HESA

Higher Education Strategy Associates

Category Archives: Statistics Canada

April 08

Early NGS Results: The Caveats

Yesterday, I showed you some charts on graduate outcomes indicating that the kids were – mostly – alright: employment steady, Full-time employment steady, graduate incomes steady, etc.  But there are three significant reasons to be cautious about over-interpreting these results.

The first is that this year’s NGS was conducted differently from previous iterations.  In previous years, the survey was conducted two years after graduation.  This year, the survey was done three years out, with graduates being interviewed in 2013 about what their situation was in 2012, to try to keep the 2-year time frame.  This creates an array of small biases in responses, though whether it creates over- or under-estimation of employment and income is hard to say.

The second caveat has to do with the survey response rate.  In 2000, the NGS response rate was 70%; this year’s response rate was 49%, which has to be one of the lowest ever seen in a Statscan survey.  Partly, this is probably an artefact of waiting an extra year to survey students, and partly it’s that students are getting harder to follow (when you survey on landlines, the caller eats the cost – on cell phones, part of the cost burden falls on the respondent, which can’t be good for response rates).

The third problem is the trickiest.  When Statscan reports income and employment figures, it does so only for those students who do not enrol in programs leading to further certification.  This eliminates some problematic data from people who are either still in school, or who have gained an additional credential (which is good), but at the same time it creates problems because the proportion excluded changes over the economic cycle.

Percentage of College and Bachelor’s Graduates Seeking Additional Certifications Within 2 Years of Graduation

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The suspicious might look at this and say: “holy moley! The NGS data on income and employment ignores half of all university graduates – surely that’s where all the underemployed  barista sociology graduates are!  Skills Mismatch Bingo!”

But slow down a minute.  First, the rates at which college and university grads attempt to acquire extra credentials mirror one another; this is not clear evidence in favour of a “college-grads-have-it-better-than-university-grads”, or a “skills mismatch” proposition.

Second, it’s not at all clear that, among university grads, the phenomenon is disproportionately due to Arts grads.  Even in the best of economic times, 40% of university grads continue on to extra credentials, and they come from all across the university.  The proportions by field of study for 2005 (2010 numbers not yet available) actually showed that Science students (62%) were more likely to do so than humanities (56%) and social science (45%) grads (Fine arts was at 38%, engineering, math, and computer science grads were at 30%).

All of which is to say: while the employment and income data from NGS are technically apples-to-apples comparisons, the fact is that the basis of these comparisons varies slightly from survey to survey.  Some of the good news on graduate income and employment rates is probably due more to students choosing to take extra education rather than brave the job market.  It thus probably isn’t entirely fair to say that the 2012 data implies things are getting better for grads.

That said, you can’t twist this evidence to support the idea that there has been a radical change in the labour market for graduates.  It’s mostly as it’s always been, with a slight up-tick in people taking more than one credential.

So can we please ditch the “everything is different” narratives and get back to real issues now?

 

April 07

Early Results from the National Graduates Survey: The Good News

Some very early National Graduates Survey (NGS) results are out, and they’re mostly good news.  The NGS – for the uninitiated – surveys university and college graduates two years after graduation.  It’s closely watched for its numbers on graduate employment, income, and debt.  Statscan has been doing this now for a little over thirty years (the first one was on the class of 1982), and since 1990 it has been conducted every five years.

Usually, when Statscan does a major survey, it “launches” with an analytical report and the release of a public-use microdata file (PUMF).  This time, however, they chose to do neither, which is more than a bit weird.  It’s welcome in the sense that it gets data into the public domain more quickly, but unwelcome in the sense that the only data available is what you get via a set of highly truncated standard tables (no debt data this year, for a start) and what people can order via custom tables for hundreds – possibly thousands – of dollars.  I’ve ordered quite a bit of data, which I can hopefully share with all of you relatively soon, but for today we’re only going with what’s in the standard tables.

Are you sitting comfortably?  Then I’ll begin.

