Understanding C omplexities in Public Policy Making Process
through Policy Cycle Model: A System Dynamics Approach’
Athor Subroto
Universitas Indonesia
Gd. Dep. Manajemen., FEUI, Kampus Baru UI Depok- 16424, Indonesia
Telp: +62217272425 Facsimile: +62217863556
email: athor.subroto@ ui.ac.id
ABSTRACT
This paper is aimed to explore theoretically the complexities and the reality in the policy
making process from the point of view causality relationships among the components or actors
within the system. The complexities’ exploration in the paper is based on the model of the policy
cycle that is widely discussed in the public policy and public administration literatures. The
sense of reality surroundings the policy-making process is perceived from some study cases that
have been observed from Australia and Indonesia literatures. Simulation throughout the paper
revealed different complexities and some pitfalls in each stage of the policy cycle model on
which should be given a proper attention from the policy stakeholder. The paper tried to
construct a different approach to understand the reality and embrace the complexities of the
policy-making process in order to present a starting point for an open discussion in public policy
field. The effort could be a learning tool for the public policy maker to build good awareness
and understanding on their roles in the complex relationship and inter-dependent environment.
Eventually, the paper can fill the gap between policy cycle model theory and the complexity in
the real situation of the policy-making process.
Key words: Policy cycle model, Public Policy Process, System Dynamics, Simulation
INTRODUCTION
Despite its important role for the Indonesia’s economic development, so far it can be said that
Indonesia still has great challenges in managing the small and medium enterprises (SMEs). The
challenge is evident when we look at especially the meaning or definition of SMEs and its data
availability. It seems that the wide variation in SMEs’ definition has a direct impact on the
SMEs’ data availability in Indonesia.
The current available data also seems very poor, where each data is still categorized as
temporary, quasi temporary, and most temporary. Even the data is still tentative; despite has been
three years from its first release. The variation in definition and availability of these data also
indirectly indicate the presence of overlapping SME development roles and efforts, although on
the other side has become common understanding that the SME is an important factor for
* The first version of the paper has been presented at II Conference of WCSA-World Complexity Science Academy,
September 26 th — 27 th, 2011, Palermo, Italy
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sustaining high economic growth in Indonesia, from the point of view of its contribution to total
GDP each year.
Various definitions and data can be found in the literature (OSMERI, 2008) or related agency
website which has links directly or indirectly in the development of SMEs; such as the Ministry
of MSME, Social, Industry, even on the website of Bank Indonesia. However, since the year of
2008 has been enacted Law No.20/2008 which gives a clearer definition of the SME.
Such enactment of regulation has been shown the will of the government to align all efforts to
improve seriously the development of SMEs’ sector that are as far done by several different
ministerial without solid coordination. Coordination among ministerial and institutions such
Ministerial of SMEs and Cooperative, Ministerial of Industry, Ministerial of Social, Ministerial
of Youth and Sport, Central Bank of Indonesia, and The Central Statistical Bureau of Indonesia
will play a significant role in developing sustainable SMEs since each institution has each own
specific contribution in theory. However, when it comes into the practice, it may look completely
on the other way around facts since the available data tells the different story. The increment of
SMEs along the period of 1999 to 2005/2006 is about 2% a year, nearly equal with the Indonesia
population growth which is 1, 54% a year (CIA Fact Book, 2011). In this concern, it can be
considered that the SMEs development in Indonesia is self-developing, which means that it
grown by itself either without any supports or unhelpful supports. Then for the period of 2006 to
2009, the data showed a very dynamics pattern which indicated an unsustainable development of
SMEs in Indonesia (Subroto, 2011).
Such unintended development of the data also can be considered as a reflection of an
uncoordinated policy and a nonsolid policy shaping process on the SMEs development. It raises
the need to develop higher awareness on the important of the policy formation process which
gives more emphasize on not only the result, but considers also the complexities during the
process, and admits the different perception of an issue among the actors for more open policy
discussion and submission, eventually will create a solid and sound public policy on the issue.
LITERATURE REVIEW
Formation of public policy is shaped through several stages; in each stage lays a multi
interaction that involves more than one actor and components. It has been some model
perspectives on the public policy development. The notion of a policy cycle, prominent in the
classical view, has its origin in systems theory and the pioneering work by David Easton
on political systems (Easton 1965, 1966). May and Wildavsky (1978) described a policy cycle
in which they include: (1) agenda setting, (2) issue analysis, (3) implementation, (4) evaluation,
and (5) termination. Similarly, Brewer and deLeon (1983) based their understanding of the
policy process on a series, they define as: (1) initiation, (2) estimation, (3) selection, (4)
implementation, (5) evaluation, and (6) termination. Hogwood and Gunn (1984) also envisage
a cycle: issue search or agenda setting; issue filtration; issue definition; forecasting;
setting objectives and priorities; options analysis; policy implementation; evaluation and
review; and policy maintenance, succession or termination. According to Colebatch (1998) the
policy cycle imagines the policy process as an endless cycle of policy decisions,
implementation and performance assessment. Howlett and Ramesh (2003) conceive of a
similar cycle but with more steps: agenda setting (problem recognition); policy
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formulation (proposal of a solution); decision-making (choice of a solution); policy
implementation (putting the solution into effect); and policy evaluation (monitoring results).
