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William Watson: The problems with trying to measure productivity

A new research group wants to speed up the nation's productivity growth. But the view from 35,000 feet doesn't always help

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Maybe you saw the piece by University of Calgary economist Trevor Tombe in the front section last week about how Ottawa, via the Social Sciences and Humanities Research Council, is giving a group he heads at UCalgary $6 million to expand its existing research on productivity into a national network.

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Tombe is one of the country’s most respected policy economists and there are many worse things the government could be doing with our money — have you read a federal budget lately? I’m sure this top-level group will produce much interesting research over the next 15 years. It may even increase my own productivity by providing grist for this always ravenous mill.

The first thing that crossed my mind, though, when reading Tombe’s column was that the group’s output won’t itself show up in the productivity numbers. The most common measure of productivity is output per hour, with output measured by the dollar value of what’s being produced. But this research won’t be sold. Like most of what’s produced in the public and para-public sectors, it will be given away. So what’s its value? Hard question with no real answer. People do try to measure public-sector productivity (which is not a contradiction in terms). The U.K. is a leader in that. But the usual resort has been to value public output at what it costs to produce. (That MP who’s refusing his pay raise is therefore reducing Parliament’s output!)

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No one thinks that’s satisfactory. And it explains why the productivity data you see is almost always for the business sector of the economy — where people do sell what they produce and what buyers pay presumably does reflect the value they place on it.

Even here there are problems, though. Say 100 autoworkers produce the same number of cars in a week as 100 autoworkers did two decades ago. You might think their productivity hadn’t changed. But what if they’re better cars? That would be reflected in people paying more for them, once you took general inflation out of the sales figures. So the workers are producing more value. There must be very few industries where what’s currently being produced is exactly the same as what has always been produced. Taking these product quality changes into account also isn’t easy.

For the productivity research the Tombe group will generate there’s the further problem of counting hours. Academics don’t punch a time clock. (Is there any other kind of clock, BTW?) They can be thinking about their research any waking hour and maybe even some non-waking. So when measuring productivity in this kind of venture both the numerator and denominator of “output per hour” are tricky.

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Doubtless the project’s leaders and the granting agency itself will monitor individual researchers’ productivity: are they completing what they agreed to do in the time they promised to do it? And is their research up to acceptable standards? (Review committees presumably will decide that.) And so on.

I’ve been reading economic research for a long time now and can attest it has experienced astonishing productivity improvements — basically because of computers. “Exponentially” is a much misused adverb. Mainly when people use it they mean “a lot!” But data availability and computing power really have increased exponentially in recent decades. A good empirical study these days is much more exhaustive and painstaking in both analysis and inference than used to be possible.

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That said, the intended output of this new research initiative is to improve public policy so as to increase economy-wide productivity, whose growth has slowed in recent years. The idea is that better evidence will produce better policy.

That sounds unobjectionable. But success will be hard to measure. If productivity growth speeds up nationwide, is it from better policy or from other things? And if you can control for these other determinants of productivity in trying to gauge your success, well, doesn’t that imply you already know the determinants of productivity?

Tombe writes that because Canada’s productivity problem “is complex and long-term, the solution has to be as well.” Is that really true? You may have a chronic health problem that, once properly diagnosed, clears up quickly. There’s also the problem of how conclusive evidence will be. Productivity growth dropped sharply through most of the rich world in 1973. The quadrupling of oil prices because of the OPEC embargo was a prime suspect but in fact prominent researchers (e.g., Nobelist William Nordhaus) were still doing “economic archeology” on it all 30 years later. And in the first decade of research there really wasn’t much of a consensus.

I hope some of the new group’s productivity research will be Hayekian. Friedrich Hayek (also a Nobelist) believed successful economies run on very micro knowledge — “the particular circumstances of time and place.” Mark Carney may well have intimate knowledge of how his office works or even the Ottawa bureaucracy writ large. But no one has intimate knowledge of the whole economy.

An interesting question for research is how many hours businesses and other organizations must devote to activities — reporting to regulators, doing their taxes, mapping their carbon footprint, ensuring their workforces are satisfactorily diverse — that do not contribute directly to producing widgets. If we insist they do all these other things, why would we be surprised if their widgets/hour stagnate?

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