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Construction Industry Economics and Policy


Updated: Feb 9

Some Recent Books

Issues around the measurement, structure and performance of the building and construction industry, and its relationship with the economy and manufacturing, professional services and materials industries, have become the focus of CE in a series of books published since 2008. The contributions developed topics identified within the scope of CE in previous work, but they ranged widely and have consolidated the boundaries of CE while continuing to introduce ideas from elsewhere in economics.

For example, the six books have contributions on the activities of large, international contractors that dominate the global construction industry, a topic that has been, and is, of continuing interest. However, the global perspective in these books, while not new, marked another expansion of the topics and issues addressed, to include developments in market analysis, contractor strategies and in particular international cost comparisons and construction data. The following summary of the topics covered in the books illustrates the scope of CE research and current areas of interest.

The first two books ranged across practical, empirical and theoretical topics. In Economics for the Modern Built Environment (Ruddock 2008) seven contributions were on macroeconomic topics such as the economic effects of capital formation and investment, using construction statistics. There were five studies of markets and contractors, with three contributors emphasising the increasing divergence between global firms and local markets and two country case studies. The book brought a great deal of data together, and updated previous work in empirical CE on measuring construction activity and the broad construction industry. Modern Construction Economics: Theory and application (de Valence 2011) took an industry economics/industrial organization approach with contributions on market structure and competition, auctions and innovation. There were two on production theory and three others on methodology and experimental methods. Three of the contributions directly attacked the model of perfectly competitive markets with price taking firms, arguing construction markets can be concentrated and oligopolistic.

Between them the two books covered many of the topics and techniques established in Period 2, and they carried on earlier debates over production theory and methodology. They included global and national research using macroeconomics, research based on industry economics, and case studies with managerial economics. Importantly, they consolidated the expansion of the focus of CE from the SIC construction industry and its activity and management, and made the case for CE being about the economics of the built environment. Bridging the gap between the urban scale of the built environment and new building and construction projects, which will typically only deliver a few percent of the total stock each year, has always been a fundamental challenge for CE.

In Measuring Construction: Prices, output and productivity, Best and Meikle (2015) put the focus on data quality and international comparisons of construction costs, raising issues in the collection and use of construction data. As their introduction makes clear “there are standard methods for measurement of physical building work, but the same cannot be said for the characteristics of the construction industry” (p. 1). The twelve contributions covered measurement of construction work, productivity and prices at the global, national, industry and project levels. Their conclusion was “there is no ‘correct’ answer to any of the questions this book explores … It is perhaps only by applying a variety of techniques to the various problems and comparing the results that we obtain that we will know if we are getting closer to developing an acceptable set of tools and methods.’ (p. 256). A multiple models approach is indeed required to tackle the ‘various problems’ with construction data.

In Accounting for Construction: Frameworks, productivity, cost and performance (Best and Meikle 2018), the dozen contributions looked at different ways of measuring construction. With chapters on construction statistics, productivity, costs and data, the book both reviewed and extended previous studies. An ‘important thread’ in the book was “the lack of consistency in the way construction industry data is collected and how it is aggregated and/or disaggregated” (p. xiii). This thread became the focus of the next book in the series, Global Construction Data (Gruneberg 2019). The ten contributions included three on construction statistics, four used cost data, and the other three covered innovation, architectural services and international contractors’ make-buy decisions. In the title the book made explicit this important agenda in CE research. The reliability and quality of construction statistics is a well-known issue, going back to the 1960s, and the shortcomings of the SNA and SIC have not been overcome in the revisions since then. Those shortcomings are also a major theme in Best and Meikle (2015, 2018).

The sixth book in the series is Gruneberg and Francis’ The Economics of Construction (2019). They provide “a game theory account of the behaviour of firms”, the approach typically taken in other branches of industry economics. They discuss aspects of firms’ business models, financing, contractual disputes and power relations at length. A feature is the use of case studies of the collapse of UK contractor Carillion in 2018, Grenfell Tower, construction for the London Olympics and manufactured housing, demonstrating how the business environment a construction firm faces has become significantly more complex over the decades. The profit maximizing firm has evolved into one primarily concerned with growth and survival.

