Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Labour Demand Elasticity and Manpower Requirement in the
Malaysian Service Sector
Rahmah Ismail*, Noorasiah Sulaiman**, Arawati Agus*** and Fariza Ahmad****
The first objective of this study is to analyse the elasticity of labour demand for output and
wage rate for two high-level occupations, which are professional labour and technical
labour. The second objective is to project the requirements of these two occupational
categories for the periods 2015 and 2020. The analysis in the study is based on data of
four select services subsectors, namely, professional business, education, health and
information and communication technology (ICT) within the 2000 to 2010 period. The
source of the data is the Service Industry Survey by the Department of Statistics
Malaysia. The results show that the elasticity of labour-output is generally positive, while
the elasticities of labour-wage are positive and negative depending on the category of
occupation and sector. The forecaset of the manpower requirement for professional and
technical category of occupations are highly dependent on the output growth of a sector
and the initial manpower stock, which are dominated by the education and ICT sectors.
Field of Research: Economics
1. Introduction
There are scores of discussions by various economists on the definition of manpower
planning. Generally, manpower planning has a broad definition and consists of a range of
activities realted to future skills needs and knowledge on the existing workers in an
organisation (Fyfe, 1988). Rahmah (2012) defines manpower planning as a process in which
an organisation or a country identifies the quantity and the types of workforce needed at a
defined time and place and the capability to use the workforce efficiently. Knowledge on the
requirements in labour demand by category of occupation, skills and education levels is
imperative to reduce mismatch between skills offered and needed and thus unemployment.
According to Hopkins (2002) manpower planning is a logical consequence to the continuous
mismatch in skills between supply of and demand for skills in the labour market, resulting in
persistent increase in unemployment. Manpower planning refers to the effort of preparing a
sufficient quantity of workers with needed qualifications to perform tasks at a right time and
place. Badillo and Vila (2013) argues that Job-worker mismatch reflects the inefficiency in
resource allocation within an economy. The investment on human capital by the labour is not
utilised at the maximum in the production activity and consequently resulting in the different
wages between workers.
For more than half a century, a range of techniques has been invented and used to
plan workforce in different countries using available data. The increasing attention on
the importance of manpower planning leads to improvements in methodologies and
their applications.
_______________________________________________________
*
Professor Dr. School of Economics, Faculty of Economics and Management, Universiti Kebangsaan Malaysia,
45630 Bangi Selangor Malaysia, email: rahis@ukm.edu.my
**
,
Associate Professor Dr. Noorasiah Sulaiman School of Economics, Faculty of Economics and Management
Universiti Kebangsaan Malaysia, 45630 Bangi Selangor Malaysia, email: rasiahs@ukm.edu.my
***
,
Professor Dr Arawati Agus UKM-GSB, Universiti Kebangsaan Malaysia, 45630 Bangi Selangor Malaysia,
email: ara@ukm.edu.my
****Dr. Fariza Ahmad, School of Economics, Faculty of Economics and Management Universiti Kebangsaan
Malaysia, 45630 Bangi Selangor Malaysia, f_ahmad@ukm.edu.my
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
The applicability and effectiveness of these methodologies are still a matter of debate
between present economists (Willems, 1996). Theoretically, there are two main school of
thoughts in the field of manpower planning analysis; the first is the structuralists, which view
manpower requirement through the lenses of the employer, and the second is the
neoclassical, which takes the vantage point of the labour who are the supplier of workforce.
These two opposite viewpoints require analyses using different models. Manpower
Requirement Approach (MRA) or Manpower Requirement Forecasting is suitable for the
demand side of manpower planning while for the supply side, the pertinent approach is the
Rate of Return Approach (RRA).
The MRA approach contends that manpower requirement forecasts are heavily related
to labour demand. The elasticity of labour demand to output is an important instrument in
manpower requirement forecasts. In fact, the original labour demand model includes three
basic factors namely output, price of labour (wage rate) and proce of capital (interest rate).
Labour demand is an important issue in analysing cost adjustment i.e. how producers adjust
labour demand when there are changes in wage rate. This situation needs to be observed
both in the short term and long term.
