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Construction Industry Economics and Policy

A BLOG ABOUT THE STRUCTURE AND PERFORMANCE OF THE BUILDING AND CONSTRUCTION INDUSTRY

Updated: Feb 10

Data on the Technology Frontier at the Industry Level


There is a commonly held view that construction is a digital laggard. A widely cited McKinsey report in 2016 argued:


“The construction sector has been slow to adopt process and technology innovations. The industry has not yet embraced new digital technologies that need up-front investment, even if the long-term benefits are significant. R&D spending in construction runs well behind that of other industries: less than 1 percent of revenues, versus 3.5 to 4.5 percent for the auto and aerospace sectors. This is also true for spending on information technology, which accounts for less than 1 percent of revenues for construction, even though a number of new software solutions have been developed for the industry.


Technology use and diffusion is an important dynamic in industry development, but good data is rare. Most surveys are of specific industries or selected firms (typically large ones) and surveys with large samples that would be more representative of firms across the economy by including small and medium size firms are scarce. This particularly affects built environment industries like construction and professional services because of the large number of small firms in those industries.


The United States Census Bureau conducts an Annual Business Survey (ABS) and in 2018 the ABS included a technology module with three questions about the extent of technology use between 2015 and 2017: the availability of information in digital format (digitization), expenditure on cloud computing services, and use of a range of advanced business technologies. The survey was a partnership between the Census Bureau and the National Center for Science and Engineering Statistics, and the first results have been released in a working paper from the National Bureau of Economic Research. The results are summarized below.


The survey data is at a high level of generality to make the questions relevant across the variety of firms and industries included. There are also issues around how the data has been modelled and analyzed, and the adjustments for high counts for firms that minimally use or are not using advanced business technologies at all. Importantly, the survey shows construction is not significantly lagging other industries in the US in digitization and use of cloud services, however it is doing less testing and development of advanced business technologies.


The main finding of the survey was “Despite increasingly widespread discussion in the press of machine learning, robotics, automated vehicles, natural language processing, machine vision, voice recognition and other advanced technologies, we find that their adoption rates are relatively low. Furthermore, adoption is quite skewed, with heaviest concentration among a small subset of older and larger firms. We also find that technology adoption exhibits a hierarchical pattern, with the most sophisticated technologies being present most often only when more-basic applications are as well.”


There were 583.000 responses to the survey. Two thirds of the firms employed under 10 people and were less than 20 years old, making this the most comprehensive survey of diffusion of advanced business technologies done so far.




The industry breakdown of firms is in Table 1 with

the built environment

industries of construction,

real estate & professional

services well represented. This makes the data

particularly interesting.




Following are extracts from the NBER paper on the results of the survey on the three technology questions: the availability of information in digital format, expenditure on cloud computing services, and use of advanced business technologies with a focus on the use of AI. The next post will discuss the survey and its implications for construction.


Digital Share of Information by Business Activity


The first question in the 2018 ABS technology module queried firms on the type of information stored digitally. In all sectors, financial and personnel information are the most likely to be digitized, followed by customer feedback and marketing. This is the case for Construction, with financial, personnel and marketing digitized. The lowest rates of adoption are in production and supply chain activities.


Figure 2 is a butterfly chart of adoption and use rates for digital information by sector, where the ranking of sectors by adoption and intensity of use rates parallel each other. The right panel of the chart represents, by sector, the adoption rates of digital information across all surveyed information types. The segments within each bar in the chart capture adoption rates by the number of information types in digital format. In all sectors, a large share of adopters report having three or more types of information digitized.


The left panel of Figure 2 represents intense use of digitization. Most firms report digitizing at least two types of information, regardless of sector, the fraction of firms digitizing only one type of information intensively is relatively small in each sector. Overall, digitization appears to be highly prevalent across sectors. Manufacturing, Information and Professional Services are among the highest adopters of digitization, with size being a primary correlate of adoption.