The picture on employment and immigration looks pretty good.  To be clear about what’s being shown here, NGS only releases data on graduates who finished a level of study but who did not take any further education.  In theory, this is a way of making comparisons more apples-to-apples: it avoids methodological problems of adding incomes of BA students who went on to do a 1-year Master’s degree in with students who just did a Bachelor’s degree.  It’s less than totally satisfactory, but it has the benefit of simplicity.  Anyways, here’s what the employment data looks like for college and Bachelor’s grads:

Full- and Part-time Employment Rates for College and Bachelor’s Graduates Two Years After Graduation, NGS 2005 (in 2007) and 2010 (in 2012)

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Got that?  From the height of the boom to the middle of the current “recession”, there’s essentially no difference either in terms of overall employment or in terms of full-time employment.  To the extent that there are people struggling and having a hard time finding a job, it’s business as usual: the proportion has not changed since the height of the boom (for both college and university grads, unemployment rates in both periods were 5%, with another 5% not in the labour force).

Ok, you say, but what about income?  Surely the recession has done a real number on graduates’ salaries?  Well, no.  Adjusted for inflation, there’s been a rise in median salaries of 7% for Bachelor’s graduates and 8% for college graduates.

Median Salaries of College and Bachelor’s Graduates Two Years After Graduation, NGS 2005 (in 2007) and 2010 (in 2012), in $2012

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My favourite crazy pieces of data from NGS, though, are the province-by-province figures.  Check out Newfoundland, which according to NGS now pumps out the country’s best-off college and university graduates:

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These are all good stories, worth keeping in your armoury next time someone whines about skills gaps or spouts some nonsense about a lost generation.  But there are a few caveats to this story.  I’ll deal with them tomorrow.

March 05

The Long-Term Benefits of Higher Education

A very good Statscan report came out last week, and didn’t get nearly enough attention.  Authored by the excellent Marc Frenette, it’s called, An Investment of a Lifetime? The Long-term Labour Market Outcomes Associated with a Post-Secondary Education, and it deserves a wide readership.

What Frenette did was link the 1991 census file to the Longitudinal Worker File (LWF), which integrates data from Records of Employment, annual T1 and T4 files, and some data on employers as well, for a 10% random sample of all Canadian workers.  From this, he created a sample of about 8,000 people who were born in Canada between 1955 and 1957 (i.e. who were about 35 years old at the time of the census), and who held jobs in 18 out of 20 years since then. From this, he worked out what the added value of university and college credentials were over that period.

Figure 1 shows earnings by education level.  For men between the ages of 35 and 55, the added benefit of a college education (vs. high school) was $153,000; for a university education it was $445,000 (for women, the figures were $115,000 and $280,000).  In addition to higher salaries, higher levels of education were also associated with higher levels of union membership, and lower frequency of layoffs.

Figure 1: Net Present Value of 20-Year Earnings of Canadian-Born Workers, by Level of Education

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Figure 1 isn’t exactly ground-breaking; more education = more money, and more so for men than women.  Where it gets interesting is when the results are disaggregated by gender, and attention is paid not just to means and medians but also at the distributional tails.  Figure 2 compares the wage premiums at various percentiles for female college and university graduates, over high school graduates in the equivalent percentiles.

Figure 2: Cumulative Additional 20-Year Earnings for Female College and University Graduates, at Selected Percentiles

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Figure 2 shows three things.  First, women with university degrees make more money than those with college or high school across all percentiles.  Second, that said, down around the 5th-10th percentile, the premiums are so low that it’s really not clear that women are better off with higher education.  And third, the premium for higher education really flattens out above the median – which, as Figure 3 shows, is not even vaguely the case for men.

Figure 3: Cumulative Additional 20-Year Earnings for Male College and University Graduates, at Selected Percentiles

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Crazy stuff.  Recall from Figure 1 that average gains for men were significantly higher than for women.  But Figure 2 and Figure 3 show that the median gains – those at the 50th percentile – are about the same.  The difference is that among males, the top ten percent – and especially the top five percent – are reaping astronomical rewards from higher education.

The last amazing thing in the paper has to do with how men and women with bachelor’s degrees fare comparatively in the public and private sectors.  And the numbers there are astonishing: in the bottom ten percentiles in the private sector, women are making less money, cumulatively, than their counterparts with just high-school education.  But what’s really interesting here is the fact that in the public sector, at least, women actually reap higher gains than men.

Figure 4: Median Cumulative Additional Earnings for Male and Female University Graduates in Public and Private Sectors

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Nitpickers will likely snub this study – it deals with a cohort that finished school 35 years ago, it doesn’t disaggregate by field of study, etc.  But methodologically, it points to ways to conduct future studies (we could do the same with a shorter period for 25 year-olds in the 2001 census, for instance), and substantively it gives us a lot to chew on, not only in terms of average earnings, but also with the distribution of those earnings.  Kudos to Marc for this work.

