Alex Usher: Financiamiento basado en el desempeño (cuatro partes)
Abril 28, 2019

Captura de pantalla 2016-10-11 a las 3.51.51 p.m.

April 23rd, 2019 – Alex Usher
Ok guys, I’m going to take the rest of the week to nerd out about performance-based funding (PBF) indicators, since clearly this is all anyone here in Ontario is going to be talking about for the next few months. I’m going to start with the issue of what indicators are going to be used—and fair warning: this is going to be long.
(Reminder to readers: I actually do this stuff for a living. If you think your institution needs help understanding the implications of the new PBF, please do get in touch).
Last week, the provincial government briefed institutions on the indicators it would like to use in its new performance-based funding formula, which in theory is meant to eventually govern 60% of all provincial grants to institutions. It won’t actually be that much, for a variety of reasons, but let’s stick to the government script for the moment. Currently, the government still has literally no idea about how it is going to turn data on these ten indicators into actual dollar amounts for institutions -I’ll talk more about how that might work later this week. But we can still make some useful observations about the indicators on their own in three respects. First, with respect to the conceptual validity of the proposed measure, second, with respect to any potential measurement problems, and third with respect to any perverse incentives the indicator may create.
Are you sitting comfortably? Then I’ll begin.
According to some briefing papers handed out by the Ontario Ministry of Training, Colleges and Universities last week (which someone very kindly provided me, you know who you are, many thanks) the new PBF is supposed to be based on ten indicators: six related to “skills and job outlooks” and four related to “economic and community impact”. One of the latter sets of indicators is meant to be designed and measured individually by each university and college (in consultation with the ministry), which is a continuation of practices adopted in previous strategic mandate agreements. One of the “economic” indicators is a dual indicator – a research metric for universities and an “apprenticeship-related” metric for colleges which is “under development” (i.e. the government has no clue what to do about this), so I won’t touch the college one for now. And finally, one of the skills indicators is some kind of direct measurement of skills, presumably using something like the tests HEQCO performed as part of its Postsecondary and Workplace Skills project (which I wrote about back here) –I will deal with separately with that one tomorrow because it’s a huge topic all on its own.
So that leaves us with eight indicators, which are:
Graduation Rate. Assuming they stick with current practice, this will be defined as “percentage of first-time, full-time undergraduate university students who commenced their study in a given Fall term and graduated from the same institution within 6 years.”. Obvious problems include what to do about transfer students (aren’t we supposed to be promoting pathways?)
Graduate Employment. The government is suggesting measuring not employment rates but the “proportion of graduates employed full-time in fields closely or partly related to one’s field of study”. This question is currently asked on both the university and college versions of Ontario Graduate Surveys but is not currently published at an institutional level. It is a self-report question, which means the government does not have to define what a “related” or “partly related” means.
Graduate Earnings. This is currently tracked by the ministry through the Ontario Graduate Surveys, but the government appears to want to switch to using Statistics Canada’s new Educational and Labour Market Longitudinal Platform (ELMLP), through which graduate incomes can be tracked through the tax system as a means of measurement. This is mostly to the good, in the sense that response rates will be high and valid and graduates can be tracked for longer (though it is not clear what the preferred time frame is here), but they will lose the ability to exclude graduates who are enrolled in school for a second degree.
Experiential Learning. The Government’s briefing document indicates it wants to use the “number and proportion” of graduates in programs with experiential learning (this is confusing because it is actually two indicators) for colleges, but substitutes the word “courses” for “programs” when it comes to universities. I have no idea what this means and suspect they may not either. Possibly, this is a complicated way of saying they want to know what proportion of graduates have had a work-integrated learning experience.
Institutional Strength/Focus. This is a weird one. The government says it wants to measure “the proportion of students in an area of strength” at each institution. I can’t see how any institution looking at this metric is going to name anything other than their largest faculty (usually Arts) as their area of strength. Or how OCAD isn’t just going to say “art/design” and get a 100% rating. Maybe there’s some subtlety here that I’m missing but this just seems pointless to me.
