Higher Education Strategy Associates

Category Archives: Technology

December 08

Innovation Ecosystems: Promise and Opportunism

We sometimes think of innovation policy as being about generating better ideas through things like sponsored research.  And that’s certainly one part of it.  But if those ideas are generated in a vacuum, they go nowhere – making ideas spread faster is the second pillar of innovation policy (a third pillar – to the extent that innovation is about new product-generation – has to do with venture capital and regulatory environments, but we’ll leave those aside for now).

Yesterday, I discussed why the key to speeding up innovation was the density of the medium through which new ideas travel: basically, ideas about IT travel faster in Waterloo than in Tuktoyaktuk; ideas about marine biology travel faster in Halifax than in Prince Albert.  And the faster ideas travel and collide (or “have sex” in Matt Ridley’s phrase), the more innovation is produced, ceteris paribus.

Now, although they don’t quite use this terminology, the proponents of big universities and big cities alike find this logic pretty congenial.  You want density of knowledge industries?  Toronto/Montreal/Vancouver have that.  You want density of superstar researchers?  U of T, McGill, and UBC have that (especially if you throw in allied medical institutes).  That makes these places the natural spot to invest money for innovation, say the usual suspects.  All you need to do is invest in “urban innovation ecosystems” (whatever those are – I get the impression it’s largely a real estate play to bring scientists, entrepreneurs, and VCs into closer spatial proximity), and voila!  Innovation!

This is where sensible people need to get off the bus.

It’s absolutely true that innovation requires a certain ecosystem of researchers, and entrepreneurs, and money.  And on average productive ecosystems are likelier to occur in larger cities, and around more research-intensive universities.  But it’s not a slam dunk.  Silicon Valley was essentially an exurb of San Francisco when it started its journey to being a tech hub.  This is super-inconvenient to the “cool downtowns” argument by the Richard Floridas of this world; as Joel Kotkin has repeatedly pointed out, innovative companies and hubs are as likely (or likelier) to be located in the ‘burbs, as they are in funky urban spaces, mainly because it’s usually cheaper to live and rent space there.  Heck, Canada’s Silicon Valley was born in the heart of Ontario Mennonite country.

We actually don’t have a particularly good theory of how innovation clusters start or improve.  Richard Florida, for instance, waxes eloquent about trendy co-working spaces in Miami as a reason for its sudden emergence as a tech hub. American observers tend to attribute success to the state’s low tax rate, and presumably there are a host of other possible catalysts.  Who’s right?  Dunno.  But I’m willing to bet it’s not Florida.

We have plenty of examples of smaller communities hitting tech take-off without having a lot of creative amenities or “urban innovation strategies”. Somehow, despite the lack of population density, some small communities manage to get their ideas out in the world in ways that gets smart investors’ attention.  No one has a freaking clue how this happens: research on “why some cities grow faster than others” is methodologically no more evolved than research on “why some universities become more research intensive than others”, which is to say it’s all pretty suspect.  Equally, some big cities never get particularly good at innovation (Montreal, for instance, is living proof that cheap rent, lots of universities, and bountiful cultural amenities aren’t a guarantee of start-up/innovation success).

Moreover, the nature of the ecosystem is likely to differ somewhat in different fields of endeavor.  The kinds of relationships required to make IT projects work is quite different from the kinds that are required to make (for example) biotech work.  The former is quick and transactional, the latter requires considerably more patience, and hence is probably less apt to depend on chance meetings over triple espressos in a shared-work-environment incubator.  Raleigh-Durham and Geneva are both major biotech hubs that are neither large nor particularly hip (nor, in Raleigh’s case, particularly dense).

It’s good that governments are getting beyond the idea that one-dimensional policy instruments like “more money in granting councils” or “tax credits” are each unlikely on their own to kickstart innovation.  It’s good that we are starting to think in terms of complex inter-relations between actors (some, but not all of which involve spatial proximity), and using “ecosystem” metaphors.  Complexity is important. Complexity matters.

But to jump from “we need to think in terms of ecosystems” to “an innovation agenda is a cities agenda” is simply policy opportunism.   The real solutions are more complex. We can and should be smarter than this.

December 07

H > A > H

I am a big fan of the economist Paul Romer, who is most famous for putting knowledge and the generation thereof at the centre of  discussions on growth.  Recently, on (roughly) the 25th anniversary of the publication of his paper on Endogeneous Technological Change, he wrote a series of blog posts looking back on some of the issues related to this theory.  The most interesting of these was one called “Human Capital and Knowledge”.

The post is long-ish, and I recommend you read it all, but the upshot is this: human capital (H) is something stored within our neurons, which is perfectly excludable.  Knowledge (A) – that is, human capital codifed in some way, such as writing – is nonexcludable.  And people can use knowledge to generate more human capital (once I read a book or watch a video about how to use SQL, I too can use SQL).  In Romer’s words:

Speech. Printing. Digital communications. There is a lot of human history tied up in our successful efforts at scaling up the H -> A -> H round trip.

And this is absolutely right.  The way we turn a patterns of thought in one person’s head into thoughts in many people’s heads is the single most important question in growth and innovation, which in turn is the single most important question in human development.  It’s the whole ballgame.