February 27

New Student Debt Numbers

So, the more stat-minded among you may have noted the release, this past Tuesday, of Statistics Canada’s 2012 Survey of Financial Security (SFS).  Though the main talking points were largely about mortgage debt, it also contained some interesting statistics on student debt.

Now, remember that these are figures on outstanding student debt.  Some of it will be in repayment (i.e. held by graduates now in the labour force), and some of it will not (i.e. held by current students).  The way to think of these debt figures is as a collective portrait of people who borrowed in the decade or so prior to the snapshot, and who had not yet fully repaid their debt (because those who had successfully completed repayment would be out of the sample).  So the 2012 figure for student debt is actually a collective picture of the outstanding debt of everyone who borrowed in the period 2002-2012, and who had not yet repaid, the 2005 figure covers the period 1995-2005 or so, etc., etc.

Anyways, the headline that the usual suspects would like you to focus on is the one about aggregate debt outstanding: $28 billion, up by $5.5 billion (23%) in real dollars since the last time the study was conducted, in 2005.  Why is that a big deal?  Because!  $28 Billion!  Big Number!  But a slightly more intelligent look at the data shows a different story.

Figure 1 shows that the average outstanding student loan was about $15,000.  That’s up about 6% from 2005, and 13% from 1999 (again, all figures are inflation-adjusted).  Why is this figure so much smaller than the one for total debt?  Simple: more people have outstanding student debt than in 2005, so it’s divided among a larger population.  That might be because people are taking longer to repay their loans – more likely, though, it’s a reflection of the fact that student numbers as a whole rose substantially over the 00s.

Figure 1 – Average Student Debt Among Holders of Outstanding Student Loans, in $2012

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Intriguingly, the data for median student debt (that is, the mid-point value, rather than the mean) tells a slightly different story, in that it fell 2% between 2005 and 2012 (though it has still risen a bit since 1999).

Figure 2 – Median Student Debt Among Holders of Outstanding Student Loans, in $2012

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How should we interpret this?  This isn’t the easiest data to unpack.  It probably means, as I pointed out back here, that student debt hasn’t been increasing.  But it also might mean that debt repayment rates have been increasing along with indebtedness, or (less likely) that a greater fraction of student loans are held by individuals who graduated from shorter programs.

Whatever the truth, what we do know for sure is that young people aren’t drowning in student loan debt.  Among family units headed by people under-35, only a quarter hold a student loan, and the loan debt constitutes just 5.3% of their total debt, down from 6.7% in 2005.  Whatever the effects of student borrowing is, it would appear that deterring graduates from taking on ever-larger mortgages isn’t one of them.

October 22

Faculty Salary Data You Should Probably Ignore

Recently, the Ontario Confederation of University Faculty Associations (OCUFA) published a comparison of American and Canadian academics’ salaries.  Using Canada’s National Household Survey (NHS) and the US Occupational Employment Statistics (OES) survey (which they described as being not quite apples-to-apples, but at least Macintosh-to-Granny Smith), they noted that average salaries for the combined college-and-university instructor population (the OES cannot disaggregate below that level) were $76,000.  In Canada, the figure was $65,000.  Hence, according to them, with the dollar at par, there is a 17% gap in academic pay in favour of the Americans… and much more of a gap if PPP is taken into account.

There are three reasons why this conclusion is deeply suspect.

First, OES and NHS are not even vaguely comparable.  One is a world-class instrument, based on administrative data collected at over 200,000 places of employment; the other: a self-report from a nonrandom sample of Canadians which has been widely panned as a steaming pile of horse manure.

Second, the actual numbers seem to be slightly off.  When I go to the OES, the category for 2- and 4-year post-secondary teachers (25-1000), I get $77,600.  The Canadian NHS files show that “university professors and lecturers” (category 4011) earn $87,978 and “college and other vocational instructors” (category 4021) earn $57,275.  Together, weighted, that’s an average of $70,033.  So, a 10% gap, not a 17% one.

Third, since the two countries don’t have identical proportions of instructors in the 2- and 4-year sectors, it’s hard to tell how well these numbers reflect differences among university professors.  Neither do we have any sense of the proportion of part-timers and sessionals in the count, on either side of the border.  In other words, this comparison is based on a hodgepodge of non-comparable data, and proves absolutely nothing with respect to relative salaries of professors on either side of the 49th parallel.

