Go figure – the ups and downs of productivity
Figures for the economy’s productivity performance haven’t looked good for the past decade, causing consternation among economists and business people. But a careful study by the Productivity Commission has failed to find any particular problem, nor anything we could do to make the figures look better.
Productivity is a measure of the efficiency with which the economy turns inputs of labour and capital into outputs of goods and services. Thus productivity is measured as output per unit of input.
The more we can improve productivity the better off we are. We have in fact being increasing it a little almost every year since the Industrial Revolution, and this is what has made us so much more prosperous. So if you believe the goal of economic management should be to increase our material standard of living (which I don’t), nothing is more important than ever-improving productivity.
The simplest (and probably least inaccurate) way to measure productivity is to take the quantity of goods and services produced during a period (real gross domestic product) and divide it by the number of hours of labour required to achieve that production.
Doing this each year shows that our ‘‘productivity of labour’’ improved unusually rapidly in the second half of the 1990s, but then showed little further improvement during most of the noughties. Over the past two or three years, however, it has returned to a reasonably healthy rate of improvement.
But you can improve the productivity of labour simply by giving workers more machines to work with. And this tells us nothing about the efficiency with which the economy’s physical capital is being used. So in recent years it has become fashionable to focus on a more sophisticated measure called ‘‘multi-factor productivity’’.
This is the growth in real GDP (output) that can’t be explained by any increase in inputs of both labour and physical capital. So, in principle, multi-factor productivity represents ‘‘technological progress’’ – the invention of better physical technology and the discovery of better ways to organise the production of goods and services. It’s technological advance that does most to raise material living standards.
When you look at our performance over the past few years you find that, though the productivity of labour has been improving at a reasonable rate, multi-factor productivity hasn’t improved. It was this that staff at the aptly named Productivity Commission set out to investigate in a study published last week. They found that the flat performance of multi-factor productivity in the market economy was explained mainly by an actual decline in the multi-factor performance of manufacturing. So they focused their investigation on manufacturing.
Estimates by the Bureau of Statistics show that between 1998-99 and 2003-04 multi-factor productivity in manufacturing improved at a rate of 1.3 per cent a year. But between 2003-04 and 2007-08 it fell by 1.4 per cent a year. Since then (up to 2010-11) it has deteriorated at the slower rate of 0.8 per cent.
Delving further, the researchers found that two-thirds of the deterioration between the first two periods could be explained by just three of manufacturing’s eight subsectors. From worst to least worse: petrol and chemicals, food and beverages, and metal products.
Trouble is, they could find ‘‘no overarching systemic reason for the decline’’. That is, no problem or problems you could tell the government it needed to fix.
What they found were several factors that made the figures look bad but weren’t actually bad themselves, plus one factor we all know about, can’t do much about, but have reason to hope will improve soon: the high dollar.
The metal products industry’s poor performance was explained mainly by a big expansion in alumina refining capacity which had yet to come on line. Obviously a temporary problem.
The petroleum and chemicals industry’s poor performance was explained to a significant extent by increased investment by petroleum refineries to meet new environmental standards. That is, there was an improvement in the quality of their output which the figures didn’t pick up.
The food and beverages industry’s poor performance was partly explained by a change in consumer preferences in favour of products made in smaller-scale, more labour-intensive bakeries. No probs if that’s what the punters want.
In both petroleum and chemicals and food and beverages the poor performance was explained also by reduced use of production capacity, caused largely by the effect of the high dollar in reducing exports and increasing competition from imports.
But now I must give you the product warning economists keep forgetting. Like so many other concepts in economics, multi-factor productivity is simple in principle but as ropey as hell in practice. Putting a number on the concept requires you to make a lot of unrealistic assumptions (perfect competition, equilibrium, for instance) and use statistics that don’t accurately measure what they’re supposed to measure.
As the researchers acknowledge, multi-factor productivity is measured as a residual: after you estimate the amount of production you subtract an estimate of the amount of labour used and an estimate of the amount of capital used (particularly dodgy) and what’s left is multi-factor productivity.
It’s what a modeller would call an ‘‘error term’’ – the net result of all the mismeasurement of output, labour input and capital input. So, as the researchers acknowledge, the figures they have used can’t be taken as a reliable measure of technological progress.
My word for it is ragbag: technological progress may be in there somewhere, but so will be a lot of other things, real and non-existent.
You can work out the figures for multi-factor productivity, but if they look good you don’t know whether they really are, nor why they are. If they look bad it’s the same.
What the Productivity Commission’s study tells me is that even with figures that look really bad, it can find nothing amiss. Worrying about measures of multi-factor productivity is jumping at shadows.
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