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Ain Shams Engineering Journal
Volume 12, Issue 2, June 2021, Pages 1575-1582
Civil Engineering
Investigating the impact of inflation on labour
wages in Construction Industry of Malaysia
Wesam Salah Alaloul a, Muhammad Ali Musarat a
Qureshi a, Ahsen Maqsoom b
, M.S. Liew a, Abdul Hannan
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https://doi.org/10.1016/j.asej.2020.08.036
Under a Creative Commons license
Abstract
Labours in construction are one of the main pillars in the construction
industry of Malaysia for projects execution. Construction labours not
only contributes to the development of the construction industry but also
impacts the Malaysian economy. Consideration of labour wages is made
in the initial phase of the project budget, however, wages are getting
changed over time. The inflation rate is one of the key factors which
affect labours wages. Regrettably, the inflation rate is being ignored while
computing labour wages for projects budget development, resulting in
cost overrun of construction projects. In this regard, the correlation
coefficient test was used to determine the impact of the inflation rate on
labour wages gathered from the year 2013 to 2019. The results showed
that a significant acceptable relationship exists among the inflation rate
and several categories of labour wages. Most of the labour wages showed a
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negative relationship with the inflation rate, indicating the deviation in
the wages, thus, result in cost overrun. To steer the cost overrun effect, it
is recommended to adopt automation system and introduce the
Industrial Revolution (IR) 4.0 in construction projects as a replacer of
labours.
Previous
Keywords
Labour wages; Inflation rate; Correlation test; Construction
industry; Automation system; Industrial Revolution (IR) 4.0
1. Introduction
Labour work is the physical effort utilized in the production of goods and
serves in economy development by providing facilities to convert raw
materials into final products. Efficient labour force exploits the scarce
natural resources effectively, act as a backbone of the nation and supports
the country to move towards the development [1], [2]. In both developed
and developing countries, labour’s share of national income is facing
decline since the 1980s [3]. In developed countries, labour’s share has
been reduced to 54% in 2018 from 61.5% since 1980. Whereas, in the
developing countries it has been reduced to 50% in 2018 from 52.5%
since 1990 [4].
The construction industry is one of the largest industry which impacts
on societies development [5], [6], [7]. The construction industry exhibits a
major contribution to the economic growth of a country [8]. Not only
economic and societies development, but also millions of jobs are created
due to construction work. Till July 2019, 7,505,000 workers are linked
with the construction industry with an estimated increase of 864,700 new
job opportunities by the end of 2026, showing an increase in growth rate
by 12% [9]. Moreover, due to the foreign trade of materials and services,
the revenue raises [10]. The importance of the construction industry
increases as it is connected with other industries as well [11]. With time
new technologies have been introduced to boost the construction work,
however, even with advancements, still, the construction industry is
facing several issues in achieving projects’ objectives [12], [13]. In the
overall project budget, 30–50% is the labour cost, showing their
importance not even in the construction industry but also in budget
Next
estimation before pursuing a construction project. Thus, the
construction industry is labour intensive, therefore to get quality results
and achieve project success, the role of labours cannot be ignored, where
the skilled labours are essential for project success [14], [15], [16], [17].
Project success is associated with its completion on time and within the
set budget [18], [19], [20]. Most of the construction projects are overbudgeted, mainly due to change in order, failing to take necessary
measures or changes in the prices over time [21], [22], [23]. Compare to
other industries, the construction industry is facing the issue of low
efficiency and cost overrun, which require improvement to fulfil the
needs of stakeholders [24], [25], [26].
Cost overrun is not a new issue for the construction industry, and it is
recognized as a global concern. Over the last 70 years cost overrun has
been occurring with an average rate of 28% [27]. Cost estimation is very
important in the initial phase of the construction project as it involves
economic consequences which need attention at the beginning [28]. That
is why efficient budget estimation is vital because it decides the financial
fate of the project. Over budgeted project increases the cost of the
construction, increases the pressure on the investors, decreases investors’
decision-making potential and at the end, a huge loss of national finance
occurs [29], [30]. In the project life cycle, the utmost operator to project
success is “Cost”. Unfortunately, in most of the construction projects cost
deviation from the initial set budget occurs, where not enough work has
been done to eliminate this issue [31].