More direct comparisons are possible.  Oklahoma State University has been doing an annual survey of salaries at Public and Land-grant Universities – the grouping of US institutions that look most similar to Canadian universities – for 40 years.  The figure below compares the 2012-13 OSU data with that of Canadian profs from Statistics Canada’s last UCASS study (2010-11), as published by CAUT.

Canada vs US Professors’ Salaries

 

 

 

 

 

 

 

 

 

 

 

 

One can quibble with this graph, of course.  The Canadian numbers have probably gone up another 6-7% in the intervening two years.  The US numbers don’t include the income professors get from summer research grants, which would probably add another 10% or so to their averages (see here for that calculation).  But effectively, there’s about a 15% pay gap in Canada’s favour, if dollars are counted at par, not a 17% gap the other way.

Naturally, one could get into arguments about purchasing power parity, living standards, and the like – that’s all fair game.  What’s not fair game is using a set of bad statistics when better ones are available, just because the bad data happens to better serve your cause. You’d think an association representing academics, of all people, would know that.

October 02

A New Study on Postdocs

There’s an interesting study on postdocs out today, from the Canadian Association of Postdoctoral Scholars (CAPS) and MITACS.  The report provides a wealth of data on postdocs’ demographics, financial status, likes, dislikes, etc.  It’s all thoroughly interesting and well worth a read, but I’m going to restrict my comments to just two of the most interesting results.

The first has to do, specifically, with postdocs’ legal status.  In Quebec, they are considered students. Outside Quebec, it depends: if their funding comes from internal university funds, they are usually considered employees; but, if their funding is external, they are most often just “fellowship holders” – an indistinct category which could mean a wide variety of things in terms of access to campus services (are they students?  Employees?  Both?  Neither?).  Just taxonomically, the whole situation’s a bit of a nightmare, and one can certainly see the need for greater clarity and consistently if we ever want to make policy on postdocs above the institutional level.

The second – somewhat jaw-dropping – point of interest is the table on page 27, which examines postdocs’ training.

Level of Training Received or Available, in % (The 2013 Canadian Postdoc Survey, Table 3, pg. 27)

 

 

 

 

 

 

 

 

 

 

 

 

 

As the authors note, being trainees is what makes postdocs a distinct group – it’s basically the only thing that distinguishes them from research associates.  So what should we infer from the fact that only 18% of postdocs report receiving any formal training for career development, 15% for research ethics, and 11% on either presentation skills or grant/proposal writing?  If there’s a smoking gun on the charge that Canadian universities view postdocs as cheap academic labour, rather than as true academics-in-waiting, this table is it.

All of this information is, of course, important; however, this study’s value goes beyond its presentation of new data.  One of its most important lessons comes from the fact that a couple of organizations just decided to get together and collect data on their own.  Too often in this country, we turn our noses up at anything other than the highest-quality data, but since no one wants to pay for quality (how Canadian is that?), we just wring our hands hoping StatsCan will eventually sort it out it for us.

But to hell with that.  StatsCan’s broke, and even when it had money it couldn’t get its most important product (PSIS) to work properly.  It’s time the sector got serious about collecting, packaging, and – most importantly – publishing its own data, even if it’s not StatsCan quality.  This survey’s sample selection, for instance, is a bit on the dodgy side – but who cares?  Some data is better than none.  And too often, “none” is what we have.

CAPS/MITACS have done everyone a solid by spending their own time and money to improve our knowledge base about some key contributors to the country’s research effort.  They deserve both to be commended and widely imitated.

September 05

Affordability

At some point in the next week or so, Statistics Canada will be releasing its annual statistics on tuition fees.  Hopefully it will be less of a fiasco than last year, when they released data a few days after the Quebec election, but didn’t bother to note that the planned tuition fee hike was being reversed.

What I want to do today is to put the inevitable “rising fees” stories that always accompany the Statscan release into some sort of context.  Students pay two types of fees – tuition and “ancillary fees”.  Statscan data on the latter is only marginally better than hopeless, so these fluctuating annual figures need to be treated with extreme caution; but they’re a non-negligible part of total tuition (15% or so), and so I include both in the graph below showing the evolution of total fees.

