A System Dynamics Model for Economic Planning
in Sarawak
Brian Dangerfield
Centre for OR & Applied Statistics
University of Salford
SALFORD M5 4WT
UK.
44-161-295-5315 (T)
44-161-295-2130 (F)
Email: b.c.dangerfield@ salford.ac.uk
Abstract
This paper describes the outcome of a research project undertaken for the
government of the State of Sarawak in E. Malaysia. A system dynamics model was
constructed so as to inform the State’s future economic and social planning to 2020.
Positive engagement with State government officials at the highest levels was a
feature of the successful completion of the work. A flexible policy evaluation tool for
use in their macro-economic planning is now available to be used by those officers
who were exposed to several training sessions in system dynamics modelling.
Introduction
Nothing manifests complexity more than an economic system. Here we observe
myriad interactions between physical production, employment, finances and
government policy. In addition there are likely exogenous effects which can impinge
on the system and are driven by international market movements, political upheavals
or even terrorism.
The development of computers in the 1950’s and 60’s together with the systematic
collection of national economic data led to a pursuit of economic planning based upon
models which attempted to make sense of this data with a view to forecasting the
future of key economic variables. The emergence of econometrics as an adjunct to
economic science was bom. Nowadays hardly any developed nations lack an
econometric model. If the central government itself have not built one, then one or
more university or private research institutions will have. However, these models have
been criticised (Black, 1982).
The reliance on econometric models for macro-economic management is common, but
it is not necessarily the best methodology for exploring, evaluating and reflecting on
policy options. Meadows and Robinson (1985) review a range of methodologies and
include system dynamics amongst them. They make a cogent case for the usefulness
of system dynamics in national economic planning.
The application described below is one rooted in a developing economy. Some of the
material described has been made available already whilst the research was a work-in-
progress (Dangerfield, 2005). Examples of systems-based approaches to developing
economies are available in the broader operational research literature (see for instance
Parikh, 1986 and Rebelo, 1986) but it is only relatively recently that SD has begun to
acquire a prominence in development planning (Bamey, 2003; Chen and Jan, 2005;
Morgan, 2005). The Sarawak project is important in the sense that it is another
contribution to empirical macro-economic modelling using SD. Moreover, the project
has exposed (and hopefully convinced) government officials as to the merits of SD as
a methodology in this sphere of application.
Determination of model purpose
The determination of a purpose for an SD model is well grounded in the literature.
Here a brief had been agreed: to provide the State with a tool to aid their future
economic planning. But this is too broad an objective. A specific purpose needed to
be defined and a period of time was spent after the commencement of the research in
reviewing the various strands of thinking in the state government and, in particular,
reading the key speeches of ministers to see what was preoccupying them. There
would be no benefit derived from the creation of some grand planning tool if it was
not consonant with the interests and ambitions of the primary stakeholders.
A proposal was eventually tabled and agreement secured to develop the model with
the following purpose:
How and over what time-scale can the State of Sarawak best manage the
transition from a production-based economy (p-economy) to a knowledge-based
economy (k-economy) and thereby improve international competitiveness?
There had been concerns raised in ministerial speeches that the resource-based
economy, which had served Sarawak well in over two decades of development, was
coming under pressure from other industrialising nations, in particular China. To
secure further international competitiveness the state needed to develop more high-
tech industry with higher value-added products and services: in short there was a
desire to shift towards a k-economy, implying the emergence of a quaternary sector.
A high level map for the model
The diagram shown in figure 1 sets out, in as economical a way as possible, the
overall structure of the model designed to address the issue above.
DYNAMIC HYPOTHESIS: hightevel map “"™ > Skill Tech Tranter
——> Capital Equipment
— Human Resources
Leakage
Overseas?
Figure.1. The high-level map used to guide the developing model
There are three main aspects to be handled and an appropriate triangulation of these
components is key to managing a successful transition to a knowledge economy:
e The supply of suitably trained human capital and entrepreneurs.
This is the output from the education sector shown towards the top left of the
diagram. Clearly the primary and secondary education sector provides the output
of students some portion of which will progress to higher education. Both arts and
science specialisms are represented and there are indications that the balance here
does not currently favour the enhancement of science skills which underpin the k-
economy. The current graduate output ratio is weighted heavily in favour of arts
courses. However, over recent years this imbalance has been tackled from the point
in School career where students choose a future pathway: Form Four (F4) level.
The vocational sector is also represented since development of a k-economy is
augmented by an important group of sub-professionals (e.g. technicians) who have
a crucial supporting role to play.
Funding for most education in Sarawak is provided by the federal government.
However, and primarily in an effort to develop the science base, the State
Government have funded certain private university developments. State funding in
this manner can play an important part in expediting the flow of suitably qualified
individuals who will stimulate the development of a k-economy.
e The demand side of a k-economy: those knowledge-based industries and
services which are emerging (in some cases as development of primary and
secondary industry) to form an ever-increasing component of the economy.