Budget development has a significant impact on project growth.
However, it is common that a project faces budget revision and gets
overrun. One of the main reasons behind project cost overrun is the
inflation rate which changes the construction cost over time and no
consideration has been made to incorporate its role in the industry [19].
The inflation rate not only deviating the prices of the goods but also
labour wages been affected [32], [33]. Regrettably, changes in the labour
wages while setting the project budget is been ignored which in later
stages cause cost overrun in construction projects [16], [34], [35]. From the
economic context, inflation is a critical factor which is directly associated
with the economic growth [36], [37], where the change in the inflation rate
is difficult to estimate [38]. The main factors linked with the inflation rate
estimation are interest rate, inflation levels, money supply, wage rate and
exchange rate [39], [40], [41]. Due to the inflation rate the purchasing
power of money changes frequently, indicating its importance in the
construction industry and the economic world [42], [43].
In Malaysia, the construction industry is the main pillar of economic
growth. In the Malaysian economy, a commendable growth was observed
in recent years due to an annual increase in the construction work. Not
only providing economic growth, but the construction industry in
Malaysia is also linked with other industries [44]. In the first quarter of
2018, construction work of RM 37.1 billion was done in Malaysia,
indicating the contribution of the construction industry in the country’s
GDP [45]. However, still, the construction industry in Malaysia is facing
cost overrun [46], [47], where labour related cost is one of the most critical
factors [48]. A significant relationship exists between labour wages,
inflation and the labour productivity in Malaysia, where the inflation rate
possesses a negative impact, hence showing its importance in short and
long duration construction projects [49]. The value of the construction
work conducted in Malaysia from the year 2011 to 2019 [50] is shown in
Fig. 1.
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Fig. 1. Value of Construction Work in Malaysia.
A firm increase in construction work can be observed, indicating the
importance and contribution of construction in Malaysia. So far, no study
has been conducted assessing the deviation of labour wages due to the
inflation rate in construction projects which results in cost overrun.
Therefore, this study aims to highlight the impact of the inflation rate on
labour wages in the construction industry of Malaysia. As the inflation
rate is considered as one of the most influential factors of cost overrun by
deviating the labour wages, thus, the correlation coefficient was
calculated via Statistical Package for Social Sciences (SPSS-24) to observe
the strength of the relationship between the inflation rate and labour
wages. Also, a solution to this issue has been provided for the betterment
of the construction industry.
2. Methodology
The methodology of this research is divided into three phases. In the first
phase, data collection of the inflation rate and labour wages was made
from the Government departments of Malaysia. In the second phase, the
linear or nonlinear behaviour of labour wages was assessed, as the
selection of the correlation test is based on the nature of the data. In the
third phase, the correlation test was performed through the Statistical
Package for Social Sciences (SPSS-24) on the collected data to find out
whether the inflation rate is significantly influential on deviating the
labour wages or not. The flowchart of this research is provided in Fig. 2.
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Fig. 2. Research Flowchart.
2.1. Data collection
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Labour wages vary from state to state in the same country i.e. the capital
and the neighbourhoods. For the study purpose, labour wages data was
taken for Selangor, Malaysia, which is published by the National
Construction Cost Centre (N3C) [51]. The collected data covers the
duration of six years, i.e. from the year 2013 to 2019. The labour wages
were provided by N3C into two phases, i.e. January and July of every year,
where average wages/day was calculated. Within the published labour
wages, three groups were made as i) Construction Workers, ii) Plant and
Machine Operators, iii) Industrialized Building System (IBS) Installer,
having 136 subcategories. For Construction Workers and Plant and
Machine Operators group wages were further categorized in terms of
local and foreign labours. Whereas, in IBS installer group only local
labours wages were available. The mean labour wages of each group are
provided in Table 1.
Table 1. Mean Labour Wages.
Group
Mean Labour Wages (RM)
2013
2014
2015
2016
2017
2018
2019
Construction Workers
82.49
93.84
92.09
86.25
83.99
86.64
90.01
Plant and Machine Operators
74.60
87.04
95.97
102.19
100.83
98.52
100.68
IBS Installer
80.71
96.18
100.92
95.76
91.86
92.11
95.22
The second group of the data (the inflation rate of Malaysia from the year
2013 to 2019) was collected from the Department of Statistics Malaysia
[52] as provided in Fig. 3.