Cloud Service Purchases by IT Function


This section describes the adoption patterns for cloud service purchases across size, age and sector. Like digitization, the highest adoption and intensive-use rates are in Information, followed closely by Professional Services and Education. The lowest rates are in Agriculture, Mining, Utilities, Retail Trade, and Transportation and Warehousing, and the Other category. Figure 4 reveals that cloud services purchases have much lower diffusion rates compared to those for digital information in any given sector.


Billing and Security are the most common IT functions for most sectors, with certain sectors, including Construction, predominantly relying on the cloud to perform collaborative or synchronized tasks. The Data Analysis function has the lowest number of firms reporting some cloud purchase, Billing and Account Management has the highest number of firms, closely followed by Security or Firewall and Collaboration and Synchronization functions.


Although the adoption rates for business IT functions in the cloud is significantly lower than the adoption rates of storing information digitally, this technology is widespread across various applications, with nearly a third of each different type of IT function being performed in the cloud and being used intensively.



Advanced Business Technologies


In this section we analyze firm responses to the business technologies question. Due to their wide technological scope, we link the responses here with the previous technology adoption questions and perform a deeper set of analyses assessing the range of response categories.


The frequency of responses that very few firms use the business technologies included in the module, and many answered, “Don’t know”. Based on our tabulation weights, only 10.3% (8.5% non-imputed) of firms adopt at least one of the listed advanced business technologies.


The highest use frequencies are in touchscreens and machine learning. For touchscreens the adoption rate is 6.1% of firms. Machine learning comes second but the rate is low at 2.9%. Voice Recognition and Machine Vision, which are can be considered examples of Machine Learning applications, have the next two highest use rates.


The overall diffusion of robotics is very low across firms in the U.S. The use rate is only 1.3%, concentrated in large, manufacturing firms. The distribution of robots among firms is highly skewed toward larger firms. The least-used technologies are RFID (1.1%), Augmented Reality (0.8%), and Automated Vehicles (0.8%).


Looking at the most common types of business technologies adopted by sector in Table 2 there is substantial variation.

All sectors (except Manufacturing adopt Touchscreens followed by Machine Learning or Voice Recognition).


Manufacturing is most likely to adopt Machine Learning followed by Touchscreens and Robotics. RFID technology is most commonly used in the Retail, Wholesale, and Transportation and Warehousing sectors, consistent with these industries tracking physical goods through supply chains.


The testing-versus-use rates across different technologies are used to assess which technologies are in earlier phase of diffusion, that is, where testing is high relative to use. In Figure 6, the vertical axis represents the ratio of the fraction of firms testing to the fraction of firms using. The technologies are represented by the circles. The size of each circle corresponds to the use rate for that technology with larger circles representing higher rates of use. Technologies are ordered in the figure by usage rate, low to high.


As shown in panel a, the technology with the highest testing-to-use ratio is Augmented Reality, where nearly half as many firms as those using the technology report testing it. The next highest ratios are observed in RFID and Natural Language Processing and the lowest ratios are in technologies that are relatively more diffused (and hence, used), such as Touchscreens, Machine Learning and Machine Vision. For Touchscreens, for instance, only about 15 firms report testing the technology for every 100 that use it. It is notable that most testing-to-use ratios are below 0.3, indicating that there are fewer than 30 firms testing the technology for every 100 using it.


The remaining panels of Figure 6 plot the testing-to-use ratio for technologies by firm size, age, and manufacturing status. Panel b displays ratios by firm size, where small firms are defined as those with 1-9 employees and large firms are those with at least 250 employees. The blue circles capture usage among large firms and the orange circles represent usage among small firms. The sizes of the circles are smaller for small firms for each technology, consistent with the earlier finding that larger firms tend to use the business technologies at a higher rate, in general.


Figure 6: Testing-to-Use Ratios




















The butterfly chart in Figure 7 provides sectoral diffusion rates for all business technologies considered together. Manufacturing leads with about 15% of firms indicating use of at least one business technology, followed by Health Care (14%), Information (12%), Education (11%) and Professional Services (10%). The lowest diffusion rates for the technologies are in Construction, Agriculture, Mining and Utilities, Management and Administrative, and Finance, Insurance and Real Estate sectors.