Research Funding and Capacity (universities only): Straight up, this is just how much tri-council research funding each institution receives, meaning it could be seen as a form of indirect provincial support to cover overhead on federal research. This seems clear enough, but presumably there will be quite some jostling about definitions, in particular: how is everyone supposed to count money for projects that have investigators at multiple institutions? Should it use the same method as the federal indirect research support program, or some other method? Over how many years will the calculation be made? A multiple-year rolling average seems best, since in any given year the number can be quite volatile at smaller institutions.
“Innovation”. Simply, they mean funding from industry sources (for universities, this is specified as “research income”). The government claims it can get this data from the Statscan/CAUBO Financial Information of Universities and Colleges Survey, although I’m 99% sure that’s not something that gets tracked specifically. Also, important question: do non-profits count as “industry”? Because particularly in the medical field, that’s a heck of a big chunk of the research pie.
“Community/Local Impact”. OK, hold on to your hats. Someone clearly told the government they should have a community impact indicator to make this look like “not just a business thing”, but of course community impact is complex, diffuse, and difficult to measure consistently. So, in their desperation to find a “community” metric which was easy to measure, they settled on…are you ready?…institution size…divided by….community size. No, you’re not misreading that and yes, it’s asinine. I mean, first of all it’s not performance. Secondly, it’s not clear how you measure community; for instance, Confederation College has campuses in five communities, Waterloo has three, etc., so what do you use as a denominator? Third: What? WHAT? Are you KIDDING ME? Set up a battle of wits between this idea and a bag of hammers and the blunt instruments win every time. This idea needs to die in a fire before this process goes any further because it completely undermines the idea of performance indicators. If the province needs a way to quietly funnel money to small town schools (helloooo, Nipissing!) then do it through the rest of the grant, not the performance envelope.
OK, so that is eight indicators. Two of these (community impact, institutional strength) are irretrievably stupid and should be jettisoned at the first opportunity. The “research” and “innovation” measures are reasonable provided sensible definitions are used (multi-year averages, the indirect funding method of counting tri-council income, inclusion of non-profits) and would be non-controversial in most European countries. The experiential learning one is probably OK, but again much depends on the actual definitions chosen.
That leaves the three graduation/employment metrics. There are some technical issues with all of them. The graduation rate definition is one-dimensional, and in most US states a simple grad rate is now usually accompanied by other metrics of progress beyond completion (e.g. indicators for successfully bringing transfer students to degree, or indicators for getting students to complete 30/60/90 credits). The graduate employment “in a related field” is going to make people scream (it might be a useful metric for professional programs, but in most cases degrees aren’t designed to link to occupations and even where they are, people shift occupations after a few years anyway) and in any case it is to be measured through a survey with notoriously low completion rates, which will matter at small institutions. The graduate income measure is technically OK but doesn’t work well as an indicator in some types of PBF systems because it does not scale with institution size (I’ll deal more with this in Thursday’s blog).
But the bigger issue with all three of these is that they conceivably set up some very bad incentives for institutions. In all three of them, institutions could juice their scores by dumping humanities or fine arts programs and admit only white dudes, because that’s who does best in the labour market. I’m not saying they would do this – institutions do have ethical compasses – but it is quite clearly a dynamic that could be in play at the margin. As it stands, there is a strong argument here that these measures have the potential to be anti-diversity and anti-access.
There is, I think, a way to counter this argument. Let’s say the folks in TCU do the right thing and consign those two ridiculous indicators to the dustbin: why not replace them with indicators which encourage broadening participation? For instance, awarding points to institutions which are particularly good at enrolling students with disabilities, Indigenous students, low-income students, etc. The first two are measured already through the current SMA process; the third could be measured through student aid files, if necessary. That way, any institution which tries to win points by being more restrictive in its intake would lose points on another (hopefully equally weighted) indicator, and the institutions which do best would be those that are both open access and have great graduation/employment outcomes. Which, frankly, is as it should be.
Tomorrow: Measuring skills.


April 24th, 2019 – Alex Usher
Yesterday,  I critiqued most of the indicators being suggested for the new Ontario PBF system. But I left one out because I thought it was worth a blog all on its own, and that is the indicator related to “skills and competencies”. It’s the indicator that is likely to draw the most heat from the higher education traditionalists, and so it is worth drilling into.
