TOWARDS A TRANSITION TO A KNOWLEDGE
ECONOMY: HOW SYSTEM DYNAMICS IS HELPING
SARAWAK PLAN ITS ECONOMIC & SOCIAL
EVOLUTION
Brian Dangerfield
Centre for OR & Applied Statistics
University of Salford
SALFORD M5 4WT
U.K.
44-161-295-5315 (T)
44-161-295-2130 (F)
Email: b.c.dangerfield@salford.ac.uk
Abstract
Accounts of the real-world use of system dynamics as a policy evaluation tool in
macro-economic management are relatively rare. This paper offers an overview of
current research being undertaken for the government of the State of Sarawak in E.
Malaysia where an SD model is being formulated to inform the State’s future
economic and social planning to 2020. Although still a work-in-progress, enough has
been achieved to enable an interim account of the research to be written. Positive
engagement with State government officials at the highest level has put system
dynamics on the map in this corner of SE Asia.
Introduction
This paper reports on the progress to date in formulating a system dynamics model designed
to provide a planning aid which can be used by senior state officals and ministers in
discharging their role in steering the development of the State of Sarawak in East Malaysia.
Sarawak is a former British colony which is now part of the federation of states called
Malaysia whose capital, Kuala Lumpur, is situated in West Malaysia. The outlying Malaysian
States of Sarawak and Sabah are in East Malaysia on the island of Borneo (see map figure 1).
Adjoining nations here are Brunei and Kalimantan (Indonesia). The capital of Sarawak and
the seat of government for the State is Kuching near the north-west coast.
JAVA SEA
Figure 1: Map of the island of Borneo
The study is a work-in-progress and this paper reports on developments to date. Conclusion of
the research and presentation of the final report is anticipated in October 2005.
Tools for Economic Modelling
There are three other principal tools and modelling methodologies currently used for
development planning purposes apart from SD, namely spreadsheets, Input-Output tables
and econometric models. Each methodology has its merits and potential usefulness as an
aid to specific aspects of the development planning process, but they are distinctively
different in terms of the input requirements, the type of data used, how the results are used
and, most of all, the modelling purpose. A good overview of the methods itemised below,
and how they differ from SD, can be found in Meadows and Robinson (1987).
Fundamental to the choice of methodology is the need to define the purpose of the model,
termed problem definition, and for this purpose to be agreed by all parties concerned.
Spreadsheet Modelling As the name suggests, the basic framework to represent the
economic activity is that of a spreadsheet depicting relationships. Characteristics of this
methodology are large discrete time steps in the analysis, no direct incorporation of
feedback mechanisms, and exogenous inputs are utilised. Examples here are the World
Bank’s Revised Minimum Standards Model (RMSM), which forms a spreadsheet
comprising of 10 columns and 5000 rows.
Input-Output Tables This is a well known methodology which measures flows in
money or goods amongst various sectors of an economy in a given year. This is,
therefore, essentially a static or snap-shot model. It uses only directly observable
economic data and presents a record of what did happen. Linear input-output relationships
are assumed, and there is no representation of delays or bottlenecks in economic activity.
The models are of value in highlighting the historic order of magnitude of the cross
impacts of various economic sectors.
Econometric Models. These are the most mathematically intensive form of modelling,
and are based upon economic assumptions and theoretical economic relationships between
variables of interest. Past data is used to estimate parameter values. In fact, an extensive
set of past time series data is needed for parameter estimation purposes, and model quality
is based upon how well the model fits the data. Econometric models are used for
forecasting, but are inappropriate when conditions might deviate from those in the historic
period used for parameter estimation.
The “Threshold 21 (121)” Model
The “Threshold 21” model (T21) is a large system dynamics based model for national
planning in developed and developing nations (Barney, 2003). It has been progressively
enhanced since its initial formulation in the late 1970’s.
It is a generic and flexible economic planning model in that it purports to be applicable to
any country and can be customised to that country’s particular situation. Custom
applications have been made to inter alia Bangladesh, China, Ghana, Italy, Malawai,
Somalia, Tunisia and the USA. (Ref ISDC Oxford).
In 1999 the President of the World Bank introduced the Comprehensive Development
Framework (CDF) detailing a holistic approach to national development and which
effectively replaced the former Country Assistance Strategy (CAS) the World Bank had
hitherto supported. The CDF engendered new criteria and standards for national
development models.