The sectors towards the bottom left of Figure 1 are split into Primary (the
production and resource based sector also called the p-economy); Secondary (the
service sectors such as tourism, finance and professional services); and the
knowledge-based sector (the k-economy or quaternary sector).
The evolution of the quaternary sector is propelled by a mixture of foreign direct
investment and State funding. But this alone is insufficient, for a flow of skilled
human capital is essential as is the quality of the ICT infrastructure which must
have attained an appropriate level of sophistication.
The growing emergency of a quaternary sector can appear to consume resources
which might otherwise have been directed to primary and secondary industry. But
it must be stressed that, contemporaneously, development of the k-economy will
mean direct skill and technology transfer benefits to the existing base of primary
and secondary industry. It is impossible to ignore the bedrock components of the
p-economy which can, in tum, be enhanced as part of overall economic
development. The sectors are currently the main providers of state revenue (via
taxation and employment) and are likely to remain so.
e The state of the ICT infrastructure, which in some senses mediates the evolution
of the drivers of supply and demand.
The quality of the ICT infrastructure can be fairly easily measured by appropriate
metrics. Two such examples are the length of the broadband data highway within
Sarawak and the estimated number of PC’s installed.
Again, it would be expected that the State government revenue would, in large
measure, underpin the enhancement of these metrics, although foreign direct
investment cannot be ruled out.
An ICT infrastructure of reasonable sophistication will also be necessary in order
to allow the development of a number of Research and Development (R&D)
Centres of Excellence, as indicated in Figure 1. The initiation of such projects is
suggested in order that best practice k-economy activities can be showcased and
publicised. These centres will make it clear that the State government is strongly
promulgating the development of the quatemary sector through provision of funds
to allow these start-up operations to proceed.
Development of a k-economy would be constrained if the supply of science
graduates and suitable sub-professional k-workers are not forthcoming, which is
why emphasis has been placed upon coincident (or even prior) educational
changes. Initial staffing of such centres may be a problem but it is possible that,
with sufficiently attractive remuneration packages, qualified Sarawak expatriates
would be tempted to return.
The proposed R & D centres can be seen as crucial catalysts in the stimulation of
the quaternary sector and they will offer a primary supply of people with the
necessary skill sets to enthuse the creation and development of knowledge-based
industry and services.
The dynamic flows to be considered are:
Skills and technology transfer
Money and all forms of financial resource
Capital Equipment
Human Resources (Capital)
Within the industry sectors (particularly the Primary Sector) there are also dynamic
flows of goods, material and orders.
Description of some important model sectors
The Population Sector
Within this sector the age-based population structure of Sarawak is represented. This
is an important input to modelling sustainable economic activity within the State. The
flow diagram for this sector is shown below as Figure 2. The main summary variables
are available e.g. population age 15 and under; population 15 to 64 (taken to be the
working population); population 65 and over, and the total population. The
dependency ratio is also computed. This is an important demographic ratio which
expresses the non-working subset of the population (the young and the old) as a
percentage of the total population. The higher is this value the greater the pressure on
the State’s working population to support the strata that is not economically active.
Extensive census data is collected for this sector usually on a ten-year cycle and thus
the formulation of the model for population flows is straightforward since the
parameters are readily available. However, estimates have to be made for years
between census dates and we have employed the 1995 estimates to initialise the
population in each age group.
\ ai intial sub
total deaths population t
{
/ pai) ae
a cohorts
death rate per 000 a.
popn (output)
= cide he deaths meen ag tne
OS sis per 000 —_ 1)
popn (output)
it oa acl “|
rate (input)
\3 moh population 65 and
under over
total BB
\ rato
Figure. 2. The Population sector sub model
The population sub-model is presented succinctly as a flow of births which adds to
population cohorts which are, in turn, depleted by deaths. A critical feature is that the
population is stratified into 15 age cohorts from 0-4, 5-9, 10-14 and so on up to 65-69
and finally 70+.
Although not shown in figure 2, the age bands are modelled separately along an
ageing chain. The Vensim software allows such a decomposition through its array
facility. Although any age group can be extracted (e.g. for plotting or to use
elsewhere in the model), it is usual to concentrate on the important broad age ranges
and the total population, as described above.
The data which provides the parameters for this sub-model is collected in an EXCEL
spreadsheet. In general, many of the model’s parameters are stored in this way
because Vensim allows the extraction of both parameter values and historical time
series from EX CEL spreadsheets and extemal data (text) files.
There are separate crude death rates for each age band (estimated from the census
data) and separate initial population values for each also. By initial population we
mean the initial number of persons in that age band at the commencement of the
simulation in 1995.
