International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

ANALYSING AND FORECASTING THE UNDEREMPLOYMENTTREND IN MALAYSIA

1,*

Mohd Aminul Islam, 2Syifa Binti Kamarudin

1

Department of CTS, Faculty of Science, International Islamic University Malaysia
Jln Sultan Ahmad Shah, Bandar Indera Mahkota, 25200 Kuantan, Pahang

2

Department of CTS, Faculty of Science, International Islamic University Malaysia*Corresponding author

ABSTRACTIn this paper we aim to examine and forecast the underemployment trend in Malaysia. We
primarily focus on two measurements of underemployment which are time-related and skillrelated underemployment for a period of ten years starting from 2005 to 2015. To forecast the
number of time-related and skill-related underemployment for 2016 we use exponential
smoothing forecast method. The data and other information related to underemployment in
Malaysia are collected from Department of Statistics, Malaysia. The study found that although
the proportion of time-related underemployment over employment is relatively stable during theten years period, the number of skill-related underemployment is increasing by years. Females
are mostly affected by both time-related and skill-related underemployment.
Keywords: Underemployment, unemployment, trend, Malaysia, forecasting, productivity
1.0 INTRODUCTION
Malaysia has enjoyed one of the best economic growth records in Asia over the last four decades.
The economy managed to achieve a stable real GDP growth of 6.2% per annum since 1970,
successfully transform Malaysia from an agriculture-based economy in the 1970s, tomanufacturing based economy in the mid-1980s, and to modern service based economy in the
1990s. Malaysia rose from the rank of a low-income economy in 1970s to a high middle-income
economy in 1992 and remains so today (Economic Planning Unit, 2015). With a target of
average 5 to 6% economic growth per annum Malaysia aspires to achieve an advanced economic
status by 2020. To materialize this aim, six strategic thrusts have been outlined in the Eleventh
Malaysia Plan so as to help Malaysia facing the challenges and opportunities waiting ahead. The
six thrusts are: enhancing inclusiveness, improving wellbeing for all, accelerating human capital
www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2018

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

development, pursuing green growth, strengthening infrastructure, and re-engineering economic
growth for greater prosperity.
Pursuing the high income nation target, one of the focuses in The Eleventh Malaysia Plan is toaccelerate human capital development which is comparable to an advanced nation. Human
capital development is a critical enabler for driving and sustaining Malaysia’s economic growth.
An efficient and effective labor market is important to attract investments and provide
opportunities for everyone to participate in and enjoy the benefits of economic growth. It is also
critical to ensure the match between supply and demand, minimize underutilization of skills, andenable inclusive and sustainable growth (Economic Planning Unit, 2015). During the previous
Tenth Malaysia Plan, Malaysia managed to succeed to achieve the full employment at thefluctuation of 2.9% to 3.1% unemployment rate in 2014 and 2015 respectively (Department of
Statistics Malaysia, 2015), which was below the natural rate of unemployment of 4%. It was oneof the highlights of the achievements of Tenth Malaysia Plan in creating more jobs andmaintaining the full employment. Even though the economy continues to be in full employment,
there are many underlying issues on labor forces that are not captured by unemployment rate.
Unemployment rate alone cannot comprehensively cater the scenario of labor underutilization orunderemployment as underemployment has been emphasized in the international labor market
issues (International Labour Organization, 1998). Underemployment is a broad conceptreflecting underutilization of the productive capacity of the employed population (Mehran,Bescond, Hussmanns, & Benes, 2008).
Underemployment in general is defined as the situation when workers' jobs do not use all theirskills, education, or availability to work. Underemployments are basically measured in terms oftime-related and skill-related underemployment. Time-related underemployment usually occurs
when employees who are working fewer hours than is typical in their field despite the fact thatthey are willing and capable to work more hours, but cannot get full-time employment. On theother hand, skill-related underemployment occurs when workers are in full-time jobs that do not
use all their skills. In other words, workers are in full time jobs that do not match their skill or
qualifications. According to Department of Statistics, Malaysia (DOSM, 2015),
underemployment occurs when persons who, at any time during the reference week worked atleast an hour for pay, profit or family gain; were employed less than 30 hours a week during thereference week because of the nature of their work or due to insufficient work and are able andwilling to accept additional hours of work.
On the international level, time-related underemployment exists when the hours of work of aperson are insufficient in relation to an alternative employment situation in which the person iswilling and available to engage. According to the definition of International Labour Organizationwww.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2019