In principle, measuring the ability of institutions to provide students more of the skills that allow them to succeed in the work force is perfectly sound. Getting a better job is not the only reason students attend university or college, but repeated surveys over many decades show it is the most important one. Similarly, while labour force improvement is not the only reason governments fund universities, it is the most important one (it pretty much is the only reason they fund colleges). So, it is in no way wrong to try to find out whether institutions are doing a good job at this. And if you don’t like simple employment or salary measures (both of which are fairly crude), then it makes sense to look at something like skills, and the contribution each institution makes towards improving them.
Problem is, there isn’t exactly a consensus on how to do that.
In some countries – Brazil and Jordan, for instance – students take national subject-matter examinations. Nobody seems to think this is a good idea in OECD countries, partly this is because there tends to be a lot of curriculum diversity, making the setting of such national tests hilariously fraught. But mostly, it’s because in many fields, there is no particularly straight line between disciplinary knowledge and the kinds of skills and competencies required in the labour market. One is not necessarily superior to the other, but they are just different things. And so was born the idea to try to measure these skills directly and more importantly, to measure their change over time.
The dominant international approach is a variation of the Collegiate Learning Assessment (CLA), pioneered about 20 years ago by the Council for Aid to Education (a division of RAND Corporation). The approach is, essentially, give the same exam to a (hopefully randomly-selected) group of students during their first and final year of studies at an institution. Assuming there has been no major change in the qualities of the entering student body (which can be done in the US by looking at average SAT scores, but is harder to do in Canada), this creates a “synthetic cohort” and obviates having to offer a test to incoming students and then waiting three years to test them again, which would be tedious. You mark the scores on both tests, and you see the change in scores over time. Not all of the growth in skills/competencies can be attributed to the institution, of course (22 year-olds, on the whole, are smarter than 18 year-olds, even if they don’t go to school), so the key is then to compare the change in results at one institution against those in the others. Institutions where the measured “gain” is very high are hence deemed “good” at imparting skills and those where the gain is small are deemed “poor”. By measuring learning gain rather than just exiting scores, institution can’t just win by skimming the best students; they actually have to do some imparting of skills.
This is all reasonable, methodologically speaking. You can quibble about whether synthetic cohorts are good substitutes for real ones, but most experts think they are. And many years of testing suggest the results are reliable. The question is, are they valid? And this is where things get tricky.
What CLA does is designed to test students’ ability to think critically, reason analytically, solve problems, and communicate clearly and cogently. It does this by asking a set of open-ended questions about a document (for some sample questions, see here) and then scoring answers on a scoring rubric (see here). This isn’t an outlandish approach, but there are questions to be asked about how well the concepts of “thinking critically” and “reasoning analytically” are measured. As this quite excellent review of CLA methodology suggests, it seems to have straddled three separate and not-entirely compatible definitions of “critical thinking”; but while it seems to capture the nature of what is taught in humanities and social sciences, it is not clear that this is the same thing as “critical thinking skills” in other fields.
(A similar problem exists with tests that claim to measure “problem-solving”.  Turns out that results of exams of problem-solving actually are highly correlated with measures of IQ. This is not the end of the world if you use the CLA synthetic cohort approach because you would be comparatively measuring an increase in IQ rather than IQ itself, which might not be such a terrible thing to the extent one could prove that the competencies IQ measures are valued in the work place. But it’s not quite as advertised.)
Now, we don’t know exactly how Ontario plans to measure skills. The Higher Education Quality Council of Ontario (HECQO) – which is presumably the agency which will be put in charge of these initiatives – has been experimenting with a couple of different tests of skills: the HEIghten Critical Thinking assessment developed by the American company ETS (some sample questionshere), and the Education and Skills Online (ESO) test, which is a variant of the test the OECD uses in its Programme for the International Assessment of Adult Competencies (methodology guide here). The former is a one-off test of graduating students, and (though I am not a psychometrician) just seems like a really superficial version of CLA or OCED’s AHELO; the ESO test uses the synthetic cohort strategy but uses a test which is much heavier on simple literacy and numeracy skills and lighter on critical thinking (technically, it tries to measure “problem-solving in a digital environment” which may come close to IQ testing, but again not necessarily a problem if you are looking at “gain”). That probably means EASI has higher levels of validity (because literacy and numeracy are pretty well-studied and firmly conceptualized), though again there has to be some doubt about the degree to which these specific skills represent what is valued/rewarded in the workplace.
