Australian Construction Productivity

Is the industry’s productivity as bad as claimed?

 

The Australian Bureau of Statistics publishes productivity measures for the whole economy, the Market Sector, and for the 16 industries that make up the Market Sector. Productivity is the ratio of output and inputs and is affected by innovation, research and development, education and training, the quality and age of the capital stock (of machinery, plant and equipment, buildings and structures), the rate of technological change and adoption of new technologies.  The effects of all these factors takes time, so productivity is a long-run measure that changes gradually. 

 

The post compares Construction productivity to the performance of the Market Sector. The data used is from the annual ABS Productivity Statistics release, which has data from 1994-95 to 2023-24 (the most recent release was February 2025). The ABS productivity indexes are based on 100 in 2022-23, however for this analysis they have been first rebased to 100 in 1994-95 to compare the long-run growth of Construction and Market Sector productivity, and then rebased at 100 in 2015-16 for comparing productivity in the short-run. 

 

Comparisons are made for labour productivity and multi-factor productivity (MFP) using both the hours worked and quality adjusted labour input measures. The quality adjusted labour input indexes take into account characteristics of the workforce like years of education, levels of training, industry of employment, age and sex. These quality adjusted measures reflect changes in the composition and skills of the workforce, and typically have a lower rate of growth than the hours worked measure. Capital productivity is also shown. 

 

As well as comparing the different measures of productivity for Construction and the Market Sector, there is data for the individual industries that shows Construction is in no way the worst performing industry, although it is far from the level of growth seen in the best performing industries.

 

 

Productivity Since 1995

 

The long-run performance of Construction includes a sharp rise during the mining boom between 2012 and 2015, followed by a gradual decline over the next few years as these major resource projects completed [1]. At the end of the mining boom productivity had fallen to around the level it was before the boom. This pattern was due to the large increase in Construction output during the mining boom because output included plant and equipment like the offshore drilling platforms and gas liquefaction plants, none of which involved much construction work and most of which was imported. Productivity increased because this statistical quirk increased output much more than employment and hours worked [1]. 

 

Labour Productivity

 

Starting with labour productivity over the long run since 1994-95, the difference between growth in the Market Sector and the lower productivity growth of the Construction industry is apparent in Figure 1. However, despite claims made that there has been no growth in Construction labour productivity, there has been an increase. Construction labour productivity has increased by 17% on an hours worked basis and 24% on the quality adjusted labour input basis which, although less than the Market Sector’s 64% and 41% respectively, is not nothing. 

 

Figure 1. Market Sector industries labour productivity

 

Source: ABS 5260. Gross value added per hour worked. Quali is the quality adjusted labour input measure. 

 

As Table 1 shows, since 1995 the three leading industries for hours worked labour productivity growth have been Agriculture, forestry and fishing 210%, Information media and telecommunications 228%, and Financial and insurance services 123%. The two industries with lower growth than Construction were Mining 6%, Electricity, gas, water 2%, and Administrative and support services had negative growth of -13%. 

 

For quality adjusted labour productivity, Construction had better growth than Rental, hiring and real estate services 4%, and there were three industries with negative growth: Mining -2%, Electricity, gas, water and waste -9%, and Administrative and support services -23%.

 

Table 1. Market Sector industries labour productivity change

 

Multi-factor Productivity 

 

The ratio of output to input of combined labour and capital is multi-factor productivity (MFP). For MFP the story is not as good as for labour productivity, because there has been only 1% growth in Construction hours worked MFP and a 3% fall in the quality adjusted measure.  Market Sector growth on the hours worked basis was 23% and on the quality adjusted labour input basis was 13%. After MFP rose and fell during the mining boom, instead of returning to the preboom level there was collapse in Construction MFP after 2015-16.

 

Figure 2. Market Sector industries multi-factor productivity

 

Source: ABS 5260. Gross value added per hour worked. Quali is the quality adjusted labour input measure. 

 

The 1% increase in Construction hours worked MFP is very small, but not the decline often claimed for the industry. Table 2 shows four industries had a fall in hours worked MFP since 1995:  Mining -28%, Electricity, gas, water -30%, Rental, hiring and real estate services -32%, and Administrative and support services -16%. The three high growth industries were: Agriculture, forestry and fishing 182%, Information media and telecommunications 64%, and Financial and insurance services 63%. 

 

Construction, however, was one of five industries with negative quality adjusted labour input MFP growth, although at -3% it had a much smaller decline than the other industries of Mining -31%, Electricity, gas, water and waste -33%, Rental, hiring and real estate services -36%, and Administrative and support services -25%. This raises the question of why Construction is singled out as the problem industry. 

 

Table 2. Market Sector industries multi-factor productivity change

 

Capital Productivity

 

Capital productivity has been falling for both the Market Sector and Construction since the early 2000s.  This is a complex measure, because estimating the stock of capital requires an estimate of annual capital investment and a depreciation rate to account for declining efficiency of the existing stock due to use and age. Although Construction capital productivity peaked in the mid 2000s and declined during the mining boom, the post-boom fall in MFP was due to the sharp decline in capital productivity, because since then labour productivity was more or less flat but capital productivity was falling. As Figure 3 shows the Market Sector also had declining capital productivity, but after 2015-16 the decline in Construction capital productivity was much worse. 

 

Figure 3. Market Sector industries capital productivity 

 

Source: ABS 5260. 

 

What these long run graphs show is that there was a downward shift in Construction productivity around 2015, when both MFP and capital productivity went into significant decline. Up until then Construction productivity had been similar to Market Sector productivity for MFP, but after 2015 the Market Sector and Construction industry measures diverged. The next section looks at productivity over the short run since that divergence.

 

Productivity Since 2015-16

 

Labour Productivity

 

Labour productivity in the short run since 2015-16 has a distinctive and interesting pattern. The hours worked measure has fallen 4% from 100 to 96 but the quality adjusted labour input measure has increased by 6% from 100 to 106, and was in fact higher then both Market Sector measures in 2023-24. The increase in the Quali index occurred in the 2019-20 year with a big jump from 95 to 104, and there has been a gradual increase in the years since. 

