PAPER – Construction Productivity – CONSTRUCTION PROJECT MANAGEMENT
Find a current research article (published within the last 4 years) on the topic of Measuring and Comparing International Construction Activities (Chapter 6 Word Document – Attached).
Write a two-page analysis of the article using at least two other peer-reviewed sources to support your analysis/discussion. You must have a title page, abstract page (minimum of 150 words), two full pages of text, and a reference page (3 References **Minimum) for this weekly assignment. References used must be in the U.S. APA format is required. No Plagiarism. Need this done by January 31, 2021, @ 1900 Central Time zone.
6 Measuring and comparing construction activity internationally
Introduction
The notion that there is a predictable relationship between construction and economic development was first put forward by Turin (1969) and subsequently developed by Drewer (1980). Since then, a body of construction industry development theory has been built on that literature, including Bon (1992, 2000), Crosthwaite (2000), Ofori and Han (2003), Ruddock and Lopes (2006) and Gruneberg (2009).
One would expect wealthier economies to have larger construction outputs. Turin’s suggestion, however, was that, as an economy grows, the proportion of construction in national output would increase until, at some point, it levels out. Bon (1992), using the same kind of data, but updated, suggested what has come to be known in the literature as the ‘Bon Curve’: that, as national output continues on a growth path, after a period of levelling out, construction will tend to decline as a proportion of national output.
Two interlinked phenomena were suggested by Turin and Bon. Firstly, there is a direct relationship between construction value added per capita and GDP per capita and, second, as countries grow wealthier, construction value added tends to increase at a faster rate than growth in the overall economy. Bon’s refinement is that, at a certain point, growth in construction value added tends to slow and, eventually, begins to decline as a proportion of GDP. More recently, Gruneberg (2009) has suggested that, while there may be an increase in construction activity in the initial stages of economic development, this may subsequently slow down. Thereafter, construction may rise and fall depending on changing circumstances, but with no particular or predictable pattern.
The studies mentioned and other analyses of construction’s contribution to the economy are in terms of construction value added, presumably because data in that form is more or less readily available from national accounts. Moreover, construction data and GDP are invariably converted to a common currency – usually $US – using commercial exchange rates. The use of value-added and commercial exchange rates is questioned in this chapter. The chapter casts a fresh eye on the contribution of construction to national economies and how that contribution varies across countries and over time.
Users and uses
Construction activity is distinct from but obviously related to the property sector. However, it does not include the value of land. Construction activity is also separate from but increasingly overlaps with construction professional services. The cost of at least some construction professional services is often included in construction contracts and, therefore, construction output data, although it may be provided by consultancy firms subcontracted to construction contractors.
The main potential users of reliable national and international construction data include construction companies (contractors and consultants), construction industry clients, property agents and developers, government agencies, researchers and the press. Their needs are varied but largely comprise market or statistical data and analyses, including data on output and new orders, costs and prices and productivity. Their interest in construction data extends to levels (absolute values) and trends in:
· the size of markets for different types of construction work, for example, housing, office buildings and power projects. There is often interest locally and internationally for particularly large programmes of work or for significant falls in the demand for construction work. A number of specialist market intelligence organizations produce construction market reports, usually based on official data.
· prices for the main construction resource inputs. Rapidly rising or falling resource prices are of interest to people inside and outside the industry. Price changes are usually an indication of local demand pressures but sometimes result from international demand for particular resources. Structural steel is an example in recent years. The construction press publishes regular articles on construction price trends, for example, Gardiner and Theobald (2012), Rider Levett Bucknall (20
13
) and Turner and Townsend (2013); there are also annual – or more frequent – price books in a number of countries, such as Rawlinsons’ Australian Construction Handbook (2013).
· output prices for particular building types or types of work. Steep increases or decreases in these also usually follow market demand. Data on output prices is often published with data on resource prices. The effects of construction price trends should not be confused with property price trends. In the UK, for example, the effect of land prices on house prices is frequently ignored, and the cause of property price increases is wrongly attributed to the construction industry.