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01(ILO: 1998), persons in time-related underemployment comprise all those in employment thatsatisfy the following three criteria:a) were willing to work additional hours (i.e. wanted another job (or jobs) in addition totheir current job (or jobs) to increase their total hours of work; to replace any of theircurrent jobs with another job (or jobs) with increased hours of work; to increase thehours of work in any of their current jobs; or a combination of the above);b) were available to work additional hours (i.e. were ready, within a specified period oftime, to work additional hours, given opportunities for additional work); and whoc) worked less than a threshold relating to working time (i.e. persons whose hours
actually worked in all jobs during the reference period were below a threshold to be
specified according to national circumstances)
Mehran et al. (2008) described two particular categories of persons in time-related
underemployment, which are: i) persons who usually work part-time schedules and want to work
additional hours, and ii) persons who during the reference period worked less than their normal
hours of work.
In addition to time-related underemployment, countries may identify employees in otherinadequate employment situations due to low income, inadequate use of skills and excessive
hours (ILO, 2013). According to Hussmann (2007) and Mehran et. al. (2008) skill-related
underemployment occurs when employees want or seek to change current work situation in order
to use their current occupational skills more fully, and being available to do so. It may also bereferred to as over qualification or over education, the employment of workers with higheducation, skill levels or experience in jobs that do not require such abilities. There are few
studies available on this topic particularly in the case of developed nations. Most of the previous
studies revealed that time-related and skill-related underemployment is higher in females thanmales (Campbell, Parkinson, & Wood, 2013; Li, Duncan, & Miranti, 2015; Shahril, 2015;
Wilkins, 2004) with the exception of the study of Sugiyarto (2007) who showed that
underemployment is significant among males. ILO has been calling long its members to address
the issue of underemployment together with unemployment with a view to stimulate economic
growth and development, raising levels of living, meeting manpower requirements andovercoming unemployment and underemployment (ILO, 1998). In its Convention No. 122 of1964, which is ILO’s principal standard on employment policy, calls on members to ensure that“there is: (i) work for all who are available for and seeking work; (ii) such work is as productive
as possible; and (iii) there is freedom of choice of employment and the fullest possible
opportunity for each worker to qualify for, and to use his skills and endowments in, a job forwhich he/she is well suited, irrespective of race, color, sex, religion, political opinion, national
www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2020

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

extraction or social origin” (ILO, 1998). Situations which fail to meet (i) are related to
unemployment, and those which do not meet (ii) are related mainly to underemployment.
However, full employment policies in a country have mainly targeted the reduction ofunemployment (ILO, 1998). Many workers around the world tend to work less hours, earn lessincome or use their occupational skills incompletely; situations related to underemployment. In
other words, they tend to carry out an activity which is less productive than they could and would
like to carry out (Greenwood, 1999).
Unemployment rate is criticized because of its inability to capture all types of labor market
downturns. Mehran et al. (2008) argued that response to economic downturn may be different indifferent areas. Employers facing economic downturn in an area with a comprehensive
unemployment scheme may lay-off or reduce number of employees to cut cost. A downturn inanother area with limited unemployment insurance coverage may resort to another option – forexample work sharing or reduction of hours of work and wages. A similar economic downturn in
two different areas results in an increase of unemployment rate in one area and no change inanother. In developing countries, rate of unemployment is generally low, and in particular lower
than in industrialized countries. There are no unemployment benefits or social scheme to beclaimed; hence most people cannot afford to be unemployed for a long period of time. They arewilling to take any kind of job to support themselves. Thus unemployment rate cannot be used asa measure of economic well-being of the people; or as an indicator that people’s aspiration forwork are largely met (Sengenberger, 2011). Besides, unemployment rate is not affected byvariations in the extent to which both the time and skills of employed labor are used for
economic development. A person may be employed but his or her potential is not optimized; beit in terms of pay, hours, skill level, education, and experience. The employee is not given the
chance and responsibilities to fully utilize his/her capacity. They lost the opportunity to fully
participate in the labour market and accumulate financial, wealth and personal benefits.
Measuring labour underutilization is important for two reasons: first, labor is a productive inputthat cannot be stored and should not be wasted; and second, ownership of productive inputs
determines income, and many people in developing countries have only labor as their income
source (Sugiyarto, 2007). As far as Malaysia is concerned, it relies solely on unemployment rate
to measure the effectiveness of labour market. As mentioned earlier, the condition of full
employment has been the highlights of the Tenth Malaysia Plan. Little attention has been given
to what is happening in the labour market micro economically. As far as underemployment isconcerned, the question arises whether the industries fully utilize labour force’s capacity both interms of time and skill. This is the focus of the present study which aims to analyse whether
underemployment is a big issue in Malaysia’s workforce by examining the pattern of timerelated and skill-related underemployment in Malaysia. The study is significant in a sense that towww.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2021