 

Figure 4. Market Sector industries labour productivity 

 

Source: ABS 5260. Gross value added per hour worked. Quali is the quality adjusted labour input measure. 

 

The increase in the Construction quality adjusted labour input measure index will be the result of changes in the composition of employment, with the combined share of Professionals and managers increasing from 15% to 18% between 2019 and 2020, and peaking at 19% in 2022. Figure 5 shows the share of Professionals increased from 4% to 6% in 2020, and for Managers the share rose rom 10% to 12% in 2020 and was 13% from 2021 to 2023. In 2024 Technicians and trades workers were 50% of Construction employment, and Machinery operators another 6%, and their combined shares in total Construction employment have decreased by 3% since 2016. The share of Clerical and administrative workers has also declined, by 0.6%. Therefore, since 2016 the overall makeup of Construction workforce has become more skilled and qualified, raising the quality adjusted labour input measures [2]. 

 

Figure 5. Share of total Construction employment

 

Source: ABS 6291

 

Between 2016 and 2024 there were large differences in the productivity performance of the 16 Market Sector industries. As Table 3 shows, on the labour productivity hours worked basis there were two industries with high growth: Agriculture, forestry and fishing 44%, and Information media and telecommunications 40%. Four industries had growth between 10 and 20%, and five had growth less than 10%. Construction -4% was one of five industries with negative growth, the others were Mining -15%, Manufacturing -4%, Electricity, gas, water and waste -15%, and Financial and insurance services -4%.

 

On a quality adjusted basis Construction was the only industry to improve on the hours worked measure, all other industries had slightly lower quality adjusted labour input growth than hours worked. The other four industries with negative hours worked labour productivity again had negative quality adjusted labour input labour productivity growth. There were only six industries with better quality adjusted labour productivity growth than Construction’s 6%: Agriculture, forestry and fishing 41%, Wholesale trade 7%, Accommodation and food services 8%, Information media and telecommunications 33%, Professional, technical and scientific services 14% and Administrative and support services 12%.

 

Table 3. Market Sector industries labour productivity change

 

Multi-factor Productivity 

 

The MFP indexes for Construction do not show the same pattern as labour productivity. Both the hours worked and the quality adjusted indexes have fallen since 2016 and have closely followed each other down, ending at 92 and 91 respectively in 2024. However, the Market Sector has not performed particularly well, with the quality index only increasing to 101 and the hours worked index increasing to 104. 

 

Figure 6. Multi-factor productivity

 

Source: ABS 5260. Gross value added per hour worked. Quali is the quality adjusted labour input measure. 

 

MFP growth since 2016 is similar to labour productivity with a couple of exceptions. Table 4 shows on the hours worked measure only Agriculture, forestry and fishing 44% had high growth, and there were three industries above 10%. Five industries had negative growth: Construction -8%, Mining -3%, Manufacturing -1%, Electricity, gas, water and waste -15%, and Arts and recreation services -1%. Again, the growth in the quality adjusted labour input measure was lower than for hours worked, with Construction -9% one of eight industries with declining productivity, including Mining -3%, Manufacturing -3%, Electricity, gas, water and waste -16%. Transport, postal and warehousing -3%, Rental, hiring and real estate services -1%, and Arts and recreation services -3%. 

 

Table 4. Market Sector industries multi-factor productivity change

 

Capital Productivity

 

The performance of capital productivity has been particularly poor for construction, falling from 100 to 85 between 2016 and 2024, while the market sector index barely increased and ended at 103.

 

Figure 7. Capital productivity

 

Source: ABS 5260. 

 

Misunderstanding Productivity

 

There are two common misunderstandings about Construction productivity. One is that increasing offsite manufacturing and use of modern methods of construction like prefabrication and modular buildings will increase measured Construction productivity. It will not, because that work will be included by the ABS in the Manufacturing industry subdivisions of Prefabricated steel and timber buildings, Concrete products, and Structural steel. In fact, one reason for the lack of growth in measured Construction productivity has been the gradual but continual shift to more prefabrication and offsite manufacture. 

 

A second misconception is that improving Construction productivity will somehow decrease the cost and increase the number of dwellings being built. This mistakes new construction for the market for housing, where in the short-run price is determined by the interplay of demand and an inelastic supply of new dwellings due to limited industry capacity to build and lengthy approval times. Increasing onsite productivity might decrease the time to complete a build but will have a marginal effect on the total cost of delivery, and the number of dwellings built is determined by project feasibility (i.e. the profitability of development) at any one time. Improving Construction productivity might help, but on its own cannot and will not solve the housing crisis. 

 

Conclusion

 

That the Construction industry has had no or negative productivity growth for the last few decades has been repeated so many times by so many commentators it has become an accepted fact about the industry. There are, however, four different measures of productivity, and commentators can focus on those that support their claims, and productivity growth rates vary considerably over different time periods, allowing selective choosing of comparisons. 

 

The four productivity measures are labour productivity on an hours worked basis or quality adjusted labour input basis, and multi-factor productivity (MFP includes the capital stock) also on an hours worked basis or quality adjusted labour input basis. The ABS productivity statistics for the Market sector go back to 1994-95, and this analysis has been for two periods, the long-run from1994-95 to 2023-24 (the latest data) and the short-run period of 2015-16 to 2023-24, chosen because 2015-16 was the end of the rapid rise and fall in Construction productivity during the mining boom. 

 

When the productivity of Construction is compared to the Market sector, despite claims that there has been no growth in Construction labour productivity, there has been an increase. Since 1994-95 Construction labour productivity has increased by 17% on an hours worked basis and 24% on the quality adjusted labour input basis which, although less than the Market Sector’s 64% and 41% respectively, is not nothing. Construction is in no way the worst performing industry, although it is far from the level of growth seen in the best performing industries.

 

In Australia there is a wide difference between a group of high productivity growth industries and a group of low or negative productivity growth industries. On the hours worked measure for labour productivity, since 1994-95 there were three high growth industries, and ehree industries with lower growth than Construction. For quality adjusted labour productivity, Construction had better growth than Rental, hiring and real estate services’ 4%, and there were three industries with negative growth. 