Malaysia has undergone a rapid growth in its economy and the quality of life of its
people has been improving despite the harsh and inconsistent global economic environment
of late. Acknowledging the vast potential of the service sector, the government has decided to
give an added attention on developing it with the aim of making the sector the main driver of
growth and employment with a focus on creating a more value added activities. During the
first half period of 2012, the services sector has recorded a 5.8% growth owing largely to
strong and sustained domestic demand (Malaysia, Economic Report 2013). The contribution
of the services sector to the Gross Domestic Production (GDP) has also improved from 46.8%
in the year 2005 to 54.2% for the year 2011. In 2011, the growth rate of the services sector
outpaced the GDP growth slightly with the sector growth at 7.0% compared to GDP growth of
5.1% (Malaysia, Economic Census Report 2011). To achieve the goal of making the services
sector the main driver of the Malaysian economy, a steady supply of educated workforce is of
the essence.
During the period of the Tenth Malaysia Plan (10-MP) the government has introduced
various programmes under the Economic Transformation Plan (ETP), which include the
introduction of the New Economic Model (NEM), which accentuates creativity and innovation
(Malaysia, 2010). Realising the need to focus on its strengths, Malaysia, under this
transformation effort has chosen 12 areas to be included in the National Key Economic Areas
(NKEA), which are projected to be the areas to drive the economy towards achieving the
ultimate goal of becoming a high-income economy by the year 2020. There are four services
subsectors identified in the NKEA, namely professional business, education, health care and
ICT. All four subsectors are the focus of this study, which examines the labour demand
elasticity and labour requirements for categories of professional and technical workers.
Considering the importance of this sector as emphasised by the government in its policies, it
is therefore, necessary that the workforce requirement of this sector is researched. This study
focuses on the high-level occupation due to the high cost of investment associated with this
type of labour compared to lower-level workers and subsequently a failure to optimally utilise
them lead to more costly waste of resources. Existing literature is lacking in studies focusing
on this specific issue and the existing studies on the Malaysian case are more general in
nature. It therefore, fits for this study to fill the lull on this subject matter in Malaysia.The first
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
objective of this study is to analyse the elasticity of labour demand for output and wage rate
for two high-level occupations, which are professionals and technical workers. The second
objective is to project the requirements of these two occupational categories in the future.
The analysis in the study is based on data of four selected services subsectors, namely
professional business, education, health care and information and communications
technology (ICT). This paper is organised into six sections. The next section is a review of
existing literatura followed by a section on theoretical framework and model specification.
The fourth section introduces the data followed by the fifth section, which discusses research
findings. The final section entails the conclusion of the study.
2. Literature Review
A study on demand revolves around its basic determinants, which are output and input price.
Studies on demand make use of a number of different approaches. For instance, Falk and
Koebel (2000) employed the Generalized Error Correction Model (GECM) to determine the
labour demand in the German manufacturing industry. In their study, labour was categorised
into three levels of skills - skilled, semi-skilled, and unskilled workers. The independednt
variables were output, total net capital stock and wage rate. They used data on the
manufacturing industry covering 26 years from 1976 until 1995. Their findings indicate that
labour demand is not elastic for all category of occupations in both short and long term.
Nonetheless, demand for unskilled labour is more elastic relative to skilled and semi-skilled
labour at least in the short term. They also found that total capital stock is a substitute to
unskilled labour in both short and long term with cross elasticity values positive at 0.27 and
0.40.
Betts (1997) used the tanslog cost function to estimate the substitution between whitecollar workers, blue-collar workers and capital in 18 manufacturing sectors in Canada
between the years 1962 and 1982. The study reported that capital and labour according to
skill levels are complementary. More recently, using the constant elasticity of substitution
(CES) production function and data from nine manufacturing subsectors in Malaysia, Rahmah
and Idris (2004) analysed the subsitution elasticity between physical capital and labour by
category of occupation for the year 1985 until 1996. Their findings indicate that the
manufacturing sector in Malaysia has a rather high elasticity of substitution with seven out of
the nine industries recording elasticity values exceeding unity. Furthermore, the elasticity of
substitution is higher for high-level occupations compared to lower category of occupations.