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

the best of our knowledge there is no research done on this topic in Malaysia. Therefore it will
motivate others to undertake more details research on this topic in the future as well as the policy
maker to take appropriate actions so as to solve problem.
2.0 DATA AND METHODOLOGY
The data used in this study are secondary data which are obtained from Department of Statistics
Malaysia (DOSM). Most of the data can be accessed through their official website. Some
unpublished data relevant to our study were collected through personal request. Data extracted
from DOSM are based on Labour Force Survey and Malaysia Economic Statistics Time Series.
DOSM website also has eDataBank, an electronic data bank where users may compile their own
tables and graphs and export these tables in different formats. Among the relevant data extracted
are:a) Gross Domestic Product (GDP),b) labour force,c) number of employed person,d) unemployment and unemployment rate based on gender,e) number of employees working less than 30 hours per week, andf) number of employees with diploma or degree working in semi-skilled occupation.
Time-related underemployment is measured in DOSM as the number of employees working lessthan 30 hours a week during the reference week. This definition is taken in this study to get an
insight on time-related underemployment. To measure skill-related underemployment, the numberof employees with diploma or degree working in semi-skilled occupation is analyzed based on 1digit Malaysia Standard Classification of Occupations (MASCO). Semi-skilled occupation iscategorized as MASCO 4 to 8 under the Malaysia Standard Classification of Occupation(MASCO) while the rest are skilled workers (1 to 3) and elementary workers (9). MASCO lists
down the descriptions and tasks as well as the code for each position listed. It contains ten
categories of major groups. Normally, Major Groups 2 to 9 take into account both education andskill levels in determining their classification. Jobs under categories 4 to 8 require only secondaryor post-secondary education. Therefore, a person with a certificate higher than diploma is
considered underemployed if he or she is employed in semi-skilled jobs.
The data available for time-related and skill-related underemployment is in terms of number of
person affected. The proportion of underemployment over employment is calculated andexpressed in terms of percentage using the following formula:www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2022

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

Number of underemployed persons
𝑥 100
Number of persons employed
Time series data is taken for a period of 10 years’ time from the year 2005 to 2015. However,some data (2005 to 2007) particularly data related to skill-related underemployment are notavailable. As such we use data over the periods 2008 to 2015.
There exist a wide range of forecasting methods in the literature. In this study, we utilize one of
the simplest but popular forms of forecast methods called the basic exponential smoothing
method to forecast the number of underemployment for 2016. Exponential smoothing uses aweighted average of past time series value as a forecast. In this study, only one weight is selectedthat is the weight for the most recent observation. The exponential smoothing equation isexpressed as the following:
Ft+1 = αYt + (1 – α)Ft
Where;
Ft+1
Yt
Ft
α

=
=
=
=

(1)

Forecast of the time series for period t+1
Actual value of the time series on period t
Forecast of the time series for period t
Smoothing constant (0 ≤ α ≤ 1)

In order to evaluate the forecasting accuracy, we have calculated the forecast errors asN

Et

MAE  t 1
N

N

E

t

; MSE  t 1
N

N

2

Et

Y

, and MAPE  100 x t 1
N

t

(2)

Where MAE is the Mean Absolute Error, MSE is the Mean Squared Errors and MAPE is the
Mean Absolute Percentage Errors. E is the forecast error at time t defined as Yt – Ft. These areused more frequently to compare forecasting performance between different data sets.
3.0 RESULTS AND DISCUSSIONS
3.1 Trend of time-related underemployment by gender:
Trend of time-related underemployment by gender is depicted in figure 3.1 showing the numberof person working less than 30 hours per week. The year 2013 recorded the highest number of

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2023

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

person affected by time-related underemployment which was 643,100. The lowest number oftime-related underemployment was recorded in the year 2008, which were 470,700.