A distinction is made between input and output for crude birth and death rates. Input
values for crude death rates are those estimated from the data, whilst the input crude
birth rates were provided directly as data up to the most recent year for which figures
are available or are derived from a linear interpolation process out into the future to
2020. Output values are calculated in the model as part of the simulation and are
presented as aggregates over the entire population. In part they act as a check on the
data entered but also provide a useful overall metric for consideration. For instance
the crude death rate in the older age bands might be expected to reduce over calendar
time as a result of improved medical care. Currently, these inputs are held constant.
This possibility, if implemented, should be reflected in the output value for the overall
crude death rate per thousand (’000s) of the population.
Education & Human Capital Sectors
Progression to a k-economy will take some time but will be propelled by the twin
thrusts of investment in people and a communications infrastructure. In respect of
obtaining a suitably qualified labour force we need to place the centre of gravity of
the model around the production of higher-educated and technically-qualified human
capital. These developments underpin a suitably skilled workforce and, in view of
the importance attached to this, a workforce sector has been added (see section
below).
The education sector is considered here and has been split as between Primary and
Secondary education (Primary 1 - Primary 6 and Form 1 - Form 5 respectively) on
the one hand and Tertiary education on the other. The extent and importance of these
model sectors means that they cover two separate views. Firstly the model of Primary
and Secondary education is described and is represented by Figure 3.
No. in Secondary
Educ
2 2 eo.inFLFD
i) <No. in F4-F5
ry Leavers after 5 (Sciences)
(Sciences)
Dropouts after F3
Dropouts after/during
Primary educ
No. in Foma
prea 5 6/Matiic
Transition to | (Sciences) aloe al
No. in Primary 4 F6/Matrc (Sciences) mal
Educ «5
Enrolments to FL )N°-@FI-F3)Transtomtto Fa-F5 transition to ‘iisnin
ry (Sciences) F6/Mattic pone
transition to F4 e
Primary enrolment —
jo. in Form
‘(im io 6/Matiic
ne >| (Humanities)
Q Transtion to F4-F5 ransition to F6/Mat Transition to Univ
(Humanities) (Humanities) (Arts)
Leavers after F5
(Humanities)
Figure. 3. The Primary & Secondary Education Sector
a
There is a significant proportion leaving full-time education after F5. Attempts to
address this are essential if the Sarawak economy is to prosper and if Malaysia as a
whole is to attain developed status. An intemal document suggested an estimate of
90% leaving education post-F5. However, it is believed that improvements in the
proportion progressing to tertiary education have been implemented since the mid-
1990's and so the figure of 0.9 is progressively reduced from the start of the model’s
runs. Experimentation here is obviously a highly critical aspect of our work: more
students entering higher education is vital for the Sarawak’s future as a potential
knowledge economy.
Transition to higher education is an almost continuous progression once students
have elected to carry on to Form 6 or, alternatively, elect the matriculation route.
Those leaving after this point in their educational progression are negligible. No such
leavers are represented in the model since the numbers are so small. Also, it has been
assumed that the choice between humanities and sciences will then reflect their
corresponding choices at university.
Once the student flow bifurcates after F4 there is a need to create aggregated
variables. Thus the totals entering F4, F6/Matriculation and university are separately
defined. Also the total in secondary education is introduced, made up the five
individual model variables which are the sub-components of “number in secondary
education”.
The tertiary education sector is defined as universities and also technical colleges or
polytechnics. In essence it is all those institutions offering full-time education
programmes to students after the level of F6 or equivalent. The model of tertiary
education developed for the current purpose is presented in outline in Figure 4.
tectnicaly
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“> enlmenis Tech labour] Tech recnuits|
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Technical education is included. Knowledge-based firms require both skilled
engineering and scientific staff but also technical support staff who have gained
qualifications from the technical/vocational education institutions existing in
Sarawak. By 1998 all secondary vocational schools were converted to State Technical
Schools with around 1500-2000 students enrolled. However, the number of such
schools in the whole of the State is less than ten. But it is progression on to higher
technical education which is the element that underpins the knowledge economy and
fulfils the necessary support role for qualified engineers.
Figure. 4. Tertiary education sector and consequent employment
After graduation there must exist job prospects, particularly for those qualified on
the science and engineering side, for significant emigration to be avoided. If such
graduates cannot obtain employment in Sarawak within one year, on average, it is
assumed they will start the process of emigration. In addition to emigration a flow of
repatriations is included. If the prospects and opportunities for skilled ex-patriots
expand in Sarawak then it would be expected that some fraction would retum to their
homeland for employment and career progression. This will add to the skilled labour
available to k-firms and to R & D centres.
Arts graduates can be re-trained and the existence of post-graduate conversion
courses to equip them with some of the skills needed in knowledge-based
employment is a possibility and is included in the sector. This initiative needs to be
progressed and it assumes a capability and willingness of the universities to mount it
and, furthermore, government funding may also be required. In the event that (more)
such courses are launched, there is yet another source of skilled labour available to k-
firms.
