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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.