· construction price level differences across countries. This information is provided on a regular basis by international firms of cost consul-tants, such as Compass International (2013). Usually these include the prices of resources and different types of construction work. Priced items relate to representative local materials and products and projects that are representative of local practice and standards; and prices are usually brought to a common basis using commercial exchange rates. There have, however, been efforts recently to take a more considered approach, for example, by Faithful and Gould (2013) and Turner and Townsend (2013).
· indicators of national competitiveness and productivity. These are of great interest to industry bodies, national governments and the press and often become a source of national pride or national concern. In fact, accurate, meaningful measures can be elusive, but that does not prevent regular publication of reports providing ‘data’ ranking countries’ performance.
However, because of the ways in which construction is measured and presented, what is measured and how it is presented provide opportunities to use the wrong data or to use it in misleading ways. These issues are discussed later. In addition, it is important that some kind of commentary or critique accompanies published construction data; this is particularly appropriate for the press and the wider dissemination of the data. The opportunities for misunderstanding or misinterpretation – deliberate or not – of construction data are great. More effort needs to be made to explain terms and concepts and how they should be used and interpreted. There also needs to be explicit acknowledgement of the shortcomings in the quality of much construction data.
Measures of construction activity
Two aspects of measuring construction activity are what is measured and how it is presented. A major source of inconsistency in international comparisons is the variety of definitions used, what is included and what is excluded from national measures of construction activity. As has been noted by Meikle and Grilli (1999), construction activity in the national accounts is not consistent either because some activities are included elsewhere in the national accounts (for example, construction design and management services are usually included in professional services), because they are not included at all or because they are only partially included.
A recent unpublished pilot survey undertaken by Meikle in 2011 on behalf of the
Africa
n Development Bank (AfDB) revealed an even more complicated situation in African countries. Unlike most developed countries, the majority of countries in Africa have a high proportion of construction activity that is informal or undertaken by households that is often unrecorded. Recorded construction activity is usually broken down into work by registered and unregistered contractors and households and may or may not include work by small firms or individual tradesmen and may or may not include very small projects. Surveys, where they are undertaken, are usually sample surveys, and the value of most work is estimated based on material consumption or other indicators. The 2011 AfDB survey indicated that different things are measured in different countries and in different ways. In some countries, only work by registered contractors is included in the national accounts; in others, virtually everything that could be included is.
There are three commonly used definitions of construction activity: construction value added, gross construction output and contractors’ output. Construction value added and gross construction output are national accounts concepts and are supposed to include all activity that produces construction output, including work by contractors, work by government agencies, work by households and informal (black- or grey-economy) construction output. The third measure, contractors’ output, is the gross value of construction work produced by construction contractors. Because the output of construction is so heterogeneous, aggregate quantity measures of construction are not feasible, and using monetary values is the only option.
Construction value added is the component of GDP that measures the value added by construction firms to inputs from other parts of the economy to produce gross construction output. Typically, it includes compensation of employees, depreciation of capital investments and gross operating surplus, crudely, labour, depreciation of capital and profit. The concept of value added avoids double counting in national accounts, as the aggregate of all value added by all economic sectors is the measure of GDP.
Gross construction output, the second measure, appears as a component of national investment, Gross Fixed Capital Formation (GFCF). It is the aggregate of construction value added and the value of contributions from all other parts of the economy that produce construction works. Gross output is the sector’s turnover or the amount paid by its customers for its products. Strictly speaking, investment in construction excludes construction repair and maintenance activity, although it includes major refurbishments and extensions, but this distinction is not always observed. The 2005 International Comparison Program (ICP) (International Bank for Reconstruction and Development 2008), for example, breaks down GDP in expenditure terms, by investment and consumption, but includes all construction expenditure as investment.
The third measure, contractors’ output, is the total work undertaken by registered contractors and, in some cases, estimates of work by public-sector enterprises and unregistered contractors. Own-account work by commercial organizations is included in the organizations’ output.