The promotion of Research and Development (R & D) centres is a crucial segment
of the strategy to transition Sarawak towards a knowledge-based economy. The
source of the labour for these centres will come from the expanding scientific and
engineering graduate output, backed up by suitable technical support staff. The
goverment have a role to play in inaugurating start-up R & D centres and must be
prepared to allocate financial resources accordingly. Over time the centres will
acquire collective experience: they will ‘mature’. It is these mature centres which both
directly and indirectly will facilitate the supply of entrepreneurs to set up k-firms. In
tum these k-firms will recruit from the scientific and engineering labour pool.
The training of scientific and engineering staff will necessitate more teachers at the
level of scientific secondary education and at university. This is another career route
for appropriately qualified new graduands and one which currently is not explicitly
included in the model.
Workforce Sector
The Workforce Sector presented in Figure 5 aims to portray the quality of human
capital available in the Sarawak economy. It defines five categories consistent with
their highest level of educational attainment. Thus we have:
No formal education
Educated up to F3
Educated to F5
Technically educated post F5/F6 (Certificate & Diploma students)
Numbers with Arts degrees
Numbers with Science degrees
The category ‘No formal education’ covers those who have dropped out during or
just after primary education (P1 -P6) or before commencing F1. Attaining just this
level of education cannot be expected to help a move to a k-economy and so the
description is justified in the current context. There currently appears to be a
significant dropout at the F3 stage and this level of attainment is therefore specifically
included. Those in the category ‘Up to F5’ education similarly have terminated their
education after F5.
Tertiary education comprises those who have gone on to further or higher education
post F5/F6. It comprises those in degree level education, either via F6 or through the
matriculation route, together with those electing a technical education. The latter have
animportant supporting role to play in the development of a k-economy.
10
Transition to withno | Retirements: strata with
working age formal edie | ho formal educ
Retraining of F3 eskiled nae:
dropouts oasis \, cin
OO Wordors. inte)
; with upto F3
Trento working [Le Workiore
age: F3 dopo ata)
pumas Retirements: strata “~ when
—— orton wer ‘Saget
Enianis to with up to ~"Retreméns; stata
workloreaterF5 | FSedu | Sea
Retirements: strata with
scifeng degrees
Workiore
with technical ieee
technically education isa
ualifieds> a
Worktorce
SS thats
Gi )L_degrees_| Retirements: strata
with Arts degrees
Figure. 5. The Workforce Sector: main view
Accumulating all the various categories together allows a calculation of the total
workforce. In addition, weighting the proportions attaining each possible level of
education against the total workforce allows a computation of the ‘mean years of
education’. This is an important international measure of development and its
improvement over the years is an indicator of Sarawak’s progression to developed
economy status in line with Vision 2020 for Malaysia as a whole.
In addition to the above, there are various indices computed, such as the percentage
of the workforce educated to tertiary level, to F3 level, F5 level and so on (Figure 6).
The proportion of professionally-skilled workers and graduates per 1000 of population
is another widely-reported index and this too is easily computed. ‘Professionally-
skilled’ is defined as those with a university degree or other post-F5/F6 qualification
such as a Diploma or Certificate.
11
percentage educated tech ie gee .
to tertiary level
perentage educated percentage with no
Hoxitaou to only F3 level Sonmal eda
D - Force>
‘ a \ OS
percentage educated 3
to only F5 level percent with gee
de
percent with Arts \ “ene degrees
degrees with
ducation> 7
: perntwih otal population>
technical educ:
No of skied workers &
graduates per 000 popn
Figure. 6. Various percentages and indices computed for the workforce sector
Manufacturing, C onstruction and Service Sectors
This section describes the representation of the manufacturing, construction and
service sectors within the model. The manufacturing sector is specified by
disaggregating it into its important individual industrial components. It is not modelled
globally. The separate sectors included are: ‘Electrical & Electronic (E&E)’;
downstream timber processing; petroleum products; palm oil and sago (starch)
production. (LNG & unrefined petroleum are categorized as part of ‘mining’ although
these export-eamning sectors are also included separately here.) The chosen groups
cover 87% of the total gross value of manufacturing output in the State based on 2000
data. The construction industry is specified separately.
Figure 7 shows the structure adopted for E&E, Construction and services. The
diagram is a simplified version of that incorporated within the model so as to aid
comprehension.
12
net growth of
construction capa
stock
construction
capital index
construction
labourindex
Construction
ret gowthof labour
constuction labour
force ee services ouput
vices output
etn vale of ouput om
\efims in services
| fraction of k-fims
itn services
| Mamsacting
net growth of ESE BEE otal
capital tock
Bec) wei fraction of kfims in
manuacring
/ Prem ERE ‘f
jes vie
Manufacturing E&E a s
net gowih of ESE py meee f cfm EGE igmeeg aE
our fore +
Figure. 7. Simplified view of the Electrical &Electronic Manufacturing,
Construction & Service Sectors
A Cobb-Douglas production function is used to model E&E output and construction
output. This type of function has a long history of use in economic growth models
and it is flexible, being capable of exhibiting increasing, constant or diminishing
returns to scale dependent upon parameter specification. The inputs which need to be
specified are (i) a constant parameter (ii) a labour index and (iii) a capital index.