800.0
600.0
400.0
200.0

2015

2014

Total

2013

2011

Year
Female

2012

2010

Male

2009

2008

2007

2006

0.0
2005

Number of person ('000)

Figure 3.1: Time-related underemployment by gender (For data refer to Appendix)

Figure 3.2 shows the percentage of employed person affected by time-related underemployment.
In the year 2013, 4.87% of the employed was found to be underemployed, which was the highest
proportion recorded. The number dropped in the following year, recording the lowest proportionof time-related underemployment at 3.91%. Although 2008 recorded the lowest number of timerelated underemployment according to Figure 3.1, the percentage of employees underemployed
was lowest in the year 2014. The percentage of time-related underemployment over employedperson for the 10 period times was relatively stable ranging between 3.80 to 5.00%. From 2005
to 2011, number of males underemployed was higher than females. However, the proportion
shows that time-related underemployment was more significant among females than males
across the time periods. It shows that 6.94% of female employees were working below 30 hours
in 2013 as compared to 3.62% of male employees.

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2024

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

8.00
7.00
6.00
5.00
4.00

Male

3.00

Female

2.00

Total

1.00

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

0.00

2005

Proportion to employed person(%)

Figure 3.2: Proportion of time-related underemployment to employed person

Year

3.2 Trend of skill-related underemployment by gender: (For data refer to Appendix)
Figure 3.3 shows the number of person facing skill-related underemployment. The data for skillrelated underemployment was only available starting from 2008 where 276,600 employees arefound to be affected. The number however has shown increasing every year and reached to682,500 in the year 2015.

Number of person ('000)

Figure 3.3: Skill-related underemployment by gender
800.0

Male

Female

Total

2010

2011

2012

600.0
400.0
200.0
2008

2009

2013

2014

2015

Year

Figure 3.4 below shows the proportion of employees affected by skill-related underemployment.
In 2008, only 2.60% employees are found to be underemployed in terms of skill and education.
The percentage however found getting bigger and by the year 2015, 4.90% employees werewww.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2025

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

affected by skill-related underemployment. The numbers of female employees affected by skillrelated underemployment are found to be significantly higher than male. The percentages offemale underemployed were more than double of male. By the year 2015, 7.50% of female
employees were found underemployed as compared to only 3.2% affected male employees.

Proportion to employedperson (%)

Figure 3.4: Proportion of skill-related underemployment to employed person
8.0
6.0
4.0
2.0
2008

2009

2010

2011

2012

Male

Year
Female

2013

2014

2015

Total

3.3 Forecasting of time-related and skill-related underemployment for the year 2016:
3.3.1 Time-related underemployment
Table 3.1 below shows the forecasted (using equation 1) number of time-related
underemployment with smoothing factor α = 0.3. We tried different values for smoothing factor
out of which α = 0.3 appeared to be the best as it exhibits less error. Based on the exponential
smoothing method, the forecast may predict that 558,700 employees are most likely to beaffected by time-related underemployment in 2016.
Table 3.1 Exponential smoothing forecast with α = 0.3 for time-related underemployment
Year
2005
2006
2007
2008
2009
2010
2011
2012
www.ijsser.org

Actual number of person (‘000)
484.4
471.9
511.3
470.7
478.7
503.0
507.9
587.0

Forecasted number of person (‘000)
#N/A
484.4
480.7
489.9
484.1
482.5
488.6
494.4

Copyright © IJSSER 2017, All right reserved

Page 2026

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

643.1
529.0
580.0
-

2013
2014
2015
2016

522.2
558.5
549.6
558.7

The results of forecast errors (using equation 2) for time-related underemployment withsmoothing constant α = 0.3 are presented as follows:
MAE

=
=
=
=
=
=

MSE
MAPE

Mean of the absolute value of forecast errors
38.08 (in ‘000)
Mean of the sum squared forecast errors
2726.51 (in’000)
Mean of the absolute value of percentage forecast errors
6.71%

Figure 3.5 exhibits the trend between actual and forecasted number of time-related
underemployment with smoothing factor α = 0.3
Figure 3.5: Actual and forecasted number of time-related underemployment

Number of person ('000)

700.0
600.0
500.0
400.0
300.0

Actual200.0

Forecast

100.0

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

0.0
Year

3.3.2 Skill-related underemployment
Table 3.2 below shows the forecasted (equation 1) number of skill-related underemployment
with smoothing factor α = 0.9. The forecasted value of persons to be most likely affected bytime-related underemployment in 2016 is 625,700.