This work continues. The ElgarCompanion to CE Research and volume 3 in the measuring construction series, again edited by Best and Meikle, are due in 2021.


Best, R. and Meikle, J. (eds.) 2019. Accounting for Construction: Frameworks, productivity, cost and performance, London: Taylor & Francis.
Best, R, and Meikle, J. (eds.) 2015. Measuring Construction: Prices, Output and Productivity Abingdon: Routledge.
de Valence, G. 2011. (ed.). Modern Construction Economics, London, Taylor and Francis.
Gruneberg, S. (ed.) 2019. Global Construction Data, London: Taylor & Francis,
Gruneberg, S. and Francis, N. 2019. The Economics of Construction,
Ruddock, L. (ed.) 2008. Economics for the Modern Built Environment, London: Taylor and Francis.

Construction 4

A new general purpose technology (GPT) becomes the basis of a system of industrial production. For the construction industry and the production of the built environment, the emerging technologies collectively known as the fourth industrial revolution* will be transformative.

Prior industrial revolutions were driven by steam in the nineteenth and electricity and computing in the twentieth century. Over this period the structure of the construction industry evolved through three stages, first from mediaeval master builders and craft guilds to contractors and tradesmen, then to the modern project manager-subcontractor structure. Interestingly, the transition to steam and the end of the guild system affected the organization of the construction industry far more than the ones to electricity and computers.

With electricity, the organization of the industry evolved from contractors to project managers in a structurally, if not contractually, similar production system. And electricity did not affect on-site construction in the same way it did manufacturing, which needed to reconfigure factory layouts, because on-site steam powered machines such as cranes and excavators were replaced by petrol and diesel ones doing similar work. Computers and information technology have restructured office work everywhere, and affected industries like retailing, travel and entertainment far more than construction.

The adoption of steam power was an earlier experience of technological disruption leading to a restructuring of the construction industry. Steam power was a new GPT and industrial materials fundamentally restructured the industry from the craft-based industry of the eighteenth century. Over the nineteenth century this led to the emergence of the architectural, engineering and quantity surveying professions, and an industry structure of contractors and tradesmen for production.

The three areas of construction that were transformed in the nineteenth century were identified in the eight case studies by Peters (1996) as industrialization, mechanization and organization:

  1. Industrialization of production methods with standardisation of components and mechanized mass production, and the development of new materials like steel, plate glass and plastics. This led to a new design aesthetic, with more modular components and internal services, and separated the envelope from the structure. The infrastructure of materials suppliers and equipment producers developed, and scientific R&D joined the industry’s traditional trial and error approach to problem solving.

  2. Mechanization of work based on steam power, with cranes, shovels and excavators common by the mid-1800s. This in turn led to a reorganization of project management, with the new form based around logistics and site coordination to maximise the efficiency of the machinery and equipment.

  3. Organization of the modern construction industry was developing by the mid-1800s. Large general contractors had emerged by the 1820s, undertaking projects on a fixed-price contract often won through competitive bidding. This system of procurement was supported by the new professions of architects, engineers and quantity surveyors, which were institutionalising in the early nineteenth century.

Automation and AI in the twenty-first century can be expected to work along these dimensions, as the fourth industrial revolution reconfigures them by linking data through the life of a project. The role of AI enhanced, cloud-based platforms that integrate design, production and delivery of components and materials with digital production technologies that allow mass customisation will be significant in the production of components and materials. Gershenfeld (2017) argues digital fabrication will follow a similar exponential development path as digital computers, with the number of fabrication laboratories (Fabs) doubling every two years and their cost halving, making local production of many objects and items possible. Gershenfeld, who founded the first Fab in 2003, suggests the technology is now ready to become widespread, at the stage PCs were in the early 1990s. If this exponential growth eventuates, much of the current construction supply chain based on mass-production of components might become redundant. For example, an on-site or nearby Fab with printers and moulders might produce many of the metal, plastic and ceramic fittings and fixtures for a building.

