The illusion of foreign workers into the national
economy has grown particularly in these two decades. From the mid-70s to the
mid-90s, the size of foreign workers increased to 1.7 million or 21.25% out of
the total number of workers of 8 million in 1997
(Bank Negara Malaysia,1997). The high inflow of foreign workers to Malaysia is
based on the country’s economic situation. From 1987 to 1993, 14 million new
jobs were created in Malaysia with a growth rate of 3.9% compared to rates of
local workers growth of 3.1% only. This gap as it is stated by the World Bank,
will be filled by foreign workers (World Bank, 1995).
The number of foreign workers has been increasing due
to economic growth, international division of labour and foreign worker’s
investment (Tan Chuic Hong,2007). From the data, the number of foreign workers
start to increase in the year 2000 according to the growth economy of Malaysia
after 1997 economy crisis. Based on Sufian (2009), ASEAN’s financial value has
dropped and faced with financial problem in 1997. From that situation, the
number of foreign workers has decreased from 204,968 foreigners to 179,402
foreigners in the year 1999 (ministry of home affairs).
Based on Immigration Department of Malaysia (2017),
foreign worker only can work in a few sectors such as maid, manufacturing,
construction, agriculture and others. In Malaysia, manufacturing sector is the
most famous sector among foreign workers based on the skills and knowledge that
the foreign workers have. Besides that, sectors that have highest amount of
foreign workers are maid and construction because the sector have been
dominated by foreign workers from Indonesia.
This study focuses on forecasting the number of
foreign workers in manufacturing sector from 2017 until 2020 to figure out the
number of new intake of foreign workers in the next year to make sure the
demand of foreign workers can be fulfilled. Based on Dato Seri Najib Abdul
Razak on his speech during present Budget 2018 (Najib 2017), the government has
created one policy to reduce the number of foreign workers which government
have spent RM 1 billion with 70 percent government guarantee loan under Working
Capital Guarantee Scheme Facility (SJPP).
According to Warta (2017), Malaysia’s economy growth pattern nowadays is very slow in growing. Malaysia’s economy is slowing down from the year 2009 until now. In 2009, Malaysia’s economy growth has dropped 6.2% from the total economy in 2007, according to Najib (2017) . This problem, gave bad impact on local and foreign workers in
Malaysia because lots of company started to close their business such as Suzuki, because decreasing in demand,
Nurhayati (2016). This issue is similar with what had happened in Malaysia in 1997. During that time, a lot of people lost their jobs and the
number of foreign workers decreased. The decrease the number of foreign workers are related to the rate of Gross Domestic Product (GDP), because based on Intan (2016) foreign
workers are one of the factors that affect the rate of GDP. The importance of this study, to
forecast the number of foreign workers in manufacturing sector in year 2017
until 2020 and the effect on rate of GDP.
The study would like
meet certain objectives. The objectives are:
To determine the
best fit model that fit to the number of foreign workers in manufacturing
sector in Malaysia.
To forecast the
future number of foreign workers in manufacturing sector in Malaysia from
2017 until 2020.
1.4 LIMITATION OF STUDY
The researcher has the limitation of data. The data
that provided by Ministry of Home Affairs is not specified by the state and
also not specified which state have more demand of foreign worker so, the
researcher have the limit to know how many demand of foreign workers for every state. The data
that provided by Ministry of Home Affairs only from 1987 until 2016 while the
researcher need more information about foreign workers to study more deeper the
impact of foreign workers in Malaysia.
1.5 SCOPE OF STUDY
This study only uses the data from 1987 until 2016 to
forecast the future value of foreign worker. This only covers 30 data to done
this forecasting. Besides that, this study only focus on manufacturing sector
that contribute by foreign workers. Lastly the data is not specific by the
state and not specified by skill category.
1.6 SIGNIFICANCE OF STUDY
The significance of this study is to see the pattern
of foreign worker in Malaysia. Hence, to explore the demand of foreign workers
in Malaysia every year. Furthermore, this study will give the benefit to
Ministry of home Affairs in order to know the number of foreign workers for the
next year. Besides that, this study can also know the Gross Domestic Product
(GDP) for every year. This is because the number of foreign workers can help to
increase the percent of Gross Domestic Product. This study will help government
to control the number of foreign worker to make sure there is no surplus of
foreign workers in Malaysia. Lastly, this data very useful for government to
make any decision about foreign workers in Malaysia.
CHAPTER 2 : LITERATURE REVIEW
Foreign workers are migrants from their home country
to other country to give their services on a temporary basis. In Malaysia,
foreign workers are one of the contributing resources of human labour to fulfil
job demands in certain sectors such as manufacturing, agriculture and so on.
Since 1980, the rate of intake of foreign workers has been increased in line
with the growth of the Malaysian economy. But, the end of the 1990s, there was
a drop in the recruitment of foreign workers in the wake of the economic crisis
in the ASEAN countries. According to Norinah (2002) the economy crisis had been
recovered before 2000. In general, foreign workers intake in Malaysia is to address
the problem of labour shortage in the sector certain sectors such as
construction, services and manufacturing.