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Fig. 3. Inflation Rate of Malaysia.
Fig. 3 shows that the inflation rate in Malaysia was not steady during the
year 2013 to 2019. The highest recorded inflation rate during this period
was 3.80% in 2017, where a sudden drop was observed in 2018, bringing
the inflation rate at 1%. Malaysia has been successful to maintain the
moderate inflation rate even during the Asian Economy Crisis and
exhibited a stable economic growth. During the year 2013 to 2019, the
inflation rate of Malaysia was not steady and shifted noticeably. There
could be many possible reasons but mainly the increase of crude oil price
and the goods by local traders for their profits which results in altering
the inflation rate and turn down the value of Malaysian currency. The
reason why the inflation rate of Malaysia fluctuates is due to the internal
factors rather than been affected externally [53].
2.2. Data analysis
To measure the relationship between two variables, the correlation
coefficient is recommended. The value of correlation coefficient sits in
the range of −1 to +1 showing the strength of the relationship which
could either be negative or positive. The range division of correlation
coefficient is shown in Table 2 [54].
Table 2. Correlation coefficient range.
S. No
Correlation Coefficient
Relationship
Range (±)
1
Very Weak
0.00–0.19
2
Weak
0.20–0.39
3
Moderate
0.40–0.59
4
Strong
0.60–0.79
5
Very Strong
0.80–1.0
The selection of the correlation test depends on the nature of the
gathered data that whether it is linear or nonlinear. For linear behaviour
data, Pearson correlation test is performed, whereas, for nonlinear data,
Spearman correlation test is performed [55], [56]. To determine the
linearity and nonlinearity of the data, the following equation was used
[57];
(1)
where, Δy represents a change in labour wages and Δx represents a
change in the inflation rate.
By using the Equation (1), if the difference between the x and y variable
comes as 1, it is a linear behaviour and if it is not 1, it indicates the
nonlinear behaviour of the data.
3. Results and discussions
In this section, first, the behaviour of the data was evaluated, based on
which the Spearman correlation test was performed to obverse the
relationship between the inflation rate and the labour wages. The
Spearman correlation coefficient results were also reported.
3.1. Data behaviour
To analyze the data behaviour, first, change in the inflation rate and
labour wages during each year was calculated. Afterwards, by using
Equation (1), the behaviour of labour wages was calculated as shown in
Table 3. In the same manner, it was calculated for each labour category,
indicating the nonlinear behaviour of labour wages.
Table 3. Nonlinearity of labour wages.
Year Difference
Inflation Rate (Δx)
General Construction Worker Δy
Δy/Δx
2013–2014
1.03
5.1
4.95
2014–2015
−1.04
−5
4.81
2015–2016
−0.02
−1.55
77.5
2016–2017
1.72
1.15
0.67
2017–2018
−2.8
6.8
−2.43
2018–2019
0.02
4.98
249
From Table 3, a clear understanding can be drawn that labour wages
possess a nonlinear behaviour as none of the Δy/Δx value is equal to 1.
Also, the nonlinear behaviour of the data can be observed visually by
plotting a scattergram as shown in Fig. 4. The imaginary trend line was
drawn to reflect how much the data points are far away from the trend
line, which clearly shows that the data is having a nonlinear behaviour.
Therefore, due to nonlinear behaviour, the Spearman correlation test was
performed to observe the relationship between the two variables.
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Fig. 4. Scattergram of Labour wages with Inflation rate.
3.2. Spearman correlation coefficient
Spearman correlation, named after Charles Spearman, is a nonparametric
measure which assesses the monotopic or nonlinear relationship
between two variables. Spearman correlation coefficient was performed to
measure the influence of the inflation rate on labour wages, where the
inflation rate was taken as the independent variable and labour wages as
the dependent variable. The test was performed on each group of labour
wages to observe the influence of the inflation rate. The summary of the
correlation coefficient of the Construction Workers group is shown in
Fig. 5.
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Fig. 5. Summary of Construction Workers Group.