For mechanization, the characteristic changeability of construction sites is challenging for automated and robotic systems, and it might take decades of investment for machines able to do site work or for humanoid robots to do human tasks. In some case a human supervisor operating a team of robots or several pieces of equipment, each with limited autonomy, might work better. A worker with a smart helmet could monitor these machines both on the project and in the site model. Beyond site preparation however, there may not be many tasks left if site processes are restructured around components and modules that are designed to be assembled in a particular way, and machines to assemble those components and modules can be fabricated for that purpose. For an industry with an aging workforce there are exoskeletons for site work, a form of human augmentation beginning to be used in the aerospace and automotive industries.

Digital platforms providing building design, component and module specification, fabrication, logistics and delivery will become widely used. Platforms provide outsourced business processes, usually cheaply because they are standardized, and are available to large and small firms. Also, platforms use forms of AI to monitor and manage the data they produce, the function of intelligent machines. Examples are Linkedin (matching jobs and people), Skype (simultaneous translation of video calls), AWS and other cloud-computing providers, and marketing, legal and accounting software systems. If these digitised business processes are cost-effective and become widely used, they can provide much of the data needed to train machines as project information managers.

The BIM model of the project, which might be outsourced, can link the design and fabrication stages to the site and the project. In 2019 the International Standard 19650 was released, providing a framework for creating, managing and sharing digital data on built assets. Digital fabrication produces components and modules designed to be integrated with on-site preparatory workand assembled to meet strict tolerances. Project management would become more focused on information management, and the primary role of a construction contractor could evolve into managing a new combination of site preparation work and integration of the building or structure with components and modules, some of which may be produced on-site in a Fab if economies of scale permit.

In this case, the industry would, perhaps slowly, reorganise around firms that best manage on-site and off-site integration of digitally fabricated parts. With outsourced business processes and standardized site and structural work, that would be a key competitive advantage of a construction firm. Firms would become more vertically integrated if they become fabricators as well, reinventing a business model from the past when large general contractors often had their own carpentry workshops, brick pits or glass works and so on.

Technological developments are combining intelligent machines with engineered materials, deep learning capabilities, human augmentation and new organizational concepts, and are pushing against established custom and practice in a mature technological system. Because the system is mature the effect of new technology and the changes it brings could spread slowly across the industry as a whole, and unevenly because of the many small and medium size firms. While this was case with twentieth century GPTs like electricity, a period of disruptive change in the construction industry occurred during the second half of the nineteenth century, and a new system of production eventually resulted in a new form of industry organization led by contractors instead of architects and engineers. That disruptive transition took several decades, as industrial materials replaced craft ones and site work was mechanized and reorganized.

Then, over the twentieth century contractors evolved into project managers and the traditional trades became subcontractors. Large contractors delivering major projects ended up at the core of the construction technological system at the end of the twentieth century. By this stage the technological system had a clear outline, and a very clear structure, for bringing together the products, suppliers and materials needed for building and engineering projects, and had stabilised around particular forms of procuring, financing and managing those projects.

With a technological trajectory for industry based on AI, digital fabrication and associated emerging production technologies, the view taken here is that there will be a transition period of perhaps a decade, possibly two, as the commercial contracting part of the industry adopts these innovations. As that happens the organization and structure of the industry will also change, from one centred on project managers to one based on integrators that combine site preparation with production and assembly of components and modules. AI as a new GPT may be as disruptive as steam power in the nineteenth century, and lead to a similar restructuring of the industry.

Neither electricity nor computing had a significant effect on the organization of the construction industry, because the evolution of the industry from contractors and trades to PMs and subcontractors was not driven by those technologies. However, the change from master builders and crafts to contractors and trades was a break from the past, and the result of industrialization and mechanization.

* This imprecise concept has been popularized by the World Economic Forum, following David (1990). Their description is: “The First Industrial Revolution used water and steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third, the digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.