Regarding public policy-making stages; Bridgman and Davis (2000) have proposed a model
called the policy cycle model. In another literature, Meredith Edwards (1993) called the model as
a policy development framework, as based on her experience that the framework is most useful
in practice, especially when chairing the government interdepartmental committees (IDCs), and
which she has used with her students of public policy in an attempt to encourage the good
practice, contains stages similar to those in Bridgman and Davis (2000).
Generally, public policy formation process as in this paper described by Edward (2001) witha
case study in Australia also occurs in Indonesia, although not at the same level. The process also
can be found in the case of the Independent Commission on Transparency and Participation
(CITP) formation in the District of Lebak, Banten Province, Indonesia. The commission was
established as a local government's response to the public aspirations for more transparency in
local government administration in order to create clean and good governance (Pramusinto,
2006).
However, according to Kay (2006), policy cycle models fail to embrace the complexity
of the policy-making process and the reality that policy rarely, if ever, develops in a
linear progression. Stages are often skipped or compressed and the idiosyncrasies, interests,
preset dispositions, policy paradigms or mental maps of the actors involved often usurp the sense
of a smooth process. There is a multitude of different processes at different scales and at
different speeds occurring simultaneously.
Edwards (2001) has been presented an insightful point of view concerning with complexities
in the policy environment. She revealed that policy environments are full of complexities,
usually involving a diverse range of players coming from different perspectives and
spawning a host of unexpected events.
METHODOLOGY
This paper uses system dynamics as a method to explore and understand the complexity in the
policy cycle model applied in some cases, which are taken from literature in public policy field.
Thus, for the longer term could be used as an initial foundation for an open discussion in the
public policy field. The suitability of the use of system dynamics as a method in this paper is
based on Sterman’s argument cited below:
System dynamics is a method to enhance leaming in complex systems. Just as an airline uses flight
simulators to help pilots leam, system dynamics is, partly, a method for developing management flight
simulators, often computer simulation models, to help us leam about dynamic complexity, understand the
sources of policy resistance, and design more effective policies (Sterman, 2000: 4).
In that regard, this paper has carried out some of the common modeling practice in system
dynamics described by Zagonel (2006), such as; system mapping’, quantitative modeling? and in
? System mapping is qualitative and inductive; involves drawing influence diagrams, CLDs, S&F diagrams, or any
form of mapping or organization of the elements forming a system; attempts to get at the key causal
interrelationships; focused upon identification of inter-organizational linkages and inter-dependencies. This step is
needed as a visual summary of a lengthier verbal or written discussion. It organizes information and may yield
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some degree also to test the hypothesis testing’ (expectation on the simulation result); which is
based on the way as much as possible to capture the process that has been described in the cases
presented in the Edwards’ book. Some necessary quantification is needed in order to simulate the
system. Simulation technique is used to give the sense of the reality condition in public policy-
making process.
The use of simulation techniques is believed has the attractive features of allowing
the construction of realistic, testable and modifiable models of real-world phenomena. This
makes them of particular interest in the policy field. Simulating the complexities in every phase
of the policy cycle model could support the awareness creation of the common goal among the
actors involved in public policy.
It has to be mentioned that at some points, the paper modeling process has to judgmentally
quantify the effect of a variable to another variable. Quantifying process is taken in the believe of
what Akkermans (1995) urged in his paper’s conclusion that in many cases, clients will not
expect a quantified model for very soft issues, in contrast to the expectations for a very ‘hard’
problem. Thus, the modeling process is not omitting such effect of important variable to another
variable in the consideration of what Sterman (2000: 879) said in his book as omitting structures
or variables known to be important because numerical data are unavailable is actually less
scientific and less accurate than using our best judgment to estimate their values. And taking
carefully the logical sense into the judgment of the important variable effect to another variable,
yet it has to be verified that it will not either overstate or understate the final simulation result
from the expected behavior. In order to support the logical sense the model, in-depth interview
has been also made with some high level bureaucrat officers from related ministerial and
institutions to enrich the insight.
The discussions in the paper are divided into several sections; such separation follows
accordingly the policy cycle stages as the followings; 1) Identifying an issue in order to put
agenda on the table, 2) policy analysis in order to prepare the green paper or recommendation
paper, 3) Discussion and decision in order to prepare the white paper or policy paper, 4)
implementation, and 5) evaluation. The end section will be dedicated to reveal the complexity
insights of the paper and its implication for further research.
IDENTIFYING ISSUE STAGE
Outline of the current subtopic can be drawn as in the following Table 2 for the system
mapping and Table 3 for the variables are included in the simulation with the initial value,
expected value, and the final value after the simulation.
preliminary dynamic insights. For example, a stock-and-flow diagram helps to understand points of accumulation
and intervention. Alternatively, causal-loop diagrams begin to explore reinforcing (R) and balancing (B) feedback.