From Fig. 5, it can be observed that the labour wages lies in each range
division of correlation coefficient where few of them are strongly
correlated with the inflation rate. Out of 72 labour wages, 7 labour
categories are showing a strong relationship, 1 is showing a very strong
relationship and 11 are showing a moderate relationship with the
inflation rate. The detailed correlation coefficient of the Construction
Workers group is discussed in Table 4.
Table 4. Spearman Correlation Coefficient of Construction Workers
Group.
Construction Workers
Spearman
Construction
Correlation Workers
General Construction Worker -
Spearman
Construction
Correlation Workers
Spearman
Correlation
General Construction Worker
Electrical Wireman PW4
General
Electrical
Building (helper)
General Construction
−0.500*
−0.214
Worker - Building, L
Construction
Wireman PW4, S,
(Helper)
Worker - Civil, L
L
0.214
Construction Workers
General Construction
Spearman
Construction
Spearman
Construction
Correlation Workers
Correlation Workers
Correlation
−0.286
−0.429
0.143
General
Electrical
Worker - Building, F
Construction
Wireman PW4, S,
(Helper)
Worker - Civil, F
F
Concretor
Bricklayer
Painter - Building
Concretor, S, L
Spearman
−0.036
Bricklayer, S, L
0.000
Painter -
−0.286
Building, S, L
Concretor, S, F
−0.393
Bricklayer, S, F
−0.286
Painter -
−0.071
Building, S, F
Concretor, SS, L
−0.500*
Bricklayer, SS, L
−0.643*
Painter -
−0.214
Building, SS, L
Concretor, SS, F
−0.414
Bricklayer, SS, F
−0.679*
Painter -
−0.143
Building, SS, F
Plasterer
Plasterer, S, L
Tiler
0.286
Tiler, S, L
Scaffolder - Tubular
0.143
Scaffolder -
−0.214
Tubular, S, L
Plasterer, S, F
−0.414
Tiler, S, F
0.216
Scaffolder -
−0.143
Tubular, S, F
Plasterer, SS, L
−0.643*
Tiler, SS, L
−0.429
Scaffolder -
−0.286
Tubular, SS, L
Plasterer, SS, F
−0.393
Tiler, SS, F
−0.429
Scaffolder -
−0.571*
Tubular, SS, F
Barbender
Barbender, S, L
0.393
Carpenter - Formwork
Scaffolder - Prefabricated
Carpenter -
Scaffolder -
0.071
Formwork, S, L
0.071
Prefabricated, S,
L
Barbender, S, F
0.071
Carpenter Formwork, S, F
−0.429
Scaffolder Prefabricated, S,
F
0.000
Construction Workers
Barbender, SS, L
Spearman
Construction
Spearman
Construction
Spearman
Correlation Workers
Correlation Workers
Correlation
−0.143
−0.357
0.000
Carpenter Formwork, SS, L
Scaffolder Prefabricated, SS,
L
Barbender, SS, F
−0.214
Carpenter -
0.036
Formwork, SS, F
Scaffolder -
−0.180
Prefabricated, SS,
F
Carpenter - Joinery
Carpenter - Joinery, S, L
Roofer
0.643*
Roofer, S, L
Plumber - Reticulation
0.214
Plumber -
−0.036
Reticulation, S, L
Carpenter - Joinery, S, F
0.214
Roofer, S, F
0.071
Plumber -
0.000
Reticulation, S, F
Carpenter - Joinery, SS, L
−0.286
Roofer, SS, L
−0.357
Plumber -
−0.393
Reticulation, SS,
L
Carpenter - Joinery, SS, F
−0.679*
Roofer, SS, F
−0.786*
Plumber -
−0.250
Reticulation, SS,
F
Steel Structure Fabricator
Steel Structure
General Welder
0.107
Fabricator, S, L
General Welder, S,
Plumber - Building & Sanitary
0.536*
L
Plumber -
0.036
Building &
Sanitary, S, L
Steel Structure
0.286
Fabricator, S, F
General Welder, S,
0.464
F
Plumber -
−0.071
Building &
Sanitary, S, F
Steel Structure
−0.036
Fabricator, SS, L
General Welder,
−0.179
SS, L
Plumber -
−0.393
Building &
Sanitary, SS, L
Steel Structure
Fabricator, SS, F
−0.071
General Welder,
−0.036
SS, F
Plumber Building &
Sanitary, SS, F
Building Wiring Installer
Electrical Wireman PW2
−0.821*
Construction Workers
Spearman
Construction
Correlation Workers
Spearman
Construction
Correlation Workers
Building Wiring Installer, 0.000
Electrical Wireman 0.607*
SS, L
PW2, S, L
Building Wiring Installer, 0.018
Electrical Wireman 0.429
SS, F
PW2, S, F
Note: L = Local, F = Foreign, S = Skilled, SS = Semi-skilled.