Construction in the national accounts
Table 6.1
sets out the breakdown of construction activity in national accounts required by the United Nations International Standard Industrial Classification (ISIC).
Table 6.1
SIC activity categories for construction
The national accounts work categories are intended to include all construction activity by all agencies, not only work by contractors and direct works organizations. The categories, however, are not particularly helpful or complete. There are only two subcategories for building work (commercial and residential) when building probably represents more than half and often more than three quarters of construction activity in most countries; there is rather more detail for civil engineering work (seven subcategories); and there is no separation of public and private work or new work and work to existing buildings.
It is fairly common but not universal in national accounts, where there is a breakdown of construction output, to provide data on residential, nonresidential and civil engineering work. This is equivalent to ISIC categories
41
.20/2, 41.20/1 and
42
. It is often not possible, however, to obtain reliable data on construction output by trade (ISIC category 43) or preconstruction activities (ISIC category 41). There is a real need to review and revise the ISIC categories to reflect statistical, industry and other needs and modern practice.
Normalizing construction data
In order to compare value data on construction activity within countries, it is necessary to be able to adjust for differences in purchasing power in terms of type of work as well as the date of construction, location and even qualitative differences. This is done with different levels of reliability in different countries. In the UK, for example, a number of methods are used, such as cost and price indices, to bring construction values for different types of work undertaken at different times to a common price basis.
Spatial variation factors, the focus of this chapter, can be used to adjust for project location. Projects in dense urban or remote locations tend to have higher price levels than projects on open sites adjacent to sources of labour and materials, though other factors can come into play. For example, projects in the South-east of England tend to be more expensive than equivalent projects in the North-east. The purpose of work type and temporal and spatial adjustment factors is to permit comparisons to be made that are based on a common value. As noted in other chapters, the difficulties of making international construction comparisons at a point in time are compounded by the need to use currency convertors based on exchange rates.
Purchasing power parities (PPPs) are an alternative to exchange rates and are applied generally to the whole economy; PPPs are also available for components of the economy. The origin, development, production and use of PPPs are described in detail elsewhere in this book. It is sufficient here to note that PPPs for a range of activities, including construction, are published by the World Bank, currently every five years or so, but the intention is for this to be more frequent in the future.
Table
6.2
sets out exchange rates, GDP PPPs and PPPs for selected components of GDP, all based on US$ = 1.00, for a selection of countries from different regions, income levels and population sizes.
In higher-income countries, PPPs are typically higher than exchange rates. In middle- and lower-income countries, exchange rates tend to be higher than PPPs. This means that in rich countries, the size of economies and the volume of national output tend to be overstated and in poorer countries, understated. Because construction activity is not internationally traded, construction PPPs tend to exaggerate these tendencies, whereas machinery and equipment, which are internationally traded, tend to have PPPs that are generally similar to or higher than commercial exchange rates. Food and clothing PPPs tend to be closer to GDP PPPs in higher-income countries and closer to exchange rates in lower-income countries. There are, of course, exceptions to these general statements that may be explained by specific national features. In Singapore, a rich country, for example, construction PPPs are lower than might be expected, because there is a reliance on low-cost foreign construction workers.
Table 6.2
Exchange rates and PPPs for selected countries, 2005
PPPs demonstrate the Balassa-Samuelson effect. This states that nontraded elements of the economy – like construction – will have low relative prices in the early stages of economic development but that, over time, their price levels will tend to converge with general prices. Using commercial exchange rates for all economic activities, the effect is not necessarily picked up, and this means that the real volume of construction activity in lower-income countries tends to be understated and that the real volume in higher-income countries tends to be overstated. This is an added reason for using PPPs rather than exchange rates for comparing the volume of construction in one country to that in another. However, PPPs can only really be applied to high-level economic concepts. As PPPs are an average of equivalent baskets of goods, they do not apply to particular products or projects. The next section applies the theoretical framework of PPPs to national construction expenditures.