Delays can also be graphically displayed. Maps facilitate the surfacing and clarification of assumptions, and thus
can help with communication
* Quantitative modeling is quantitative and descriptive; involves formulation and simulation; largely system-
focused; emphasizes stocks and flows dynamics and the effects of delays; requires specification of the decision rules
governing interrelationships; focused on representing and tracking consequences; sometimes rich in detail
complexity
* Quantitative and deductive; requires stating a hypothesis that explains dynamic behavior from the causal structure
of the system; largely problem focused; emphasizes feedback-rich dynamics, learning, and exploration of the effect
of changes in system structure; focused upon understanding and insight.
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Table 2: Identifying issue system mapping outline
The Actors Resource Strategic Intermediate Control | Final Final
Result _| Indicator
The Government: Ministerial | Perception on Issue Change in Perception
Department Task Force Interest on Initiative on Issue and Pressure
groups interest
Change in Initiative
interest
Pressure Groups Opposing Perception on | Change in Opposing Sense of
an Issue perception on issue | Green | broad
and government Paper” agreement
interest in society
Press/Mass Media Press coverage Perception Gap on an
issue
Public Opinion Discourse Change in Discourse
Need of Information. intensity
Information
Fulfillment
Table 3: Initial, expected, and final value of the identification stage
No Variable Type Initial | Expected | Final | Unit
Value | Value _| Value
1 Interest pressure release Constant | .15 - - dmnl
2 Pressure Group Perception Stock 1 ~GP 438 dmnl
(PGP)
3 Government Perception (GP) | Stock 1 ~PGP .097 dmnl
4 Issues clarity Auxiliary | .97 1 99 dmnl
5 Standard Press Coverage Constant | 2 - : %
6 Normal total report in one Constant | 200 - - report
edition
7 Effect of report to public Constant | .001 - - Per
intensity addition report
8 Public discourse intensity Stock Al 0 .03 dmnl
9 Publicly available Stock 99 0 .00013 | dmnl
Information Need (PAIN)
10 Publicly available Stock 01 1 -99986 | dmnl
information
11 Normal Public information Constant | .5 > - dmnl
need
13 Normal Information Constant | .0015 | - - Per
Fulfillment per report report
14 Time to report Constant | 1 - - Week
(wk)
15 Broad Agreement (BA) on Stock 0 ~1 .9927 | dmnl
Issues Initiatives
> Government discussion paper usually with issues, options and sometimes proposals as a basis for public
consultation, typically developed before a white paper
5
No Variable Type | Initial | Expected | Final | Unit
Value | Value_| Value
16 Potential Broad Agreement Stock 1 0 00728 | dmnl
on Issues Initiatives
17 Time To Change in BA Constant | 1 - : mo
18 Public Interest on Initiatives | Stock 5 ~1 -729 dmnl
(PII)
19 Potential PPI Stock 5 ~0 27 dmal
20 Ministerial Interest on Stock 001 1 -7311 | dmnl
Initiatives (MII)
21 Maximum Normative MII Constant | 1 : : dmnl
22 Potential MII Stock 999 [0 .2688 | dmnl
23 Time to Observed PII Constant | 1 : : wk
24 Time to adjust MII Constant | 1 : : mo
25 Normal pressure from poll Constant | .5 - - dmnl
26 Incumbent Party Electability | Stock 4 1 70 dmnl
projection (IPE)
27 Potential Addition to IPE Stock 6 0 30 dmnl
28 Normal pressure from IPE Constant | .5 - - dmnl
Projection
29 Time to revise IPE Constant | 2 : : yr
Identifying the issues is the initial stage when an issue demands government attention
and where the nature of the problem is clarified and articulated. Nevertheless, the
empirical evidence is that commonly the policy process is initiated from within government
(Howlett and Ramesh 1995: 105; Hall et al. 1986).
However, Cobb and Elder create two categories in the policy agenda setting as the beginning
of a policy formation process. The two categories are; first, the “formal agenda,” also referred to
as the institutional or governmental agenda, consists of items that have been placed for
consideration on the policy agenda by Congress or the executive branch. Second, the “systematic
agenda” or “agenda of controversy” consists of issues that have received enough attention to
ensure public awareness, that reflect a concern shared by some members of the public that action
is required, that are seen as appropriate for redress by government, or that are subject to
resolution by citizen initiative (Cobb and Elder, 1972).
Inthis paperthe discussionon _ the issue identification stage will be based on the
second category of thecategory ofthe policy agendaset _—_forth by Cobb and Elder,
although in simulations it is also possible to initiate the agenda by the government. In the final
stage of this phase is expected to emerge a general consensus on an issue thus the next stage can
begin.
In her book, Edwards urged more or less the same tone on the broad consensus at the end of
the stage i.e. a key question to address early on, therefore, in the context of the case
studies she provided in her book, is how the problem got on the agenda and how it was
articulated. Until there is broad acceptance of the nature of the policy problem, it is
difficult to move on.
In this stage, some actors who have prominent roles can be identified as follows; government,
specifically ministerial departments, pressure groups, the press or mass media, and the latest are
the public. Each actor has a strategic resource that can affect the interaction with other actors in
the system. In general, the government, represented by the ministerial department and pressure
6
group for example, each of them has its own perception on an issue. The difference on
perception (perception gap) is what caused the onset of negotiation of interests between them.
