Construction Productivity Bibliography 2016

Over the years I've done a few papers on construction productivity, most recently a book chapter in 2015. This bibliography started as their combined reference lists, which for the book chapter needed updating. After adding some more recent publications this is now the 2016 update.

The bibliography is extensive, with over 200 references, most journal articles, but not exhaustive. In particular, there is no attempt to collect all the work sampling literature, which is vast. There are dozens, probably hundreds and possibly thousands of conference papers and the like on 'productivity of bricklayers in country X' or similar. These are not included, but many journal articles across several decades on craft and project productivity are.

The basic criteria for inclusion is the focus on construction productivity, although multi-industry and multi-country studies are also listed. That means relevant and related research, for example on innovation and R&D, capital expenditure, or skill formation and training, is not included. Most entries have the words 'construction productivity, in their title. That is somewhat restrictive, but helps keep it manageable.

 The intention is to try a keep this reasonably current, and notice of a new update will be posted when ready. Any worthwhile additional references that readers email me will be added. 

Construction Productivity and Project Managers

McKinsey on High Performing PMs

One of the reports covered in the recent post on construction productivity came from the McKinsey Global Institute, the think tank for the management consultancy.

McKinsey argues that productivity is a major issue for the construction industry, with delays, blown budgets, and quality issues common. In their 2016 infrastructure report they claim “Cost overruns for large projects average 20 to 45%. We often see cost differences of 50 to 100% in similar projects carried out by different countries, even those in similar income levels. If countries apply the best practices that have already been proven effective elsewhere, they can achieve remarkable results.” 

Interestingly, McKinsey seems to argue that while these variations are endemic to the construction industry, their causes can be addressed on individual projects. A key factor is the quality of the project manager, who can determine outcomes. Their research “across thousands of projects indicates that top quartile project managers consistently deliver projects ahead of time and below cost, whereas the opposite is true for the bottom quartile”. Their argument is that project management capabilities are the key to success, as shown below.

 

They identify ten contributions to weak construction productivity growth, and suggest these are broadly understood yet hard to measure:

1. Fragmentation. The construction industry has many small-scale players. For example, about half of construction output in the United States is produced by firms with fewer than 50 employees.
2. Skills. Research shows that educational attainment has decreased over the years for the average US construction worker at age 30. This has implications for sector performance. The skill level of supervisors and project managers is critical for good on-site productivity, but it can vary greatly among employees across the same firm. Skill gaps also limit the introduction of new technology.
3. Insufficient planning and design. Large projects typically require more than 5 percent of total investment during the planning phase to run smoothly. This up-front investment is often not made, resulting in time-consuming problems and change orders.
4. Ineffective procurement processes and contracts. One-round lowest-price bidding processes, for instance, can encourage firms to use changes and claims as a core revenue stream.
5. Workflow split. The differing skill sets and working styles of architects and engineers affect the way they work with contractors and can prevent the right degree of cooperation and overlap needed for optimizing the design-build process.
6. Limited use of industrialized construction techniques. Approaches such as lean construction, the use of big data-driven building information modelling (BIM) systems, full prefabrication methodologies, and construction flow balancing (that is, the full optimization of material flow and team rebalancing to eliminate downtime) are often not applied to their full potential.
7. Limited use of technology. The sector is perceived as being slow to innovate—in fact, most construction work looks just like it did 50 years ago. Recent MGI research found that the construction sector lagged behind most other parts of the US economy in the intensity of digital assets, usage, and labor.
8. Risk aversion. Construction is typically a low-margin business. This tends to create a preference for proven technologies and approaches, since there is an insufficient financial buffer to support experimentation and innovation. Furthermore, failures tend to be highly visible, with direct impact on future business, as well as being costly and hard to correct.
9. Significant dispersion of performance. There is a wide gap between frontier firms and the average firm in the construction sector—and there are enormous gaps across geographies.
10. Uniqueness of projects and project mindset of companies. There is a tendency to approach each project as a unique case. Even if that stems from a desire to provide the client with craftsmanship or personalized service, it has the unfortunate effect of limiting standardization of designs and construction modules or prefabrication. It also discourages contractors.

 

Many Dimensions of Productivity

Productivity is Not a Magic Pudding

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

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

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

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

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

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

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

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

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

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

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

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

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

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