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CHAPTER 4
FACTORS INFLUENCING CONSTRUCTION INDUSTRY-LEVEL
TFP GROWTH
4.1 Introduction
In the previous chapter, the real TFP growth in the Singapore construction industry
was computed. For monitoring and controlling purpose, it is necessary to analyse the
causes and motivations of TFP growth in the construction industry. To this end, it is
necessary first to study factors affecting TFP growth in the construction industry. The
objective of this chapter is to identify factors influencing construction industry-level
TFP growth.
Section 4.2 provides a literature review of factors affecting productivity, serving as
foundation for the whole chapter. It involves literature review of labour productivity
growth accounting in the construction industry; labour productivity growth accounting
in the economic academia; technology progress; interrelationship among factors
affecting productivity; classic and new growth theory.
Based on the literature review of section 4.2, section 4.3 conducts a theoretical
identification of factors affecting TFP growth in the construction industry. The
mechanism and indicators of each factor affecting TFP are explained correspondingly.
Finally, section 4.4 summarizes the theoretical factors affecting TFP growth in the
construction industry.
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4.2 A theoretical review of factors influencing TFP growth in the construction industry Little study has been done on factors influencing construction industry-level TFP
growth. Since TFP is the defined as a related measurement of technological progress,
which is in turn defined as advances in scientific and technical knowledge, and
improvements in the methods of organisation and management, any factors affecting
advances in knowledge and improvements in the organisation would be regarded as
factors affecting TFP. Therefore, it is necessary to review factors identified as
technology progress in LP growth accounting studies. Meanwhile, because factors
influencing productivity growth highly interacted (National Research Council, 1979;
and Fabricant, 1983), it is also necessary to review those factors interacting to
technological change. Furthermore, conventional productivity growth accounting was
built on exogenous growth theory, which has been greatly challenged by the new
endogenous growth theory in recent years. Hence, it is also necessary to review the
impact of the new growth theory on the productivity accounting studies. The following
sections provide a comprehensive literature review on factors influencing the TFP
growth for construction industry.
4.2.1 Conventional Growth accounting studies in the construction industry
Most of the works identifying factors that influence productivity of the construction
industry are focused on project level or site level (Herbsman and Ellis, 1990; Allmon,
et al., 2000). Katavic et al. (1993) argue that since very rarely are two buildings
identical, their levels of construction productivity are not comparable and hence
productivity in the entire construction industry, is simply not measured. Only a few of
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the studies looked at construction industry-level and without exception they addressed
labour productivity. Dacy (1965) made a first attempt to explain labour productivity
trend of the construction industry in the US, based on six explanatory sources: increase
in capital per worker, shifts in the construction product mix, shifts in the geographical
distribution of construction, increase in the corporate share in contract construction;
the declining average age of construction workers, particularly in the intermediate post
war years, introduction of new techniques in building and the substitution of labour-
saving building materials for others. Following Dacy (1965), Stokes (1981) and Allen
(1985) have conducted growth accounting studies of productivity decline in the
construction industry of the US between 1968-1978. They examined the impact of
capital-labour ratio, economies of scale, labour quality, the composition of output,
regional shifts, percentage of unionised workers on productivity growth. However, a
sizeable productivity growth remains unaccounted. Allen attributed the “residual” to
the possible factors such as R&D expenditure and variation of ratio of new
construction to repair and alteration construction.
4.2.2 Conventional Growth accounting studies in economics
The growth accounting and its “residual” problem are not unique in the construction
industry. They originated from the field of economics. Essentially, productivity growth
accounting is a part of economic growth (or the output growth) accounting study in
that the sources of output growth have two components: the growth of inputs and the
growth of productivity.
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In mainstream economics, one of the best-known productivity growth accounting
studies is that of Denison (1972; 1974). Dension (1972) classified 3 main sources of
productivity growth: (i) economies of scale; (ii) shifts in resources allocation; and (iii)
growth of knowledge (including scientific and technical knowledge and improvements
in the methods of organization and management). He attributed the unexplained
residual problem to incompletely quantified factors such as advances in knowledge and
the rate of diffusion of knowledge and possible error in estimates. Two following
investigations by Griliches (1980) and Kendrick (1977) indicated that about 24-59%
percent of Denison’s residual could be attributed to advances in knowledge generated
by investment in R&D activities.
