Measuring the Built Environment Sector

Public Policy and a BES Satellite Account 

The building and construction industry links to other industries in a variety of ways, and an earlier post suggested measuring the extent of the built environment sector in a satellite account is the best representation of the dense network of firms involved in the creation of the built environment. The data required to measure the contribution to GDP and share of the economy of the built environment sector is available, but scattered across separate industry data collections. This understates the macroeconomic importance of the built environment sector and the role it plays in improving the performance of cities.

Because building and construction is so diverse it is hard to get an overview of the industry. With a vast variety of projects in all possible locations made out of materials ranging from primitive to rustic to ultra-sophisticated the industry, particularly on a global scale, is so broad that some system of classification and categorization is necessary. With the development of the Standard Industrial Classification the industry has come to be defined by the data collected by national statistical agencies, but the role of the industry is much wider and deeper than the statistics show.

The typical view of the industry called ‘construction’, is an industry made up of three sectors, residential building, non-residential building and engineering construction. Because statistical data on activity and work done is presented in this form, most of the discussion and reporting of the industry also follows this pattern. This is not a bad thing, but is not truly reflective of an industry as diverse and wide-ranging as building and construction. Industry output statistics represent the industry as a set of functional projects, like detached housing or retail and railways or hospitals, despite the fact that many buildings are mixed use. Also, there are many other ways of classifying and categorizing building and construction projects.

The idea that the construction industry as measured in the national accounts or using the standard industrial classification of industries, is only one part of the creation and maintenance of the built environment and the range of industries that encompasses is not new. David Turin in the 1960s and the Bartlett International Summer School series in the 1980s advocated looking at the sector that produces the built environment in broad and integrative terms. The industry’s extensive linkages with other sectors, measured through the industry’s high multiplier effects gives the industry an important macroeconomic role. Through those linkages the impact of construction activities on other parts of the economy is much greater than their direct contribution.

The Built Environment Sector

 

The term that arguably best encompasses the extraordinarily large number and range of participants in the creation and maintenance of the built environment, from suppliers to end users, is the built environment sector (BES). Research suggests adding the contributions to economic activity and output from other industries like manufacturing, materials and technical services is about the same as the direct contribution from construction. For example, in most OECD countries construction is between 5 and ten per cent of GDP, so the BES would be between ten and twenty per cent across those countries.

Measuring the BES would help public policy and macroeconomic management for two reasons. Firstly, the macroeconomic contribution of the BES to aggregate demand and employment is large, and possibly the largest in many countries. It is also one of the most volatile components of the economy, with annual rates of growth or contraction greater, and often much greater, than changes in GDP, making the BES a key driver of the business cycle. A satellite account collects those characteristics and thus provides data on trends in activity and output that have a significant effect on the national economy. Perhaps more importantly, changes in the composition of output of the BES would be a leading indicator of future demand as current new work completes, reflecting changes in the early stage project preparation activities required for future work. Through industry linkages and lags, such slowdowns or pickups in project preparation can be strongly procyclical, exacerbating the peaks and troughs of the business cycle. Because of the number of small firms found across the BES, the employment consequences of changes in activity levels are also significant.

Secondly, measuring the BES provides a way to measure the effectiveness of discretionary fiscal policy, when that involves changes in expenditures on building and construction. Discretionary fiscal policy, as a response to the business cycle, is an increase in public spending to counteract a downturn in the business cycle or a recession, typically targeting public investment in both social and economic infrastructure. Tracking the impact of such expenditures through the economy is difficult but would show up in a BES satellite account. This would also allow a finer-grained analysis of the employment effects of different types of projects and programs.

In this context, it is worth noting city policies involve significant infrastructure spending, and is often their main focus. However, it is the associated induced development around the new infrastructure that drives longer-term growth. A satellite account would capture all that activity over time, thus giving a measure of the effectiveness of city policies in promoting urban growth and development. It may be that regional or city-scale satellite accounts would be most useful for urban planning and management.

A satellite account for the building and construction industry would also reflect changes in the range of activities and types of firms that contribute to the built environment. In the broad view of the industry, and in a satellite account, more of these activities would be included, and the role and development of the sector better understood.

Groak, S. 1992. The Idea of Building. London: E. & F.N. Spon.

Turin, D. A 1969. The Construction Industry: Its Economic Significance and Its Role in Development, UNIDO, New York.

Technological Diffusion Takes Time

How Rapidly Will New Technology Spread Across an Industry?

One of the underlying ideas here is that the industry we call ‘construction’ is a ‘technological system’, a densely connected network of firms and organizations that produces and maintains the built environment. This system can be divided into three different levels (local, national and global) and three distinct sub-sectors (residential and non-residential building and engineering construction), each with their own characteristics and therefore each with their own development trajectory. The driver of these differing development trajectories will be how rapidly firms adopt the range of new twenty-first century technologies now emerging.

These technologies will greatly enhance our abilities to reorder the physical world, the ultimate purpose of a technological system. These abilities are increasing through continued improvement in hardware, both mechanical and silicon, and software, with new applications and programs and the development of machine intelligence.