The T21 model most probably meets the new standards and certainly exceeds the
capabilities and utility of the spreadsheet based models used for some time by the World
Bank and the IMF. However, being an ‘off-the-shelf’ model implies that T21 is not
principally designed for addressing specific issues concerning economic development
which pertain in a particular country.
For this reason it was considered more suitable to formulate a bespoke SD model designed
to focus on an agreed issue (see below). In addition, in the case of Sarawak, we were
concerned with a State and not a nation and needed to specifically tailor any model to
handle the State’s interface with the federal government.
Engagement with System Dynamics
Given the preponderance of the above methodologies, it was difficult initially to secure
acceptance of the SD approach (Forrester, 1961; Coyle, 1996; Sterman, 2000). Admittedly
there had been relatively little modelling activity undertaken in Sarawak, the state
government perhaps being willing to accept that the centre of gravity for analysis and
research resided in the federal capital, Kuala Lumpur. Here there was a tradition of using
econometric and input-output analyses and so knowledge of these methods had gained a
certain amount of currency amongst officers in Sarawak.
Prior to the contract being signed a number of visits had to be undertaken to explain the
nature of SD and to attempt to engage the state government such that they went ahead with
the contract. This took nearly two years but ultimately they decided to go ahead and the
contract was signed in June 2003. In retrospect this can be seen as quite a bold step
especially as the State Planning Unit (the contact unit) had hitherto seen its role solely as
provider of (extensive) statistical data and its rudimentary analysis. It might have taken a
lot less effort to engage them with econometric modelling, but it was felt that SD had a
great deal more to offer. Perhaps a pivotal moment in their acceptance of, and subsequent
enthusiasm for, SD as a methodology was when they had the opportunity to see a
prototype model actually running complete with stock-flow diagrams and graph plots. The
software tool employed was Vensim™ and seeing the reaction to the SyntheSim™ feature
was quite illuminating.
It was considered quite appropriate to display views direct from the software. Whilst
scrutiny of equations was avoided, government officers could easily follow the ideas in the
flow diagrams and critically comment where they felt something had not been properly
specified. In particular they were vocal in situations where our chosen nomenclature for
variable naming was inconsistent with their usage and this criticism was encouraged as it
demonstrated that they could contribute to the emerging model.
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
The diagram shown in figure 2 attempts to set out, in as economical a way as possible, the
overall structure of the prototype model designed to address the issue above.
DYNAMIC HYPOTHESIS: high-level map oo. Money
“> Skills/Tech Transfer
<=suRPLy-> . Leakage
[Secondary _ |
——> Capital Equipment
— Human Resources
Overseas?
LX
et
Figure 2: A proposed high-level map for the emerging model
There are three main foci:
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.
Approximately only 20% undertake a science degree, with around 20% of
those graduates undertaking teacher training.
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.
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 3 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 turn, 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.
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 2. 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 quaternary 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.
Transformation to a k-economy: the Challenge
The high-level map suggests that the three components of: supply, demand and the
ICT infrastructure need to be broadly in balance if the transition to a k-economy is to
be achieved smoothly. Any mis-alignment in this triangulation will most likely lead
to fluctuations in progress and during any downturn there may well be political
repercussions.
Consider the hypothetical scenarios shown in figure 3.
Growth of k-
economy
(2005=100)
100
Time (years) —>
Figure 3: Hypothetical scenarios for the growth of the k-economy component
Scenario A is an ideal case. There is a smooth growth progression suggesting an
effective triangulation.
Scenarios B and C achieve growth, but it is uneven. Economists call this ‘higgledy-
piggledy’ growth. In the case of scenario C there is an alarming medium term crisis
while the components adjust and, while this scenario may be something of an
exaggeration, if such an out-turn should manifest itself it is possible other dynamics
(maybe political) will emerge during the downturn. This may effectively stifle any
further progress.