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2027

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

Table 3.2: Exponential smoothing forecast with α = 0.9 for skill-related underemployment
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016

Actual number of person (‘000)
276.60317.20354.60441.00473.90547.90619.50682.50-

Forecasted number of person (‘000)
#N/A
276.6
313.1
350.5
431.9
469.7
540.1
611.6
625.7

The calculations of forecast errors (equation 2) for skill-related underemployment withsmoothing constant α = 0.9 are presented below:
MAE

=
=
=
=
=
=

MSE
MAPE

Mean of the absolute value of forecast errors
63.30 (in ‘000)
Mean of the sum squared forecast errors
4397.26 (in’000)
Mean of the absolute value of percentage forecast errors
13%

Figure 3.6 below exhibits the trend between actual and forecasted number of skill-related
underemployment with smoothing factor α = 0.9

Number of person ('000)

Figure 3.6: Actual and forecast amount of number of skill-related underemployment
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
-

ActualForecast

2008

2009

2010

2011

2012

2013

2014

2015

2016

Year

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2028

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

The analysis revealed the fact that in Malaysia skill-related underemployment is increasing overyears although unemployment rate is still below the natural rate of unemployment. An estimateof 625,700 employees is forecasted to be affected by skill-related underemployment in 2016. The
analysis also revealed that in Malaysian labour market females are mostly affected by both timerelated underemployment and skill-related underemployment. This finding is in line with theprevious studies of Campbell et al. (2013), Li et al. (2015), Norshyahman (2015) and Wilkins(2004). As the Government of Malaysia projects the number of skilled workers to be comprised
35% of employees by 2020, the increased number of skill-related underemployment is going topose a big challenge in the future. If the number of underemployment employees continues togrow, it will have negative effect both on the employees and on the economy. The employees
will be deprived of getting better pay as they will not be able to utilize their potential ability at
fullest extent. They may loss ability to update their skills which may downscale their lifestyleand forced them to remain in long term underemployment condition.
In other words, they will not be on the right career track. Underemployment on the other handwill affect the economy’s gross domestic product negatively. This is due to the underutilizationof human capital which will lead to produce less than the potential level and hence lower income.
Low income means low consumption demand and hence slower economic growth as in the caseof increased unemployment rate. To address this issue, the Government may focus on raising thewages of semi-skilled jobs or to identify the factors leading to skill-related underemployment inorder to reduce the number. As far as the female underemployment problem is concerned, theGovernment aims to increase female participation in the workforce to 59% by 2020. However
this effort should be hand in hand with providing or creating the job opportunities that match
their capability and skills.
4.0 CONCLUSIONSIn this paper, we have analysed the trend of underemployment in Malaysia both from timerelated and skill related underemployment perspective. The study found that in terms of number,time-related underemployment fluctuates between 470,700 and 643,100 during the ten years
period. The number of skill-related underemployment is found trending upward by years. It was
276,600 in 2008 which increased by 405,900 to 682,500 in 2015. It is predicted that 625,700
employees are most likely to be affected in the year 2016. Female are found mostly affected byboth time-related and skill-related underemployment. One of the limitations of this study is thepossibility of overlap between time-related underemployment and skill-related underemployment.
It is possible that an employee facing time-related underemployment is also suffering from skillrelated underemployment. He or she may be working less than 30 hours a week; without fully
utilising his/her skill and education attainment in the occupational world.
www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2029

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

As such a deeper research on underemployment based on age cohort, industry, strata, field ofstudy and educational attainment can be undertaken in order to get clear picture of the problem.
It will help balancing the supply and demand of the labour force. On the other hand, data on fieldof study of employees affected by skill-related underemployment will facilitate the Government
to identify the critical skills demanded by the industry and the creation of skilled jobs. Since thenumber of skill-related underemployment is increasing, it is also advisable to identify the factors
leading to underemployment, especially among female employees.
REFERENCES
Campbell, I., Parkinson, S., & Wood, G. (2013). The housing security consequences ofunderemployment. AHURI Positioning Paper (No. 152). Australian Housing and Urban
Research Institute: RMIT Research Centre.
Department of Statistics Malaysia. (2016). Labour Force Survey Report: Malaysia 2015.
Department of Statistics, Malaysia.
Economic Planning Unit. (2015). Eleventh Malaysia Plan 2016-2020. Putrajaya: Economic
Planning Unit.
Greenwood, A. M. (1999). International definitions and prospects of Underemployment Statistics.
International Labour Organization Working Paper, ILO: Geneva
Hussmanns, R. (2007). Measurement of employment , unemployment and underemployment Current international standards and issues in their application. ILO Bureau of Statistics: Geneva
International Labour Organization. (1998). The measurement of underemployment. Retrieved
from http://www.ilo.org/public/libdoc/ilo/1998/98B09_207_engl.pdf
International Labour Organization. (2013). Statistics of work, employment and labour
underutilization: Geneva
Li, J., Duncan, A., & Miranti, R. (2015). Underemployment Among Mature Age. Economic
Record, 91(295), 438–462. http://doi.org/10.1111/1475-4932.12219
Mehran, F., Bescond, D., Hussmanns, R., & Benes, E. (2008). Beyond Unemployment:
Measurement of Other Forms of Labour Underutilization. ILO: Geneva
www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2030