There are other factors that influence labour demand. A case in point is a study by Siti
Hajar et al. (2011), which used Foreign Direct Investment (FDI), economic openness, number
of technological agreements, output and wages to study labour demand in the services
selected subsectors in Malaysia. Their findings indicate that FDI, number of technological
agreements and economic openness are positively related to labour demand, while wage rate
is negatively related to labour demand in the services sector. An earlier study, Saenset et al.
(2008), studies the relationship between demand for agricultural products and labour demand
and wage rate in the agriculture sector in Chile. Quarterly data from the 1996-2005 period
was used to estimate agriculture labour demand and the Cobb Douglas production function
was used as the basis for model estimation. From the Cobb Douglas production function the
minimum cost function was estimated. Meanwhile, the linear log function was used to
measure labour elasticity. The main finding of the study was that demand for output is
positively related with labour demand with elasticity value of 0.38, while the wage elasticity is
negative at 0.88.
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Fajnzylber and Fernandes (2009) examines the effects of three economic activities at
international level, namely import, export and FDI on demand for skilled labour in Brazil and
China. The findings show that import and FDI are positively related with skilled labour demand
in Brazil. Conversely for China, they found that skilled labour demand reduces with increase
in import and export. The logical explanation is that for both sectors in China, the demand is
more for unskilled labour. Ilham and Mahmoud (2008) studies the impact of international trade
on labour demand elasticity in Tunisa's manufacturing sector. In their study, labour is divided
into two categories: contract workers and permanent workers. The study found that demand
for contract workers is more elastic than demand for permanent workers.
Bishwanath et al. (2013) estimates the substitution elasticity between capital and
labour input in the manufacturing sector in India. They estimated two types of industries
selected at two digit level during a research period of 1980 and 2007. Using CES model, they
found that the elasticity of subsitution between capital and labour are between 0.54 and 0.97.
The findings in Bishwanath et al. (2013) corroborates some earlier findings like in Virmani and
Hashi (2009) which also found elasticity values of less than unity. This indicates that the
possibility of subsitution between the two variables is low for the case of India. However,
Upender (2009) found that subsitution elasticity value between capital and labour for India's
manufacturing sector is more than unity.
There are various methodologies that can be applied to make projections on
manpower requirement in an economy. Methods like econometric, regression and time-siries
model are often used. All these models require adequate and suitable data to produce
accurate projections. If data is not sufficient or not following desired distribution pattern, a
model will not produce accurate projections (Ho., 2010). Scheffler et al. (2008) projected the
size of requirements for demand and supply of medical experts at the global level for the year
2015. The study used data of per capita medical expert during the period between 1980 and
2001 for 158countries obtained from the World Bank and World Health Organization (WHO).
The researcher applied the needs-based model and demand-based model to estimate the
need for medical experts in the future. The need-based model approach is based on the sin1-log model, which is related to the ratio of medical expert and skilled medical assistants to a
weighted population size. Meanwhile, the demand-based model uses Gross National Income
as the indicator to demand for and supply of medical experts for every 1000 citizens. The
study projects that in 2015, global demand and supply of medical experts will be in balance.
However, they also predict that shortage may occur in the African region while other regions
like Europe, South East Asia, West Pacific, East Meditteranean and America may not
experience such issue. In another instance, Chung et al. (2010) studies the demand and
supply of nurses in Korea due to the rising demand for healthcare in the country using the
system dynamic model to estimate future requirement for nursing workforce. They forecast
that demand will exceed supply during the the projection period of 2006-2020.
Labour-output elasticity can be further used to make projection of workforce using the
MRA approach. Most countries use this approach due to its ease of use and it can help policy
makers decide the needed training and education policies to achieve intended economic
targets (Neugart and Schomann, 2002). Nakajima (2008) studied the relationship between
manpower requirement and value added productivity for 24 select services industries in
Japan, Korea and America for every five years from 1960 until 1985. Using the input-output
table from the international publication The 1985 Japan US E.C. Asia Input Output Table and
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
applying Leontief ‘s Modeling, the researcher found a significant relationship between value
added productivity and manpower requirement.
