Two or Three Scenarios

Near Versus Far Future Thinking

In The Making of a New Industry David Hawk envisaged a widening separation between the traditional, local industry of small firms and small to medium sized projects and a technologically driven, increasingly oligopolistic global industry. In identifying the key trends driving this change of industry structure, Hawk was clearly correct in his view that the new industry would be far more product focused than the traditional industry.

In the three scenarios outlined previously, the traditional industry more or less fits into the business as usual approach of scenario one, and the global industry rather looks like it’s been following the upgraded and modified path in scenario two. These two scenarios cover the likely outcomes of near future developments, and they are both firmly based on well-established fundamental characteristics and trends that we observe today. The two scenario argument is that the near future should be sufficient for our strategic thinking and planning, and the challenges the industry faces will be resolved at both of these two levels, local and global.

Why then have the third scenario? The sort of advanced buildings and structures scenario three envisages will not be technically feasible for some time, it could take several decades before the experimental work being done today becomes the standard technology of the future. Nevertheless, this experimental work is the basis of the industry tomorrow. For example, there is a lot of work being done in labs around the world on molecular engineering of materials and new forms of production processes, and some of this new tech is starting to appear on site.

Energy is a particular focus. Solar facades and various forms of embedded collectors and sensors are, if not common, no longer outlandish. Since 2015 new buildings in France must have either a solar roof or a green roof, and the new HQs Google, Apple, Amazon and Facebook are building in 2016 take building design and energy efficiency to new levels. They are also installing very sophisticated building management systems. Elsewhere, sensors are being placed in structures to monitor their condition, scanning is replacing visual inspections for cracks and fatigue, and remote sensing is well underway. The scientific and technological base of the new industry today will be one driver of the development of the transformed industry of tomorrow.

The other driver of scenario three is IT and increasing digitisation. The rapid pace of development in machine learning and the rollout of the Internet of Things (IoT) will create many currently unthought of possibilities in their application to construction and the built environment. The IoT will produce a network of billions of connected objects, appliances and systems, most of which will be in buildings that will act as the nodes in the network. With major players like Cisco, Microsoft, Esri, IBM and a multitude of others pushing smart and connected cities as the big new thing, there is no shortage of ideas or possibilities. Then there is big data, with the release of huge data sets by some cities and the opportunities analytics offer.

It’s not just in the university and corporate labs and R&D facilities that new thinking is taking place. We are also seeing proposals for adventurous new buildings and structures that are at the limit of what we are currently capable, some of which may turn out to be test beds for transformational technology. Examples are the various biospheres that have been attempted, the sea-steading movement associated with Peter Theil, and Bruce Bigelow’s inflatable space modules.

It was at the first public demonstration of virtual reality (VR) headsets in 1990 that William Gibson made his now famous observation that the future is unevenly distributed¹. Those early, primitive, nausea-inducing systems were clunky and expensive, but after a couple of decades of development the costs of the key components, particularly small high-res screens and sensors, had fallen to the point where consumer products were possible. The big gadget releases in 2016 are the VR headsets from Oculus, Microsoft and Samsung, and everyone from architects to zookeepers have started thinking about how this ‘new’ technology could be used.

This trajectory, where it takes two or three decades for a technology to move from the periphery to widespread adoption and use is very common. American industry did not fully switch from steam to electric power till the 1930s, the internet had been around for over 20 years before Netscape made it accessible in 1994 by allowing graphics (it had been text based). At that time, globally, there were about 600 websites and a couple of million connected computers. Amazon and Ebay launched in 1995. There are many examples, technology proceeds a step at a time as the necessary system components come together and get improved. The question is ‘What early, primitive systems around today might be the foundations of the transformed construction industry of tomorrow?’.

¹ As told by Kevin Kelly in The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, p.215.

Many Dimensions of Productivity

Productivity is Not a Magic Pudding

Of all the tasks given to national statistical agencies, measuring productivity is one of the most difficult. There is an immense, intricate infrastructure of data definition, collection, and management required to produce estimates of annual growth that typically are around one or two percent a year, plus or minus.