Negotiation of interest is facilitated by the press and the mass media because they have some
degree of the communication role to the public in an opinion forming. These negotiations will
continue until the perception of an issue to be approximately the same and did not attract public
attention anymore. At that time, in theory, it has been already reached what is called by Edward
as a broad agreement. In Figure 1 can be viewed in detail on how the process of general
agreement on an issue.
Figure 1: Issue identificati ‘ock and flow diagram
While the process itself will be started from the existence of an issue where every single issue
could become public interest and gain attention from the government. The reason of putting
"issue" as the starting point of the policy commencement development process is that the issue
per se will always exist in a dynamic society. While the government on one side already has its
own agenda more or less like what was promised to the constituents, so that the government will
choose the agenda based on a popular issue, routine programs, and short cut and generic solution.
Thus, the more obvious and populist an issue will lead to the more government’s positive
perception. While on the other hand, pressure groups are set to always have a different
perception from the government in some degree. The difference of perception will be even
7
greater if the pressure group is always opposed to the government’s move, in other words the
pressure groups would not reduce the pressure on the government, despite all efforts by the
government.
In the simulation in the paper, it is deliberately made that between government and the
pressure group are in the different position and perception on an issue. Precisely, issues on which
the government has a positive perception will be perceived differently by the pressure groups.
However, either government or pressure groups will use the each other perception as a reference
to change their current perception on an issue. Thus, perception gap could be minimized along
the process (B1).
The perception gap then will be perceived and publicized by the press where the more gap
will have the more coverage since “the bad is the news” for them. On average the press has a
normal coverage on an issue and that is why the perception gap will have an effect to the
coverage which is supposed nonlinear in this paper. The nonlinearity of the effect is assumed and
can be seen on the Figure 2 below:
Figure 2: Effect of perception gap on press coverage
Multiplier effect of PG to
Press Coverage
Perception Gap (PG)
The behavior of press coverage is expected to follow the commonly believed which grows at
a small percentage in the beginning period when an issue starts to be perceived by public, then
keeps growing until the maturity period is reached and then starts to decline; akin behavior is
called as growth and collapse in many system dynamics literatures. As some example of such
behavior, it can be observed on one of the following figures:
Figure 3: Media coverage on the swine flu
Story are: Media and swine flu’ Beside figure (the red line) tells how the behavior of
the press coverage on an issue. It shows the behavior of
growth and collapse
Figure a: Twitter trending analysis The graph compares ‘people talking about #opic' and
‘people talking about talking about #opic'.
Beside, graph reflects of how peoples are interested on
a topic and it shows more or less similar with the
‘growth and collapse’ behavior phenomenon in the
system dynamics literatures.
‘suoquew jo ¥
Time (hours)
Source: Pickard (2009)
With normal press coverage of 2% the actual press coverage will be like as can be seen
on the Figure 5;
Figure 5: Simulated press coverage | | Figure 6: Simulated public discourse
= intensity
i ,
/ 8 (are F ;
Fs / \ LB a Pa \
A NYE Vai %
a el Mec,
tan Feb war Ar way hun ao ‘aug 809 Oc nev Dae Tan "Feb ar he way Ton ut *n* Sep "0a "tn "ee
As can be seen on Figure 5, the simulation result meets the expectation that generates an
identic behavior with the example graphs’ behavior. Further, press coverage will have impacts
on public discourse. The more press coverage means the higher public discourse intensity.
Comparison between Figure 5 above and Figure 6 below shows the similar shape of behavior
with some time delay effect on the former figure. The time delay in this case is depended on how
long the public discourse will have a net change which been set up in every 2 weeks.
And somehow public discourse will have effect on the issue clarity. The paper’ quantification
effect of public discourse intensity on issue’s clarity can be seen on the Figure 7. It is widely
considered that if there are a lot of discussions about an issue, then in the same time the issue
must be still unclear. Within such circumstance, the more discourse on public means the less
clear the issue which eventually will be perceived differently by the pressure group (R1) and the
government (R2).
Figure 7: Quantification of effect of | |Figure 8: Effect of PAIN to discourse
public discourse intensity to issue intensity reduction fraction
clarity 1
5
08 i oe
zz” ££ 06 5
3 6804 +
3 #202 4
a Ez 5
ie) 0.5 1
The Ratio Actual to Normal
Public's Discourse Intensity Need of (PAIN)
As it has been mentioned in the former paragraph, public discourse intensity is affected by the
net change on its intensity. Nevertheless, the net change of its intensity consists of two changes
which are ‘addition to intensity’ and ‘reduction of intensity’, the former is depended on the press
coverage and the latter is depended on the actual intensity of public discourse and the reduction
fraction. The intensity reduction fraction is the resulted from multiplication of ‘normal reduction’
for a time period and effect of ‘publicly available information need (PAIN)’ on the fraction.
Then, such effect is driven by the ratio of ‘normal level need of public information’ from the
actual ‘need of information publicly available’. In this case, the effect is supposed to be
nonlinear; specifically the more need the lesser effect on the reduction fraction which is can be
seen on the Figure 8.