Figure 1 – Average Tuition, Canada, Nominal Dollars

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1 is the graph that the zero-tuition crowd love to show: steady 5.1% annual tuition increases from 1995 to the present.  That’s actually a trick of scale – in fact, during the era of maximum government skintness (the 90s) tuition was going up about 9% per year to make up for cuts in government grants.  After 1999, the economy improved, public finances improved, and the rate of fee increase fell to just about 4%.

There is, however, a little thing called inflation.  It’s kind of important if you want to understand real prices over time.  Here’s what the tuition graph looks like if you take inflation into account.

Figure 2 – Average Nominal and Real ($2103) Tuition, Canada

 

 

 

 

 

 

 

 

 

 

 

 

This changes things a bit.  Those annual increases since 1999-2000?  Just two percent, after inflation.

But, as apparently nobody in the press or politics seems to understand, those increases in fees have been accompanied by increases in subsidies, too.  The most important of these are the increases of various forms of tax credits.  Say what you want about them – they reduce the actual cost of education by about a third.  Their value is eroding slightly at the moment due to inflation, but they are still worth $2,220 to the average Canadian student.

Figure 3 – Average Nominal and Real ($2013) Tuition plus Net Real Tuition Canada

 

 

 

 

 

 

 

 

 

 

 

 

Finally, if we’re looking at affordability, we also need to take into consideration a measure of ability-to-pay, because cost on its own is meaningless.  Televisions cost more than they did, say, 40 years ago, but no one thinks they’re “less affordable”, because incomes have risen even more quickly.  So to compare affordability across time, what we need to do is look at cost over time with respect to a measure of purchasing power, such as average family after-tax income.  Which I do, below.

Figure 4 – Real Net Tuition as a Percentage of Average After-Tax Family Income

 

 

 

 

 

 

 

 

 

 

 

 

So, is tuition less affordable than it was?  Well, a bit, yes.  Fifteen years ago, it took up 4.8% of average, after-tax income; now, it takes up 5.2%.  But calling it a crisis, the way the usual suspects routinely do, is a bit of a stretch.

And we haven’t even taken into account need-based student aid yet.  We’ll do that tomorrow.

June 04

Some Insights Into Medium-term Education Outcomes

As I noted yesterday, Canada is unnecessarily bad at looking at medium-term outcomes of education. The only place where we have data on university graduates even five years out is in BC, and they publish the data in such a weird format (seriously: check it out) that no one really explores them.

It could be worse. In 2005, Statscan, did a 5-year follow-up of the class of 2000 and elected not to publish any results relating to employment or income. *Facepalm*, as the kids say.

However, because I have nothing better to do, I have put together three interesting figures on how graduates fare between years 2 and 5, in select disciplines (chosen because of sample size). It’s all courtesy of that same BC data on the graduating class of 2004. I won’t bore you with employment both at 2 and 5 years, it’s uniformly quite low. Let’s start instead by looking at incomes five years out. It turns out that while some disciplines do have precarious earnings in the first two years after graduation, median incomes rise across all fields by 35% between years 2 and 5 (that’s more than 10% per year, if you’re counting). Just for comparison, the median earnings among all Canadian workers in 2009 was $46,500. So, even in the “soft” disciplines, the ones that allegedly leave people without valuable skills like English and History, graduates five years out show median incomes above the national average.

Figure 1 – Median incomes, 2 and 5 years out, BC class of 2004, selected disciplines

Ah, you say: but are they using their skills? Aren’t they, perhaps, underemployed? Well, not really. Figure 2 shows the percentage who are in jobs which have been classified by the National Occupation Classification system as either being managerial or requiring university education. In the three disciplines where that percentage is lowest after 2 years (Biology, English, and Business) the rates of employment in high-skilled jobs jumps by 50-65% in the following three years. Five years out, the difference between history grads and computer science grads is only five percentage points.

Figure 2 – Percentage of graduates in jobs classified as “Management” or “Skill Level A” by NOC, 2 and 5 years out, BC class of 2004, selected disciplines

What’s perhaps most interesting is how graduates feel about how their education changes over time (figure 3). Across the board, graduates five years out feel less satisfied with their education and are less likely to say they’d do the same program again that they did at two. But while there’s a generalized malaise among students, the regret factor is clearly a lot higher in arts and science programs than it is in professional ones.