Analysis of the International Comparison Program 2005 data
The 2005 ICP Results (International Bank for Reconstruction and Development 2008) give nominal or commercial exchange-rate adjusted data for GDP and construction expenditure in $US for 1
28
countries (
30
African, 23
Asia
n, 10 Confederation of Independent States [CIS], 45
Europe
an and OECD, 10 Latin American and Caribbean [LAC], and 10 West Asian); they also give GDP and construction expenditure for the same countries using real or PPP adjusted data. Two distinct features of the ICP data are that it provides data on expenditure, not value added, and that it is brought to a common currency basis using both commercial exchange rates and PPPs, both whole-economy PPPs for GDP and construction-specific PPPs for construction expenditure.
The ICP data set allows for an examination of the effect of the use of PPPs on construction volume and on the relationship between GDP and construction expenditure. There are, of course, concerns about the reliability of such value data and PPPs, but the ICP data set is the largest single-source data set available. It is almost certain that the ICP data is more useful than comparisons based on exchange rates.
Table 6.3
sets out GDP, construction expenditure and construction as a proportion of GDP for the
128
countries in their regional groupings using commercial exchange rates and PPPs. The numbers in brackets indicate the numbers of countries for which there is construction data. Coverage is good for Europe/OECD, Asia, the CIS and West Asia but rather less so for Africa and Latin America and the Caribbean.
Table 6.3
illustrates the effect of using PPPs compared to using exchange rates on both construction volume and construction expenditure as a proportion of GDP. Using PPPs, construction expenditure increases markedly in every region except Europe and the OECD, and construction expenditure as a proportion of GDP increases most in Asia and West Asia. There are increases in Africa and Latin America and the Caribbean, but only relatively small ones, as the increases in construction expenditure in the latter are more or less matched by the increases in GDP. The results with PPPs are almost certainly more credible interpretations of the data than those with exchange rates.
The data, of course, represents regional averages, and countries in each region will vary around the mean. Nevertheless, using averages helps to identify broad regional trends. These are in line with expectations, although it is probable that the regions with more complete data – Asia, CIS, EU/OECD and West Asia – will be more reliable than Africa or Latin America and the Caribbean, where there is less complete coverage.
The differences in Asia and West Asia are a result of overvaluation of price levels using commercial exchange rates, particularly for construction. The reasons for this distinction in the two regions are subtly different. Asia comprises mainly middle-income countries with a few high- and low- income countries, a high proportion of locally or regionally manufactured materials and access to low-cost labour. West Asia also comprises high- and middle-income countries with access to low-cost resources and low-cost foreign workers in the case of the high-income countries. The similarities in both GDP and construction exchanges rates and PPPs in the European and OECD countries are as expected in developed economies.
Table 6.3 GDP and construction expenditure in 2005 using exchange rates and PPPs
The reasons for the decrease in construction expenditure as a proportion of GDP in the CIS states are possibly more complex and deserve further study. Although both GDP and construction may be overvalued using commercial exchange rates, this appears to be more so in construction than in GDP. Using the data in Table 6.3, there is a decrease in the proportion of construction to GDP using PPPs (9.47%) compared to exchange rates (10.40%). The difference, however, is less than 1%, which may not be significant bearing in mind the data quality. Overall, using PPPs for 128 countries, GDP increases by around 29%, but construction expenditure increases by 67%, and construction expenditure as a proportion of GDP increases by 30%.
It seems, therefore, that the volume of construction expenditure using exchange rates compared to PPPs tends to be overstated in developed countries and understated in developing countries. Previous research, based on commercial exchange rates, looked at the relationship between construction output (measured as value added) per capita and GDP per capita.
Figures 6.1
and 6.2 present the relationship between GDP per capita and construction expenditure per capita using exchange rates and PPPs.
The trend line in
Figure 6.1
indicates that, on average, construction expenditure represents around 12% of GDP using commercial exchange rates.