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

Ministry of Human Resources. (2013). Malaysia Standard Classification of Occupations 2013.
Putrajaya: Ministry of Human Resources.
Raze Norshyahman, bin Shahril.. (2015). Terlebih pendidikan dan inflasi pendidikan dalamsektor awam di putrajaya. International Journal of Business, Economics and Law, 8(3), 40–46.
Sengenberger, W. (2011). Beyond the measurement of unemployment and underemployment
The case for extending and amending labour market statistics. Retrieved fromhttp://www.ilo.org/stat/Publications/WCMS_166604/lang--en/index.htm
Sugiyarto, G. (2007). Measuring Underemployment: Establishing the cut-off point. Retrieved
from http://trove.nla.gov.au/work/8315862?selectedversion=NBD42222835
Wilkins, R. (2004). The Extent and Consequences of Underemployment in Australia. Melbourne
Institute of Applied Economic and Social Research, Working Paper (No: 16/04): University of
Melbourne

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2031

International Journal of Social Science and Economic Research
ISSN: 2455-8834
Volume:02, Issue:01

Appendix
Total Employment ('000)
Year
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

Total
10,045.30
10,275.40
10,538.10
10,659.60
10,897.30
11,899.50
12,284.50
12,723.20
13,210.10
13,532.10
14,067.80

Male6,470.50
6,618.60
6,747.10
6,851.10
6,955.70
7,707.80
7,889.80
8,093.50
8,237.50
8,350.10
8,691.10

Female3,574.80
3,656.80
3,791.00
3,808.50
3,941.60
4,191.70
4,394.70
4,629.70
4,972.60
5,182.00
5,376.70

Total Employment ('000)
Year
2008
2009
2010
2011
2012
2013
2014
2015

Total
10,659.60
10,897.30
11,899.50
12,284.50
12,723.20
13,210.10
13,532.10
14,067.80

Male6,851.10
6,955.70
7,707.80
7,889.80
8,093.50
8,237.50
8,350.10
8,691.10

Female3,808.50
3,941.60
4,191.70
4,394.70
4,629.70
4,972.60
5,182.00
5,376.70

Time-Related
Time-Related Underemployment
Underemployment ('000)
as a % of Total Employment
Total
Male Female
Total
Male
Female
484.4
256.5
227.8
4.82
3.96
6.37
471.9
253.8
218.1
4.59
3.83
5.96
511.3
264.3
247
4.85
3.92
6.52
470.7
251.1
219.6
4.42
3.67
5.77
478.7
249.4
229.3
4.39
3.59
5.82
503
260.3
242.7
4.23
3.38
5.79
507.9
261
246.8
4.13
3.31
5.62
587
279.2
307.8
4.61
3.45
6.65
643.1
297.9
345.2
4.87
3.62
6.94
529
207.9
321.1
3.91
2.49
6.2
580
243.8
336.2
4.12
2.81
6.25
Skill-Related
Skill-Related Underemployment
Underemployment ('000)
as a % of Total Employment
Total
Male Female
Total
Male
Female
276.6
111.8
164.8
2.6
1.6
4.3
317.2
130.8
186.4
2.9
1.9
4.7
354.6
146.8
207.8
3
1.9
5
441
182.1
258.9
3.6
2.3
5.9
473.9
191.7
282.2
3.7
2.4
6.1
547.9
236
311.9
4.1
2.9
6.3
619.5
252.5
367
4.6
3
7.1
682.5
277.8
404.7
4.9
3.2
7.5

Source: Department of Statistics, Malaysia

www.ijsser.org

Copyright © IJSSER 2017, All right reserved

Page 2032