‘PAIN’ is more like a “potential” or the normative need of fully available information for the
public. Thus for the initial time, it is set to have value of 0.9. The value level is set to that level
for the initial time because of the sense that in the society must already available some degree of
shared public information in any case; it is set in this case to the value of 0.1.
On the other hand, in the same time, the ratio of ‘PAIN’ to ‘Normal PAIN’ will also have an
effect on the information fulfillment rate on a nonlinear basis, specifically the lesser the ratio; the
lesser information will be supplied. The effect of the ratio on the fulfillment rate can be seen on
the Figure 9:
Figure 9: Effect of PAIN to normal Figure 10: Simulated Information
information fulfillment per report Fully Available for Public (IFAP)
rT}
Effecton
Normal information
Fulfilment per report
The Ratio Actual to Normal
‘Need of Publicly Aavailable Information (PAIN)
Jan Feb Mar for May Jon il Aig Sep Oc Nov Dee
Along the simulation, PAIN will gradually be transformed to Information Fully Available for
Public (IFAP). The transformation is hinged on the information fulfillment rate where the higher
10
information fulfillment rate the lesser the PAIN, and the IFAP will increase until its normative
level is reached (R3). The simulation’s result is shown on the Figure 10.
Information fulfillment rate has also influence on the public interest on initiatives to cope with
the issue. The public interest on initiatives itself is governed by its own net change over the time
which is influenced by the rate of information fulfillment adjusted with the available normative
potential public interest. Strictly speaking, this net change will transform the available normative
potential public interest on the initiatives which is set to 0.5 to the actual public interest which is
set to 0.5 initially (apathy).
On the other side, the government is assumed to monitor closely the public interest
development; however, there will be a probability of delay, since the respond from the
government usually is late. In other words, development in public interest on initiative will
influence the net change of government’s interest to take the initiative. Some other variables that
also influence the net change are pressure from the poll and the electability projection for the
next round general election. In the short term basis, polls give real pressure to the government
than the electability projection which is depended on the government accomplishments during
the administration mandate. The dynamic development of government and public interest on
initiative is shown on the Figure 11:
Figure 11: Simulated interest of the government and the public
0.4 — Ministrial Interest on Initiatives
— Public Interest on Initiatives
— Interest GAP
0.2:
0.0:
Jan Feb Mar Apr MayJun Jul Aug Sep Oct Nov Dec
As can be seen on above figure, the government tries to follow what the public wants on the
issue even though there is still a gap between the interests of the two actors along simulation
time. The gap per se reflects a bargaining process between governments and public in general
thus it will determine accumulation rate of broad agreement sense in community through a
nonlinear effect. In this paper as can be seen on the Figure 12, the nonlinear effect is defined as
following; the more gaps on interest, the lesser effect on the change in broad agreement.
11
Figure 12: Effect of interest gap on the broad agreement
i, 0.5
63 04 2 SSS SSeS
a — —————
g 3 E 0.2 SS SS]
Pepe NU
Fs é < 5 SS
no 0 0.5 1
Interest Gap
In fact, it will not be easy to reach the extreme interest gap either 0 (zero) or 1 (one), and the
gap will never reach the value of 0 (zero); that is because no party always takes an extreme
position and has to stand a different 180 degree all the time. Thus for the initial time, it is set the
value of the interest gap up to 0.5 for a moderate case.
For initial state, there is no sense of any such broad agreement that might be caused of no
social awareness about the issue. The sense of broad agreement is developed by the change on
the sense, which is driven by the effect of interest gap between government and the pressure
group, the normative potential broad agreement that society can maximum reach which is set to 1
(one), and the allocated time (deadline) for broad agreement to established. Figure 13 shows the
simulation result of the first stage of the policy cycle model.
Figure 13: Simulated broad agreement
‘—Time allocated for broad agreement
aa — Broad Agreement on Issues Initiatives
0.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
The change rate of the sense will actually transform the normative potential broad agreement
into the actual one. Sense of broad agreement will be reached when the level reaches closer to 1
(one). The closer to value of 1 will indicate the confidence in the society for the initiative
willingness on the issue and be a signal for government to move on the next step of the policy-
making process.
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POLICY ANALYSIS STAGE
Outline of the policy analysis stage can be drawn as in the following Table 4 for the system
mapping and Table 5 for the variables are included in the simulation with the initial value,
expected value, and the final value after the simulation.