David, P. A. 1990, The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox, American Economic Review. 80, 355 - 361.
Gershenfeld, N. 2017. The Science, and The Roadmap, in Gershenfeld, N., Gershenfeld, A. and Cutcher-Gershenfeld, J. Designing Reality: How to Survive and Thrive in the Third Digital Revolution, in New York: Basic Books. 95-116, and 159-182. 

Peters, T. F. 1996. Building the Nineteenth Century, Cambridge, Mass.: MIT Press.
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Updated: Feb 10

General Purpose Technologies and the Construction Industry

The modern construction industry had its roots in the take-off of industrialization in the early nineteenth century, and there was a comparable period of rapid, disruptive technological development not unlike the present one in the late nineteenth century.

Between 1860 and 1900 building and construction was restructured as an industry by the rise of large, international contractors, and project management and delivery was reorganized around steam powered machinery and equipment. Major projects like the Suez Canal, railways, tunnels and the new factories for mass production were typically built by new, global European contractors employing workers from around the world on their projects. These projects also required a new organizational form that integrated components, systems and processes.

In materials, the disruptive new technologies of steel, glass and concrete, which came together in the last decades of the century, led to fundamental changes in both processes and products, along Peters (1996) three dimensions of industry development: industrialization, mechanization and organization. Over a hundred years later construction is a mature system, based around standards and professional roles, with a high degree of technological lock in due to the age of the system. The ‘embeddedness’ of the construction technological system is found across the various combinations of the complex array of professional institutes and organizations, trade and industry associations, government regulations and licensing, standards and codes, and insurance and finance providers and regulators.

The impacts of new technology on a mature technological system like the construction industry are often thought to be gradual, changing industry practice over time without significantly affecting industry structure or dynamics. This was the case for twentieth century General Purpose Technologies like electricity, computers and the internal combustion engine. These became universal without significantly restructuring and reorganising construction in the way steam powered mechanization did, because they essentially upgraded existing capabilities. At the starting point for a cycle of development is a new GPT, then industries and products evolve and develop as the underlying knowledge base and technological capabilities increase and become more complex.. If, after a period of development, this GPT gives a technological shock to an existing system of production, it leads to a transition period where the firms involved have to adjust to a new business environment, which in turn leads to a restructuring and consolidation of the industry. This is what happened to construction in the second half of the nineteenth century, with iron-framed and steel-reinforced concrete buildings the industry had to not only master the use of these new materials, but also develop the processes and project management skills the new technology required. With electricity, computers and the internal combustion engine in the twentieth century, the construction industry adopted these new GPTs and used them to improve efficiency, but they did not require a major change in the form of industry organization that had emerged during industrialization and mechanization in the nineteenth century.

How and why a new technology spreads through the economy and society is determined by many factors, however studies of historical cases such as tractors, electricity, TV and phones have given good examples of technology diffusion and its dynamics. A GPT takes time to diffuse through the economy because parallel changes in forms of organization, methods of production and patterns of consumption are required, and these are not decisions firms and households make quickly or easily. Studies on the introduction of new technologies found it takes 15 to 30 years for a new technology to reach 90 percent of its potential market, for example 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 example is how the tractor displaced horses and mules in US agriculture between 1910 and 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 to 4.5 million by 1960. One reason for the slow spread of tractors was the incremental innovation needed to increase their reliability. A second was an increase in farm wages after 1940. The relative price of labour and mechanization has been found to be the most significant factor in technological innovation, diffusion and automation of work.

How firms utilise technological capabilities differentiates them within a diverse, location-based technological system. It is widely recognised there are differences between industries in the way that technology is adopted, adapted and applied, but differences within industries generally get less attention. 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, and 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. For building and construction this is significant, not only because of the number of small and medium size firms, but because of the size and reach of the major firms. A global contractor might have over 50,000 employees, suppliers of basic materials and sophisticated components are large multinational or multilocational industrial firms, many of these firms are publicly listed, and so on. These firms have the management and financial resources required to invest in twenty-first century technology, if and when they decide to do so. The issue may be the ability of incumbent firms to capture knowledge externalities, adopt new technologies, and adapt to the impacts of emerging technologies and their requirements.