How firms use technology, in the way it is adopted, adapted and applied, varies widely within the construction technological system, and this is a significant driver of change. After 100 years the construction technological system is well developed, and as a mature system it is also conservative. Mature technological systems accumulate capital and skills, and this investment in the existing system gives it great inertia until some disruptive change emerges. How and why a new technology spreads through the economy and society are determined by many factors, but studies of historical cases like steam power, tractors, electricity, phones and the internet have given us good examples of technology diffusion and its dynamics. 

The rate of adoption of technologies within the firms that make up an industry, which is affected by a range of factors, has also been studied. The technology adoption literature discusses rank effects, which are the different individual characteristics of firms such as their size, and how they affect the rate and extent of adoption of new technologies, also the effects of competitive dynamics, which is how the adoption of new technology by one company in an industry influences the adoption of technology by other companies in that industry.

From economic history, we know major new technologies take time to diffuse through the economy because they require parallel changes in forms of organization, methods of production and patterns of consumption. These are not decisions firms and households make quickly or easily, due to the investment in upgrading machinery and equipment usually needed. New technologies are ‘embodied’ in this new physical capital, in the way a 20 year old car incorporates the technology of two decades ago when it was made. In the literature there are many studies of the introduction of new technologies, and the consensus is that it typically takes 15 to 30 years for a new technology to reach 90 percent of its potential market. A well-known example is Paul David’s research on electrification in the US, which took 30 years from 1900 because of the fundamental changes industry and households needed to make to take advantage of electrical power.

Another good example is tractors. The figure below shows the tractor slowly displacing horses and mules in US agriculture from 1910 to 1960. Horses and mules declined from about 26 million in 1920 to about 3 million by 1960, while the number of tractors rose from zero in 1910 to 4.5 million by 1960. One reason for the slow spread of tractors was the incremental innovation needed to increase their capabilities, which made them more attractive over time. A second was an increase in farm wages after 1940, which made tractors more economic. 

The spread of new technologies through society and the economy often takes longer than expected, based on the benefits the new tech typically brings. Paul David observed many people "lose a proper sense of the complexity and historical contingency of the processes involved in technological change and the entanglement of the latter with economic, social, political, and legal transformations. There is no automaticity in the implementation of a new technological paradigm, such as that which we presently discern is emerging from the confluence of advances in computer and communications technologies." The same could be said today about the advances in machine learning and artificial intelligence.  

Generally, discussion of construction industry development over the next decade shares a view that the impacts of new technology, including BIM, will be gradual, changing industry practice over time without significantly affecting industry structure or dynamics. Given the entanglement of economic, social, political, and legal factors in the construction technological system this might be the case, however there are good reasons to think this may be wrong because these studies either predate the current surge in machine learning or do not consider the potential of AI. Machine learning, AI, automation and robotics are an interconnected set of technologies that are evolving quickly, enabled by expanding connectivity and the massively scaleable hardware available today. 

Technologies have to be adapted into solutions for specific tasks, with their role in the workplace evolving over time as machines reach the levels of performance required. Only when their cost is low enough will they be adopted, but developing and engineering new technologies takes time and money. Once established, the single most important factor in technology uptake is the price/performance relationship, or the gain in productivity or other measure (time, quality, safety) the new technology delivers for a given level of investment. To successfully displace an older technology a new technology often has to provide an overwhelming economic advantage to overcome the inbuilt conservatism of an existing industry, due to the investment by incumbents in the current system. This is a significant barrier because, at present, the price/performance trade-off is not there for the many small firms in building and construction because the investment required to upgrade capability is too large for the size of these firms. However, for larger firms the issue is not whether to invest, in BIM for example, but how much, if they intend to compete in the upgraded and modified category of firms in the industry.

A period of technology-driven restructuring of the building and construction industry may be about to start, similar to the second half of the 1800s when the new materials of glass, steel and reinforced concrete arrived, which led to new methods of production, organization and management. There are many implications of such a restructuring. Some firms are rethinking their processes in response to developments in AI, robotics and automation as capabilities improve quickly and the range of new products using these technologies expands. Many firms, however, are not. Meanwhile, frontier firms are exploring new tech and pushing the boundaries of what is possible, and are inventing new processes.

There is understandable skepticism across the industry about the extent of impact of new technology, the ‘business as usual’ approach has worked well for many firms. With the characteristic changeability of construction sites particularly challenging for automated and robotic systems, it might take decades of investment for machines to learn how to do site work. On the other hand, once a robotic system has learned how to do something, that skill can be copied and replicated at minimal cost, and that applies as much to engineers, architects, quantity surveyors, urban planners and project managers as it does to crane drivers, bricklayers and electricians.

 

Rodolfo E. Manuelli and Ananth Seshadri 2014. Frictionless Technology Diffusion: The Case of Tractors, American Economic Review.
Paul David 1991. Computer and Dynamo: The Modern Productivity Paradox in a Not-too-Distant Mirror, in Technology and Productivity: The Challenge for Economic Policy. OECD.