Figure 6.2
uses separate GDP and construction PPPs and indicates that the percentage increases to 15%. The figures reflect Turin’s view that there is a direct relationship between GDP per capita and construction expenditure per capita; they also reinforce the theory behind the Balassa-Samuelson effect. They do not, however, strongly support Bon’s proposition that the proportion of construction expenditure increases in the early stages of development but then levels off or declines in maturity.
The following figures look in more detail at the data by region, by income level and by population.
Figures 6.3
to
6.6
show GDP and construction expenditure for Africa, the Americas, Asia and Europe In all four figures, the scales are the same, in $US millions using PPPs; the vertical scale is construction expenditure per capita; the horizontal scale is GDP per capita.
Figure 6.1
GDP and construction expenditure for 128 countries in 2005 in $US using commercial exchange rates
Figure 6.2
GDP and construction expenditure for 128 countries in 2005 in $US using PPPs
Figure 6.3
GDP per capita and construction expenditure per capita in 2005 $US PPPs by region: Africa
Figure 6.4 GDP per capita and construction expenditure per capita in 2005 $US PPPs by region: the Americas
Figure 6.5 GDP per capita and construction expenditure per capita in 2005 $US PPPs by region: Asia
Figure 6.6
GDP per capita and construction expenditure per capita in 2005 $US PPPs by region: Europe
In Africa and the Americas, with the exception of Canada and the US, both GDP per capita and construction expenditure per capita are low. National income appears to be a major constraint on construction expenditure. In Asia and Europe, on the other hand, greater construction expenditure appears to be associated with higher national income. The regional data generally confirms that the relationship between GDP and construction expenditure indicated in Figures 6.1 and 6.2 holds at the more detailed level.
Figures 6.7
to
6.10
group the countries by national income per capita in US$ PPPs. The 128 countries have been divided into five groups, four with
25
countries each and one with 28 (the income bands for these five groups are set out in
Appendix A
). In these figures, the middle group has been omitted. The labels for the vertical and horizontal scales are the same as in Figures 6.3 to 6.6, but the values vary to suit the groups.
Figure 6.7
GDP per capita and construction expenditure per capita in 2005 $US PPPs by income group: Lowest Income Group
Figure
6.8
GDP per capita and construction expenditure per capita in 2005 $US PPPs by income group: Second-Lowest Income Group
Figure 6.9
GDP per capita and construction expenditure per capita in 2005 $US PPPs by income group: Second-Highest Income Group
Figure 6.10
GDP per capita and construction expenditure per capita in 2005 $US PPPs by income group: Highest Income Group
Generally, as the variance is greater, there is much less sign of a relationship between GDP per capita and construction per capita in the lower income groups (Figures 6.7 and 6.8). Bearing in mind that previous authors have emphasized the importance of national income in predicting construction expenditure, this is surprising. The somewhat better relationship between per capita GDP and construction in the higher income groups (
Figures 6.9
and 6.10) may be a result of more geographical homogeneity.
A third grouping of countries was examined – by population size – and a statistical analysis of the results of this exercise and the other two exercises is set out in
Table 6.4
.