Figure 2 suggests the possible ramifications arising from a lack of balance between
the three components (see the unshaded wide arrows).
e Should the supply of qualified k-economy human capital substantially exceed the
capacity of the Sarawak economy to utilise such skills then these people would
likely move abroad to further their careers (‘leakage overseas’ in figure 2). There
are suggestions that to an extent this has already happened and hopefully positive
future developments in Sarawak will attract them back.
e Should the emergence of high-tech industries (higher value-added), funded by
foreign direct investment and state and federal government monies, race ahead of
available skills, then Sarawak might see wholesale closures of schemes or a series
of white elephants until balance has been restored. It is arguably the case that this
“excess demand’ would have more serious repercussions than ‘excess supply’.
e The ICT infrastructure, the third component of the triangle, is a necessary, but not
sufficient, condition for the emergency of a k-economy component in Sarawak.
Without such an infrastructure (indeed without the provision of electricity — a
relevant condition in some geographically remote parts) the k-economy cannot
even be initiated. But its over-rapid development leads to wasted resources, since
much of the funding for this infrastructure will likely come from government.
25 |
Remember competing uses for this funding extend across the entire range of
social, health and poverty reduction policies of the state. It is important to ensure
that funds spent on developing the ICT infrastructure (which may initially be
located in the urbanised areas in the north and west) do not lead to alienation by
certain other groupings who might prefer funds to be spent elsewhere.
Current status of the model
The model presently consists of the following sectors:
Population
Education & Human Capital
Workforce (including certain measurement indices)
R&D/ ICT Infrastructure / k-firms
Manufacturing (incl Electrical & Electronic); Services (incl tourism) &
GDP
Timber (including downstream processing)
Palm Oil: trees
Palm Oil: products
Liquefied Natural Gas & petroleum production
Sago production
Government finance
It is not possible to cover all of the sectors developed thus far and attention is given
just to the Education & Human Capital sector along with the Workforce sector since
these areas have a primary role in the evolution of a k-economy.
Education & Human Capital
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. Because of a
desire to focus upon the achievement of a suitably qualified labour force, it was
considered that the model needed to feature the output of higher-educated and
technically-qualified human capital (see figure 4). These developments underpin a
suitably skilled workforce and, in view of the importance attached to this, a workforce
sector was also formulated.
frac electing tech
ane, a technically
jualifieds
2 fraction terminating pe) Fata sual
after/during primary ‘tranation th education
é aus educ ecuponteig SC
aver sear rave anual 4 <iacionerminating Teh ue
primary iieaaeae Semic cori Sina ie darag sesindary tions to Govt
duration duration - R&D centres: av. number of scientific
re ao mm ee personnel required:
Sy sessid :
pimar} cuclmest a teaaney Theat transition (seen)
‘enrolment university (sciences)
frac electin,
@
fraction terminating
ratio of technical to
scientific personne
tech recruits to
population ei after/during secondary sciences Tesh lbOUF] “RAD centees
cohorts: education ec ee
frac electing arts ‘kins
transition to total at university b labour
university (arts) required per firmy
Tertiary tech/vocetional educ N tech recruits to
enrolments ahs — oS graduation enim
univ edue duration
ransition to
university (sciences)
Noat 7 Ye,
University [Aris graduates
(arts) ame es
Graduation (arts) Conversion rate courses
into work
P/G course
duration
frac converting to
(sciences)
No.in tertiary IcT
educ.
education
skilled conversions
» laa
‘new openings of
k-firms
gj
additions to Govt,
R&D centres:
initial skilled labour
available to k-firms
No. at University
Skilled ex-pats
time to average
recruitment rate
(~
av. recruitment rate
emigration
SBE] Seentne 2S
cial lable scientific recruits to
available to
“ k-firms R&D centres
time to complete
repatriation
time to complete
scientific recruits to
av. number of scientific
k-firms
personnel required
k-firms’
ee to k-firms
scientific recruits required pi
Figure 4: Education and Human Capital Sector
-10-
Technical education is included. Knowledge-based firms require both skilled
scientific staff but also technical support staff who would gain qualifications from the
technical/vocational education institutions existing in Sarawak.
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 return to their homeland for employment and career progression.
This will add to the skilled labour available to k-firms and is depicted in the flow
diagram for this sector (see figure 4).
Those leaving before, during and just after primary education (P1 — P6) are explicitly
included because such individuals do join the workforce ultimately. The reduction of
the numbers of these dropouts is an important policy issue in the context of this
research. There are also a number who terminate formal education either during or
following secondary education (F1 — F5). In fact the definition of secondary education
is taken to include the 6" form (F6) for our purposes as the skill set, although higher
in F6 pupils than F5, is some way short of that of a graduate or technician who can
directly contribute to the development of a k-economy.