The smart money is that Ontario is planning on using some variant of EASI (final report here, my take thereon here) in order to measure institutional outcomes. Which means the focus will be on literacy and numeracy, which are not ridiculous things to want post-secondary graduates to have and hence not ridiculous outcomes for which to hold institutions accountable. The problem is really twofold.
First, HEQCO (or whoever is eventually put in charge of this) is going to have to pay a lot of attention to sampling. In the first initial test done in 2017, they basically got whoever they could to take the test. Now that actual money is on the table, the randomization of the sample is incredibly important. And it’s going to be hard to enforce genuine randomization unless some students all agree to take a test, which, you know, might be tricky since the government can’t actually force anyone to do so. Second, it’s not clear how well the synthetic cohort strategy will work without a standardized measure like SAT scores to back it up: currently, there’s no good way to tell how alike the entering and exiting cohorts actually are. I don’t think it’s beyond HEQCO’s wit to devise something (obvious possibility: use individual students’ entering grades, normalized by whatever means Ontario institutions use to equalize scores from high schools with different marking schemes), but they need something better than what they have now.
There will be some who say it is a grave perversion of the purpose of university to measure graduates on anything other than their knowledge of subject matter. Hornswoggle. If universities and college aren’t focussing on enhancing literacy and numeracy skills as part of their development of subject expertise, something is desperately wrong. Some will claim it will force institutions to change their curriculum. No, it won’t: but it will probably get them to change their methods of assessment to focus more on these transversal skills than on pure subject matter. And that’s a good thing.
What it boils down to is the following: students and governments both pay universities and colleges on the assumption that an education there improves employability. Employability is at least plausibly related to advanced literacy and numeracy skills. We can measure literacy and numeracy, and subject to some technical improvements noted above, we can probably measure the extent to which institutions play a role in enhancing those skills. This is – subject to caveats about not getting too excited about measures of “critical thinking” and paying attention to some important methodological issues – generally a good idea. In fact, compared to some of the other performance measures the government of Ontario is considering, I would say it is among the better ones.
But do pay attention to those caveats. They matter.



April 25th, 2019 – Alex Usher
The biggest missing piece in the Ontario government’s proposed performance-funding system is any discussion of the algorithm by which data on various indicators gets turned into an actual allocation to institutions. The lack of such a piece is what leads most observers to conclude that the government has no idea what it’s doing at the moment; however I am a glass-half-full kind of guy and take this as an opportunity to start a discussion that might impact the government’s thinking on this.
There are lots of different ways to run performance-based funding systems, but if the model is other North American jurisdictions with 50%+ of funding coming through outcomes, then there is probably only one real model, and it is the type associated with the Tennessee model. Fortunately, Tennessee has published an unholy amount about its model and how it works, so those of you interested in nerding out on this subject may want to just go there now and play around a bit (I had a blast doing so; your mileage may vary). In case that’s not your bag, here’s an explanation.
The calculation starts with each institution presenting data for each indicator. Each indicator is then transformed or “scaled” into a certain number of points (e.g. one point for every two graduates, or every three graduates, or whatever), leaving each institution with a certain number of points for each indicator. These points are then “weighted”; that is, multiplied by a number between zero and one depending on the stated importance of that indicator, with the sum of the weights equal to one (in Ontario, the government has promised to give institutions the ability to partially manipulate their  weights to reflect institutional mission; Tennessee does this, too, but there are some pretty strict limits on the degree to which variation is permitted). Multiply each indicator by the weighted value, then sum the products and what you get is a composite score for each institution. Divide your institution’s composite score by the composite score of all institutions combined, and you get your institution’s share of whatever amount of money is on the table from government (which if you apply to current budgets and what Ontario says it wants to do with performance-based funding would seem to be about $2 billion for universities and $1 billion for colleges).
So far, so good. In some ways, we do this now with the weighted student enrolment formula. Indicators are different baskets of programs with similar program costs (Arts students are type 1, science/engineering type 2, etc.). The number of students in each indicator is weighted by whatever the government thinks the cost of each program is worth. The indicator values are multiplied by the weights, and your institutions’ “share” is whatever your share of the total aggregate weighted values are (ok, it’s actually a bit more complicated than this, but in principle this is how it works). Research intensive institutions – the ones with lots of science, engineering and medical students – get larger shares of the pie not just because they have more students, but because their students are worth more on average because of the weighting scheme.