Importantly, there is a class of more nimble, faster growing firms that have been identified as technology leaders, some of which are incumbents but often are not. Andrews et al. (2015) called these ‘frontier firms’, or firms pushing at the technological frontier through experimentation and development. Frontier firms bring with them radical new production technologies that rely in various ways on smart machines, like the three studied by Hall et al. (2019) and firms like Katerra, Esko, FBR and Daqri (from Table 3). Those firms are new entrants, but incumbents are also on the frontier. Examples are Trimble and Autodesk, Skanska embedding wireless sensors in buildings, Arup’s data collection systems and Atkins water infrastructure design system.

The technological frontier

The construction technological system is wide and diverse, and the various parts of the digital construction technological system are in various stages of development (Gruska et al. 2017). There are many possible futures that could unfold over the next few decades, recent industry scenarios for AI include Agarwal et al. (2016), WEF/BCG (2017) and Quezada et al. (2016), but there is little probability of some breakthrough technology that leads to some different, new industry. Instead, development of AI and associated digital fabrication and production technologies will more likely reshape the existing industry, led by fundamental changes in demand (the function, type and number of buildings), design (the opportunities new materials offer), and delivery (through project management). The fourth industrial revolution has already affected demand for structures like renewable energy sources and buildings like data centres, warehouses and retail, ‘dark’ kitchens and supermarkets for online delivery services. Some of these buildings and structures already use forms of applied AI in their management and operation.

At the end of the second decade of the twenty-first century, automation technology is at the point where intelligent machines are moving from operating comfortably in controlled environments, in manufacturing or social media, to unpredictable environments, like driving a car or truck. In many cases, like remotely controlled and autonomous trucks and trains on mining sites, the operations are run as a partnership between humans and machines, or as Brynjolfsson and McAfee (2014) put it “running with the machines not against them”. These innovations might reasonably be expected to affect site processes and project organization, as concrete and steam power did in the past. Table 1 has examples of where the technological frontier is in 2020 for plant and equipment, also for construction materials, as an indication of the range and extent of this wave of innovations. Missing from these lists is smart contracts using blockchain.

Invention and innovation based around BIM, digital twins, digital fabrication and advanced manufacturing technology, is starting to fundamentally affect the production system through economies of scale. Over time this will alter the balance between on-site and off-site production of building modules and components, and how they are handled, assembled and integrated. The combination of BIM and digital fabrication could be transformational if it allows on-site production of building components, fundamentally altering the economies of scale in the industry. Mass production will always have a role, but market niches currently occupied by some manufacturing firms may disappear, replaced by new production technologies based on digital fabrication and online design databases. Adding new materials to the fabrication palette through molecular design and engineering may be significant, or other new materials, or upgraded versions of existing structural materials. Combining robotic and automated machinery with digital fabrication and standardized parts opens up many possibilities. Exoskeletons combine human skill with machine strength.

While firms involved in construction of the built environment are facing technological advances that will affect many aspects of the technological system, this is a process that happens over years and decades. Lipsey et al. (2005: p. 211) found “the gestation period of individual GPTs does not seem to have shortened much since the industrial revolution” and it takes 50 years between invention of a GPT and its use becoming widespread (their examples were discovering the double-helix and biotechnology, the dynamo and electricity, and the first electronic computers in the 1940s). For the tractor and electrification cases used above, starting from the date of invention of the internal combustion engine and dynamo would add around three decades to those timelines.

In fact, how long a transition to a new technological system built on automation and digital fabrication coordinated by AI takes is unknown. While machines can replicate individual tasks, integrating different capabilities into solutions where everything works together is another matter. Combining a range of technologies is needed for workplace automation, but solving specific problems involves specific and organizational technical challenges, and once the technical feasibility has been resolved and the technologies become commercially available it can take many years before they are adopted. Importantly, this suggests there will be many new jobs in construction over coming years, for project information managers, BIM supervisors, integration specialists and other fourth industrial revolution roles. Because these jobs will be primarily on new projects, they will not quickly replace the many existing jobs in the industry required to maintain the built environment. McKinsey (MGI 2017) sees construction as an industry where AI does not significantly reduce the number of jobs. In their paper on ‘Construction 4.0’ de Soto et al. (2019) conclude: “there will be a time in which conventional construction and robotic technologies will coexist, leading to a higher job variability and new roles.”