The imbalance between science-based and arts-based students at university is
accepted and an assumption had to be made about this imbalance. From what little
data existed on course choices, it appeared that around 60% choose an “Arts” course
(used to represent a non-science course) whilst 20% take the science route with a
further 20% opting for a technical education post-Form 5/6.
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
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
operationalised, there is yet another source of skilled labour available to k-firms.
The inclusion of a population sector means that the initial flow of pupils into primary
education is taken from the outflow of the age band 2-4 yrs modelled within the
population sector. Although school starts at seven years in Sarawak, this is the mid-
point of the next age band (5-9 yrs) and it is felt that the numbers flowing out of the 2-
4 yrs band would not be significantly different from those emerging post 6yrs.
Workforce Sector
This aims to portray the quality of human capital available in the Sarawak economy
(see figure 5). It defines five categories consistent with their highest level of
educational attainment.
w TT
pe) Workibree Oo
Transition to pant... | Retirements: strata with
working age — no formaledue Av working lifetime:
transition time
strata with no formal
edue y
Workioree
FP) with up to rea)
F6 edue Retirements: trata Wotkionce
NP F6 edue with science!
Mean years of degrees [¥—_
Av working lifetime: education ‘emigration:
strata wp rene
W
— — A btrements: strata with
TorkTRwe cience degrees
wa technical snes) sone dee
technivally education Retgemeps: te
‘cclngaly “*——_
quite qualtied ‘Av working libtine of
=e time
technical staff
Workioree ane
OSS wins $$ Wo
Graduation (arts) L_@28#2€8_] Retirements: strata Total workforce fal
into Ne ES, MS,
a Ay working lifetime Workforce with S Work
of graduates teh
Figure 5: Workforce Sector: main view
The five categories are:
No formal education
Educated up to F5/F6, including F3 to Technical school
Technically educated post F5/F6 (Certificate & Diploma students)
Numbers with Arts degrees
Numbers with Science degrees
The category ‘No formal education’ is those who have dropped out during or just after
primary education (Pl —P6) or before commencing F1. Attaining this level of
education alone cannot be expected to help a move to a k-economy and so the
description is justified.
Those in the category ‘Up to F5/F6’ education similarly have terminated their
education either during Fl — FS, after F5 or after F6. Tertiary education comprises
those who have gone on to further or higher education post F5/F6. It includes those in
degree level education, together with those electing a technical education. The latter
have an important supporting role to play in the development of a k-economy.
w TDi
Specimen runs of the prototype model
Increase in % of students studying science at University (Example 1)
An initial run assumed that the percentage of students studying science at university
increases from 20% to 60% over a 10 year period. Suppose this transition rate is
significantly accelerated. The strip graph at figure 6 shows the consequences.
The graphs show adjacent relevant variables from the stock-flow diagram in figure 4.
The graduation rate in sciences has increased by about 5,000 persons per annum by
year 20, but only 1,000 of these graduates end up in k-firms with around 4,000 per
annum emigrating.
The reason for this is that the lack of availability of ICT resources, and consequently
the growth of k-firms, is now the primary determinant of capacity. There are too few
opportunities relative to the skilled graduate output, with the consequence that around
80% move to take suitable positions overseas.
Higher transition to sciences
Base run
"Skilled labour available to k-firms"
4,000
3,000
2,000
1,000
0
= emigration
Higher % 8,000
growth rate for 6,000
transition to 4,000
sciences is 2,000
better but ~giea waits (sciences)"
higher 10,000
emigration 7,500
5,000
2,500
0
"recruits to k- firms"
4,000
3,000
2,000
1,000
oO
N
N
h
0 10 20
Time (Year)
Figure 6: Strip Graph of higher % growth rate for transition to sciences
Delaying the Shift to Science (Example 2)
The shift to sciences at university is set to start at year t= 2. But suppose political
and/or financial considerations delay this start date by a further two years. The
Paki
comparison graph shown as figure 7 enables an assessment to be made of the effects
on new openings of k-firms. Initially, around 5 new k-firms are being created each
year and by year 20, on the base run assumption of a start date of year t= 2, this has
reached just over 15 new openings per year. The delayed start date for the policy shift
has not had a serious effect on new openings. In the middle years there is a slight
reduction relative to the base run case but by year 17 there is hardly any discernible
difference between the effects of the two start dates on new openings of k-firms.