Rahmah et al. (2012) analysed manpower requirement for high level category of
occupation in the services sector, namely administration and management as well as
professionals and technical workers. Five services subsectors were analysed to include:
electric, gas and water; wholesale and retail trade; hotels and restaurants; transportation,
storage and communications; finance, insurance and business services; and government
services as well as other related services. A time siries data from 1990 until 2008 was used.
The outcome was that output has a positive and significant influence on the demand for both
types of labour. More than half of labour requirement for the period between 2010 and 2015 is
for professionals and technical workers. The highest demand for labour was projected for
wholesale and retail trade and hotels and restaurants subsectors.
A study on manpower requirement in the food and beverage industry in Malaysia for
the year 2010 based on 2003 stock using econometric modeling was conducted by Rahmah
and Nur Ellia Nadira (2009). Three scenarios of output growth was used in making this
projection. the first scenario assumes that the projection period is the same as past trends.
The second scenario assumes that output growth is low and the third scenario assumes that
output growth is higher than past growth. The findings showed that the average growth rate
for all three scenarios has increased during the 2003-2010 period. The findings also indicate
that the food industry requires more labour compared to the drinks industry for all three
scenarios. Nurul Ain Moktar et al. (2012)used the input-output model framework as their
research technique and made projections of manpower requirements in the tourism sector
which include hotels, restaurants, transportation, entertainment, recreation and retail trade
subsectors. The study found that the entertainment, recreation and retail trade are the
subsectors that contribute to msot employment for the year 2015.
3. Methodology
Labour Demand Estimation Model
The estimation model for labour demand for both labour categories in the present study is
split into two types as below;
Labour Demand Estimation ARDL Model and PMG procedure
 ln PROFit   0  1 ln PROFi ,t 1  11 ln OUTPi ,t 1   21 ln WAGEPi ,t 1   31 ln WAGETi ,t 1
p 1
q 1
q 1
q 1
j 1
j 0
j 0
j 0
  1 j  ln PROFi ,t  j    11 j  ln OUTPi ,t  j    21 j  ln WAGEPi ,t  j    31 j  ln WAGETi ,t  j   1t
(5)
 ln TECH it   1   2 ln PROFi ,t 1  12 ln OUTPi ,t 1   22 ln WAGEPi ,t 1   32 ln WAGETi ,t 1
p 1
q 1
q 1
q 1
j 1
j 0
j 0
j 0
  2 j  ln PROFi ,t  j    21 j  ln OUTPi ,t  j    22 j  ln WAGEPi ,t  j    32 j  ln WAGETi ,t  j   2t
(6)
Labour Demand Estimation Model SUR procedure
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
ln PROFit   3   31 ln OUTPit   32 ln WAGEPit   33 ln WAGETit   34 Rt   3t
(7)
ln TECH it   4   41 ln OUTPit   42 ln WAGEPit   43 ln WAGETit   43 Rt   4t
(8)
Where, PROF is the number of professional, management and executive workers (will
be referred to as professionals after this), TECH is the number of technical and associated
professional workers (will be referred to as technical after this), OUTP is the real output value,
WAGEP is the monthly wage rate of professional workers, WAGET is the monthly wage rate
of technical workers, R is the interest rate, i is the services subsector (i=1,2,3,4), t is the year,
ln is the natural logarithm. Data for output and wage rate are based on 2005 price.
The same model is applied for the Labour Demand Estimation Model by category of
occupation and four services subsectors. In this estimation, data of types of services
subsectors based on the Malaysian Standard Industrial Classification (MSIC) at 3 digit level is
used.
Source of Data
Data of four selected services subsectors for the years 2000-2010 were obtained from the
Services Industry Survey conducted by the Department of Statistics Malaysia. These sectors
include professional business, education, health and information and communications
technology (ICT). For every subsector, data at MSIC three digit level were prepared. The
estimation of the PMG model according to the Autoregressive Distributed Lag (ARDL) include
four subsectors and 11 years, while SUR estimation used data at 3 digit level and a period of
11 years. The number of subsectors at three digit level is different for the four sub sectors
mentioned earlier. For example, the professional business sector has five subsectors, health
has four subsectors, education has four subsectors and ICT has seven subsectors.