The third question asked directly about the use of advanced “business technologies,” including those typically categorized as “AI.” These technologies include automated guided vehicles, machine learning, machine vision, natural language processing, and voice recognition software. Respondents are presented with a list that covers robotics (i.e., “automatically controlled, reprogrammable, and multipurpose machines”), various cognitive technologies (i.e., applications that help machines to “perceive, analyze, determine response and act appropriately in [their] environment”, a standard definition of AI), radio frequency identification, touchscreens/kiosks for customer interface, automated storage and retrieval systems, and automated guided vehicles.


Across all AI-related technologies, the aggregate adoption rate for all firms in the economy is 6.6% meaning that approximately 1 in 16 firms in the US are utilizing some form of AI in the workplace. This adoption rate is significantly lower than the adoption rate highlighted in the AI survey by the European Commission and other private surveys by McKinsey, Deloitte, and PwC. However, it is important to consider the sampling methods of those surveys. None of the other surveys claim to be nationally representative and tend to focus on larger, publicly traded companies. In contrast, the ABS sample includes many small firms where AI adoption is very low. This is important because AI adoption rate varies greatly by firm size. Adoption rates (defined as usage or testing) increase from 5.3% for the group of firms with the smallest number of employees to 62.5% for firms with 10,000+ employees.


In other words, scale appears to be a primary correlate of AI usage, likely due to both the large quantities of data and computing power required to fully realize the most popular types of AI currently available. This may potentially have far-reaching implications on topics such as inequality, competition and the rise of “superstar” firms, especially if AI is shown to have widespread productivity benefits. If only a select group of firms are able to fully realize the benefits of AI, we can expect further divergence for the “frontier” and most productive set of firms.


Our data and explanatory variables are simply too crude to provide a reliable predictor for the precise types of firms that adopt certain technologies and those that do not. While we can claim that size is a reliable predictor of adoption, even amongst large firms, we see heterogeneous patterns of adoption depending on the technology type. In other words, there are simply too many unknown factors that cannot be measured by traditional metrics (such as firm size, age and industry) that appear to drive technology adoption.


Conclusion


We have provided an introduction to the technology module in the 2018 ABS and placed it in the larger context of related work at the Census Bureau to collect comprehensive data on technology adoption and use by U.S. firms in order to provide a more accurate picture of the state of advanced technology use in the U.S. economy. Because of the large pool of respondents (about 850,000 firms) in the 2018 ABS, the module represents a unique opportunity to offer insights on technology adoption and use across all sectors of the economy and across a variety of key firm characteristics. The same technology module is expected to be a part of the 2021 Annual Business Survey.


A primary contribution for the paper is to develop a nationally representative set of technology adoption and use measures based on the survey results, which in public use tabulations report aggregate response counts for each technology question (see public use tabulations at: https://www.census.gov/data/tables/2018/econ/abs/2018-abs-digital-technology-module.html ).

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Industries, Clusters and Sectors


Parts of the economy that involve many different contributors and participants are often called an industrial or economic sector, an example is the non-profit sector with its wide variety of organisations. Although the idea of an industrial sector has no precise meaning, it is often used to describe a loose collection of firms with one or more common characteristics, like ‘manufacturing’ or ‘the business sector’, though firms in these sectors come from many different industries.


The starting point is the concept of an industry, which is defined in the Standard Industrial Classification (SIC) used by national statistical agencies as a group of firms with common characteristics in products, services, production processes and logistics. These firms are classified into a four-level structure. The highest level is alphabetically coded divisions such as Agriculture, forestry and fishing (A), Manufacturing (C) and Information and communication (J). The classification is then organised into two-digit subdivisions, three-digit groups, and four-digit classes.