The reason for this is that there is less emigration under the delayed start. The
outflow of new science graduates does not overwhelm the system’s ability to absorb
them into suitable posts — hence a smaller emigration rate. The skilled labour
available to k-firms can, towards the end of the revised run, match that of the base
run, allowing new openings of k-firms to ultimately coalesce with that of the base run.
It should be realised, however, that the above refers to new openings per annum. The
cumulative total of k-firms in existence in the economy is lower under the delayed
start time for the shift to sciences.
The final plot on figure 7 reflects the greater transition rate in the shift to sciences
described in Example 1 above. Here the start date of the shift is the same as for the
base run (t= 2). Obviously there is in excess of a doubling of new k-firm openings in
this scenario but recall the message underlined in 1 above. The 1,000 or so extra
graduates p.a. by year t= 20 has allowed around an extra 20 or so new openings of k-
firms, but it also propels an additional emigration rate of 4,000 persons p.a.
A later start of the shift to sciences
makes things marginally worse
new openings of k-firms
60
45
30
15
0
0 2 4 6 8 Tn nT |)
Time (Year)
"new openings of k-firms" : Later start of shift to sciences ———————_firms/Year
“new openings of k-firms" : Higher transition to sciences ————————_firms/Year
“new openings of k-firms" : Base run —=—===$=$>_<$_<__—_———————_firms/Year
Figure 7: New openings of k-firms under three policy scenarios
wide
Provision of ICT Resources (Example 3)
A third illustrative experiment revolves around the provision of ICT resources. If this
provision is undermined from the start then the ICT infrastructure becomes the
constraining variable with the consequences for new openings of k-firms as shown in
figure 8.
Initially, it is assumed that the stock of available ICT resources (measured in arbitrary
J-units) is 2000 and increases at the rate of 1000 I-units per annum. On the
assumption that the average ICT resources required per new k-firm is 100 I-units, this
means that, at most, a minimum of 20 new k-firms per year could be opened in the
early years. However, the fact that this does not happen is because the initial skilled
labour availability is set at 300 persons with an average requirement per k-firm of 50
such persons. This means only 6 new k-firms can open in the early years, and each
one is phased-in over a period. It takes time for any new firm to become fully
operational.
The experiment portrayed in figure 8 involves cutting back ICT resources initially to
as low as 500 I-units and assuming this increases by only 10 I-units per annum. With
such a low and continuing provision of ICT infrastructure resources, it means that we
see no more than around 5 new openings of k-firms per annum, recalling that each
new k-firm is assumed to require 100 I-units. The ICT infrastructure is now the
constraining variable (rather than skilled labour). The assumed change reveals that a
considerable initial and continuing shortfall in ICT resources will clearly impede
progress in development of a K-economy. Hardly any new k-firms are opening and
emigration (not shown) is extremely high because skilled labour cannot gain suitable
employment in the domestic economy. The message here is simple: ICT resources
are a necessary but not sufficient condition for the emergence of a K-economy.
Harmonisation of development in both factors of production — labour and capital — is
required.
= 15 i=
Cutting Back on ICT will definitely impede progress...
new openings of k-firms
60
45
30
15
0
0 2 4 6 8 10 12 14 16 18 20
Time (Year)
"new openings ofk-firms" : Fewer initial ICT resourees. ————————————_ firms /Year
Later start of shift to sciences. ———————————_ firms /Year
Higher transition to sciences —————————— firms /Year
: Base run firms/Year
"new openings of k-firms
"new openings of k-firms
"new openings of k-firms
Figure 8: New openings of k-firms consequent upon fewer ICT resources
Numbers of Annual Enrolments at Various Stages of Education (Example 4)
It is also instructive to examine the numbers enrolling per annum at various stages of
education. This gives an immediate view of the losses being experienced as
successive phases of education are entered (see figure 9). It is the gap between the
curves which needs to be minimised. Here, put quite starkly, is a critical policy issue
in the education sector and one to which the Sarawak government needs to give
urgent attention.
Given the duration of each phase of education, the curves should successively follow
each other down towards the right of the graph. Further, ideally there should be only a
small gap between each phase if a significant fraction of each cohort progress to the
next level.
= 16
Educational enrolments p.a.