 

For MFP the story is not as good, because since 1994-95 there was only 1% growth in Construction hours worked MFP. That 1% increase in Construction hours worked MFP is very small, but not a decline. Market Sector growth on the hours worked basis was 23%, and on the quality adjusted measure Market Sector growth was 13%. On the hours worked basis there were three high growth industries, and four industries had a decline. Construction was one of five industries with negative quality adjusted labour MFP growth, although at -3% it had a much smaller decline than Mining -31%, Electricity, gas, water and waste -33%, Rental, hiring and real estate services -36%, and Administrative and support services -25%. This raises the question of why Construction is singled out as the problem industry. 

 

Construction capital productivity peaked in the mid 2000s and falling MFP was due to this decline in capital productivity. The Market Sector also had declining capital productivity, but there was a downward shift in Construction productivity around 2015, when both MFP and capital productivity went into significant decline and the Market Sector and Construction industry measures diverged.

 

There is a notable difference between the quality adjusted labour input measures and the hours worked measures for Construction labour productivity since 2015-16, because the hours worked measure has fallen 4% but the quality adjusted labour input measure has increased by 6%. The increase in the Construction quality adjusted labour input measure index will mainly be the result of changes in the composition of employment, with the combined share of Professionals and Managers increasing from 15% to 19% in 2022. The Construction workforce has become more skilled and qualified, raising the quality adjusted labour input measures.

 

Between 2016 and 2024 on the labour productivity hours worked basis there were two high growth industries, four industries had growth between 10 and 20%, and five with growth less than 10%. Construction -4% was one of five industries with negative growth, the others were Mining -15%, Manufacturing -4%, Electricity, gas, water and waste -15%, and Financial and insurance services -4%. On a quality adjusted basis Construction was the only industry to improve on the hours worked measure, and there were only six industries with better quality adjusted labour productivity growth than Construction’s 6%.

 

For MFP growth since 2016 on the hours worked measure only Agriculture, forestry and fishing had high growth, and there were three industries above 10%. Five industries had negative growth: Construction -8%, Mining -3%, Manufacturing -1%, and Electricity, gas, water and waste -15%. Again, the growth in the quality adjusted labour input measure was lower than for hours worked, with Construction -9% one of eight industries with declining productivity, including Mining -3%, Manufacturing -3%, and Electricity, gas, water and waste -16%.

 

Clearly, Construction is far from the worst performing industry, which raises the question of why it is so often singled out for low productivity growth. There were only six industries with better quality adjusted labour productivity growth than Construction. And are industries that have had declining productivity like Mining or Electricity, gas, water and waste not important? Should their productivity performance not be scrutinised? 

 

Maybe Construction could do better, but there have only been a few high growth industries in Australia over recent decades. Construction is one of a group of low growth industries, and compared to those industries its performance has been much better in both the long and the short-run. Instead of complaining about low productivity growth, attention should be focused on addressing the issues that have negatively affected Construction productivity, such as the number of micro and small firms, lack of standardisation of structural elements, the low level of investment in software and capital stock, state based occupational licensing and building codes, procurement methods, financing and project management, and education and training systems [3].

 

 

[1] See The long cycle in Australian construction productivity

 

[2] See The changing composition of construction employment

 

[3] See Housing productivity report a missed opportunity

Can AI Solve the Construction Productivity Problem?

 Will artificial intelligence reduce hours worked or time taken to complete a project?

 

 

There has been little or no growth in construction industry productivity for many decades, and there have been many different reasons given. These include larger and more complex buildings, longer and more global supply chains, increased regulation and compliance requirements, temporary project teams, the cycles of boom and bust in construction work, increasing use of prefabrication and offsite manufacturing, the number of low productivity small firms, low industry capital intensity, and a lack of workforce skills and training. All these and more contribute to a complex problem. 

 

Over the decades various solutions have been proposed, such as total quality management in the 1980s, lean construction in the 1990s, BIM in the 2000s, and digital twins in the 2010s. Often criticised as top-down, technocratic solutions when imposed by governments, with BIM mandates for example, the reality is the great majority of firms in the industry, particularly small and medium sized ones, have not adopted them unless required by clients. Clients, in turn, have not been prepared to increase costs or risk innovation on their projects to benefit the industry, a classic catch 22 situation.

 

Now there is a new solution to the productivity problem. Since the launch of Chat-GPT in November 2023 there have been dozens of artificial intelligence (AI) models released, and widely used software from majors like Microsoft, Oracle, Trimble and Autodesk now has AI incorporated into their systems. Will this time be different, or will AI follow BIM and the other solutions proposed in the past, and be adopted piecemeal when required without changing industry productivity and performance? 

 

In the previous post around 100 companies with construction related AI systems were included, divided into six areas: preconstruction and estimating; project and document management; site monitoring and safety; equipment management and maintenance; design and planning; and materials. This post discusses the potential of AI to solve the construction productivity problem. It looks at how AI might reduce the number of hours worked or decrease the time taken to deliver projects, considering its potential effects in equipment management and maintenance, preconstruction and estimating, project and document management; and site monitoring and safety. Issues in worker and workplace monitoring are discussed, because there is also the possibility AI will make the industry a worse place to work, which has been the experience of call centres, warehouses and Uber drivers, industries where AI is already intensively used. 

 

Can AI Improve Construction Productivity?

 

Labour productivity is the ratio of an industry’s inputs, usually the number of hours worked, to output, the value of production minus the cost of inputs. To improve productivity the relationship must change, for example increasing output faster than hours worked or decreasing hours worked while maintaining output will both increase productivity. Although that seems pretty straightforward, there are many issues that complicate measurement, ranging from how data is collected and managed to adjustments for price and quality effects. Those data issues are more difficult for capital productivity, which measures the contribution of buildings, structures, plant, machinery and equipment to output, adjusted for age and depreciation. 