As countries like Australia move from an industrial economy to an economy that is largely made up of various services, the challenges in measuring and understanding productivity growth become more complex. And, it must be said, more interesting. It is, at present, much harder to capture output and economic activity data in the digital economy, so statistical agencies are developing new data sources that tap into sources like business tax information, accounting software and other digital data.

This may be one reason for the productivity slowdown we are currently in, a topic that is currently the focus of much attention that will be in another post. If output is not being recorded or is underestimated while the workforce is increasing, the growth rate and level of productivity will also be underestimated.

In an agricultural economy, the two resources that determine growth are land and labour, with a rising population driving increases in output. If output and labour input move together there is little or no increase in productivity, because productivity is the ratio of inputs and output.

The creation of an industrial economy required lots of investment in the built environment, infrastructure, and machinery and equipment. The stock of physical capital from that investment, when combined with labour, resulted in a higher rate of productivity growth than previously possible. As that higher growth rate was sustained over decades of industrialization it allowed incomes and living standards to rise.

The benefits from increasing productivity are similar to the gains from compound interest, small annual amounts can, over enough time, lead to large gains. However, because it is incremental and compounds slowly, productivity is not really suitable as a policy target. Unlike other targets such as business investment or employment, the long-term nature of productivity growth means year-to-year rates should not be used as a policy target. There are also short-term fluctuations due to the business cycle, as the productivity growth rate tends to move up and down with GDP growth rates.

The reason productivity is such a central idea in both economics and politics is this role it plays in long-term economic growth and development. Productivity growth comes mainly from invention and innovation, and these in turn come mainly from new knowledge in the form of scientific and technological discoveries.

This is why the political agenda linking R&D and innovation to productivity and incomes and living standards is so attractive, to those who understand the dynamic and value science. It seems to be a bottomless source of growth and prosperity. This is, of course, not really the case, because to get productivity growth requires substantial investment. Productivity is not a magic pudding.

Investment in workforce skills and training, known as human capital, has joined physical capital as a driver of productivity. Thus education in all its forms, from preschool to postgraduate, has become an important part of the policy mix that aims to improve Australia’s productivity performance. In a knowledge economy intellectual capital is as significant as physical capital. The graphs below are from a 2016 Austrade publication, and the international comparison shows Australia is not doing too badly across the industries that make up the economy.

The high rate of productivity growth in the 1990s is attributed to the rapid development of information technology and telecommunications (ITC) industries, both from the surge in investment in these industries and because these are what is known as general purpose technologies. Their products and service are widely used in other industries, and are adapted for a wide variety of uses.

The expectation is that new technologies like nanotechnology, molecular biology and genetic medicine, machine learning and artificial intelligence, additative manufacturing (3D printing) and remote sensing will be the drivers of growth and productivity in the coming decades. Obviously, these industries need highly skilled people and will require capital on a large scale, so getting the policy mix right is crucial.

But productivity has many different dimensions. The marvelous thing about productivity is its malleability, it can be used to support a wide range of policy options (the magic pudding aspect). We hear of productive cities and boosting R&D productivity by better linking industry and academic research. It can support arguments for more infrastructure, tax breaks for business investment or corporate taxes in general, more spending on education, energy diversification, enabling start-ups and venture capital, microeconomic reform such as competition policy and labour markets, increasing connectivity and so on.

All these policies can support improved productivity, if they are well designed and implemented. That is a significant caveat. However, many of the current policy settings were put in place when we had an industrial economy and are not really suited to the knowledge and services orientated post-industrial economy of the 21st century. If raising productivity is the goal, many different policies need to work effectively and reinforce each other. And they will only work over the medium to long-term.

The other issue is the unequal distribution of gains from productivity growth. Australia, like the US and UK, has had wages and average earnings growing well below the rate of growth in productivity since the financial crisis in 2007-08. That spills over into the wider debate about the increasing inequality of income distribution, its causes and consequences, the reasons for the slowdown, and appropriate policy responses to the slowdown in productivity.