4. Findings
Unit Root Test Analysis
This reasearch applied two approaches to test the stationary, namely, Augmented Dickey
Fuller (ADF) and Phillip-Perron (PP). The outcome of the unit root test for both procedures are
as reported in Table 1. Table 1 shows that all the variables are stationary at first difference
I(1) at both conditions, without trend and with trend at 1%, 5% and 10% significance levels.
These conditions implied that the spurious regression can be avoided since all variables are
stationary at first difference with trend. Since the panel data are not stationary at level I(0) but
is stationary at first difference I(1), the use of the ARDL model is thus appropriate.
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Table 1: Results of Unit root test ADF and PP
Variable
Without
trend
ADF
With Trend
PP
Without
With Trend
trend
Level
InProfessional
-9.81
-4.6762
0.69151
-4.67622
(0.21274)
(0.31868)
(0.31213)
(0.31868)
InTechnical
-2.3144
-4.1781
-2.3815
-8.9355
(0.20301)
(0.29557)
(0.2030)
(0.29557)**
lnWage Technical
-1.95812
-3.30622
-1.95811
-5.7346
(0.36351)
(0.31248)
(0.36351)
(0.31248)
lnWage
1.66091
-3.9546
-2.4354
-3.95467
Professional
(1.0848)
(0.37309)
(0.33545)
(0.37309)
InReal output
2.54226
0.1522
8.54219
3.17677
(0.12354)
(0.31275)
(0.12531)
(0.31275)
Difference
∆InProfessional
-6.7689
-9.0857
-8.97713
-11.6629
(0.21664)** (0.15856)** (0.15857)** (0.13012)**
∆InTechnical
-4.8317
-4.39940
-10.8748
-9.9732
(0.28311)*
(0.31648)* (0.28311)*** (0.31648)**
∆InWage Technical
-4.3130
-4.3147
-10.9587
-9.68774
(0.32871)*
(0.37973)* (0.36408)*** (0.40140)**
∆InWage
-6.0195
-6.91948
-6.0353
-6.9195
Professional
(0.36353)** (0.36883)** (0.36353)** (0.36838)**
∆InReal output
-1.5547
-3.34517
-5.17683
-7.02705
(0.37289)*
(1.08186)*
(0.39836)*
(0.45431)*
Note: *** Significant at significance level 1%, ** Significant at significance level 5% and
*Significant at significance level 10%. Upper value is the coefficient value, value in bracket is
the standard deviation
Results of Labour Demand Model
Table 2 reports the results of labour demand model by category of occupation using the
Pooled Mean Group (PMG) approach. The selection between the PMG and Mean Group
(MG) approaches was made using the Hausman test and the outcome indicate that the Chi
squares values are not significant. This connotes that the PMG is the more efficient estimator
compared to MG. The General Method Moment (GMM) approach is not suitable as the data
have a smaller cross section relative to time series.
Result of estimation shows that the ECT value is significent and less than one meaning
that there is a short run and long run relationship between the dependent and dependent
variables. The study also found that in the short run, the significant determinants for
professional labour demand are real output and real wage rates. Both variables signify
consistency with the theory, which propose that an increase in output will result an increase in
demand for labour, while an increase in wages will reduce demand for labour for the
professional category. However, for the technical workers a similar result was only obtained
for the output but there a positive impact of the wage rate on its labour demand. This implies
that an increase in the wage rate will increase the demand for labour of this type.
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
This kind of relationship can be maintained in the long run especially for teh
professional workers. However, in the long run, all the variables are significant and are not
conflicting with the theory especially for the output and wage rates effects on labour demand
of every job category. Moreover, result also showed the impact of changes in wage rates of
technical workers on demand for professional labours is negative, implying that these two
types of labour are compliment. Nonetheless, the outcome of the estimation of labour demand
for the technical workers shows a positive impact on demand for technical labour in the event
of an increase in professional labour wage rates. This shows that the substitution condition
that illustrates the influence of changes in wage rate for both categories of occupation are
different.