 

Also, productivity does not change much from year to year, currently increasing by less than one percent a year for the Australian economy as a whole, and so is gradual and cumulative, and works over the long-run. Even in high growth periods and economies, annual productivity increases of around three percent are common. Therefore, productivity is not a good target for industry policy. Finally, the statistics used for construction include onsite work and people employed by contractors and subcontractors only, and thus exclude important activities like offsite manufacturing and prefabrication, and design, technical and professional services.

 

There are three factors that will affect improvement in industry productivity from AI. First is the adoption rate of AI by firms, which will be the key driver because the more firms that use AI the bigger the effect. However, second, AI productivity gains may be concentrated in a few firms, lowering the overall level of industry productivity improvement. Third, some or many physical or manual tasks may be affected by AI through robotics, which is outside the discussion here. Onsite construction robots were covered in this recent post. 

 

A November 2024 OECD publication on the productivity effects of AI over the next decade included industry estimates of the share of tasks affected by AI using two measures, a baseline exposure for the share of tasks that time for completion substantially decreases using Generative AI, and an expanded capabilities measure that includes tasks where gains are achievable if other software is developed on top of current systems. The largest gains are in ‘knowledge-intensive services that rely strongly on cognitive tasks, such as Finance, ICT services (including telecoms), Publishing and Media, and Professional services. The least exposed sectors include sectors with a strong manual component, such as Agriculture, Mining and Construction.’

 

Figure 1. Share of tasks affected by AI

 

Source: Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence. OECD Working Paper. 2024: 19.

 

The OECD study looked at tasks and the extent to which AI can do or help with. High exposure means it has a large share of tasks that AI can assist with, and there are only half a dozen high exposure industries. Although construction is at the lower end of the range, 20 to 30 percent of tasks could be AI assisted, which is comparable to metal and plastic manufacturing and warehousing, and not far behind transport, fabrication, motor vehicles, and water and waste. There are, therefore, plenty of potential productivity gains for construction from AI.

 

How AI Might Improve Construction Productivity

 

There are two ways AI might affect construction productivity: it could reduce the total number of hours worked on a project or it could decrease the time taken to complete a project. Note, however, if AI had both of these effects they would cancel out and productivity would not change.  

 

The most straightforward use of AI is for equipment management and predictive maintenance. If these increase the availability and reliability of machinery and equipment there will be an increase in productivity from less downtime for operators, hours worked might not change but output would increase, and that would also decrease time taken to complete. The impact and importance of this on productivity depends on how unreliable equipment currently is. If contractors and operators are aware of the liabilities and costs of breakdowns and do adequate maintenance, any improvement from AI will be at the margin, however both labour and capital productivity would improve. 

 

In preconstruction and estimating, AI could cut time and reduce mistakes. All construction firms do estimates, and using AI and image recognition for automated quantity take-offs from drawings, PDFs and BIM, and scanning drawings to count items and check for missing information and risk assessment seems an obvious way to save time. That data can be given to estimating systems that also do BQs, cost plans, bidding automation, indirect costs, KPI analysis, and predictive analytics. For larger firms doing design and build work, AI systems that do generative design, site layouts, materials selection, planning and code compliance checks would be useful. Although important for individual firms, the effect on overall industry productivity of reducing hours worked in these offsite functions would be offset by the much larger number of hours worked onsite. AI could make a major difference to productivity if it improves project management.

 

Because of the large number of different project documents used, copilots, AI assistants and chat bots that search, summarise and analyse those documents on request is another obviously useful task for AI that would be relevant to most firms in the industry. If site and project managers spend much time searching for information and retrieving documents, and suppliers and subcontractors have to wait for instructions, this would be an efficiency gain that could reduce both hours worked and completion time for projects. 

 

Document management can also include project management functions like bidding, billing, payments, RFIs, claims and compliance management. Other AI assisted PM offers are task plans and schedules, risk assessments, status reports, workflows, resource management, ITPs and quality assurance, and site management. Some systems have collaboration and communication tools. How much difference could using AI for these make to PM and productivity? Tasks like scheduling, work plans and status reports are time intensive, so AI can improve productivity by reducing the hours required. Better onsite collaboration and communication can potentially reduce the time to complete, particularly on large projects where coordination is more important. 

 

For AI enhanced PM to improve industry productivity it would have to become widely used, and that depends on how much it improves the capability and competitiveness of firms using AI. If there are significant benefits to early adopters of AI they will be able to out-compete other firms, that will then have to become fast followers to survive. Whether or not AI will deliver meaningful reductions in hours worked, and those significant benefits are available and achievable, is an open question at this point. 

 

Finally, site monitoring and safety. Reality capture and computer vision systems match site work to digital twin and BIM models to track progress.  These design vs as-built comparisons can be done by drones or hand-held cameras and provide daily or real-time progress tracking, with time saved on reports and documentation a useful improvement, and possibly leading to an important reduction in disputes and defects. However, the main effect on productivity will depend on how well this information is used to improve project management, particularly collaboration and communication. 

 

Safety systems monitor sites and detect hazards, and dangerous behaviour by people, and can prepare safety plans. While important, this will not have much effect on productivity, although reducing injuries and time off work for recovery would have an indirect effect. Some of these systems also identify people using sensors, cameras and wearables like beacons and badges, and can also be used for access control, headcounts, timesheets, task and workforce management. Again, there can be time savings in these administrative functions, however, worker and workplace monitoring raise a number of important issues. 

 

Monitoring and Algorithmic Management

 

In Australia workplace monitoring and surveillance is covered by a mix of federal and state laws. The Privacy Act 1988 doesn't explicitly address workplace surveillance, but has 13 Australian Privacy Principles that employers must follow when handling personal information and data collected through monitoring. Employers can monitor employees to ensure they are doing their work and using resources appropriately, but should only monitor employees to the extent necessary to achieve a legitimate business purpose. Employees have to agree to have their data shared. The states have two laws that regulate workplace surveillance, one is a Privacy Act requiring compliance with the Australian Privacy Principles (in NSW a Workplace Surveillance Act), and the other a Surveillance Devices Act that regulates the use of surveillance devices in public places and workplaces [2].  