Table 4: Policy analysis stage system mapping
2 Intermediate Final Final
The Actors Resource Strategic Contesl Result Indicator
The Government: MSC: Confidence | MSC: change in
Ministry Steering | on the policy | confidence on
Committee (MSC) analysis result, key | policy _—_ analysis,
Interdepartmental Task | policy found, and | key policy found,
Force (ITF) clarified objective and clarified White Polic
ITF: Number of | objective Paper’ 6 tone
Submission ITF: Submission P P
received and | rate and change in
Confidence in | confidence on
policy option extracted policy
option
Public, Submitted opinion | Submission rate Submitted | Number of
including departmental on policy review or | Submission
national/local branch opinion and
coordination meeting Options
Table 5: Initial, expected, and final value of the policy analysis stage
No Variable Type Initial Expected | Final Unit
Value Value Value
1 | Average Contribution on Policy | Constant | 1 - * option/
Option from a Submission unit
2_| Time to Extract Constant | 2 : mo
3 | Interdepartmental Interest Constant | .5 - dmnl
Friction
4 | Weight on Steering Committee [| Constant | .3 : dmnl
5 | Supportive Extemal Review Constant | .3 - dmnl
Report on Past Policy
6 | Submission time Constant | .5 : : mo
7 | Time to revise Credibility Constant | 2 : : mo
8 | Positive Submission Stock 0 Max 32 unit
9 | Proposed Policy Options Stock 0 Optimum | ~3 option
10 Confidence on Positive Policy | Stock 0 al .99 dmnl
Analysis
W Potential Confidence Positive Stock 1 0 01 dmal
Policy Analysis
12 | Credibility Ministerial Steering | Stock 5 1 97 dmnl
© Statement of a government’s policy intention in a particular area, traditionally printed on white bond paper.
(Bridgman and Davis 2000: 174)
13
No Variable Type Initial Expected | Final Unit
Value Value Value
Committee
13 Potential Credibility Ministerial | Stock o 0 .03 dmal
Steering Committee
Confidence on Key Policy Stock 0 1 399. dmal
14 i
Questions
15 Potential Confidence on Key Stock 1 0 01 dmnl
Policy Questions
16 | Objectives clarified Stock 0 1 999 | dmnl
17 | Potential Objectives clarified Stock i 0 001 | dmnl
On her book, Edwards has made a slight modification of the concept of the policy cycle
model from Bridgman and David. In the modified form, she described the policy analysis phase
contains three major activities with some overlap in time, which are collect relevant data and
information, clarify objectives and resolve key questions, and develop options and proposals
(Edward, 2001., p.4; 24).
System mapping that has been drawn in this stage contains three major loops, one for
reinforcing and the others two are balancing. The loops are assumed to begin after the ministerial
task force has issued the green paper or discussion paper in which it is indicated the sense of a
broad agreement has been reached among stakeholders. The detail system mapping of the current
stage can be seen on Figure 14 below:
Figure 14: The policy analysis stage stock and flow diagram
WeahtonSbaing
Comite
Wet on Sapte
ser Revew Repoton
vi Fest Poy 7
meet \ y Cae
Solty 8) Ehectof Poly ton Key Poy mer
4 Anas onl +. Sacng
EHetoflS New \ Fr XK
T Submisontie Posie ibnsson | 7) \ Neto
\ \ Inriegstnent Spat Exel iy Seating
Vv - Ines Fon Reve Reporton Past
fc of Pui Discouse Eetof1S onefi je Oe
iy mest 4+ exact ply option fom
son ; Sibson Cones on 4
Ss nae Poste Poly Se so)
Submision} a EtntonPoste
simi ——Y'# Poly Aras
Desde
al
Trad Posie Work Tine for
Agreement on Submission| + + + Policy Analysis
Iss nates
2 a
5 Cre Mes bgt a
Serig Comme { :
Working Time for Working Time ft
Proposed MSC Charing ect:
icy Opto
Tad Aditonte Crattiy, | CBRE
ped Mansral Stenng ca
culate | connie
4h
fet of Poy Opts to 1 arte
The Crebity on MSC res
14
Establishment of a steering committee holds an important role that would affect the success of
the others next stages. The credibility of the team is automatically embedded on the committee
membership figure and its term of reference (TOR. This means that the more experience in the
field of the committee member and the clearer the TOR will have the higher change to have an
easy effort to analyze available past relevant policies for the benefit of incoming one. Such facts
of the committee somehow also have a good perceived of authority to cope with friction that
usually emerge between the others ministerial department involved in the process. The higher
ability to deal with the friction means the more solidity from the supporting department. Thus the
higher solidity means the more idea, policy review or opinion coming onto the committee table
and will give more policy option for the committee. Quantification is taken to reflect the effect of
the interdepartmental solidity to the policy review and idea submission that can be seen on
Figure 15 below:
Figure 15: Effect of Figure 16 Effect of ITFS to
interdepartmental task force extract policy options from
solidity (ITFS) submission
Effect of IS to New
Positive Submission
°
Effect of ITFS on effort
to extract policy option
from Submission
0 0.5 1
Interdepartmental Solidity
Interdepartmental Solidity
However, the credibility of the committee will be affected also by how it chooses the optimal
option which is not too much or too less (too much options are chosen means the more ambiguity
of purpose). These series of causalities create a balancing loop (B1), as can be seen on the former
Figure 14.
Another series of causality which has created reinforcing loop (R1) also can be seen on the
former Figure 14. Akin of loop emerges as interdepartmental solidity has another nonlinear
effect to another variable, which is an effort on digesting and extracting options from the
submitted idea about the policy. The effect is to follow the logic of when the interdepartmental
solidity is high then the extraction efforts will less and lesser to produce options with more
quality, parsimonious, and robustness. The quantification effect can be seen on Figure 16.