Wolff (1985) argues that it is perhaps most convenient to use a production function to
investigate the factors of productivity growth. He then suggests seven causes. The first
three are related to the inputs: the rate of capital formation, the composition of labour
force and energy price. The fourth is related to the residual in the production function:
R&D expenditure and technological progress. The fifth concerns the shares of the
economy’s different products: the composition of output. The remaining two are
government regulation and business cycle.
4.2.3 Technology progress
The generally accepted definition of technology change is by Schumpeter given the
threefold distinction among invention, innovation and diffusion of innovations.
Construction technology is the combination of construction methods, construction
resources, work task and project influences that define the manner of performing a
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construction operation (Tatum, 1987). Invention is the process by which new idea is
discovered or created. Construction innovation is the first use of a technology within
the construction firm either in the process or in the product. Diffusion of innovation is
the company’s performance for seeking, recognising and implementing a new
technology to improve its functions (Pedersen, 1990). To quantify technology change,
Kendrick (1981) identified three chief factors affecting technology progress: domestic
R&D outlays, changes in the average age of fixed capital goods and the rate of
international technology transfer.
4.2.4 Interrelationship among factors
Factors influencing productivity growth are highly interrelated. The effects of one
cannot be determined without some reference to the others; nor can their total effect be
calculated by a simple composition of forces or without recognition of the fact that
their interrelationship evolve over time (Fabricant, 1983).
Technical progress must often be incorporated into new tangible capital goods and
used by appropriately trained labour, if it is to be effective. As workers operate the new
machine that incorporate the latest technological breakthrough, they progressively
become familiar with it, know it better and learn how to obtain the most out of its use.
Meanwhile, in the process of adapting to the new machine, they often devise new
forms of organisation of production and find new ideas to improve on the machine
itself. This process is known as learning by doing (Valdés, 1999). On the other hand,
investment in tangible and human capital are stimulated by the opportunities that
technological advancement opens up, and R&D activity is in turn influenced by the
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country’s educational level. Hence, what technology or capital or education contributes
to productivity growth then depends in part on the rate of increase in the other factors,
as well as its own rate of increase. It is a fact that more tangible capital, better-trained
workers, and greater technical and other knowledge as well as other sources of greater
efficiency tend to appear together to increase TFP in all countries.
4.2.5 New endogenous growth model
In recent years, the conventional growth accounting theories have been
overwhelmingly challenged by new endogenous growth theories. The conventional
model assumes constant return to scale, determinants of growth are determined outside
the model, and advances in knowledge or technical progress are independent of
activities of economic agents and government has no direct role to play (Valence,
1996). In contrast, the new endogenous theories incorporate the determinants of
growth into the model, view advances in knowledge as deliberate investment by
economic agents, and emphasize the public good characteristics of knowledge and
externalities (Romer, 1986; Lucas 1988). The endogenous economic growth theories
are important not only because they focus on the factor that is the key for long-term
economic growth, but also because they offer new explanations for old problems,
particularly for the residual unexplained.
Romer (1986) emphasizes the public good nature of knowledge as the primary source
of growth, suggesting spillovers from R&D expenditure and from learning by doing
effects. Lucas (1988) emphasizes the role of human capital as a complementary input
into production alongside physical capital. Based on the new endogenous theories and
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the most compelling evidences, Dowrick (1995) proposed six factors as the main
determinants of long-run growth. These include: the initial level of development; the
growth of capital and labour inputs; fertility and labour supply; education and human
capital; government expenditure; and research and development.
Although the new theory provides a great deal of intellectual excitement, it is still
debatable as to whether this new endogenous theory is actually better at explaining the
observed productivity growth than the old theory (Valence, 1996). Oulton (1994) also
pointed out that it is not yet clear that the factors which are emphasized in new growth
theories are actually the crucial ones empirically1. Consequently, there is no ground to
discard the former in favour of the latter, since the old and new theory treat different
aspects of the creation of knowledge, namely its exogenous and endogenous
components, it is reasonable to expect that both theories will come to be integrated in a
future theory of the production of technology (Valdes, 1999). However, in any case, it
must be realized that the new growth theory can only affect the interpretation rather
than the validity of TFP calculations (Oulton, 1994), and that factors emphasized in the
new growth theory are still suggestive if not decisive.