 

Monitoring with AI becomes controversial when it is used for ‘algorithmic management’, where workers are monitored, ranked, rated, given instructions, potentially harmed or auto-fired by an AI system. In the current dispute between Woolworths and the United Workers Union a worker performance management program called ‘The Coaching and Productivity Framework’ that was introduced across warehouses is a major issue. The December 7^th Sydney Morning Herald article on the dispute said the ‘point of contention is the disciplinary action workers face if they fail to achieve 100 per cent adherence to pre-determined pick rates. What was previously a non-enforced goal is now a mandatory requirement.’

 

In the US the Federal Trade Commission has authority to block unfair trade practices, and a recent speech by commissioner Alvaro Bedoya argued algorithmic management is an illegal unfair trade practice. Because it causes substantial injury, can’t be reasonably avoided, and isn’t outweighed by a countervailing benefit, Bedoya says algorithmic management satisfies the three criteria for an unfair trade practice.

 

He gives three examples. First, on substantial injury, Bedoya describes a warehouse worker injured while working for ecommerce sites monitored by an AI that required him to pick and drop an object off a moving belt every 10 seconds, for 10 hours a day. Workers are tracked by a leaderboard, supervisors punish workers who don’t make quota, and the algorithm auto-fires them if they fail to meet it. Second, Bedoya describes the experience of New York rideshare drivers when the apps start randomly suspending them, telling them they can’t book a ride. Drivers who stop for coffee or use a bathroom are locked out for hours as punishment. Third, on average a call-centre worker is subjected to five forms of AI, with AI video and voice monitoring to measure empathy, an AI timing calls, and two more to analyse calls. AIs produce transcripts of calls, but workers with accents find them ‘riddled with errors’ and their performance assessment is based on the transcripts. 

 

Given these considerations, construction employers who hope AI monitoring of workers will improve productivity should approach this idea with great caution. Workers have the right to know what data is being collected, who it’s being shared with, and how it’s being used. 

 

Conclusion

 

Industry productivity is the combined effect of both capital and labour productivity, and AI adoption can lead to broad and long-run gains in construction productivity as AI assistants take over time consuming tasks like report writing and document management, and AI systems improve resource and project management. The potential productivity gains from AI are significant, but there are factors that could limit the effect, such as the willingness of firms to adopt AI, their capabilities, skills, and access to data. Also, AI adoption across the industry is likely to be uneven, and the productivity benefits of AI will tend to go to firms that use other advanced technologies and have IT skills. 

 

Two construction tasks that AI could significantly improve are estimating and project management, because it can synthesise, summarise and interpret data, and provide insights and suggestions, although it cannot replace expertise and requires supervision and checking of results. AI would also make a major difference to productivity by saving time used for document management, through quicker access to information, by reducing mistakes, disputes and defects, and could lead to more and better collaboration and communication. 

 

To improve construction industry productivity, AI has to increase efficiency and reduce the hours worked or decrease the time taken to complete a project, although if AI had both of these effects they would cancel out and productivity would not change. Therefore, productivity is not the most appropriate metric to use for industry-wide effects of AI, rather it should be used to measure productivity improvements in specific tasks where AI can assist workers. 

 

In addition to productivity there are other labour market implications not discussed here, such as the effects of AI on recruitment, retention and wages, and ongoing demographic changes such as the ageing workforce. Also, the history of technologies like electricity, internal combustion engines, computers and the internet show it typically takes two to three decades for a new technology to become widely used and to significantly affect the economy and productivity. AI may be different, but probably not. Modern AI began with Alpha Go in 2016, so we are almost a decade on already and the next decade could see rapid uptake of the AI systems surveyed in the previous post.

 

At this stage it is impossible to know whether AI will deliver on its potential productivity gains. That will depend on how the technology continues to develop, how quickly AI is adopted and successfully applied, associated innovation-boosting effects, and how government policy on incentives, digital infrastructure, AI regulation and data develops. 

 

                                                                     *

 

[1] Progress in AI may be slowing down. GPT-4 is nearly two years old, but The Information said OpenAI’s new models have only incremental improvement, and Reuters quoted Ilya Sutskever, co-founder of OpenAI, saying results from scaling up pre-training have plateaued. 

 

[2] For more details see the Office of the Australian Information Commissioner about workplace surveillance: https://www.oaic.gov.au/privacy/your-privacy-rights/surveillance-and-monitoring/workplace-monitoring-and-surveillance

 

 

 

 

 

 

Recent Research on Construction Productivity

 Four papers on US statistics and McKinsey’s latest report 

 

 

 

The lack of growth in construction productivity is a well-known and universal issue. This post reviews US research into construction productivity, with summaries and comments on four recent research papers, followed by the most recent McKinsey report on improving construction productivity. Two papers develop a physical measure of productivity as houses per employee, providing alternative measures to the official statistics.

 

The first paper discussed argues that increasingly strict land-use regulation has resulted in more small, low productivity firms in residential construction, and this has lowered the overall industry level of productivity. The two papers that follow looked at problems in the statistics and methods used to measure it, and found strong evidence that mismeasurement does not explain the lack of productivity growth. The fourth paper also addresses issues in the statistics and develops four deflators for four specific construction sub-industries. 

 

The McKinsey report starts with industry level analysis, then goes on to address project level issues. It lists seven issues in construction that lead to low productivity, and offers five ‘transformative approaches that owners and delivery partners could adopt’.  

 

 

Why Has Construction Productivity Stagnated? The Role of Land-Use Regulation

D'Amico, L., Glaeser, E., Gyourko, J., Kerr, W., and Ponzetto, G. 2024. 