The study also found that the output elasticity for the professional occupation in the
long run is higher than in the short term. A 1% increase in the output will increase labour
demand for this category by 0.788 % in the short run and 1.0219% in the long run. The
elasticity of wage rate is lower in the long run, indicating that changes in wage rate has more
stable effect on the demand for labour in the long run. The findings indicate that a 1%
increase in wage rate reduces professional labour demand by 0.56732% in the short run
compared to 0.48797 in the long run. The technical labour output elasticity is lower than that
of the professional workers but the elasticities of wages are negative and higher in the long
run.
Table 2: Results of estimation for professional and technical categories of occupation
Pooled Mean Group (PMG)
Variable
lnProfessional
lnTechnical
ARDL (0,0,0,1)
ARDL
(0,0,1,0)
Short run effects
∆InReal Output
∆InReal Professional Wage
∆InReal Technical Wage
Constant
Error Correction Term (ECT)
Long run effects
InReal Output
InReal Professional Wage
InReal Technical Wage
0.78885
(0.19583)***
-0.56732
(0.13630)***
0.56732
(0.13346)
0.92968
(0.50327)**
-0.38982
(0.1307)**
0.51381
(0.10886)***
-0.36940
(0.24902)
0.46829
(0.1185)***
0.90659
(0.56573)*
-0.44407
(0.35835)**
1.0219
(0.02516)***
-0.48797
(0.04176)***
-0.97554
(0.12221)***
0.59766
(0.11207)***
1.67633
(0.25609)***
-1.71134
(0.25425)***
Note: *** Significant at 1%, ** Significant at 5%, *Significant at 10%. Figures in bracket are
standard deviation
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Proceedings of Annual Tokyo Business Research Conference
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The results of the estimation of labour demand model using the Seemingly Unrelated
regression (SUR) approach for all sectors are shown in Table 3. The value of R2 for the
professional labour demand model is 0.6451, while for the technical labour is 0.9345. This
means that 64.51% and 93.45% of the variations of the respective dependent variables can
be explained by the independent variables included in the model. The estimation outcome
indicate that the influences of wage rate and output are still consistent with the labour demand
theory with output playing a positive and significant role in determining labour demand for the
two categories of occupation. Meanwhile, the wage rate influences demand for labour for both
occupational categories negatively and significantly. The cross effect results from changes in
wage rate are still similar to PMG results.
Results on the estimation of labour demand model for the professional and technical
labour for every subsector is presented in Table 4. The value of R 2 is more than 0.8, showing
that more than 80% of variations in the dependent variable is explained by the independent
variables in the model. The influence of output on labour demand for both categories and for
all sectors are positve and significant. The negative effects of wage rate apply to all
subsectors except the professional business. For cross wage rate, estimation results show
that technical and professional labour are complements in the professional business and ICT
sector but are substitutes in the health and education sectors. A consistent cross elasticity
was found in the professional and technical labour demand estimation model for education,
health and ICT sectors.
Table 3: Results of estimation for professional and technical categories of occupation
Seemigly unrelated regression (SUR)
***
Variable
InReal Output
InReal Professional Wage
InReal Technical Wage
Interest Rate
Constant
R2
Chi2
p-value
No. of observation
InProfessional
0.48665
(0.07369)***
-1.44367
(0.17830)***
-0.09993
(0.19016)*
-0.00464
(0.00897)
12.4167
(0.81327)***
0.6415
78.75
(0.0000)***
44
InTechnical
0.07695
(0.07596)**
2.33905
(0.18379)***
-0.56428
(0.19601)**
-0.01046
(0.00923)
-3.44178
(0.83831)***
0.9345
628.01
(0.0000)***
44
Note:
Significant at 1%, ** Significant at 5%, *Significant at 10%. Figures in bracket are
standard deviation
Elasticity of labour-output is very high and there some that approach unity like in the
case of professional business sector. A 1% increase in output increase demand for
professional labour by 0.954% for the professional business sector, 0.774% for health sector,
0.768% for education sector and 0.815% for ICT sector. A similar trend was recorded for
technical workers with a 1% increase in output resulting in a 0.877% increase for professional
business sector, 0.781% for health sector, 0.759% for education sector and 0.967% for ICT
sector. Regarding wage elasticity, there are sectors that recorded elasticity values exceeding
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
unity, suggesting high elasticity. For instance, in the professional business sector, a 1%
increase in wage rate of professional workers increase the demand for professional labour by
1.563%, while a 1% increase in wage rate of technical workers increase the demand for this
category of labour by 1.061%. The own wages elasticity is negative in other sectors for the
professional category of occupation, but they are positive for technical workers. The cross
wage ealsticity is negative for the professional workers in the professional business and ICT
implying the complementarity between these two types of jobs. But for the health and
education sectors, the cross wage elasticities are positive implying the substitubility between
the technical and professional occupations.