4.3 Theoretical identification of factors influencing TFP growth in the construction industry
Based on the theoretical review, the following sections aim to identify factors
influencing TFP growth of the construction industry of Singapore, by combining
factors conventionally listed as influencing technology progress in the construction 1 See Crafts (1992; 1993) for a specific evaluation and for a demonstration that there is still life in Solow model (classical model), see Mankiw et al. (1992). O’Mahony (1992) finds evidence that part of the productivity gap between UK and German manufacturing can be explained by an externality generated by the higher proportion of skilled workers in Germany.
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industry, factors interrelated with technology progress, and factors emphasized in the
new endogenous growth theory that are believed to be crucial as environmental factors
for all industry’s productivity growth. A comprehensive list of common factors
influencing construction industry-level TFP growth is discussed below with the
mechanism in which each factor affects TFP growth being illustrated respectively.
4.3.1 Changes in the composition of output
Industry-level TFP growth is an aggregate of TFP growth of each sector. Hence, it can
be divided into two major components: TFP growth in each industry sector and
changes in value share of each sector in the industry. If the composition of output is
shifting to sectors with low average TFP, this could cause aggregate productivity to
rise slowly or even decline although productivity continue to rise in each of component
sectors (Stokes, 1981; Schriver and Bowlby, 1985). An index of sum of weighted
value-share of each sector in the industry can be derived as an indicator to reflect the
change in composition of construction products, with weights given by average TFP of
each sector.
Another factor to be considered is the trend in the ratio of new construction to
maintenance and repair construction works since maintenance and repair works
generally has relatively low productivity compared with new construction.
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4.3.2 Technology progress
Technology progress has been recognized as the long term, key determinant of
economic and productivity growth. Construction technology embraces the materials,
plant and equipment, organisation, procedures and information systems used in
planning, designing, constructing, maintaining, repairing, altering and demolishing
building and infrastructure (Ofori, 1994). Technology change involves two aspects:
advances in knowledge and rate of diffusion of new knowledge.
4.3.2.1 Advances in knowledge
Advances in knowledge include invention and innovation activity in the construction
industry, which come from two sources: organised R&D activity and job-practice.
Organised R&D activity is the prime source of technology advances. R &D affected
productivity through advances that reduce the unit cost of final outputs already
available, or through the introduction of new products. (Wolff, 1985). In addition,
R&D raises the threshold by which adaptation of technology abroad becomes more
feasible (Kendrick, 1981). Another significant but subsidiary source is informal R&D
through job-practice. From the use of the new techniques or job practice, workers
derive new ideas to improve upon the organisation, instruments and the new
techniques itself. Young (1993) and De Long and Summers (1992) argue that there is
likely to be substantial learning and innovation involved in the implementation of new
ideas, especially when new technology is embodied in capital equipment. Better-
educated and more knowledgeable people develop new ideas more easily. The
underlying idea is that the level of education and training in the construction industry
can be used as a proxy for innovation through job-practice.
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To quantify the impact of organised R&D activity, the ratio of R&D expenditure in the
construction industry of Singapore to construction output (or value of contracts
awarded) can be used as a proxy. Meanwhile, construction is closely related to other
sectors of the economy, increasing scientific and technical knowledge outside the
construction industry make new materials, equipment and methods available for
application (CIB, 1989). Hence, the expenditure of domestic R&D activity can also
affect construction technology change. Besides, the new endogenous growth theory
also emphasizes the good quality of public knowledge and its spillover from R&D
expenditure and from learning by doing effects (Romer, 1986). Since ratio of
aggregate of domestic R&D expenditure to GDP is widely accepted as an indicator to
measure R&D activities in a country (Kendrick, 1981, Wolff 1985), it will be also used
in Singapore as a factor reflecting the level of technology progress of the economy as a
whole. To quantify innovation through job-practice, the average educational level of
construction workers and training is used as a proxy. This proxy will be discussed later
under quality of labour.