Table 6.4
Statistical tests by region, national income and population size
|
Construction per head as a function of GDP per head |
Number of countries |
R2 |
Mean |
Standard error |
|||||||
|
By geographic regions |
|||||||||||
| Africa | 30 |
0.79 |
72 |
185.1 |
|||||||
|
The Americas |
13 |
0.93 |
518 |
405.9 |
|||||||
| Asia | 42 |
0.82 |
361 |
1,567.2 |
|||||||
| Europe | 41 |
0.73 |
1,528 |
1,009.8 |
|||||||
|
By GDP per capita |
|||||||||||
|
Lowest-income countries |
25 |
0.15 |
115 |
99.6 |
|||||||
|
Second lowest |
0.23 |
478 |
543.8 |
||||||||
|
Mid-range |
0.18 |
950 |
424.2 |
||||||||
|
Second highest |
0.63 |
2,111 |
677.9 |
||||||||
|
Highest-income countries |
28 |
0.53 |
4,556 |
2,483.7 |
|||||||
|
By population size |
|||||||||||
|
Smallest by population |
0.80 |
1,989 |
2,073.6 |
||||||||
|
Second smallest |
0.84 |
949 |
805.2 |
||||||||
|
0.83 |
611 |
504.7 |
|||||||||
|
Second largest |
533 |
377.8 |
|||||||||
|
Largest by population |
0.75 |
983 |
768.9 |
||||||||
|
All countries in survey |
128 |
440 |
1,301.6 |
The table gives the mean and standard errors by geographic region, GDP per capita and population. The regions are shown to be relatively homogenous, as are the groups by population size; while income level in a country is a poor predictor of likely expenditure on construction, regional location and population size are relatively good predictors. In all cases, the standard errors are relatively high compared to the means, leaving wide margins for error in predicting individual country values.
Conclusion
This chapter reviews and comments on some of the main issues concerning the measurement, presentation and comparison of construction activity nationally and internationally. It uses ICP 2005 data to assess the relationships between national income and construction expenditure and concludes that previous theories are, at best, not proven and that more work is needed on defining, measuring and normalizing data on construction activity.
Consistent approaches are required as to what is included in or excluded from construction activity and how variables should be measured and presented. This needs to take account of the data requirements of statisticians, policy makers, international bodies, industry, researchers and others. It is an international issue and needs to be addressed at an international level; construction is too important a sector of the economy to be measured so poorly.
The current ISIC breakdown of construction activity is not particularly helpful to any user groups. It requires distinctions to be made among residential, nonresidential and civil engineering work and between a few subtypes of civil engineering work. It does not distinguish between construction investment (new work and improvements) or construction consumption (repair and maintenance) or among publicly sponsored, privately sponsored and mixed-funded work. Detailed breakdowns of construction activity could also address the different providers of construction output: construction contractors, the informal sector, households and so forth, although it needs to be acknowledged that providers often overlap.
Gross construction output is often a more useful measure of construction activity than construction value added, although the latter has its uses. Gross output data is needed to measure the size and capacity of the sector and its growth rate; value-added measures are needed for measuring construction productivity and the share of on-site activities in gross domestic product. In developed countries, contractors’ output can be viewed as broadly representative of construction value; in less developed countries, there is more likely to be greater activity by noncontractors, mainly households.
Construction prices tend to change over time relative to prices in the wider economy both nationally and internationally. Measures of construction activity, therefore, are subject to both quantity and price changes; in these circumstances, construction values are not necessarily good proxies for construction volumes. Indeed, the patterns of national and construction-sector development reported by Turin, Bon and others may be, at least partly, an outcome of relative price trends and may not necessarily reflect real activity. Using the best data available, we cannot confirm Turin and Bon’s hypotheses. If regional location and population size are better predictors of construction output per capita than national income, it must be accepted that the basic data as it is currently defined and collected is not as helpful as it might be.
In discussing the use of exchange rates or construction PPPs, a consensus is emerging that exchange rates are poor deflators, or normalizers, in international comparisons of construction activity. Although there are problems with PPPs in general and construction PPPs in particular, there is a rationale for using PPPs in preference to exchange rates for converting construction output and GDP. Furthermore, as PPPs are refined, they can be expected to improve over time, and it is important that their development is encouraged.
Because of the issues and difficulties raised in this chapter, construction activity data – national and international – can be misinformed and misreported. Governmental statistical agencies and industry commentators require improvements in the official definitions of construction and the way data is presented. There is a need for better information on the various measures of construction activity.
In many countries, the construction sector is one of the largest components of the economy. Industrial strategies and policy decisions depend on consis- tent and reliable measures, and decisions to invest in infrastructure and the built environment remain essential for economic and social development.