The robustness of extracted options in the simulation has been defined as how many options
the committee will propose for the next round stage. The definition sounds like a little shallow in
this way. However, the definition comes out from the fact that top executives usually demand
only a few options, i.e. two or three options on their table. Thus, the higher number of options
proposed by the committee will be surely to have an impact on its credibility. Specifically, the
higher number of option proposed will then reduce the credibility since the more options
proposed by the committee reflects a high ambiguity on understanding the policy vision of the
top executive. The effect quantification of the number of option proposed to the MSC credibility
can be seen on Figure 17.
15
Figure 17: Effect of proposed policy
options to the ministerial steering
committee’s credibility
‘y
a
25
62 os +
>
222
es
sg 0
ra 0 5 10
Proposed Policy Options
As can be observed on the above figure, the committee will be expected to propose about 2 or
3 options to the top executive which are cabinet meeting in this case. The more options proposed
means the lesser credibility of the committee.
Some series of causal have also been indicated in the former Figure 14: The policy analysis
stage stock and flow diagram creates the second balancing loop (B2). Along the policy analysis
proses there is another process is done by the committee to search the policy’s key questions and
to clarify the policy objectives as vivid as possible. The clarity of intended policy objective will
determine the success of the committee to filter the policy review and idea from public or
ministerial local branch through national coordination meeting and other interested party to make
a robust policy option proposal (white paper).
The development of committee confidence on the white paper is supported by the confidence
on the other activities which are key policy question and policy objective clarification.
Graphically, the development of the three confidences on the three variables can be observed on
the Figure 18 below:
Figure 18; Simulated confidence on policy analysis, key question on policy, and clarified
the policy objective
As can be seen on the above figure, it clues the confidence development on the policy analysis
report is preceded by the development of the key question and clarified objective of the policy.
However, the confidence on policy objective is preceded by the confidence development on the
policy’s key question.
16
CONSULTATION AND DECISION STAGE
Outline of the consultation and decision stage can be drawn as in the following Table 6 for the
system mapping and Table 7 for the variables are included in the simulation with the initial
value, expected value, and the final value after the simulation.
Table 6: Consultation and decision stage system mapping
The Actors Resource Strategic Intermediate Final Result Final
Control Indicator
The Government: 1. PM: a) | PM: a) change in| Policy option | Consultation
Cabinet Meeting (PM) Confidence on the | confidence on the | selected to the
Criterion definition | Criterion definition | supported with | parliament
and Options | and Options | reasonable proceeded
Selection, b)Budget Selection budget
b) available budget
2. DS: a) Capability, | DS: a) addition to
Departmental Staff (DS) b) Detail policy capability b)
operationalization confidence on Staff's
policy Policy capability and
operationalization | Operationali- | Policy’s detail
zation in detail | increment
and
confidence on
the policy
operationali-
zation
Law Maker (Legislative Approval Vote Supporting and | Parliament Policy
Body) Opposing rate Majority enactment
Pressure Group Opposing vote Change in | Parliament Blocked
(Opposition Party) opposition standing | disagreement | policy
Table 7: Initial, expected, and final value of the consultation and decision stage
End of Second Y ear
No Variable Type Initial | Expecte | Final Unit
Value d Value
Value
1 | Potential Contradiction Opposition Stock 5 3 593 dmnl
Party Point of View
2 | Contradiction Opposition Party Point of | Stock 5 5 406 dmnl
View
3__| Time to review point of view Constant | 5 : : 0
4 | Potential Confidence on Criterion Stock 1 0 .028 dmnl
Selection
5 __| Confidence on Criterion Selection Stock 0 1 972 (mn
6 | Working time intensity to Find Constant | 10 - - la
Criterion
7 | Potential Confidence on Policy Stock L 0 001 mn]
Decision
8 | Confidence on Policy Decision Stock 0 1 -999 dmnl
9 __| Working time intensity to decide Policy | Constant | 15 : : la
17
No Variable Type Initial | Expecte | Final Unit
Value d Value
Value
10 | Normal Against meeting Constant | 2 : : er mo
11_| Against Lobby Effectiveness Constant | .1 - : dmnl
12 | Supportive Legislative member Stock 50 100 94.99 [Person
13 | Against Legislative member Stock 50 0 5.01 erson
14 | Legislative member Constant | 100 : : erson
15 | Supportive Lobby Effectiveness Constant | .05 - - dmnl
16 | Potential in Detail Policy Operational Stock 1 0 201 dmnl
17 | Detail Policy Operational Stock 0 1 99 dmnl
18 | Working time intensity to Constant | 2 - - la
operationalize policy
19 | Potential Department Staff Capability Stock 9 0 06 dmnl
20 | Department Staff Capability Stock 1. 1 94 dmnl
21 | Induction time intensity Constant | 10 - : la
In the next step, the MSC has to bring what they have done in the former stage of policy
making i.e. policy analysis and can be said MSC has three important roles in the current stage.