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Table 4: Estimation results for services subsectors using Seemigly unrelated regression (SUR)
Variable:
InReal output
Professional Business
Health
Education
lnProfessional
lnTechnical
lnProfessiona
l
lnTechnical
lnProfessiona
l
0.95459
0.87756
0.77426
0.78157
0.7681
(0.12226)***
(0.57619)***
(0.11127)***
-0.95345
(0.12468)***
ICT
lnTechnical
lnProfessiona
l
lnTechnical
0.75945
0.81555
0.90666
(0.05768)***
(0.27531)**
(0.02639)***
0.74737
(0.24562)**
-0.94056
(0.12481)***
0.75945
(0.27531)**
-0.75571
(0.16175)***
(0.03645)**
*
-0.12087
(0.22146)
InReal wage
Professional
1.56348
(0.49846)**
(0.12034)**
*
1.06085
(0.49065)**
InReal wage
Technical
-2.1642
(0.52695)***
-1.52755
(0.51869)**
0.11836
(0.10076)**
0.12598
(0.20774)**
0.119
(0.10076)*
0.04371
(0.22226)**
-0.16014
(0.18498)*
0.35376
(0.25327)
Interest Rate
-0.03094
(0.02671)
-0.02563
(0.02629)
0.00075
(0.00591)
-0.12110
(0.01179)
0.00051
(0.00591)
-0.02390
(0.01303)
-0.02408
(0.01154)
-0.01831
(0.01581)
-2.9050
(1.92001)**
-2.53476
(1.89001)*
3.4854
(0.60809)***
(5.9308)
(1.2.454)***
3.4946
(0.60810)***
-4.9803
(1.34139)***
2.30599
(1.21301)*
-6.54067
(1.67469)**
*
0.8417
238.48
(0.0000)***
44
0.8790
319.42
(0.0000)***
44
0.8418
234.08
(0.0000)***
44
0.8602
270.77
(0.0000)***
44
Constant
2
R
λ2
p-value
N
0.8527
318.44
(0.0000)***
55
0.8170
245.48
(0.0000)***
55
0.9258
960.15
(0.0000)***
77
0.8958
662.20
(0.0000)***
77
Note: *** Significant at 1%, ** Significant at 5%, *Significant at 10%. Figures in bracket are standard deviation. Professional Business subsector at 3 digit
levels consist of Advertising, Accounting, bookkeeping and auditing activities; tax consultancy, Architectural, Engineering and Other Technical Activities,
Veterinary Activities, and Legal activities. Subsector of health includes human health, Hospital activities, Medical and dental practice activities, other human
health activities. Subsector of education includes Pre-primary and primary education, Secondary Education, Higher Education and other education. Subsector
of ICT includes Data base activities, Data processing services, Hardware consultancy, Maintenance and repair of office, accounting and computing
machinery, other computer related activities, Software consultancy and supply and Telecommunications.