For the system to deliver \ different results from what we currently have, it would need to produce results in which institutional “shares” are radically different from what they are now under the weighted student unit system we currently use. And we cannot tell if this will be the case simply from the chosen indicators (which I looked at back here); we need to know what kinds of values are going to be produced and what kinds of scales and weights are going to be used to arrive at an aggregate points total. And this is where there are a few things to watch.
Under the current system, the input units are “students”. Big schools have lots of them, smaller schools have fewer of them. That is to say, they are already scaled to institutional size to some degree. It’s not clear what the input units are going to be in this new indicator system. If you use “graduates” then big schools will get more and small schools will get less, which is more or less as it should be. But if you use graduation rates, those are not scaled to size. Ditto salaries and whatever “skills” score we end up using. Big research institutions might get better values (grads earn $50K instead of $45K, for instance), but it’s not clear how those values get translated into dollars without some other mathematical jiggery-pokery accounting for size.
This is where scaling comes in. I guarantee people will focus on the weights allotted to each indicator, but actually the impact of weighting is probably secondary to the scaling. Let me show you, using some truncated data from Tennessee as an example. To simplify the story and not completely overwhelm you with numbers and charts, I am going to use three real institutions from Tennessee (UT Chattanooga, UT Knoxville and UT Martin) and three real indicators (bachelor’s graduates, the six-year graduation rate, and income for “research, service and sponsored programs”). To show you how this works. First, the raw indicator results for 2016-17:
Now let’s scale these, as they do in Tennessee: one point for every graduate, 100 points for every percentage point on the graduate rate, and one point for every $15,000 in outside income. That leaves us with the following points totals for each school.
Now let’s assume the three indicators are weighted 45% to graduates, 45% to graduation rate and 10% to research income. (this isn’t quite how they are weighted in Tennessee because they are six other indicators, but these three do account for about 75-80% of the total and 45-45-10 is close to their proportions). Multiply each of the cells above by the appropriate weight, add them up, and you get a weighted point total. Since each institution gets its share of total weighed points, it’s then easy to work out how much money each institution gets. In this case, if the total budget was $100 million, then Knoxville would get $50.7 million, Chattanooga $27.1 million, and Martin $22.2 million.
My guess is most of you can work out in your head what would happen if we played around with the weights: for instance, Knoxville would do better if we put more weight on income. But even if you leave the weights unchanged, you can completely alter the results by changing the scales. So, for instance, what happens if we leave the scale for graduation rates unchanged, but change the scale for graduates from one for each graduate to one for two graduates and offer one point for every $5,000 in income instead of $15,000? I’ll skip a step to save space, but here are the final point outcomes:
See? No change in weights but a change in scales, and suddenly Knoxville grabs seven million from the two smaller institutions.
This is tricky stuff. And not easily visible to the casual observer.
Now, in Tennessee it seems that a major function of the scales – at least at the start – was explicitly to calibrate the new system with the old one and make sure that there was as little disruption as possible in funding levels between the old system and the new one (and if there was a slight drop in the share of money going to each institution, they were usually made whole by the fact that the budget was rising every year – which is not going to be the case in Ontario). Tennessee was happy to let funding go wherever the formula told it to do *after* it was introduced, but they seem to have made quite a big effort, mainly through the scales, to ensure that at the start it was as seamless as possible.
The question is whether Ontario intends to do the same.


April 25th, 2019 – Alex Usher
Yesterday, I explained how the distribution of funds might occur in a single-envelope PBF system (that is, the dominant system in North America, where indicators generate scores for each institution which then govern the distribution of a pre-set amount of money). And while that is the likely way a PBF system will work in Ontario, it’s not the only possible way and indeed the government has left some hints that it is thinking about an alternative method.
The way the notion of performance funding was introduced seemed to tie the whole thing to Strategic Mandate Agreements (SMAs), which currently require institutions to report performance for different indicators (an example of which you can see  here ). And this ties in with the notion, which some of the political staff kept pushing at the briefing, that universities and colleges wouldn’t be competing with each other, they’d be competing against themselves : SMAs, after all, use common indicators, but the standards each institution is asked to achieve is different. So in a sense, the idea would be: $X if you hit a given level of achievement on each indicators, and less if you don’t.