The sum of the indices determines the degree of retums to scale. At present, E&E is
specified with increasing returns to scale. Inputs of labour and capital are estimated
based upon a simple growth function and the numbers used equate with existing data
on labour and capital in the E&E industry.
The influence of k-firms enhancing the output of the E&E sector is modelled by
uplifting the constant value in the Cobb-Douglas function. This is in line with sound
practice in economic growth theory. As the number of k-firms in the State gets bigger
this will have a progressive effect on E&E output value.
The growth of services is set at 5% per annum and a compound growth function is
adopted. An examination of the recent growth rates of the various categories of
services reveals a significant variation from less than 1% to in excess of 12%. The
choice of 5% per annum is indicative of the majority of sub-sectors. The emergence
of a knowledge component in the service esector will enhance the output in excess of
the 5% p.a. specified initially. The number of k-firms is split as between E&E
manufacturing and services. Currently the ratio is 0.7 for E&E and 0.3 for services.
This serves to emphasise that developments in a knowledge economy are not
13
restricted to ICT-based manufacturing industries but can, and are expected to,
permeate into a range of service sectors also.
Output from the construction industry is modelled in the same way as the E&E
sector in manufacturing. That is to say a Cobb-Douglas production function is
employed and the labour and capital inputs over time specified by simple compound
growth functions. Presently this industry has indices of 0.2 for capital and 0.5 for
labour, the sum equating with decreasing returns to scale.
State Finances and GDP
The formulation of the State Finances and GDP sector follows a logical path: State
income allows for State expenditure. Reserves, which are built up or run down as
desired, exist as a buffer between the two flows. State income is derived from the tax
take on various industries such as palm oil, timber and LNG, together with an
allocation from the Federal government. This is split into operating funds and
development funds. For instance a large slice of operating expenditure is devoted to
education, social welfare and medicine and health.
For our purposes the flows of funds from different sources can be aggregated,
although expenditure is necessarily split into operating expenditure and development
expenditure. This is reflected in the diagram for this sector below.
‘fraction of revenue spent ES:
snopeangexendare
ore operating
q aa
ah 4 ~
olterreeme incl Tol Reveme aia
feel toae
ovbinoter | pee
4 | : coeds ae
f | orb ene
\
}
pe ama |/
ae
i
GDP-12 Monts
Cy)
Figure. 8. State Financial sector together with GDP and GDP annual growth rate
Whereas development expenditure will normally follow development revenue
allocations from the Federal government, as financial reserves allow it would be
14
prudent for the State government to take the initiative in releasing further monies for
development. State R & D centres would be one such use of these funds.
The growth of ‘other revenue’, which covers all sources of revenue not derived
from the State tax take on the industries specifically included in the model, is set to
grow at 2.5% per annum from a base of 4 billion RM in 1995. It should be
emphasised that the model is not aiming to faithfully reflect the full detail of financial
flows into and out of the State Treasury, but rather to allow a consideration of
variations in development expenditure, the primary engine of endogenous growth.
Gross Domestic Product is formulated by aggregating the output data from ten
separate industries and sectors specifically included elsewhere in the model. The
availability of total population data from the population sector means that GDP per
capita is readily computed. It is common to find annual GDP growth rates in all
countries’ published data and so this measure is also included. It is worth repeating
that all monetary data is specified in real terms based upon 1995 prices, the starting
point for the all the simulation runs.
Research and Development and the K -industries Sector
The genesis of the emergence of the k-economy is to be found in R& D. Here is the
instrument which any government in a developing country can deploy in order to
effect an economic transformation. he sequence is normally to provide seedcom
funding for ‘start-up’ R & D centres which are staffed by qualified engineers backed
up by technical support staff. These centres may not necessarily be ICT
manufacturing industry based, but may embrace elements of the service economy
such as health and biotechnology.
Whatever the nature of the activities ongoing in the R & D centres, they all require a
supply of skilled engineers, technical backup and a high-tech ICT infrastructure.
These three components are specifically included as all three together represent a
necessary and sufficient condition for R & D centres to emerge. However, the
creation of k-firms requires, additionally, experienced high-tech engineers and
scientists. Where such people are not available the k-firms need to source their
expertise from outside of Sarawak. Endogenous growth of k-firms therefore is
restricted in the absence of a supply of experienced engineers and scientists. Raw
graduates, however appropriate their degree subject, do not equate with experienced
staff.