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Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Projection of Manpower requirement
Table 5 conveys the projection of high-level manpower requirements for four services
subsectors included in the study. The growth rates for all four subsectors are quite high i.e.
between 8% to 12% between the years 2000 and 2010 with the exception of the health care
subsector, which only recorded growth of 3%. This projection took into account the labouroutput elasticity and the output growth rate. Manpower requirement is dominated by the ICT
and the education sectors followed by the health sector. The output growth rates for the two
sectors reach 12% resulting in high demand for labour for the next five and 10 years. This is
in line with the mission of the government to make these two subsectors the pillar of the
services sector and thus important sectors in the economy.
Professional business and ICT sectors recorded the highest labour-output elasticity.
However, it should be noted that the professional business sector went through a rather
slow growth in output and have a small manpower inventory leading to a small manpower
requirement. Despite that, its annual manpower requirement growth rate is encouraging
especially for the professional category of occupation, which is anticipated to grow at 4% a
year for the 2010-2020 period. It is less heartening for the health sector, which may only
grow at 1.7% a year during the same period. This owes partly due to the sufficiency of
present workforce in the sector leading to lower growth in demand for future recruitment.
Meanwhile, the education and ICT sectors' annual manpower requirement growth rate is
also projected to be 4%, which is a good sign for employment in the future.
Table 5: Employment projection by services subsector
Employment
Sector
LabourOutput
Elasticity
Value
(βij)
Output
Manpower
2015
2020
Annual
Growth
Inventory Projection Projection Growth
Rate
2010
Rate
(2000(Lijo)
20102010)
2020
(gr)
(%)
Professional business
Professional
Technical
Total
0.955
0.878
0.0842
0.0842
33098
1638
34736
Health care
41082
2001
43083
49068
2368
51436
4.0
3.7
Professional
Technical
Total
0.774
0.782
0.0382
0.0382
58251
7534
65785
Education
63513
8209
71210
68773
8884
77657
1.7
1.7
Professional
Technical
Total
0.768
0.759
0.1248
0.1248
65701
6050
71751
Communications
84593
7762
92355
103481
9137
112618
4.6
4.2
Professional
Technical
Total
0.815
0.906
0.111
0.111
45158
51697
96855
54842
63764
118606
4.5
4.9
35472
39634
75106
12
Proceedings of Annual Tokyo Business Research Conference
15 - 16 December 2014, Waseda University, Tokyo, japan, ISBN: 978-1-922069-67-2
Notes :
1. Calculation of output growth rate (gr) use output data for professional and technical (year 2000 until
2010) using the following formula;
*( √
)+
2. Projection of Lijt is obtained using this formula: Lijt = Lijo + (Lijo x n x βij x gr)
5. Conclusions
Research findings show that the most important factor influencing demand for labour in the
services sector is output. Wage rate also influence labour demand albeit negatively. This
supports the labour theory except for the case of professional business subsector for
professional category of occupation, which showed a positive influence of wage rate. This
difference indicates that the sector is still short of professional expertise and the competition
to attract this type of labour leads to positive influence of wages on professional labour
demand. For technical labour, the relationship is negative only in the professional business
subsector, while other sectors reported positive influence of the wage change. This signifies
that requirement for technical labour is still high and thus increasing wages still result in
higher demand for technical labour in the education, health and ICT sectors.
The projection of professional and technical labour requirements show that the ICT
and education sectors need more labour parallel with the bigger inventory. On annual
growth rate of labour requirement, technical occupation in ICT is expected to have highest
growth between 2010 and 2020, while professional and technical occupations in health is
projected to experience the lowest growth. The implication of these findings is that an effort
has to be made to increase labour requirement growth rate for high-level occupations to
reduce unemployment amongst prospective unversity graduates. A wage rate that helps
improve the welfare of workers can still be increased since it can still increase demand for
labour in some sectors under study. Besides, the encouraging growth of demand for these
two types of labour implies that present efforts of education and training need to be
continued. A sufficient supply of labour is imperative for a sustained economic growth and
this can be done through the education and training system. However, the researchers
concede that this study has not covered the supply side of the labour market and thus
unable to project the difference between supply and demand of high-level labour in the
services sector. Such study would need to include projection of supply with tertiary
education graduates as indicator. A more comprehensive study shall be conducted as an
extension to the present study to include supply of high-level labour in the services sector.
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