Figure 3 – Percentage-point change in graduates indicating satisfaction with program and indicating they would take same program again, 2 and 5 years out, BC class of 2004, selected disciplines


Anyways, that’s just what one bored dude can do with available data on a crappy 12-hour flight. Imagine if governments actually wanted to improve data and analysis in this area! Possibilities: limitless.

June 03

A Better Way to Track Graduates

The real problem Canada has with respect to the whole “does-education-pay” debate is data. It’s not that we don’t have people collecting data – we do, lots of them. The problem is that they’re all collecting data over time frames so short as to be largely meaningless.

The gold standard used to be the National Graduate Survey, which surveyed every fifth graduating class two and five years out. Now the 2-year survey is a year behind schedule and the 5-year follow-up has been discontinued. That’s right, folks – at the start of the recession, when Statscan took a look at their suite of surveys and decided which ones to can and which ones to keep, they decided that the one on medium-term educational outcomes was among the least policy-relevant and canned it. You know, so they could keep funding their monthly poultry storage reports .

For about a decade now, a number of provinces (all except MB, SK and NL) have started collecting data too; indeed, they have been doing so on a biannual basis, which is much better than Statscan could ever manage. However, most only track them out to 24 months, so the issue of long-term outcomes is still unaddressed. BC is the only province which does 5-year reports, and they’re quite interesting (more about them tomorrow).

The long-term outcomes of degrees and programs clearly matter a great deal. So why can’t we measure them? Cost, mainly. Anything further out that about 24 months is expensive to do well (BC’s 5-year response rates are disappointing, for instance), and so – penny-wise pound-foolish nation that we are – we don’t do it.

But there actually is a very cost-effective way to do this; namely, to link student records to tax records. Virginia, Tennessee and Arkansas have already linked their grads’ data to unemployment records and others seem poised to follow. In Canada, we could quite easily do the same thing by having Statistics Canada link its Post-Secondary Student Information System (PSIS) to the T1 family file. Instantly, with no new data collection expenses, you’d have income data by institution, program of study – what have you – as many years out as you like. As always with Big Data, there are some privacy concerns, but frankly none of them are very convincing, certainly not compared with the major public policy gains available.

Linking administrative databases is cheaper, faster and more accurate than what we do now. Why we haven’t moved to this system already is one of the biggest mysteries in Canadian higher education policy.

May 02

Those Statscan Cutbacks

Many will have seen news yesterday about large cutbacks in the works at Statistics Canada. On the basis of the news that lots of PSAC members had received notices that their jobs may be “affected,” a number of pro-Statscan commentators rushed to say that the agency needed to be saved because it provided such fantastic, non-partisan analysis.

Well, yes. But yesterday’s notices appear not to have gone to any analysts, since they are not PSAC members.  The employees who got notices would appear to be the ones involved in data collection, field interviews, data processing, etc.  Not to put too fine a point on it, these units are the ones that make Statistics Canada a target, because they are unbelievably expensive.

I have had occasion in the past to compare prices between a private sector data collection agency and Statistics Canada. Using exactly the same methodology, Statistics Canada costs were between two and three times that of the private sector agency. On any given survey, that’s going to be a seven-figure difference.

Now, obviously, quality costs money. And Statistics Canada, just by dint of being itself, has some advantages over a private sector provider in terms of getting higher response rates (people are a lot more willing to stay on the phone with “Statistics Canada” rather than some outfit running out of RackNine). But it’s not that big a gap; in the project in question, the difference between our project and a comparable one Statistics Canada had done was about twelve percentage points (65% vs. 77% if memory serves). Every percentage point matters, of course, but that’s a lot of money per point.

Besides, it’s not as though private sector firms are incapable of dispassionate and highly professional data collection. South of the border, data collection and analysis of the entire suite of post-secondary education surveys (the National Post-Secondary Student Aid Survey, the Beginning Post-Secondary Survey and the Baccalaureate and Beyond Survey) conducted by the U.S. Department of Education has always been awarded via competitive tender. As anyone who has used these studies knows, the work is world-class.

And, who knows? If budget cuts for surveys are significant enough, then Statscan might have to get serious about pursuing more solutions based on administrative rather than survey data, a development which – as I’ve argued before – could actually improve the quality and timeliness of the information.

So while the Tories’ past behavior has given all Canadians cause to worry about the honorability of their intentions, let’s not jump to the conclusion that a budget cut is necessarily a disaster. It might actually open the door to a better statistics agency.

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