1995 2000 2005 2010 2015 2020
Time (Year)
primary enrolment : Base Case persons/Year
secondary enrolment : Base Case persons/Year
Tertiary enrolments : Base Case persons/Year
Figure 9: Educational enrolments at start of a phase of education (Base Case)
Birthrate Sensitivity Testing in respect of Annual Enrolments (Example 5)
Sensitivity to the assumed birth rate is an obvious consideration. The original
assumption of a medium birth rate (19 live births per 000 population) in figure 9
differs little from the picture for a high birth rate (figure 10) in educational terms.
Primary enrolments pass 50,000 p.a. just after 2010 in the high birth rate scenario
whereas this is delayed to nearly 2015 in the Base Case (medium birth rate). In
addition, differences in secondary and tertiary enrolments across the two figures are
imperceptible. Clearly more than just demographic possibilities are part of a future
agenda if the gaps between the curves are to be lessened. Proactive policy is needed.
wT Jin
Educational enrolments p.a.
1995 2000 2005 2010 2015 2020
Time (Year)
primary enrolment : high future birth rate ——————————————_ perssons/Year
secondary enrolment : high future birth rate —————————————__ persons/Year
Tertiary enrolments : high future birth rate —————————————_ persons/Year
Figure 10: Educational enrolments at start of a phase of education (High future
birth rate)
Calculation of the Mean Number of Years of Education (Example 6)
An important international measure of development is the ‘number of years of
education’. This is computed in the model. It weights the numbers in the various
strata of education by the duration of each strata and divides by the total numbers
being educated.
Mean years of education
10
9
8
7
6
1995 2000 2005 2010 2015 2020
Time (Year)
Mean years of education : medium future birth rate) ————————_____ Year
Mean years of education : Primary dropouts eliminated ———————————._ Year
Mean years of education : Secondary leavers reduced by 5% pa ———————_ Year
Figure 11: Effects of changes to school dropouts on mean years of education
= 1B
The average number of years of education for Sarawak in 2005 (figure 11) is shown
as just in excess of 8 years. This compares with some other nations as follows.
Nation No of Yrs | Year attained
Singapore 8.1 2000
USA 12.2 1992
Japan 10.8 1992
UK 11.7 1992
Improvements are computed under two scenarios — firstly assuming the dropouts at
the primary school level are removed altogether from 2006 and secondly that the
dropouts and/or leavers at the secondary level are reduced progressively by 5% p.a.
over the period to 2010, the termination date of the go” Malaysian Plan (5-yearly
cycles).
Leveraging change in this important international measure of development is clearly
not easy. Although the reduction of 5% p.a. for secondary leavers and dropouts might
be achievable, that of eliminating the dropouts (11%) at primary level at once is a
much taller order. But even if these challenging changes could be achieved, the
outcome is just a 0.3 yr increase in the mean number of years of education by 2020.
Conclusion
Despite this research being a work-in-progress, it is evident that some useful insights
are capable of being unearthed and this augurs well for the successful completion of
the work. Positive engagement with the various civil servants and government
officials associated with the project has accelerated as they have come to appreciate
the capabilities of the SD methodology and associated software. (After a recent
presentation the State Secretary himself expressed a wish to have the software and the
model installed on his own PC!)
The earliest written critique of modelling methods which Forrester penned in 1956
(Forrester, 1956/2003) was primarily directed at tools for economic (not business)
modelling. Maybe this work represents one manifestation of the vision he had nearly
five decades ago.
= 19.
References
Barney G O (2003) Models for National Planning. In Proceedings of the 2003
International Conference of the System Dynamics Society, New York (CD-ROM).
See also www.threshold21.com (accessed May 27 2005).
Coyle RG (1996) System Dynamics Modelling: a practical approach, Chapman &
Hall, London.
Forrester JW (1961) Industrial Dynamics, MIT Press, Cambridge, Mass. (Now
available from Pegasus Communications, Waltham, Mass.)
Forrester JW (1956) D-Memo Zero: Dynamic Models of Economic Systems and
Industrial Organisations, MIT Sloan School of Management. Reprinted in System
Dynamics Review, 19:4, 331-345, 2003.
Meadows DH and Robinson JM (1985) The Electronic Oracle, Wiley, New York.
Sterman J (2000) Business Dynamics, Irwin McGraw-Hill, Boston.
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