The boundaries around an industry are tightly defined by the SIC, to allow identification of individual industries as producers of goods and services and measurement of their contribution to output and employment in the economy. However, to produce something supplies are needed, purchased from other producers, and these relationships between industries are also important. For example, bricks are manufactured products supplied to property developers to provide buildings for their customers. Many industries are structured around such supply chains and production networks, and when enough firms share sufficient characteristics they are often described as an industry cluster.


An industry cluster brings together a group related firms and was originally applied in the 1990s to specific locations like the wine industry in California’s Napa Valley or Bordeaux in France. Over time, the concept itself broadened as different types of clusters were identified, such as creative industry hubs or knowledge centres. Two types of industry cluster are:


1. Geographical – industries using the same resources in a specific location

  • Movies – Hollywood US, Bollywood India;

  • IT – Silicon Valley CA, Silicon Alley NY, Silicon Glen Scotland, Bangalore India;

  • Leather goods, spectacles and glasses – Italy;

  • Health – Boston US, Oxford England, Chennai India;

  • Electronics – Guadalajara Mexico, Cordoba Argentina, Guangdong China;

  • Finance – London England, New York US, Geneva Switzerland; and

2. Vertical – a hub and spoke value chain from suppliers to end products

  • Automotive – Detroit US, Dusseldorf Germany, Turin Italy, Curitiba Brazil;

  • Aerospace – Toulouse France (Airbus), Seattle US (Boeing);

  • Smart phones – Guangdong China (Apple), Hanoi Vietnam (Samsung).

Some industries do not have central locations like the clusters in IT, wine, finance etc., or major hubs where production is concentrated like automobiles and aerospace. These industries are built around decentralised production, distribution and delivery networks that make their products widely available to clients and customers. Four examples are:

  • Pharmaceuticals – a globally distributed industry, with countries combining some form of domestic production and imported supplies;

  • Shipbuilding – brings many suppliers together in a few locations;

  • Electricity generation – brings many suppliers together in many locations;

  • Building and construction – the world’s most ubiquitous industry, sharing the most widely used materials of wood, clay, glass, steel and concrete. Is this really a cluster?

Building and construction, in fact, is only one of the many industries involved in the production of the built environment. There is a diverse collection of industries that create, manage and maintain the built environment. On-site work links suppliers of materials, machinery and equipment, products and components, and all other inputs required to deliver the buildings and structures that make up the built environment.


Consultants provide design, engineering, cost planning and project management services. Once produced, buildings and structures then need to be managed and maintained over their life-cycle, work done by another group of related industries. The built environment also needs infrastructure and services like water and waste disposal, provided by yet more industries.


A dense network of many different firms and participants such as this is often called an industrial or economic sector, because it is too diverse and distributed to be a cluster. There is no definition of an industrial sector, beyond a broad collection of firms with one or more common characteristics, like ‘manufacturing’ or ‘the business sector’, though firms in these sectors come from many different industries. There are also sectors based around a definable market, two examples being:

  • Defence - there is no defence ‘industry’ because suppliers come from many different industries like IT, aerospace and shipbuilding, but as a sector share resources and clients; and

  • Tourism - which brings together the contributions of industries like accommodation, tour operators and entertainment. Australia has an annual Tourism Satellite Account produced each year (cofounded by industry and government).

If the built environment encompasses the entirety of the human built world, then the built environment sector (BES) is the collection of industries responsible for producing, managing and maintaining the buildings and structures that humans build. To be included in the BES an Industry needs a direct physical relationship with buildings and structures.


Those industries can be divided into those on the demand side and those on the supply side, like materials or specialised tradesmen, Demand side industries like property developers and facility managers pull output from the supply side, both for new output and for servicing and managing existing assets.


Therefore the BES is a sector more like defence than tourism, because it also produces long-lived assets for clients outside the sector (governments and owners respectively) that require repair and maintenance, and that R&M generates significant ongoing revenue for firms across the broad industry sector that produces those assets.


The concept of the BES is broad and extensive, so cannot be precise and exact. While the boundaries of industries and markets are important, in practice the data and SIC definitions are the starting point for the data used. The industries included are selected because they clearly have a relationship with construction, management and maintenance of the built environment.