Based on Ramesh (2012), the intake of foreign workers
from abroad have positive and negative impact towards Malaysia. One side of the
community claimed that foreign workers are involved in crime such as murder and
robbery. Bahrom (2014), reported that 9496 cases are related with foreign
workers in Malaysia which consist of 11.2% from 85,029 cases have been
recorded. This problem was a serious issue that had been faced by Malaysian
Foreign workers also give a positive impact in economy
growth. Empirical studies on the impact of foreign workers on the economic
growth of the country receiving the conclusion that foreign workers contribute
positively to economic growth. Sabrina et al (2014) foreign workers help
Malaysia to increase the economy growth by help in production that give impact
in Gross Domestic Product (GDP). GDP is the total of overall goods market value and the end services that
have been produced in a country over the specified time period Gregory (2013).
The uses of GDP is to measure the economy level for the country annually to
understand the National economy pattern. The aspect included to calculate GDP
is the total of government expenditure, investment expenditure, consumer
expenditure, export value and minus import value, Saiful (2015). Based on
Investopedia (2017), GDP represents the large impact within economy. For
example, when the GDP is growing and in good situation, that means the economy
growth and GDP in a line of growing that will affect the number of job
opportunities. The large number of job opportunities is significant that the
company have good economy growth.
The excess demand of labour in this rapid economic growth and the lower
cost of worker needed are the reason of increase rate of foreign workers year
by year. Erika and Teodoras (2013), with the tight labour’s condition, foreign
workers that migrate helped to ease, especially in the manufacturing sector,
labour shortages and skill deficiencies are filled up by the foreign workers
thus beneficial to the economy. Based on Zaleha et (2011), Foreign workers are
elastic for being a substitute at various job categories while they complement
the capital By employing foreign workers, Malaysia can remain as a competitive
country in the world market. Sectors that are physically demanding and labour
intensive usually the main contributors to the GDP, Rahmah and Ferayuliani
Based on Rahmah (2015) there is a problem of shortage of foreign workers
in many sectors in Malaysia. That problem forced the government to import the
foreign workers to work at these industries to overcome the problem. Simon (1998)
discovered that some findings said that when the economy grows positively, it
will create more job opportunities. A
study conducted that the foreign workers show complementary relationship where
foreign workers will encourage more local workers to be more productive. The
manufacturing sector is important to the economic growth in terms of
contribution in gross domestic product, external trade and also in job creation
in Malaysia. Based on journal Cindy (2017) the manufacturing workforce is based
on the context of demand in Malaysia and the demand exceed from year to year.
Today, to ensure that economy growth smoothly, companies or businesses need to
build up an improvement to these foreign workers.
In February 2016, the Malaysian
government took an extreme measure of stopping the intake of foreign workers
whilst reviewing their employment policies. Reuters (2016) government stated
they would utilize this mechanism until they are satisfied with the manpower
needs of the working sectors. This sent a wave of shock to the industries,
specifically the manufacturing sectors. These kind of sectors are not the kind
of sector local citizens would work in because they are considered as dirty,
dangerous and difficult. These industries can only willingly participated by
According to article from The Star (2016) this mechanism to control the
intake of foreign workers gave a bad effect on businesses from those
industries. The Master Builders Association Malaysia (MBAM) begged the
government to lift the suspension on the intake of foreign workers as fast as
possible, mentioning the ban caused a shortage of manpower that will affect
productivity and delay projects.
From the previous study,
this study wanted to forecast population of foreign workers that suit with
mathematical method. The mathematical methods, involve the charting of past and
present population data, the figure of “trends” and the projection of
these present population trends into the future. There are two types of
mathematical projection arithmetic and geometric. Arithmetic projection assumes
the continuation of the amount of population change observed in what is defined
as the base period, the period from which the projection is started, through
successive equal intervals of time. Arithmetic projection, since it has been
employed during periods of population increase, has generally been used to show
population growth in fixed amounts. The geometric projection method has been
much more popular. It looks at population changes in terms of percentage
changes rather than numerical changes. Based on Pallavari (2016), the concept
of mathematical methods to forecast about foreign workers is based on average
forecast method because there is five step to done in mathematical method.
First step, find the average of the population. Second, estimate increment
rate. Third, find the geometric mean. The forth step is substitute the
information to population forecasting formula and the last step is conclude the
outcome. The overall method of mathematical method is average forecast method.
Therefore, based on study have done by Rajchakit (2014), the researcher
use Variance-Covariance Control method
(VAR-C) to forecast the number of
foreign workers in Chiang Mai, Thailand. In this study, the researcher will use
average forecast method to forecast the number of foreign workers in
manufacturing sector in 2017 until 2020.