 

The paper discusses construction productivity in the US and argues land-use regulation has affected the size and productivity of residential construction firms, and uses a new productivity measure using housing units started per employee. They admit this is an ‘imperfect’ series, because the employment data has missing years and excludes some subcontractors, but nonetheless is used in the comparison with manufacturing and car production. Despite the data problems, this is a useful measure of physical productivity (as output per employee) that avoids the issues associated with productivity estimates from official construction statistics 

 

Figure 1. US house building labour productivity 1900-2020 

 

The paper says “From 1935 to 1970, homes produced per construction worker grew at a pace that at times exceeded the growth in the number of cars produced per automobile industry worker or the growth of total manufacturing output per industrial worker. Since 1970, these three series have diverged sharply. Car and manufacturing output per worker continued to soar but houses built per worker fell dramatically.” [1]

 

Figure 2. Construction compared to manufacturing and cars

 

In the 1950s the US developed new towns and suburbs with thousands of houses, using a production line method that moved site workers from one building to the next in a highly coordinated system integrated with the supply of materials. D’Amico et al. suggest such large scale developments with their economies of scale cannot be done today, due to the “increase in regulatory tightness in construction from the mid-1970s to today. Developing large projects and coordinating construction teams over different projects all working at the same time is becoming more and more difficult … It is also hard to obtain permits to develop a single type of housing unit on large plots of land given that zoning laws discourage such types of projects.”

 

The argument is that land-use regulations affect firm size by reducing the average size of home builders, and firm size restricts returns from scale and diminishes incentives to invest in technology [2]. With reduced technology investment comes lower productivity: “Project-level regulations have been put in place that reduce innovation, not by barring it, but by limiting project and firm size. The small scale of the firms, and the fact that they could not grow dramatically even if they made a breakthrough, then limits innovative activity.”  

 

They argue there will be less construction activity in areas with stricter land use regulation, because stricter regulation leads to smaller establishments and forces contractors to undertake many small projects. This stretches their span of control, creates inefficiency and causes lower productivity. Therefore, in more regulated areas residential construction firms will be smaller and less productive.

 

Their econometric model links project-level regulation, firm size and productivity, and they suggest firm size could explain a significant fraction of the low productivity seen in American residential construction. For bigger firms, fixed costs are relatively less important and productivity is higher. Smaller firms are less productive and in jurisdictions where regulation is more intense, average firm size and firm productivity are both lower.

 

Using the model, there is a detailed analysis of residential construction firm productivity, with regressions against regulation, size, revenues, housing units produced, and profits. For regulation the Wharton Residential Land Use Regulation Index is used, and the model finds a one standard deviation increase in the Index is associated with an 12.8 % reduction in revenue per firm, a 5.4% decrease in revenue per employee, and a 4.3% reduction in employment in large building firms’

 

Comment

This is an interesting paper built on the well-known fact of lower productivity in smaller firms. The intuitively appealing argument is that increasingly strict land-use regulation has resulted in more small firms doing more small projects. Using regression analysis they find this is has indeed been the case in the US sine the 1970s, when construction productivity stopped growing. 

 

However, does stricter land-use regulations lead to smaller firm sizes, and therefore lower productivity, in construction? The answer is more maybe than definitely, for two reasons. First, the comparison with the 1950s and 1960s house building boom is misleading. That period was uniquely different because of the size of the sites available and repetition of a large number of houses, in many places developments had over 10,000 houses built over a few years. Those sites are no longer available and developments of that size are not possible today. That is not due to regulation, but because the opportunities are not there. 

 

Second, construction is a local business, There is a physical limit to managing projects based on the time and cost constraints of distance. Small firms work within their local area, and larger firms typically operate as a series of semi-independent project offices. Regulation might enhance the effect of location through local planning laws, but does not create the diseconomies of distance. 

 

Finally, does project-level regulation reduce incentives to invest in technological innovation? The paper does not provide any evidence for that beyond firm size, but it is a well-known characteristic of small firms that they do not invest in innovation and have limited capex budgets. That is because they are small, not because they are over regulated. 

 

The paper links regulation, firm size and productivity. Basically, land-use regulations reduce project and firm size, leading to smaller and less productive firms. ​The analysis finds some support for that argument, because there is less building in places where regulation is more intense, but there are other good reasons why there are so many small residential construction firms. This is original research, but does not conclusively prove increased regulation is responsible for the prevalence of small firms in house building in the US. 

 

 

The Strange and Awful Path of Productivity in the U.S. Construction Sector

 Goolsbee, A. and Syverson, C.  2023. 

 

This paper is from two experienced researchers into the construction industry, and they look at several key issues in measurement of productivity. Their intent is to show that the lack of growth in construction productivity is not due to problems in the statistics or method used to measure it. The time period covered in their research is 70 years, from 1950 to 2019. 

 

They focus on measurement problems as an explanation of poor performance: ‘we update some of this previous work and extend it to some new data sources and hypotheses. Together, these new approaches seem to reinforce the view that the poor performance is not just a figment of measurement error.’ 

 

First, mismeasurement of capital stock or labour inputs is not the problem. They find both capital intensity (capital stock per full time employee) and intellectual property have grown at rates comparable to the whole economy since 1970. The growth rate of labour inputs was lower after 1970 than before, not faster as a mismeasured productivity slowdown would imply. Therefore, the problem should be in measuring output.

 

Second, in the US, construction’s nominal value added has grown at a similar rate to the economy, but the construction output deflator and the GDP deflator start to diverge in the late 1960s: ‘From 1950-69, the average annual growth rates of the construction and GDP deflators were almost identical—2.40 percent and 2.42 percent. From 1970 on, however, the GDP deflator averaged annual growth of 3.37 percent, while the construction value added deflator grew 5.47 percent per year.’  

 

By deflating nominal construction activity with the whole-economy deflator, construction productivity will look like overall productivity. Is this the smoking gun? Unfortunately, no. The issue here is the change in relative prices, which is the price change of inputs in construction compared to the changes in overall prices in the GDP deflator. They find the increase in the construction price deflator cannot be explained by increasing relative prices of construction inputs. 

 

They then measure productivity in physical units in residential housing construction, using units per employee and square feet per employee for houses and apartments. These also show declining or stagnant productivity, although the multi-family series are extremely variable, falling by half in 2001 and 2010 during economic downturns. 

 

Figure 3. US house building labour productivity 1972-2020

 

 

Finally, they consider construction’s ability to transform intermediates into finished products. Again, performance has deteriorated. They also provide evidence that something keeps producers in areas where construction is more productive from growing, and ‘this problem with allocative efficiency may be accentuating the aggregate productivity problem for the industry.’

