I’ve been working on a piece about productivity, targeted as an Economics Briefing for the Fin Review. This used to be the Schools Brief, but they’re trying to broaden the appeal a bit. The target audience, I think falls into two groups
* Students with a year or two of economics under their belts
* The subset of the Fin audience with an interest in economic policy issues, as opposed to business news
Any suggestions or criticisms will be much appreciated.
By the way, I hope no-one minds being used as unpaid editorial advisers. The payoff is that you get to read stuff well before the print audience.
Productivity may not be the only thing contributing to a difference between high and low living standards, but it is easily the most important. By comparison, factors such as mineral resources and other natural advantages are almost insignificant. Australia’s mineral resources, for example, contribute about 4 per cent of our national income. This is less than the amount added through productivity growth every three or four years.
Ultimately, all productivity growth comes from new knowledge about science, technology and economic organisation. The standard of living enjoyed by the average Australian today could not have been achieved by any society 100 years ago, no matter how hard people worked, how much they saved and invested or what government policies were adopted.
However, the links between technical progress and productivity growth are many and varied. Some are clear and direct, but others are complex and poorly-understood. Partly because of this, there are many different concepts of productivity, and many complex disputes about its measurement.
The simplest form of productivity improvement is called ‘embodied technological change’. This is what happens when Intel or IBM comes out with a new computer chip that is faster, smaller and cheaper than the one it replaces. From the point of view of a country like Australia, which mostly imports its computers, this kind of embodied technical change is like a free gift. When we replace our old computers with new faster ones, we can do more of whatever we do with no additional effort on our part.
Improvements in physical capital are not, however, the most important way in which improvements in knowledge contribute to productivity. What matters even more is the skills and knowledge of workers, or ‘human capital’. In part, this depends on the progress of knowledge in the world as a whole – doctors in 1900 could not possibly know about antibiotics, for example. But more significantly, human capital depends on the availability and effectiveness of education (of course, education is costly, and this cost needs to be taken into account when we think about improving productivity through education).
Technical innovations and improvements in human capital create the potential for profitable new investments, thereby giving rise to a third potential source of productivity growth, known as capital deepening. Capital deepening is usually said to arise when the value of the stock of capital per worker increases. In this sense, there has been almost continuous capital deepening ever since the Industrial Revolution. However, the evidence suggests that the ratio of physical to human capital has been more or less constant (if you assume that workers are paid roughly in line with their contribution to output this observation follows from the fact that the wage share of national income has been stable over time).
Finally, there may be improvements in economic organisation, including improvements in economic policy. An important example of an improvement in economic organisation is the invention, in the mid-19th century, of the limited liability corporation. In Australia, it is commonly claimed that changes in policy during the 1980s and 1990s, collectively referred to as microeconomic reform, were a source of productivity growth.
Assessing the contribution of different factors in promoting productivity growth is a complex exercise, fraught with error. Consider the simplest possible concept of productivity, which is output per unit of labour. The output of an economy is typically measured by Gross Domestic Product. This aggregate measure has well-known problems, but there’s not enough space to discuss them all here. Even more problems arise when we try to measure the value added in some particular sector of the economy.
But the real issue is with the measurement of labour input. Until relative recently, it was usual to look at output per worker. This made sense when most workers were full-time employees, with standard hours of 35 to 40 hours a week common to most of the workforce. But when some workers have one or more part-time jobs, and others are working 60 hours a week, this no longer makes sense, and it’s more usual to look at output per hour of work.
But even output per hour is not really the right measure. Over the last fifteen years or so, there’s been a lot of pressure to speed up the pace and intensity of work. This squeezes more labour input into a given time, but there’s no real change in productivity. Since there are no good measures of work intensity, there is no easy way to tell how much this change has affected measured productivity, but it’s been argued that the bias could be 5 or even 10 percentage points, which would account for much of the observed increase in productivity between, say, 1989 and 1999 . On the other hand, many economists dismiss claims about increases in work intensity during the 1990s as being based on little more than anecdotal evidence.
While labour productivity is interesting, the discussion above suggests that it’s necessary to take account of capital as well. The standard approach, known as growth accounting involves estimating how much of any observed increase in output can be explained by increases in inputs of labour and capital. The unexplained residual is given various names, of which the most common is ‘multi-factor productivity’.
The size of the residual attributed to multi-factor productivity varies depending on how much the capital input is adjusted to take account of embodied technological progress and how much the labour input is adjusted to take account of human capital. The more quality adjustment that goes on, the less is left for the residual to explain. But, almost invariably, there is a positive residual.
In Australia, estimates of multifactor productivity have been produced by the Australian Bureau of Statistics since the mid-1990s. On statistical grounds, the ABS divides the data for the period since 1964 into subperiods called ‘productivity cycles’. In general, productivity cycles are about five years in length and a typical macroeconomic cycle of ten years contains two productivity cycles, one corresponding to the upswing and the other to the downturn.
This series has excited a lot of interest because, after declining, with occasional upticks, during the 1970s and 1980s, the estimated rate of multi-factor productivity growth rose substantially in the productivity cycle from 1994 to 1999. Initial estimates suggested a growth rate of 2.4 per cent, an all-time record, leading a number of commentators referring to an economic ‘miracle’, or the emergence of a ‘New Economy’. Subsequent revisions yielded an estimated growth rate of 1.8 per cent, comparable to the ‘Golden Age’ of the 1960s, but not unprecedented.
Sceptics argued that the increase in measured productivity was, at best, a once-off blip in the data, and at worst, a statistical illusion. This implied that improvements in measured productivity growth would prove transitory.
The case that the productivity ‘miracle’ was illusory had three main elements. The first, and most important, was that of increased work intensity. It was argued that the apparent improvement in productivity simply reflected unmeasured increases in labour input arising from a faster pace of weak, the loss of informal tea breaks and so on.
The second element of the argument related to the ABS concept of productivity cycles. It was argued that productivity moves in line with the economic cycle, being weak in recessions and strong in recoveries. Thus the upsurge in productivity growth in the mid-1990s was partly due to things like improved utilisation of capital due to economic recovery.
Finally, there were technical disputes about the composition of the index. The MFP index is constructed for the market sector, but excludes a range of business services, which expanded rapidly in the mid-90s as a result of contracting out. On the other hand, the index includes agriculture, and was boosted somewhat by generally favourable seasons in the mid-1990s.
Sceptics claimed vindication when the measured rate of productivity growth dropped sharply after 1999. The incomplete productivity cycle beginning in 1999 has shown MFP growth of less than 1 per cent per year, below the long-term average.
By contrast, supporters of the view that microeconomic reform has boosted productivity have argued that productivity growth is merely ‘taking a breather’ as a result of random shocks such as the drought conditions that have been widespread over the past few years. They point to the long economic expansion since 1992 as evidence that the economy has become more resilient and adaptable. However, some have argued that reform has slowed down under the Howard government and that this is already being reflected in the productivity statistics.
Economic disputes are rarely resolved quickly, and the dispute over productivity growth seems likely to be no exception. A slide into recession, with an accompanying decline in productivity might settle the argument in favour of the sceptics. Conversely, a renewed acceleration in growth would strengthen the case for an Australian economic ‘miracle’. It is quite possible, however, that the actual outcome will be somewhere in between, a slowdown in growth but not an outright recession, leaving the issue unresolved.