15
Startup RED Mahue RED
‘addins t startup cenes | trnstion b mala centres
RGD certs R&D certies
instar
ICT msouces
reqd per im
potential mmber of nw
fms based on CT zat of cl
ee peso
o = item
nh < { tech about ‘nfastuctue
in| aan os
iene = Ee
poelaneat | undp
oll
/
peice Ps
‘ke firms based upon skilled ee
ae
Figure. 9. Research & Development Centres, knowledge-based firms and the ICT
infrastructure (simplified from that in the model)
The diagram above shows that ‘start-up’ R&D centres eventually mature over a
period of years, assumed to be on average five years, and it is from the mature R&D
centres that a source of experienced high-tech staff will emerge. Start-up centres are
assumed to require 20 scientists and engineers, with a further 30 being employed as
the centres mature. Each start-up R&D centre is estimated, on average, to cost RMS
million to set up, the number of such schemes being determined by the overall annual
Statebudget for R&D.
The role of the technical backup staff, both in R&D centres and, more significantly,
in k-firms should not be underestimated. Within Sarawak there are indications that the
supply of this element in the workforce could be constraining the State’s
development. We have assumed that, for both R&D centres and k-firms, the technical
labour force is 20%. In other words, there would be one technical officer for every
five qualified engineers and scientists.
Remaining sectors
For the sake of brevity the remaining model sectors are not described in detail. These
sectors comprise those which make up the current resource-based economy in
Sarawak. Timber is a prominent example along with the emerging pulp wood
industry. Palm oil is a prominent export earner and its yield comes from oil palms
planted in clearings as hardwood trees are felled. However, there is considerable
attention paid to the sustainability of the hardwood species and forestry is managed
closely by the government. This also applies to land made available for oil palms.
Along with the timber resources there are mining resources which contribute
significantly to GDP. Oil and petroleum products is one case in point together with
16
liquefied natural gas (LNG) which is exported to Japan. In 2003 crude petroleum and
LNG together accounted for some 63% of Sarawak’s exports by value (Source:
Sarawak Facts & Figures, 2003/2004). In contrast timber logs and sawn timber
contributed 7.5% in 2003, down from almost 28% in 1990 and reflecting the
government’s management of timber resources referred to above. Within the timber
industry the development of plywood and veneer products (higher value-added items)
has offered a counterpoint to the situation with logs and sawn timber. Plywood and
veneers accounted for almost 9% of exports in 2003.
Specimen Scenario Runs
Introduction
There are innumerable scenarios which can be undertaken with the model as currently
configured. It consists of a total of 15 sectors (sometimes called ‘sketches’ or ‘views’ )
and in excess of 450 individual relationships, parameters and mappings. In keeping
with the objective of the model purpose, we restrict consideration to those changes
which impact most closely on the knowledge economy.
Reduction of dropout rates from various stages of education.
Here we have assumed that the major crisis points for dropouts, namely after or
during primary education and after lower secondary education (F3), are addressed.
Over the years of the 9MP we assume that in the case of primary school dropouts this
is reduced to just 5% by 2010 and for F3 dropouts to just 2%. Also, the improvements
in shifting the proportion doing science and engineering at university, a near doubling
from 20% to just under 40% (over 1997-2004) are incorporated.
The effects on enrolments in tertiary education are notable, allowing over 6,000 extra
students per annum by as early as 2015 (see figure 10). It must be stressed that this is
without any reduction being made in those leaving after F5. Although there has been a
reduction in F5 leavers in recent years, bringing the proportion down from 90% in
1995, it is still quite high as compared to normal progression rates to tertiary
education in developed nations.
17
Tertiary enrolments
40,000
30,000
20,000
1995 2000 2005 2010 2015 2020
Time (Y ear)
Tertiary enrolments : Current persons/Y ear
Tertiary enrolments : School Dropouts Reduced ——---—--————_ persons/Y ear
Figure. 10. Increased enrolments in tertiary education after school dropouts reduced
The beneficial effects of these extra additions to tertiary education follow the obvious
links of cause and effect. Providing the ICT infrastructure is adequate then this extra
stream of students will permeate through the economic system and, with many more
becoming scientists and engineers, this will mean more k-firms opened and
consequent improvements in GDP. The latter aspect is charted below (figure 11). It is
interesting to note the delayed effects. The policy to reduce school dropouts was
assumed to be initiated in 2005, but we do not begin to see the effects on GDP until
around 2017.
18
GDP
400 B
300 B
200 B
1995 2000 2005 2010 2015 2020
Time (Y ear)
GDP : Current RM/Y ear
GDP : School Dropouts Reduced RM/Y ear
Figure. 11. Effect on GDP of a reduction in school dropouts
A policy precept can be asserted: resources should be found to address dropouts at
both the P1-P6 and F3 stages of education with a view to adding to the initiatives
already undertaken in this regard.
