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Baumol and Big Data
October 2, 2012 - 9:20pm


“Trash removal costs go up not because garbage collectors become less efficient but because less labor is needed to manufacture a single computer, for instance, and wages in that industry (and others, as well) continue to climb.” – William Baumol, The Cost Disease, p. 44

If you read only one book about higher education this year, read The Cost Disease, by William Baumol.  It’s essential, brilliant, and even readable.  And it answers an important question – why the costs of health care and education keep going up – more intelligently than anything else I’ve seen. (My book doesn’t come out until January, so he can have this year…)

William Baumol is an economist who gained fame in the 1960’s for discovering what came to be known as “Baumol’s cost disease.”  It’s the observation that rates of productivity growth are uneven across industries, and that the products or services of the industries with lower productivity growth will gradually and inexorably become more expensive than those of the industries with higher productivity growth.

In the original paper, Baumol looked at a Mozart string quartet.  It takes just as many musicians just as long to play today as it did two hundred years ago; in real terms, the productivity increase for string quartets has been zero.  By contrast, the productivity growth in telecommunications, driven almost entirely by technology, has been astronomical.  The rate of growth in telecom has been so high that companies can lower prices and increase profits at the same time.  (Bless him, Baumol prefers to look  at increased wages, rather than increased profits.  Suffice it to say that actual telecoms don’t always work like that.)

Baumol isolates human labor as the key factor. (There’s a vague echo of Marx’ labor theory of value, though Baumol doesn’t acknowledge it.)  Industries that have been able to replace expensive human labor with inexpensive machinery (when amortized over lots of production, anyway) have been able to lower costs for everyone. The share of GDP they’ve forfeited by lowering costs has moved over to what he calls the “stagnant” sector, which is the set of industries in which production is still very hands-on and therefore hard to speed up.  Education and health care are the most conspicuous examples, though it also holds for live entertainment, restaurants, and legal services.

To take an easy example from the book, in the 1800’s, most Americans worked in agriculture.  Now only about three percent do, yet that three percent produces far more food than the majority of the country was able to produce just a few generations ago.  Technology has been the key.  Even in manufacturing, though the job losses since the midcentury peak have been dramatic, there’s still plenty of stuff being produced; the key change has been that the producers have become so much more efficient that they don’t need as many employees.

Education and health care haven’t enjoyed those gains.  They’re still producing in much the same way they did fifty years ago.  As a result, salary increases for people who work in education and health care aren’t paid for through increased production; they’re paid for by charging more.  Instead of the virtuous cycle of higher wages and lower prices that the more “productive” sector has enjoyed, we’ve endured the vicious cycle of lower wages and higher prices that comes from a long-term productivity squeeze.

Baumol is oddly content with that.  He argues throughout the book that when the economy grows enough as a whole, the fact that a few sectors are taking up more of it really doesn’t matter much.  I can’t share his confidence, since I can’t help but notice that middle class wages have stagnated for decades even in the face of annual productivity increases.  We’ve had a self-reinforcing distribution problem that has concentrated the gains of productivity in the top tier, leading to a politics of resentment among the rest.  (One could easily explain the election as the two parties offering different versions of who to resent.)  Baumol is right that GDP as a whole can certainly handle increased tuition, but it doesn’t follow that therefore a middle class family can.  To paraphrase Tug McGraw, ya gotta disaggregate.

Still, the real breakthrough of the book for me is the discussion of Big Data as a way around the cost disease.  The cost disease happens – entirely without villains, corruption, or ill intent – when some sectors increase productivity faster than others for an extended period.  Baumol notes that one way around the problem is to zero out productivity growth altogether.   He’s right, mathematically, but I don’t think we want to live that way.  So the other way around it – which the book explores in the context of health care, but largely ignores for education – is for the slow-growth sectors to pick up their pace.

Baumol contrasts car manufacturing to car repairing: the former allows for easy productivity improvements, but the latter doesn’t.  As Tolstoy would have noted, every new car is the same, but every broken car is broken in its own way.  (That’s from “Vanna Karenina,” about a minivan in a bad spot.)  It’s harder to cut costs on the repair side because the diagnostic step still requires trained human intervention.

And that’s where we in higher ed have an opportunity, though Baumol  doesn’t raise it himself.  We’ve tried to use midcentury standardization – “take out your number two pencils” – with mostly terrible results.  But we haven’t used Big Data more strategically, as a diagnostic.

Amazon.com does this all the time.  When I log on, it offers suggestions based on what I’ve bought or looked at before.  When I look at a particular book, it offers “others who bought this also liked…”  It looks simple, but there’s an impressive amount of data crunching that makes that possible, and the data crunching has only become possible as computers have hit their stride.

As an industry, we still use the artisanal mode of production.  Until recently, there was a good argument that it was the best we could do.  But I’m not sure that’s true anymore.

To me, the really exciting prospects offered by Big Data and MOOCs and, yes, outcomes assessment, is in helping us allocate human intervention most effectively.  In health care, for example, evidence-based reforms have led to procedural checklists that have resulted in better patient outcomes at remarkably low cost.  Instead of basing incredibly expensive institutions on anecdotes and self-reinforcing hunches, we have the option of starting to base them on what actually works.  Instead of denoting learning in units of time – the credit hour – which defeats any possibility of productivity improvement, we could experiment with different ways of improving student capabilities.  If we find a way to get a student to mastery faster, well, that’s productivity.

Oddly for such an insightful book, Baumol seems to take for granted that certain activities are simply trapped, and must be trapped forever.  That strikes me as a failure of imagination.   Since I don’t share his political complacency, I also don’t share his sense that, say, higher education will just keep on getting more expensive forever and that’s okay.  It’s not okay – I have polls and an Occupy movement to prove it – and it’s not necessarily inevitable.  We’ll need to be willing to make some pretty drastic changes, but that’s what smart people who intend to survive do.  It’s how it’s done.  We can do this.

Still, Baumol’s formulation is the only serious answer I’ve seen to why the costs of education, law enforcement, and health care have gone up drastically around the world for decades.  It isn’t about this administrator or that one; if that were the issue, we’d see exceptions.  The fundamental problem – the issue that colleges have tried to evade with adjuncts – is structural.  It’s Baumol’s cost disease.  Until we come to grips with that, we’ll be stuck in the politics of resentment.
 

 

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