UNDERSTANDING OF THE
The data used is from the Ministry of Home
Affairs from year 1987 until 1996. This data shows the number of foreign
workers that enter the country year by year. Furthermore, this data only
focused on the manufacturing sector in Malaysia.
this research, the seasonal component was found upward based on the figure in
this study, the researcher used average model in order to analysis the data.
The reason why the researcher use the average model is because the model used
from the previous study to forecast the number of foreign workers by year is
average model. The model that the researcher used is average model. In this term, there
are 2 model which are average change model and average percent model.
Frequently, the average change model are used within the organization since it
is known for its stability and practicability. For information this model also based on the premise that the forecast
value is equal to the actual value in the current period plus the average of
the absolute changes experienced up to that point in time. Another method which
is the Average Percent Change which its assumption that the forecast of
dependent variable are equally to the current value of that variable time
period plus the average of the changes percentage from one time to another for
the next period.
Based on this figure, it show the upward
movement through the year and also the decrement in 2016. In this case, the
decrement is caused by the government of halting the intake of foreign workers
in that year and it is also caused by the economy scenario.
3.2 DATA PARTITION
part, the data is divided into two section which is on part for the data
evaluation and another one is for the data partition with the ratio of 70 : 30
respectively. the below table shows the simple view about the data partitioning:
Methods of Averages
i. Average / Mean Forecast
The formula to calculate average forecast is
Where is knows as forecast for
m-step-ahead made at period t, then ? would we refer to the arithmetic
mean of the actual historical time series and characterized as,
Where T is the aggregate number of observation
in the time series.
This method assumes that the forecast value is
equivalent to the average value of the data series over the recorded time
period the information was gathered. Moreover, the mean forecast method
performs most acceptably when the historical time series contains no discernible
pattern, significant drop or growth.
ii. Average Change Model
This method is generally utilized by a few
associations since its stability and practicality.
It is given as;
Formula for Average of Change is as below
In any case, where the significant changes are
seen in the data series, then the average of more than two changes will be able
to stabilize better the ‘Average of Change’.
This model is similar to the naïve with trend
model with exception that is less influenced by all historical and observations
and it responses relatively quickly to changes in the actual time series.
On the other hand, if these changes are
randomly dispersed, the average change model will overreact to these random
changes. This model is suitable for short data, a common phenomenon in most
On the diverse side, this model tends to lag
behind turning points and that all period are weighted equally, irrespective of
their importance, when deriving the forecast values. However, the averages
change model can provide useful short-term to intermediate-term forecasts when
the actual time series is commanded by a consistent pattern of change.
iii. Average Percent Change Model
The model is assumes that the forecast of the
dependent variable equals to the actual level of that variable in the present
time period in addition of the average of the percentage changes from the one
time period to the following. It can be formally expressed as,
Where the Average of Percent Change is;
The most crucial aspect of this model is that
the forecast are produced based on percentage changes in the historical data.
Subsequently, it is most appropriate for time series that exhibit a constant
percentage growth rate. Again, this model is also suitable for short data
series. This technique might be unsuitable for forecasting beyond one or two
months period since the compounding effect will, over time, produces very high
2.4 ERROR MEASURE
2.4.1 MEAN SQUARED ERROR (MSE)
This is the error measure
used by most practitioners for assessing the model’s fitness to a particular
series of data and is provided by most statistical software’s. This measure is
also commonly used for comparing the model’s forecasting performance.
To show the
mathematical form of Mean Squared Error (MSE), assume a series be, …,and the corresponding
fitted values are be , …,. The MSE is given as
is the actual observed value in
time t and
is the fitted value in time t
ANALYSIS OF THE DATA
4.1.1 Average forecast
of average forecast model
Average forecast model
Based on the figure 4.1, the average forecast model shows that
straight line movement because the average number of foreign workers is same
from year 1987 to 2006. For this model, the mean square of the model shows the
value of 44757467880.0982.
Average change model
of average change model
Average change model
on the figure 4.3, the model shows no movement form year 1990 to 1993. It shows
slightly upward started from 1994 to 1998 and also the upward movement at year
1999. For year 2000 to 2001, it shows the downward movement but in year 2002
shows the upward movement same as year 1999. For the end of the year, it shows
aggressive upward movement compare to the original data. For this model, the
mean square error value is 85499707495.0119.
Average percent change model
of average percent change model
Average percent change model
average percent model is used for analyze the data. The fitted value is
generated after analyze the data. For the average percent model, in year 1990
to 1993 it shows the slightly upward movement and the increment started from
year 1994 1997. In the middle of the year, it shows the huge increment and also
form year 2003 until 2007. For the mean square error, it shows the value 8494521106.
4.2 EVALUATION PART
Summary of the Model
Average Change Model
Average Percent Model
this part, this will determine the best model that need to use in the real
forecasting data. If the MSE value has the lowest value, that model is chosen.
Note that MSE is the degree of error that may be in a model. Based on the table
4.4, the best model chosen is average forecast method.