Re-training of F3 dropouts for Technical education
Over the past, before the policies designed to tackle lower secondary dropouts was
invoked, there has been a cohort of young people who have already entered the
workforce with a limited secondary educational attainment. Our estimates are that as
many as 150,000 persons in the Sarawak labour force would come into this category
in 2005. It was thought by many that this pool represented a resource to the economy
that is being wasted: they could be given further training and enter the technical
labour force. Recall that this often ignored sector of the labour force is vital to the
evolution of a knowledge economy. We are assuming that each k-firm or R&D centre
employing 100 engineers will also require 20 technically qualified people.
This scenario assumes that a modest increase of 500 per year of the F3 dropouts are
now being placed into technical education. By 2010 this means 2,500 per year will be
entering technical education. The change has the effect of both reducing the numbers
educated to only F3 (see figure 12) as well as increasing the percentage of the
workforce with a technical education.
19
Effects of retraining on numbers in technical education
20
15
10
5
0
1995 2000 2005 2010 2015 2020
Time (Y ear)
percentage with technical educ : retraining for F3 dropouts ———————————_ %
percentage educated to only F3 level : retraining for F3 dropouts ————————_ %
Figure. 12. Percentage with technical education and with only lower secondary
education
Unfortunately there is a limited impact in terms of the knowledge economy. Unless
the numbers of scientist and engineers are also increased, these newly-retrained
technicians enter the technical labour pool but are not employed in a high-tech
environment. They can contribute to economic development in other ways (such as
motor engineers) but the evolution of k-firms and R&D centres requires action across
more than one front. At present the supply of labour skilled in science and
engineering is not stretching the requirements for the numbers of technically qualified
persons. This is not to denigrate the policy of retraining. The economic benefit to the
individual is accepted (and there is an economic benefit to the State also but which is
not explicitly captured in this model which addresses the k- economy). The policy
precept is: retraining of F3 dropouts as technicians needs to be accompanied by
increases in the numbers of science and engineering graduates if the additional
supply of technicians are to gravitate to employment in the high-tech sectors of
the economy.
20
Increasing R & D expenditure
It is instructive to assess the effects of an increase in R & D spending. The method by
which a k-economy can evolve is through increasing the number of experienced
engineers by nurturing their capabilities in State-financed R & D centres. Accordingly
it might be expected that to increase the proportion of development expenditure
committed to R & D would be a beneficial policy.
The outcome of this policy is counter-intuitive. To see the basis for this assertion
consider a policy to increase the percentage of development expenditure devoted to R
& D, currently 1.5% p.a. (assumed), to 2%. This is accomplished in two ways: firstly
by gradually increasing the percentage to 2% over a five-year period from 2005-10
and, alternatively, by stepping up the percentage to 2% abruptly from 2005.
The policies have a clear beneficial effect upon the number of R & D centres as
can be seen from figure 13.
Startup R&D centres
400
300
200
100
0
1995 2000 2005 2010 2015 2020
Time (Y ear)
"Startup R&D centres" : Current schemes
"Startup R&D centres": R&D up to 2% over 5yr —_—-—— schemes
"Startup R&D centres": R&D stepped to 2% at 2005 -————————_ schemes
Figure. 13. Effects of increased R & D spending on startup R & D centres
But this policy attracts many of the highly skilled scientists and engineers into R & D
on graduation from university. Consequently there is a surplus in that sector which
crowds out the correspondingly fewer skilled persons available to become employed
in k-firms. The numbers of experienced engineers is far in excess of what is required
to found new k-firms as compared with the outflow of ‘raw’ graduates, which are then
in short supply for k-firm startups.
21
potential number of new k- firms based upon skilled labour
60
45
30
15
0
1995 2000 2005 2010 2015 2020
Time (Y ear)
“potential number of new k- firms based upon skilled labour" : Current firms
“potential number of new k- firms based upon skilled labour" : R&D up to 2% over Syr firms
“potential number of new k- firms based upon skilled labour’ : R&D stepped to 2% at 2005 firms
Figure. 14. Effect of increased R & D spending upon the potential number of new k-firms
We see the effect conveyed through figure 14. The number of potential new k-firms is
depressed under the raised R & D policy because there is not the supply of new
graduates to accompany the experienced ones. As with the conclusion in the previous
section, action must be taken more systemically if the policy is to be effective. Besides
an increase in the quantum of R & D spending, a move to increase the supply of
science and engineering graduates is also called for.
A final graph on the effects of this particular policy initiative is to portray what
might be suspected from examining figure 14. The number of firms in k-industries is
indeed lower under the increased R & D spending policy (see figure 15).
An additional insight here is that the choice of the rate of change in R & D
spending is also important. The policy option of phasing the change in over a period of
years, rather than abruptly changing the percentage, while still being inferior to the
base case with respect to the number of k-firms in the economy, at least is not as
detrimental as is the abrupt change. A slower phased approach would also allow for
concomitant changes to increase the supply of graduates trained in the sciences and
engineering.