 

Comment

By working through the likely suspects of mismeasurement of capital and labour inputs, output and the deflator used to adjust for price changes, the paper finds that measurement error is ‘probably not the sole source of the stagnation’, i.e. the statistics may have some issues, but the problem is real. Using alternative measures like the physical measure of housing units produced per employee and the use of intermediate inputs also finds no increase in productivity. 

 

Construction productivity, despite the improvements in materials, tools and techniques over the last few decades, has not increased. And this is not unique to the US, for countries around the world, the same result has been found. 

 

 

Can Measurement Error Explain Slow Productivity Growth in Construction?

Garcia, D. and Molloy, R. 2022. 

 

Garcia and Molloy are economists at the US Federal Reserve, and for them the answer was no: ‘we estimate that productivity was essentially flat in the construction sector from 1987 to 2019.’  However, it was not as low as the negative -0.05% annual change found by official statistics when they adjusted for what they call ‘unmeasured structure quality’ of houses. Quality changes were improved energy efficiency, better finishes, more bathrooms and larger buildings. These improvement were real but ‘undramatic’, and adjusting for improved quality gave an annual increase in productivity of 0.02%.

 

Their analysis found a small upward bias in deflators related to unobserved improvements in structure quality, ‘but the magnitude is not large enough to alter the view that construction-sector productivity growth has been weak. We also find only small contributions from other potential sources of measurement error.’ They conclude construction is very labour-intensive and there have not been many labour-saving innovations due to low investment in intellectual property.

 

They found the average length of time from start to completion of single-family homes increased from 6.2 months in the mid-1980s to 7.0 months in 2019, ‘suggesting that any time-saving productivity improvements have been more than offset by delays elsewhere in the construction process.’ Although this could be due to increased regulation causing delays, they did not find evidence for increased costs from regulation.

 

Another potential restraint on productivity growth is more construction taking place in already dense areas: ‘new homes built between 2016 and 2019 were more likely constructed in tracts with a population density above 3000 persons per square mile, while new homes built between 1991 and 1994 were more likely to be built in tracts with less than 100 persons per square mile’. Higher density makes building on small parcels of land more expensive because it is more difficult to take advantage of scale, compared with large developments of hundreds of new homes.

 

Comment

The result of this research is that a small increase in productivity has been absorbed by higher but unobserved (i.e. not in the data) quality, therefore there has been no growth in measured construction productivity since 1987. Using a variety of sources on building quality, land and house prices, and potential bias in the deflators from measurement of output and labour input, their adjusted measure finds a small average annual increase rather than the small annual average decrease in official statistics. While something is better than nothing, the gap between construction and other industries’ productivity growth remains. 

 

 

Measuring Productivity Growth in Construction

Sveikauskas, L., Rowe, S., Mildenberger, J., Price, J. and Young, A. 2018. Updated 2024.

 

Addressing the problem of measuring real output in construction, Sveikauskas and his colleagues at the US Bureau of Labour Statistics estimated real construction value added per hour worked in four construction sub-industries, using four specific deflators and including subcontractor hours. The research methods were published in 2018. 

 

Using more recent data for a time period comparable to the papers discussed above, between 2007 and 2020 productivity fell in single-family residential and multiple-family housing construction, but rose in industrial and highway, street, and bridge construction, following a rising volume of work in the latter two sub-industries. Overall productivity for the four sub-industries was flat because these rises and falls balanced out.

 

Figure 4. Productivity for four construction sub-industries 

Source: Bureau of Labor Statistics, 

 

Comment

The BLS research addresses the deficiencies found in construction deflators. There is a downward bias to output estimates because there is no adjustment for quality changes in buildings and structures.  If real construction value added is underestimated due to the deflators used, construction productivity has also been understated.

 

These estimates used four different deflators, providing high quality estimates of real construction value added per hour worked in those industries, including subcontractor hours. The BLS research improves on previous research by using appropriate output deflators to develop measures of productivity growth, these measures are more reliable because the deflators are specifically designed for each sub-industry. The 2024 update for 2019 to 2023 shows labour productivity falling for single family residential and highway and bridge construction, but rising for multi-family residential and a large increase in industrial construction productivity. 

 

 

Construction Productivity is No Longer Optional

Mische, J., Stokis, K., and Vermelfoort, K. August 2024

 

The article starts with data on the lack of growth in labour productivity, defined as the value added per hour of work, adjusted for increases in construction and input prices, for 42 countries with about 90% of construction value added. McKinsey has released a series of report on construction productivity since 2017, and this latest addition updates the data without changing the picture of lower growth in construction compared to other industries. [3] 

 

Figure 5. Construction compared to manufacturing and global economy

 

 

There are seven specific issues holding back measurable productivity gains (links below are in original):

 

1.        The construction industry’s uptake of technology has been slow over the past several decades. Historically, construction companies spent an average of less than 1 percent of revenues on IT, less than a third of what is common, for example, in automotive and aerospace. Technological innovations in construction largely focus on increasing control or other priorities, such as design, safety, and usage of new materials, and less on direct workforce productivity

2.        Improvement in projects don’t scale across the entire project portfolio. Companies typically start projects as soon as possible with a smaller team that focuses on technical aspects, procurement, and project deliveries rather than on improvement initiatives. Projects have little incentive to act as the pilot for the benefit of future projects. 

3.        In some cases where the industry has improved its productivity and where construction companies could have improved their margins, many of these benefits are passed upward (to suppliers) or downward (to their customers) in the value chain. tender teams factor gains from productivity into cost estimates within proposals, keeping margins thin

4.        Tender dynamics and low margins limit investment in productivity.

5.        Current approaches to risk-sharing and cost estimation cannot keep up with the ever-growing increase in project complexity, risk, and scope.

6.        Productivity declines because of firms having to bring inexperienced workforces onto projects. A large part of skill level is built on tenure and apprenticeship. New workers may require additional training and control and, consequently, achieve lower rates of productivity.

7.        Timely delivery takes priority over productivity improvements. Reducing idle times across all subcontractors and tasks while meeting throughput requirements would require systemwide efforts to improve workflows, reduce bottlenecks and variability, balance loads, and improve project production rates.