*
acceptable correlation coefficient ± ≥ 0.5.
In Construction Workers group, the positive acceptable correlation was
shown by “General Welder, Skilled, Local” and “Electrical Wireman PW2”
proving that if the inflation rate increases their wages will also be
increased. However, majority of the labour wages such as “General
Construction Worker - Building, Local (Helper)”, “Concretor, Semiskilled, Local”, “Bricklayer, Semi-skilled, Local”, “Bricklayer, Semi-skilled,
Foreign”, “Plasterer, Semi-skilled, Local”, “Carpenter - Joinery, Skilled,
Local”, “Carpenter - Joinery, Semi-skilled, Foreign”, “Roofer, Semiskilled, Foreign”, “General Welder, Skilled, Local”, “Plumber - Building &
Sanitary, Semi-skilled, Foreign” and “Scaffolder - Tubular, Semi-skilled,
Foreign” showed a negative relationship with the inflation rate,
indicating that if the inflation rate increases, a decrease in the wages will
occur and vice versa.
The summary of the correlation coefficient of the Plant and Machine
Operators group is shown in Fig. 6.
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Spearman
Correlation
Fig. 6. Summary of Plant and Machine Operators Group.
Out of 54 labour wages, 4 labour categories showed a strong relationship
and 1 showed a very strong relationship, whereas 14 showed a moderate
relationship with the inflation rate. The detailed correlation coefficient of
the Plant and Machine Operators group is discussed in Table 5.
Table 5. Spearman Correlation Coefficient of Plant and Machine
Operators Group.
Plant and
Spearman
Plant and Machine
Spearman
Plant and
Spearman
Machine
Correlation
Operators
Correlation
Machine
Correlation
Operators
Operators
Excavator Operator
Excavator
−0.214
Operator, S, L
Excavator
Backhoe Loader Operator
Motor Grader Operator
Backhoe Loader
Motor Grader
−0.214
Operator, S, L
−0.107
Backhoe Loader
Operator, S, L
−0.214
Motor Grader
Operator, SS, F
Operator, S, F
Operator, S, F
Pile Rigger
Off Road Truck Operator
Slinger / Dogger
Off Road Truck
Slinger / Dogger,
Pile Rigger, S, L
−0.162
−0.429
Operator, S, L
Pile Rigger, S, F
−0.214
Off Road Truck
−0.75*
Off Road Truck
−0.143
−0.75*
Roller Operator
Roller Operator,
S, L
−0.429
Off Road Truck
−0.214
Slinger / Dogger,
−0.214
S, F
−0.357
Operator, SS, L
Pile Rigger, SS, F
−0.357
S, L
Operator, S, F
Pile Rigger, SS, L
−0.357
Slinger / Dogger,
−0.464
SS, L
−0.536*
Slinger / Dogger,
−0.393
Operator, SS, F
SS, F
Roller / Compactor Operator
Forklift Operator
Roller / Compactor
Forklift Operator, −0.571*
Operator, S, L
−0.107
S, L
Plant and
Spearman
Plant and Machine
Spearman
Plant and
Spearman
Machine
Correlation
Operators
Correlation
Machine
Correlation
Operators
Roller Operator,
Operators
−0.036
S, F
Roller / Compactor
0.036
Operator, S, F
Roller Operator,
−0.393
SS, L
Roller / Compactor
S, F
−0.500*
Operator, SS, L
Roller Operator,
−0.536*
Roller / Compactor
Forklift Operator, −0.179
Forklift Operator, −0.607*
SS, L
−0.179
Forklift Operator, −0.429
SS, F
Operator, SS, F
SS, F
Scrapper Operator
Wheel Loader Operator
Tower Crane Operator
Wheel Loader
Tower Crane
Scrapper
−0.321
Operator, S, L
Scrapper
Operator, S, L
−0.234
Operator, S, F
Scrapper
Wheel Loader
−0.500*
Wheel Loader