4.3.2.2 Rate of diffusion of new knowledge
Technology progress embraces two aspects: new discoveries and the know-how to use
them in production. Technology progress can only be effective to enhance productivity
when it has been requested, transmitted, received, understood, applied, diffused widely
and improved. If the rate of diffusion of technology changes, it affects the rate of
advancement in technology and productivity. Construction technology may be diffused
by various means: subcontracting, licensing, training, joint venture, and trade and
professional literature and conferences (Kendrick, 1981; Ofori 1994). New knowledge
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can be diffused through modernising capital goods or through international technology
transfer.
Changes in the time lag between the dates at which business structures and equipment
are installed (incorporating knowledge of design at that date) and the dates they are in
use can be altered by modernising capital goods, in that to an important extent,
innovations are embodied in new plant and equipment. Therefore, a declining average
age could indicate a more rapid infusion of higher quality equipment, or simply the
discarding of obsolete equipment. This would accelerate diffusion of knowledge and,
hence, boost TFP growth. Thus, the average age of real fixed-capital stock in the
construction industry can be used as a proxy to estimate the rate of diffusion of
knowledge in the industry.
The fact that part of the new capital equipment is used to comply with safety
regulations and pollution rules instead of increasing productivity implies that when
calculating the average age of real fixed capital stock, this kind of capital should be
excluded. The rapid increase in energy price after 1972 may also have slowed down
the rate of new capital formation in a way that higher energy prices made part of
existing capital stock uneconomical and obsolete. Energy price will be discussed
separately as an individual factor affecting TFP. Factors influencing the rate of
modernising capital goods such as government regulations, rates of taxation on capital
expenditure and labour union restriction will be discussed later respectively.
Changes in the rate of diffusion of knowledge due to factors other than changes in the
average rate of fixed capital is international technology transfer
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New technology is impossible to hide; it spills over. Thus, scientific and technological
knowledge is international. Coe and Helpman (1993) found that the stock of
knowledge in one country, quantified by accumulated R&D expenditure, raises
productivity in the home country as well as foreign countries. The international
technology transfer is taking place much faster than before. It is viewed as reflecting
the narrowing “technological gap” ---catch up, between developed industry and
developing industry. Multinational firms are the main propagator of technology
diffusion. R&D intensive multinational corporations are the main actors in the
technology transfer market (Nadiri, 1993).
International technology transfer can be realised through: foreign direct investment and
joint projects; turnkey projects, international tender invitations; performance of R&D
abroad, either through subsidiaries or on a cooperative basis with foreign firms; trade
in goods and services which containing the newest technology; personnel exchange;
publication; international visits and conferences; teaching and training (Kendrick,
1981).
In the construction industry, the most preferred vehicle for international technology
transfer is joint venture (Walker & Flanagan, 1987; Sridharan, 1995). Another
important vehicle is counterpart training (Uko, 1987). Although the joint venture
appears the most preferred vehicle, it is not universally a successful one. Problems that
hinder the construction technology transfer include: international contractors who are
unwilling to nurture potential competitors; technology transfer may face extra costs,
project delays, managerial complexity, risky business of contracting overseas and the
uniqueness of construction projects hindering learning from experience (Ofori 1994).
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The rate and effectiveness of international technology transfer is difficult to quantify.
Usually, the technology transfer is measured in terms of money of technology
purchased by the recipient country (Lall, 1982; Rosenberg and Fischtak, 1985). But
this measure would omit indirect channel such as publications and would not reflect
the effectiveness. Gruber and Marquis (1969) suggest a technology transfer function of
source, nature of technology, channels of transfer and features of recipients. Baranson
(1976) observes the time for technology transfer depends on: the technology, mode of
transfer, absorptive capabilities, capabilities and motivation of suppliers and
technology gap between supplier and recipient. Ono (1976) adds another two factors:
role of producer’s association and role of government.
4.3.3 Quality of labour The productivity of women and younger worker is generally regarded as lower than
that of labour force as a whole because they have fewer skills and less experience
(Eckstein, 1980; Perloff and Watcher 1980). Therefore, a shift in labour force
composition towards this group would lower overall productivity. Besides age and sex
factors, the level of education and training of construction worker and staff will affect
productivity as well, since the better educated and trained worker learn faster and
develop new ideas (innovation) more easily. Consequently, increased educational
qualifications tend to facilitate technological advancement and productivity growth.
To quantify labour quality, an index to reflect labour quality change is needed. First,
labour force is cross-classified by sex-age-education. Under the assumption that a
worker’s pay reflect their marginal productivity, the index can be developed as the sum
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of weighted value-share of each type of worker, with weights given by their
corresponding median weekly earnings.