Nevertheless, the technological frontier is moving again, and new construction projects will generally utilise the most cost-effective technology. Current AI technology provides services such as GPS navigation and trip planning, spam filters, language recognition and translation, credit checks and fraud alerts, book and music recommendations, and energy management systems. It is being used in law, transport, education, healthcare and security, and for engineering, economic and scientific modelling. Advanced manufacturing is almost entirely automated. As expected with a new GPT, there are many new applications under development (Mitchell 2019).

The next level of AI envisages those capabilities extended in the near future to a group of intelligent machines that have been individually trained to collect and manage data from the stages of a construction project, and that outsourced business processes can provide such data for intelligent machines, supervised by users and helping them manage complicated processes. An AI acting as an overall project data manager could integrate the data from many sources to continually update a project’s schedule, work plan and cost estimates, matching progress and performance to iterate those plans for the project’s managers. This AI assists users’ decision-making by generating and evaluating options. Such a system would be operated by a voice activated interface, with the progress updates included and access to expert systems for specialist areas provided. It would generate design options and provide full visualisation of a shared BIM model linked to the schedule and site work plan. There would be real-time supply chain data on fabrication and logistics through cloud-based platforms. The AI can iterate the schedule and cost plans for a project, based on that data, allowing the project management team to match performance with plans, in real-time, for every aspect of a project. The data required for the coordination and management role of intelligent machines can come from widespread use of standardized, outsourced cloud-based business processes. That data then becomes a series of training sets needed for deep learning, the current level of AI technology.

Agarwal, R., Chandrasekaran, S. and Sridhar, S. 2016. Imagining Construction’s Digital Future, McKinsey & Co
Andrews, D., C. Criscuolo and P. N. Gal, 2015. Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries, OECD Productivity Working Papers, 2015-02, OECD Publishing, Paris.
Brynjolfsson, E. and McAfee, A. 2014. The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies, New York: W. W. Norton & Co.
de Soto, B. J., Agustí-Juan, I., Joss, S. and Hunhevicz, J. 2019. Implications of Construction 4.0 to the workforce and organizational structures, International Journal of Construction Management, DOI: 10.1080/15623599.2019.1616414
Gruszka, A., Jupp, J. R. and de Valence, G. 2017. Digital Foundations: How Technology is Transforming Australia's Construction Sector, Sydney: Startup Australia.
Lipsey, R. G., Carlaw, K. I. and Bekar, C. T. 2005. Economic Transformations: General Purpose Technologies and Long-term Economic Growth, Oxford: Oxford University Press.
MGI, 2017. A Future that Works, McKinsey Global Institute.
Mitchell, M. 2019. Artificial Intelligence: A Guide for Thinking Humans, New York: Farrar, Straus, and Giroux.
Peters, T. F. 1996. Building the Nineteenth Century, Cambridge, Mass. MIT Press.
Quezada, G., Bratanova, A., Boughen, N. and Hajowicz, S. 2016. Farsight for Construction: Exploratory scenarios for Queensland’s construction industry to 2036, CSIRO, Australia.
WEF/BCG, 2017. Future Scenarios and Implications for the Construction Industry, World Economic Forum and the Boston Consulting Group, Geneva.
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About the Blog

This blog is interested in the organisation of the building and construction industry and its role in the creation of the built environment.

Like many industries, construction is being reshaped by rapid and widespread advances in materials, technology and capability. The blog collects data and discusses these trends and their effect on industry structure and performance.


The economic perspective focuses on firms and industries rather than individual projects.

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Gerard de Valence

I studied politics, philosophy and economics at Sydney University and worked in the private sector for a decade before becoming an academic in the School of the Built Environment at the University of Technology Sydney and UCL’s Bartlett School of Construction and Project Management in London. The ABOUT page has my bio.