22
Number of firms in k- industries
400
300
200
100
0
1995 2000 2005 2010 2015 2020
Time (Y ear)
“Number of firms in k- industries" : C urrent firms
“Number of firms in k- industries" : R&D up to 2% over Sy. —————————_ fims
“Number of firms in k- industries" : R&D stepped to 2% at 2005 -——————_ firms
Figure. 15. Effect of increased R & D spending upon the number of k-firms
The policy precept here is: increases in R & D spending, to allow more State-
financed startup projects, need to be phased in over a period of years with
concomitant increases in the supply of high-tech graduates to avoid a skewing
of skills towards R & D centres, which thereby constrain the growth of new
private sector k-firms.
Microworlds: sharing and disseminating insights from complex system analysis
Introduction
System dynamics models can lever insight in complex systems such as an economy.
However, it has been recognised that there is a need to promulgate those insights
beyond the confines of the modelling team. To thrust a model into a policy support
role means more than conducting experiments in workshop situations. The assembled
audience, despite being impressed, may not be entirely convinced because the agenda
may look to be well-rehearsed.
With an official as high as the State Secretary requesting the model be made available
on his own computer, it was essential that we delivered a platform which would allow
of easy use, but still have the potential to deliver insights. Thus it was decided to
create a microworld shell around the model. This would allow anyone to run,
experiment with and, crucially, reflect upon the results without interference by the
modelling team. If the model was truly to leverage insight and stimulate thinking that
would result in policy interventions then it was considered that the greater likelihood
23
of this outtum would stem from personal interaction with the model by State officials
rather than a guided programme offered by the modelling team. A Venapp
environment in the Vensim software (at the Decision Support System version level)
allows this enhancement.
The concept of the microworld in system dynamics was initially highlighted and
explained by authors such as Morecroft (1988) and Kim (1992). It has since been
extended to allow an element of competition between participating teams who are
allowed to intervene and make various decisions as the model progresses. In these
circumstances the entire package is usually sold as a commercial item and is used in
generic training exercises rather than the dedicated policy support role envisaged here.
In the figures which follow, various screen shots (figures 16-19) are depicted which
reflect the way in which a State official would interact with the model and the nature
of the tasks allowable in the context of the microworld. The main menu (see figure
16) allows the used to view the stock-flow diagrams, examine the past data against
simulated model output. As the model runs from 1995 to 2020, the comparison
against data is currently restricted to, at most, some ten years. Scenario runs can be set
up using simple slider bars and the results depicted for a collection of the important
variables in the model. Finally it is possible to see the formulation of the relationships
expressed in the model using the causes and uses trees available as standard in the
Vensim software tool.
24
on Environment
System Dynamics Analysis Tool for Sarawak KE Development
Main Menu
Acknowledgement
View Stock and Flow Diagram
Baseline Simulation and Historical Data
Set and Perform Scenario Runs
Analyze Causal Relationships
‘The cofware you are using allows you to asplore the bebaviour aad
censtvity ofthe Sarawak model developed in CORAS, tae University of
Salford The software allows you to explore the model by means of
‘buttons, menus, and ouput screens, To activate a burton, or to select a
‘menu item, click on it
Figure. 16. The opening screen depicting the Main Menu
25
on Environment
Baseline Simulation and Historical Data
GDP per capita
60,000
45,000
30,000
15,000 =
o
1995 2000 2005 2070 2015 2020
Time (Year)
GDP per capita: current RMi(persons* Yen)
GDP per copita:: sarawakdata EMi(persons*Year}
Back to Base Runs Menu
Figure. 17. A screen showing the comparison with past data feature
System Dynamics Analysis Tool for Sarawak KE Development
Scenario Setup
Fraction termmnating afteriduning primary education
fection of development expenditure on R&D
Fraction terminating after F3
fraction of k-fems in manufacturing
rT
Enver a new name for the ceenacio (<8 chars):
un the scenario and see the simulations
Exit back ta main menu
Figure. 18. Slider bars allow the alteration of parameters prior to scenario runs
26
cation Environment
Scenario Simulation
GDP
2008
1508
100B
1995 2000 2005 2010 2015 2020
Tune (Year)
GDP: current RM/Year
GDP: Scenario am RM/Year
Back to Scenarie Setting Menu
Figure. 19. The results from a scenario run
Conclusions
The paper has described an empirical study conducted over 27 months and which has
resulted in an SD tool for macro-economic planning being made available in a
developing country. Prior to this there was little or no use of formal modelling
techniques although the collection of a proliferation of statistical data was evident. As
well as the availability of the tool, knowledge of the capability of system dynamics as
a modelling methodology has taken root, reinforced by a technology transfer element
in the project. It is confidently expected that further policy issues of concer to the
Sarawak State government will also be subject to analysis using the SD methodology.
27
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