−0.214
Wheel Loader
Tower Crane
−0.306
Operator, S, F
−0.857*
Operator, SS, L
−0.500*
−0.306
Operator, S, L
Operator, S, F
Operator, SS, L
Scrapper
−0.179
Tower Crane
−0.571*
Operator, SS, L
−0.571*
Tower Crane
−0.536*
Operator, SS, F
Operator, SS, F
Operator, SS, F
Paver Operator
Mobile Crane Operator
Crawler Crane Operator
Paver Operator, S, −0.107
Mobile Crane
Crawler Crane
L
Operator, S, L
Paver Operator, S, −0.107
Mobile Crane
F
Operator, S, F
Paver Operator,
−0.750*
SS, L
−0.393
Mobile Crane
Operator, SS, F
−0.143
Operator, S, L
0.321
Crawler Crane
0.071
Operator, S, F
−0.179
Operator, SS, L
Paver Operator,
SS, F
Mobile Crane
0.179
Crawler Crane
−0.286
Operator, SS, L
−0.179
Crawler Crane
Operator, SS, F
Note: L = Local, F = Foreign, S = Skilled, SS = Semi-skilled.
*
acceptable correlation coefficient ± ≥ 0.5.
In Plant and Machine Operators group none of the labour wages showed
any positive acceptable correlation with the inflation rate. However, a
−0.306
negative acceptable correlation was observed by “Pile Rigger, Semiskilled, Local”, “Pile Rigger, Semi-skilled, Foreign”, “Off Road Truck
Operator, Semi-skilled, Foreign”, “Roller / Compactor Operator, Semiskilled, Local”, “Roller Operator, Semi-skilled, Foreign”, “Scrapper
Operator, Semi-skilled, Local”, “Wheel Loader Operator, Semi-skilled,
Local”, “Scrapper Operator, Semi-skilled, Foreign”, “Wheel Loader
Operator, Semi-skilled, Foreign”, “Paver Operator, Semi-skilled, Local”,
“Tower Crane Operator, Semi-skilled, Local”, “Tower Crane Operator,
Semi-skilled, Foreign”, “Forklift Operator, Skilled, Local” and “Forklift
Operator, Semi-skilled, Local”, indicating that if the inflation rate
increases, a decrease will occur in their wages and vice versa.
The summary of the correlation coefficient of the IBS installer group is
discussed in Fig. 7.
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Fig. 7. Summary of IBS Installer Group.
Out of 12 labour wages, 2 labour categories showed a strong relationship
and 1 showed a moderate relationship, whereas none of the labour
categories showed any very strong relationship with the inflation rate.
The detailed correlation coefficient of the IBS installer group is discussed
in Table 6.
Table 6. Spearman Correlation Coefficient of IBS Installer Group.
IBS Installer
Spearman
IBS Installer
Correlation
IBS Precast Concrete Installer
IBS Precast Concrete
Spearman
Correlation
IBS Lightweight Panel Installer
−0.214
IBS Lightweight Panel Installer, S, L 0.286
−0.714*
IBS Lightweight Panel Installer, SS,
Installer, S, L
IBS Precast Concrete
−0.250
Installer, SS, L
L
Lightweight Blockwall Installer
System Formwork Installer
Lightweight Blockwall
−0.214
System Formwork Installer, S, L
0.000
−0.679*
System Formwork Installer, SS, L
−0.571*
Installer, S, L
Lightweight Blockwall
Installer, SS, L
Roof Truss Installer (Timber)
Roof Truss Installer
Roof Truss Installer (Light Gauge Steel)
0.214
(Timber), S, L
Roof Truss Installer
Roof Truss Installer (Light Gauge
0.107
Steel), S, L
−0.214
(Timber), SS, L
Roof Truss Installer (Light Gauge
Steel), SS, L
Note: L = Local, F = Foreign, S = Skilled, SS = Semi-skilled.
*
acceptable correlation coefficient ± ≥ 0.5.