 

The transformative approaches that owners and delivery partners could adopt are quoted in full:

• Adopt project steering 2.0. Conventional project management relies on earned-value-management systems, but these s-curves can disguise performance issues and delay intervention, enabling further cost and schedule delays. Project teams can follow the lead of manufacturing and shift their focus to production rate metrics, such as meters welded, volumes excavated, and drawings reviewed. Steering projects in this way will allow teams to be more proactive and help them resolve issues before they materialize.
• Nurture a supplier ecosystem across projects. Supplier ecosystems can help delivery partners provide owners with transparency, credibility, and predictability by providing teams more stability, which will help improve learning curves, interfaces, ways of working, and innovation. Instilling habits across an ecosystem can build the trust and better practices required to gradually promote positive end-to-end change across projects.²⁷ Owners will typically set up these partnerships and role model the desired way of working.
• Upskill project staff. Skilled labour shortages pose a massive upskilling and leadership challenge for the industry. Given high-pressure and short timelines, project staff are tempted to fall back on suboptimal behaviours and ways of working. Leaders can foster an aptitude for learning among their teams to help team members upskill regularly. Technology-supported learning journeys, systematic apprenticeships, and project academies focused on hard and soft skills will all become more popular in years to come.
• Scale initiatives across project portfolios. There are many examples of successful productivity improvements within specific project teams. However, rolling out improvements across a project portfolio is difficult. Change management in a project portfolio context is even harder than in an individual project. Tailored approaches and different capabilities are needed to establish improvement at scale.
• Apply technology in ways that have a direct impact on productivity. Technologies such as generative AI could fundamentally transform how capital projects are delivered. Investments in technology should shift from the “shiny objects” like drones or monitoring software to technology that streamlines and accelerates engineering, procurement, and construction.

 

Comment

The McKinsey report does not specifically address residential construction, which has been the focus of the other research discussed above. However, the issues and recommendations apply as much to house builders as to larger contractors in non-residential construction who are more likely to be McKinsey clients. Their seven issues are common to construction companies everywhere. As McKinsey notes, productivity is not how companies measure the success of projects, and ‘companies prioritize hitting the delivery date over any other goalpost.’

 

Their recommendations are less ‘transformative’ than statements of the obvious. These are all well-known and widely appreciated, and have been discussed in government reports and the academic literature for decades. The problem for the industry is that all these ‘transformative approaches’ require upfront investment that clients do not, and will not, cover (including government), and few contractors have the scale necessary to spread those investment and capital costs across enough projects to make them feasible. And for those companies, profits do not always a follow the investment. 

 

A more germane question, that McKinsey does not answer, is why it is so difficult for so many construction companies to make their recommended changes if they would improve margins and profits? This is the core issue: what or where is the new business model that construction companies can use that will both improve productivity and increase profitability. How do companies benefit from these changes?

 

 

Conclusion

 

A recent paper on the lack of productivity growth in US construction by D’Amico et al. argued increased regulation leads to more small, low productivity builders in residential construction. However, the structure of the residential construction industry is many small firms working on small projects and contracts in a local area, but the authors do not consider the possibility that industry structure is aligned with this demand pattern.

 

Other recent research into construction productivity growth in the US has focused on issues with the statistics used to measure it. That is because problems in measurement and particularly deflation of output are widely accepted reasons for the low rate of growth. The research is thorough and approaches the problem in several different ways, and concludes that while there is some mismeasurement that understates growth, it does not account for the lack of growth in the long-run.

 

Goolsbee and Syverson looked at potential mismeasurement of capital and labour inputs, output, and the deflator used to adjust for price changes. They find the statistics may have some issues, but the problem is real. Using a physical measure of the number of housing units produced per employee also finds no increase in productivity.

 

Garcia and Molloy found a small increase in productivity since 1987 in their adjusted estimate, but it has been absorbed by higher but unobserved quality that is not included in the data, such as larger houses, better finishes and improved energy efficiency. Therefore, there has been no growth in construction productivity as measured by official statistics.

 

Sveikauskas et al. estimated real construction value added per hour worked in four construction sub-industries, using alternative deflators specific to the four types of building. Their estimates found growth in two of the sub-industries but declines in the other two, thus no growth in overall productivity as these rises and falls cancel each other out. 

 

McKinsey’s prescription for transformation of the industry has a focus on projects, technologies, and information, in a top-down approach that has to date not delivered any meaningful changes in the industry or productivity. McKinsey does not address why it is so difficult for construction companies to make their recommended changes, or how they improve margins and profits. What or where is the new business model that construction companies can use to improve productivity and increase profitability?

 

That construction needs to improve productivity is something all industry stakeholders can agree upon. Despite all the time, effort, and words expended on this over many decades there has been no increase in industry-wide productivity. There is good evidence for differences between firms [4], with large firms having significantly higher productivity than small ones, but the great majority of firms are small and many are doing labour intensive repair and maintenance work. This structural characteristic of the industry has and will continue to limit growth in construction productivity.

 

*

 

[1] A previous post argued against comparison with manufacturing. 

 

[2] Ed Glaeser has done previous research on this, in Glaeser, E. L. and Ward, B. A. 2009. The causes and consequences of land use regulation: Evidence from Greater Boston. Journal of Urban Economics, 65(3):265-278. See here for Gyourko, Saiz, and Summers, 2006, on their Wharton Residential Land Use Regulatory Index.

 

[3] The article uses data for 42 countries that represent about 90 percent of construction’s global VA. Sources used include the International Labour Organization, IHS Markit and S&P Global, the OECD, the United Nations, and national statistical agencies, such as the National Bureau of Statistics of China and the US Bureau of Labor Statistics.

 

[4] A 2024 post on Australian firms noted ‘large firms are less than one percent of the number of firms, they employ 14 percent of the people and produce 21 percent of industry value added. Medium and large firms have much higher levels of productivity, measured as IVA per employee. For large firms this is $195,000, nearly twice the micro firm IVA per employee of $100,000.’