This is a perfectly sensible idea. When Quebec premier Francois Legault was Minister of Education in Quebec about 20 years ago, he introduced a similar idea called “contrats de performance” (the Liberals dropped the scheme when they took power in 2003). Denmark and Austria also have institutional performance contracts as part of their funding systems (though to be fair, these seem to work more along the lines of the current SMAs than what the Ontario government is talking about). There are examples out there that could serve as workable models.
Except performance contracts by design are meant to give institutions targets they can hit fairly easily, if they are paying attention, which does not really line up well with the budget commitment that “60% of funding will be delivered through performance contracts”. They also tend to be designed as ways to introduce “new” money into the system. In the event that institutions don’t “earn” all the money to which they are theoretically eligible, they still don’t find themselves in a weaker funding position.
Plus, there’s also the issue of what happens if an institution doesn’t hit its targets. In an envelope system (the kind I described yesterday ), the government has to spend all the money in the envelope – the formula just tells you how the envelope gets divided.  Some people view this as bad because institutions are “competing against one another” but the good news is the money stays within the system because  someone  gets the money. Under a contract-based system, if an institution misses its targets then the money goes back to government – and in this case, where aggregate system funding is emphatically frozen for the next couple of years, that means system-wide funding will be cut. Possibly by quite a lot, though again it depends on the targets and how easy they are to hit.
(Small technical aside: given that the government seems to prefer indicators with an economic focus, an envelope system makes way more sense than a contract system. Under a contract system, if a recession hits and brings down employment rates and salaries, every institution risks losing money because their targets may suddenly become unhittable. Under an envelope system, everyone will “lose points” in the scoring system, but as long as everyone loses at more or less the same rate, no one really loses because what matters is everyone’s  share  of points. Another reason to be wary of the contract approach, and another reason to think that even if this is what government has in mind right now, it’s not where it may necessarily end up.)
Basically: you can genuinely have 60% be performance-based, you can have contract-based performance funding, and you can have stable system financing. Pick two.
Now hopefully this tour d’horizon over the last few days gives you a sense of the possible ways this performance-based funding initiative could play out (i.e. almost infinite). It is a bit ridiculous that the scheme has been announced with literally no explanation on the key elements. Here, to my mind are the key questions to which we need answers most urgently:
  1. Is this meant to a contract-based performance system or an envelope-based system?
  2. If the answer is a contract-based system, what happens to money “at risk” which is “lost” by institutions? Does it go back to treasury? All of it, no matter what? And could institutions really lose *60%* of their funding this way, or are you going to rig it so they will never lose more than a couple of percent while still pretending the 60% number is real?
  3. If we are going to use a contract system and we are going to keep employment-related metrics, can the government credibly explain how institutions will not end up financially penalized in the event of a recession?
  4. Can we get rid of the thick-as-two-short-planks indicators for “community engagement” and “area of focus” and replace them with indicators that encourage widening participation?
  5. Can we get rid of them NOW?
  6. How does the government intend to ensure that the sampling strategy used to test for skill gain at institutions will be done in a manner that produces results which are valid and credible?
A final note here. I am, in the main, a fan of performance-based funding; my snarkiness on details should be taken as frustration with a poor roll-out rather than opposition to the concept. But it has occurred to me over the past few days that one thing that feels like it’s missing from this proposal is a conception of future direction for the system. PBFs are fine for focussing institution’s attention on tasks they are already supposed to be doing. But to drive a system to new places, you still need some funding to actually take it there. Want to facilitate access in the North? Want more microcredentials, or whatever type of tech degrees happen to be hot this year? You need money for that. I think this is where the goofy ideas around community engagement and “areas of strength” come in; I think they wanted to find ways to funnel some special money to northern institutions and to institutions like Waterloo (respectively). But just because a policy goal is valuable, doesn’t mean it is performance-related. You can fund worthy goals without conceptually shoe-horning them into a category where they don’t belong.
My advice to the Government is therefore: take a step back. Breathe. Think about what you want to accomplish. A PBF system can work and work well as part of  a healthy funding system. Don’t make everything about performance; you can and should steer the system in other ways as well.
And get rid of those two damn silly indicators. Seriously, they are embarrassing.
Have a good weekend, all.


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