In IBS installer group none of the labour wages showed any positive
acceptable correlation with the inflation rate. However, a negative
acceptable correlation was observed by “IBS Precast Concrete Installer,
Semi-skilled, Local”, “Lightweight Blockwall Installer, Semi-skilled,
Local” and “System Formwork Installer, Semi-skilled, Local”, indicating
that with the increase in the inflation rate, wages will face a decrease and
vice versa. The acceptable correlation coefficient value lies between ±0.5
to ±1 [58]. Table 7 shows the acceptable number of labour wages in each
group.
Table 7. Acceptable Correlation Coefficient.
0.179
Construction Workers
Plant and Machine Operators
IBS Installer
Total
12
14
3
29
By looking at the acceptable rate, it can be observed that several labour
wages of each category are been influenced by the inflation rate which
results in the cost overrun of construction projects, as no consideration is
made for the inflation rate during estimation of the project budget. Also,
majority of labour wages are having a negative relationship with the
inflation rate, indicating that if the inflation rate decreases, it will
increase the labour wages and vice versa, which is not beneficial for any
construction project. An inverse relationship of the inflation rate with
labour wages is harmful to the project. The deviation is also harmful to
the economy because if the construction industry is unstable due to cost
overrun effect, it will not allow the economy to grow with a smooth pace.
Therefore, foremost attention is required to incorporate labour wages
while estimating the budget of any construction project. In this study, a
comparison between the labour wages of various countries has not been
made as there are many influential factors within a country, based on
which the wages get decided. Besides that, the role of foreigner workers
also plays a significant role in determining the daily wage. The role of
labour wages in cost overrun needs vital attention. With labours, direct
and indirect costs are associated while executing a construction project.
Even if any budget estimation model or technique is introduced to
encounter the deviation in labour wages occurred due to the inflation
rate, still, there as maximum chances that a project might face the cost
overrun due to indirect cost which is difficult to estimate. When labour
starts working on a site, he is not only entitled for the wage but also
accommodation cost, medical insurance and food cost are linked up with
him which burdened the client and increases project cost. A major
reform can be brought in the construction industry by adopting the
automation system. Labours should get replaced with the machines,
where not only the direct but also the indirect cost will be reduced,
resulting in the project completion on time and within the set project
budget. In short, the Industrial Revolution (IR) 4.0 is required to be
implemented in the execution phase of construction projects to bring
major reforms in the construction industry by overcoming the cost
overrun.
4. Conclusion
Labour wages have a major impact on project budget but unfortunately
least consideration is given to it, which results in cost overrun of the
project. Spearman correlation was performed on the labour wages, where
the inflation rate was taken as the independent variable. Based on
correlation coefficient values, it was observed that all three groups of
labour wages been affected by the inflation rate. Majority of the
acceptable correlation was negative, showing that if the inflation rate
increases or decreases it will decrease or increase the wages. Both the
phenomena are harmful to construction projects budget as the labour
wages are getting change each year, however, consideration of wages into
the budget is just considered at the beginning of the project which
results in the cost overrun of construction projects. The inflation rate is a
critical factor and therefore needs a vital consideration while finalizing
labour wages into the project budget. The issue of cost overrun through
labours can be resolved by introducing automation system. Replacing
labours with machines can reduce labour cost and thus project can easily
achieve its objectives. As a solution, the Industrial Revolution (IR) 4.0 can
be introduced to minimize the cost overrun effect.
5. Contribution and limitations
This research provides a benchmark to introduce the Industrial
Revolution (IR) 4.0 as a solution to reduce cost overrun effect by replacing
labours in the construction industry. It requires a further investigation
that how IR 4.0 can be successfully implemented in the construction
industry. Also, this research considered labour wages from the year 2013
to 2019 which was only available at the time of conducting the research.
To investigate the relationship of labour wages with the inflation rate in
more depth, high observations are required which will portray the
seriousness of the issue in a better manner.
Declaration of Competing Interest
None.
Acknowledgement
The authors would like to thank Universiti Teknologi PETRONAS (UTP)
for the support provided for this research.
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Muhammad Ali Musarat is a Civil Engineer, having specialization in Construction
Engineering and Management. He has good experience in academia and published
several articles in national and international journals and conferences.
Peer review under responsibility of Ain Shams University.
© 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams
University.
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