This may not capture every last contribution to the BES, but it does allow the development of a profile of the sector. Measuring the BES provides data on its relationship to the wider economy and is relevant to a wide range of policies and issues currently facing the built environment.

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An Innovation Platform for the Built Environment

Edited by Anil Sawhney, Mike Riley and Javier Irizarry


A new book on Construction 4 from Routledge. As the table of contents below show, it is a comprehensive review of the state of play as the technologies of industry 4 get adapted and adopted to construction. The book is good evidence that the built environment industries can (should? will?) be a leading sector for application of these technologies. From the book's introduction:


Modelled on the concept of Industry 4.0, the idea of Construction 4.0 is based on a confluence of trends and technologies that promise to reshape the way built environment assets are designed, constructed, and operated. With the pervasive use of Building Information Modelling (BIM), lean principles, digital technologies, and offsite construction, the industry is at the cusp of this transformation. The critical challenge is the fragmented state of teaching, research, and professional practice in the built environment sector.


This handbook aims to overcome this fragmentation by describing Construction 4.0 in the context of its current state, emerging trends and technologies, and the people and process issues that surround the coming transformation.


Construction 4.0 is a framework that is a confluence and convergence of the following broad themes discussed in this book:


  • Industrial production (prefabrication, 3D printing and assembly, offsite manufacture)

  • Cyber-physical systems (actuators, sensors, IoT, robots, cobots, drones)

  • Digital and computing technologies (BIM, video and laser scanning, AI and cloud computing, big data and data analytics, reality capture, Blockchain, simulation, augmented reality, data standards and interoperability, and vertical and horizontal integration)

The book has 28 chapters. Part 1 has 4 chapters discussing the idea of cyber-physical systems. Part 3 has 4 case studies. The core of the book is Part 2 where the elements of C4.0 are identified and current developments explained. These chapters are:


Potential of cyber-physical systems in architecture and construction


Lauren Vasey and Achim Menges


Applications of cyber-physical systems in construction


Abiola A. Akanmu and Chimay J. Anumba


A review of mixed-reality applications in Construction 4.0


Aseel Hussien, Atif Waraich, and Daniel Paes


Overview of optoelectronic technology in Construction 4.0


Erika A. Pärn


The potential for additive manufacturing to transform the construction industry


Seyed Hamidreza Ghaffar, Jorge Corker, and Paul Mullett


Digital fabrication in the construction sector


Keith Kaseman and Konrad Graser


Using BIM for multi-trade prefabrication in construction


Mehrdad Arashpour and Ron Wakefield


Data standards and data exchange for Construction 4.0


Dennis R. Shelden, Pieter Pauwels, Pardis Pishdad-Bozorgi, and Shu Tang


Visual and virtual progress monitoring in Construction 4.0


Jacob J. Lin and Mani Golparvar-Fard


Unmanned Aerial System applications in construction


Masoud Gheisari, Dayana Bastos Costa, and Javier Irizarry


Future of robotics and automation in construction


Borja Garcia de Soto and Miroslaw J. Skibniewski


Robots in indoor and outdoor environments


Bharadwaj R. K. Mantha, Borja Garcia de Soto, Carol C. Menassa, and Vineet R. Kamat


Domain-knowledge enriched BIM in Construction 4.0: design-for-safety and crane safety cases


Md. Aslam Hossain, Justin K. W. Yeoh, Ernest L. S. Abbott, and David K. H. Chua


Internet of things (IoT) and internet enabled physical devices for Construction 4.0


Yu-Cheng Lin and Weng-Fong Cheung

Cloud-based collaboration and project management


Kalyan Vaidyanathan, Koshy Varghese, and Ganesh Devkar


Use of blockchain for enabling Construction 4.0


Abel Maciel

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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 and maintenance of the built environment.

Like other industries, it is being reshaped by rapid and widespread advances in materials, technology and capability. How those advances might affect an industry changes slowly over time is, I think, an interesting question.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.