4.3.4 Materials quality
Stocks (1981) identified the substitution of labour-saving materials with others as an
important factor affecting productivity. Chau (1988) argues that utilisation of new
materials that are of better quality and easier to handle or work with will contribute to
productivity growth. Moreover, Thomas et al. (1999 and 2000) found that materials
management also has significant effect on labour productivity.
The utilisation of labour-saving building materials has been transferring some labour
from the site to off-site. The widespread use of transit-mixed and precast concrete is an
outstanding example. The experience of countries with strong construction industries
(e.g. Japan) confirms that the focus of higher buildability is crucial in raising
productivity and efficiency (Construction 21, 1998). Generally, a positive correlation
between buildablity and productivity exists; the higher the score, the higher the level of
buildability and, hence, the productivity (Ng, 1997). Since higher buildability is
mainly achieved through promotion of buildable designs that allows for the use of
prefabricated materials, both prefabrication level and buildable score can be used as
indicators to represent quality of materials (Rapporteur, 1987).
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4.3.5 Economies of scale
Economies of scale come with the growth of local, national and international markets,
greater specialisations of personnel, equipment, plant and firms, and the spreading of
overhead costs over increasing quantities of output. Results of Allen’s productivity
accounting study in the construction industry of US during 1968-1978 shows that there
is a positive relationship between average hours per establishment and productivity.
However, the economies of scale in the construction industry are limited. Stokes
(1981) argues that although it is generally true that value-added per employee in the
construction industry rises as the number of employee per firm rises, this may, but not
necessarily, suggests economies of scale since larger firms may well be producing a
different product than smaller firms. Ofori (1990) observes that considering the labour-
intensive characteristic and non-standard products of the construction industry, the
industry does not have the advantage of economies of scale.
Although the effect of economies of scale is debatable in the construction industry, it is
still regarded as a potential factor influencing TFP for study and test purposes. To
quantify economies of scale, factors that can be used as proxies include: index of
output per employee by different sizes of firm (Stocks, 1981) and average working
hours per establishment (Allen, 1985).
4.3.6 Government regulations
Government, as an external factor to the construction industry, plays both a positive
and negative role in productivity growth. On the one hand, government expenditures
and services to the industry and government investment in infrastructure such as
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transportation and communication help to promote productivity growth (Kendrick
1980; World Bank, 1993). On the other hand, government regulations may reduce
productivity growth. Crandall (1980) suggests that there are two ways in which
government regulations may reduce productivity growth. First, government regulations
restrict competition and protect regulated firms from new technology and new
competitors. Innovation and productivity growth have rarely flourished in a protected
industry, which will reduce the incentives for management and labour to innovate
(Baumol and McLennan, 1985). Second, health, safety, and environmental regulations
divert large quantities of resources (such as capital and labor) from productive
purposes, thereby reducing normally measured output-to-input ratios. In addition, the
application of new regulations is associated with risk and uncertainty, which tends to
lead to misallocation of resources and discourages new investment and innovation and
hence slow down capital formation and productivity.
To quantify the government’s positive role in productivity growth, government
expenditure in construction industry, and investment allowance provided by
government for construction companies, can be used as proxies.
To measure the government’s negative effect on TFP growth, the proportion of capital
used to meet health, safety, and environmental regulations for non-productive purpose
in the total real stock of capital, and the proportion of managerial staff and worker
diverted from production and innovation to complying with regulations, can be used as
proxies.
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4.3.7 Cyclical factors
Cyclical factors, such as energy prices and inflation rate are usually regarded as
affecting TFP growth in the construction industry.
4.3.7.1 Energy prices
It is generally agreed that the oil shock in 1973 and the subsequent sharp increase in
energy prices had a negative effect on productivity growth (Baumol and McLennan,
1985; and Levitan, 1984). First, the steep rise in energy prices render some existing
energy-intensive capital goods obsolete, which leads to a slow down in the rate of
capital formation. The resulting obsolescence of capital was probably more rapid than
is indicated by the depreciation rates used to estimate the net capital (Baumol and
McLennan, 1985). Hence, statistics on real capital may be overestimated. Second, the
rising energy price reduces investment in capital goods as energy and capital are
complementary (Norsworthy and H. Malmquist, 1985) 2. The sharp rise in the energy
price encouraged producers to use less energy and less capital. In addition, uncertainty
as to future energy prices and inflation caused by energy prices worsen the capital
investment climate.