It also happens to be what higher education is about.  The teaching function of universities is partially about getting certain facts to go H > A > H (that is, subject matter mastery), and partially about getting certain modes of thought to go H > A > H (that is, ways of pattern-seeking, sense-making, meta-cognition, call it what you will). The entire fight about MOOCs, for instance, is a question of whether they are a more efficient method of making H > A > H happen than traditional lectures (to which I think the emerging answer is they are competitive if the H you are talking about is “fact-based”, and not so much if you are looking at the meta-cognitive stuff.  But generally, “getting better” at H > A > H in this way is about getting more efficient at the transfer of knowledge and skills, which means we can do more of it for the same price, which means that economy-wide we will have a more educated and productive society.

But with a slight amendment it’s also about the research function of universities.  Imagine now that we are not talking H > A > H, but rather H > A > H1.  That is, I have a certain thought pattern, I put it into symbols of some sort (words, equations, musical notation, whatever) and when it is absorbed by others, it generates new ideas (H1). This is a little bit different than what we were talking about before.  The first is about whether we can pass information or modes of thought quickly and efficiently; this one is about whether we can generate new ideas faster.

I find it helpful to think of new ideas as waves: they emanate outwards from the source and lose in intensity as they move further from the source.  But the speed of a wave is not constant: it depends on the density of the medium through which the ideas move (sound travels faster through solids than water, and faster through water than air, for instance).

And this is the central truth of innovation policy: for H > A > H1 to work, there has to be a certain density of receptor capacity for the initial “A”.  A welder who makes a big leap forward in marine welding will see his or her ideas spread more quickly if she is in Saint John or Esquimault than if she is in Regina.  To borrow Matt Ridley’s metaphor of innovation being about “ideas having sex”, ideas will multiply more if they have more potential mates.

This is how tech clusters work: they create denser mediums through which idea-waves can pass; hence, they speed up the propagation of new ideas, and hence, under the right circumstances, they speed up the propagation of new products as well.

This has major consequences for innovation policy and the funding of research in universities.  I’ll explain that tomorrow.

April 08

ATMs and the Future of Education

I recently came across a fascinating counterintuitive piece of trivia in Timothy Taylor’s Conversable Economist blog.  At the time ATMs were introduced in 1980, there were half a million bank tellers in America.  How many were there 30 years later, in 2010?  Answer: roughly 600,000.  Don’t believe me?  See the data here.

Most people to whom I’ve told this story tend to get confused by this.  ATMs are one of the classic examples about how technology destroys “good middle class jobs”.  And so the first instinct many people have when confronted with this information is to try and defend the standard narrative – usually with something like “ah, but population growth, so they still took away jobs that could have existed”.  This is wrong, though.  When we look at manufacturing, we see absolute declines in jobs due to (among other things) automation.  With ATMs, however, all we see is a change in the rate of growth.

The key thing to grasp here is that the machines did not put the tellers out of business; rather, they modified the nature of bank telling.  To quote Taylor, “tellers evolved from being people who put checks in one drawer and handed out cash from another drawer to people who solved a variety of financial problems for customers”.

There’s an important truth here about the way skill-use evolves in the economy.  When most people think about technological change and its impacts on skills, they initially tend to presume “more machines → high tech → more tech skills needed → more STEM”.  But actually this is, at best, half the story.  Yes, new job categories are springing up in technical areas that require new forms of training.  But the more important news is that older job categories evolve into new ones with different kinds of requirements, and requiring a different skill set.  And in most cases, those new skills are – as in our bank teller example – about problem-solving.

Now, as a society, every time we see job requirements changing, our instinct is to keep kids in school longer.  But: a) pretty soon cost constraints put a ceiling on that strategy; and, b) this approach is of limited usefulness if all you’re doing is teaching the same old things for longer.

At a generic level, it’s not hard to teach in such a way that you’re giving students necessary skills to thrive in the future labour market.  Most programs, at some level, teach problem-solving (identifying a problem, synthesizing data about it, coming up with possible solutions, evaluating them, and coming up with a solution), although not all of them test for them explicitly, or explain to students how these skills are likely to be applied later on.  More could be done with respect to encouraging teamwork and interpersonal skills, but these aren’t difficult to add (although having the will to add them is something different).

The more difficult problem has to do with understanding where technology is likely to replace jobs and where it is likely to modify them.  What do driverless cars mean for the delivery business?  At a guess, it means an expanded market for the delivery of personalized services during commuting time.  Improved automatic diagnostic technology or robot pharmacists?  More demand for health professionals to dispense lifestyle and general health counselling.  Increased automation in legal affairs?  Less time on research means more time for, and emphasis on, negotiation.

I could go on, but I won’t.  The point, as Tyler Cowen makes in Average is Over (a book whose implications for higher education have been criminally under-examined) is that the future in many fields belongs to people who can best blend human creativity with the power of computers.  And so the relevant question for universities is: to what extent are you monitoring technology trends and thinking about how they will change what you teach, how you teach it, and how you evaluate it?  Or, put differently: to what extent are your curricula “future-ready”?

In too many cases, the answers to these questions land somewhere between “not very much” and “not at all”.  As a sector, there is some homework to be done here.