4.3.7.2 Inflation rate
Inflation can impede productivity (Baumol and McLennan, 1985; Levitan, 1984). It
discourages capital formation. Inflation leads to higher interest rates, thus it makes
2 Energy and capital are complements in the production process. They are used in close conjunction with each other: when the capital input rises, so does the associated energy input. Because the sharp rise in energy prices discourages energy use, it also discourages the use of the complementary good—capital.
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investment in capital riskier and more expensive. Inflation also exacerbates future
uncertainties about the cost of long-term investment and future course of government
policy to combat inflation. Innovative investment, the source of future productivity
growth, will be postponed. Instead, investment portfolios tend to focus on non-
productive assets to hedge against inflation.
4.3.8. Industrial relation policies
Industrial relations involve the structuring of relationship between the employer and
workers. Industrial policies that are usually identified as affecting construction
productivity include changes in work rules and motivation policies. Productivity
altering work rules include: subcontracting limitation, labour-management committees.
Motivation polices include: incentive wage payment, and union-management
relationship. Productivity can be enhanced or hampered by changes in work rules,
which permit more or less efficient use of labour force and technology (Stokes, 1981).
To quantify these factors, the clauses in construction contracts can be used to capture
the data. The data needed are:
• Percentage of contracts that allow the establishment of labour-management
committees to review production procedures;
• Percentage of contracts that allow for incentive wage payment;
• Percentage of contracts that allow for subcontracting; it was generally believed
that multi-layered subcontracting will impede productivity (Construction 21,
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1998.). Because of the multi-layered sub-contracting system, many
subcontractors operate in a single project. It causes many small firms to exist
within the industry. According to the EDB’s report, 70% of firms in the
construction industry of Singaporeare small firm. Many of the small firms are
poorly managed, lack the incentive and ability to invest in new technology and
in training, they are unable to reap economies of scale, resulting in much
wastage. Chong et al (1996) suggested construction industry in Singapore is
adversely affected by factors including poor planning and management of
human resources; and the inadequate attempt to co-ordinate the overall
construction process, with much of the work subcontracted; and
• Percentage of unionised workers, or ratio of union memberships to
employment. According to Allen’s test (1985), decline in percentage of
unionised workers will cause a decline in productivity.
4.3.9 Construction accident
The labour accident frequency and occurrence will cause productivity to decrease, as
the site activities disrupted will lead to a delay in progress. Data on industrial accidents
are needed.
4.3.10 Other factors
The last category is residual, reflecting the net effect of variables not classified above.
A major residual factor is changes in those aspect of the legal, institutional and social
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environment within which business operates that impact on unit real costs and changes
in exports and international trading. More research is needed to identify and quantify
other causal factors.
4.4 Chapter summary
This chapter completed a theoretical identification of factors influencing TFP growth
in the construction industry. It serves as a foundation for Chapter 5, which conducts
the statistical identification of factors influencing TFP growth in the case of the
construction industry of Singapore.
Section 4.2 provided a holistic literature review on factor influencing productivity in
construction industry as well as economic academia. It reviewed the following relevant
areas: (i) labour productivity growth accounting in construction industry; (ii) labour
productivity growth accounting in economics; (iii) technology progress; (iv)
interrelation among factors affecting productivity; and (v) classic and new growth
theory. From the literature review, the conclusion can be drawn that a comprehensive
study on factors influencing TFP growth in the construction industry should consider
aspects relating to interrelations among factors and the new endogenous growth theory
as well as technological change factors.
Section 4.3 presented a list of theoretical factors influencing TFP growth in the
construction industry. A total of ten factors have been identified. They are:
1. composition of output;
2. technological progress;
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3. quality of labour;
4. quality of materials;
5. economies of scale;
6. government regulations;
7. cyclical factors;
8. industrial relation and policies;
9. construction accident; and
10. other factors such as social and institutional change.
The factors listed above serves as a foundation for the next chapter to undertake
statistical identification of factors affecting TFP growth in the construction industry of
Singapore.