First, its credibility is still an important part to boost the cabinet meeting positive gesture with its
high confidence on the white paper. It means that the more MSC credibility will make the
cabinet meeting in a higher positive gesture. However, the condition of the economic situation,
ie. the fiscal condition will give more pressure in the cabinet meeting. The tighter the fiscal
environment will lead to the lesser allocated total budget. Somehow, total budget will affect the
cabinet meeting gesture, specifically the lesser the budget the lower positive gesture in cabinet
meeting. The effect has been quantified in this paper’s simulation as can been observed on the
following Figure 19:
Figure 19: Effect of fiscal environment to cabinet meeting gesture
B
uioen
|
|
|
|
|
|
|
1+ t 1
1 2 3 4 5
Millions
Cabinet meeting
positive gesture
Total Budget Allocated (euro)
The total budget per se is calculated from the expected delivery of policy implementation and
average budget spent for per percentage (1%) delivery, further will be discussed in the next
section.
Next, the cabinet meeting must decide the criterion to select the best policy proposal. To make
the best choice of policy this means to choose with high confidence and must be preceded with
high confidence on the criterion selection. In other words, the more confidence on the criterion
selection will lead to the more confidence in the policy decision. Thus, it will ease the
18
departmental staff in making the details of the policy decision. The confidence development
along the simulation can be seen on the Figure 20a, b:
Figure 20a: Criterion selection Figure 20b: Policy decision
§
5 08 £§ 08
co oF
99 05 $8 06
fe Ha
c
Og 04 Tr 04
25 es
ES 02 09 92
85 be
0.0+ + + + 1 0.04 t t + 1
JanO1 — Jul01 Jan 01 ult Jan O1 JanOt 01 Jan 01. ult. Jan 01
On two above figures, after the certain level of confidence is reached then the policy
decision can be taken and at a given time the confidence on it will be accumulated to reach the
maximum level which is 1 (one) in order to be ensued to the other step ie. policy
operationalization.
The credibility of MSC, on the other side has a second important role to be akin of ‘bridge’
from the top-level government to the lower staffs in the ministerial department as the
implementation team members who are standing in the front line to make the policy
implementation is successful. The higher credibility of the MSC will make the probability of
success to spread the policy’s vision to the ‘front liner’ higher through an induction process, such
as training, seminar, national or local coordination meeting etc. Bruijn, in his book called such
role as “boundary spanners” who are the actor operating on the boundary between the managerial
and the professional system (Bruin, 2002, p.66).
Figure 21a: Team Capability Figure 21b: Policy in detail
Detail Policy Operational
Departement Staff
Capability
k + +t + 1 00+ + + + i
Jnl Jul1 Jan 01 Jul dant Jan 2 julo1 Jan o1 Jud Jan 01
Specifically, the more MSC credibility will lead to the more induction that they can make and
the more addition to the capability of the implementation team. However, this capability addition
is depending on the current capability that the staffs have already (.1), its normative potential
(.9), and the induction time, specifically the more actual capability will lead to the more ‘addition
to the capability’ and the more capability of the staff will be (R1). Eventually, the actual
19
capability of implementation team will affect the confidence on the policy operationalization
which is the more capable the team, the higher confidence on policy operationalization will be.
The development of implementation team capability and policy operationalization in detail along
the simulation can be seen on the Figure 21a, b.
As can be observed on the above figure, at the beginning it is set that the staff capability to
implement such policy is not 0 (zero). It follows the sense that it does not make sense if staff
capability is zero, since they have a minimum requirement to have their job. At the end of the
current stage’s simulation, the value of team’s capability and policy operationalization
successively are .94 and .99 as it has been shown on the former Table 7.
The third another important role of the MSC as the boundary spanners is to deal with the law
makers or legislative body through lobbying activities. The higher MSC’s credibility will make
them able to organize gathering, meeting, seminar, workshop, etc. for idea and vision sharing.
These efforts eventually can increase understanding from the legislative body members to
support the policy implementation. It means that the more understanding rate, the more
legislative member supports the policy (R2), meanwhile the more understanding rate will reduce
the legislative member who against the policy implementation, and the lesser the number of
opposing member, the lesser lobby to support the policy (B1). The complete series of causal
relationship can be seen on the Figure 22.
Figure 22: The consultation and decision stage stock and flow diagram
Working time intensity
to Find Crieron ‘Hania BSE
Criterion
a
Crtenon Finding
=
ra Cabinet meeting
aa postive geste
Deadline to decile ORE! Vet * F
policy
{ Pie Paver att tot budget
; cated to cabinet meeting
Taba Pay ‘Tie reiew
Dame | | aoa : of
|
Agi Lot
inl si Le el
th decide policy ia a sm ola Baget Credibility Mins
Zs Againls méeting fw Agi Lobby 3 Allocated Stering Commitize
Pol “ae B3 LX +
Decision u ] Opposstig rate dition to Credty
mu ae]
oath Legsave menber| ] BA OS ener {\ Commitee
\
|
|
Lei
‘yd
, Lobby
= E Supportve, Effoctveness |
ees a oe |
: ee wa
“nde, Inuctontie XS Noma mcg
: ites
odin ae
Tape]
Policy | Team
Workig tine intensity Capability ‘Adttonin
‘operationalize poy RI roe Tom
Another case, the opposition party as the real pressure group that stands to challenge the
incumbent party’s majority, will keep observing their opponent electability projection. The
20
higher electability projection of the incumbent party (IPE) is perceived by the opposition party as
a threat to their electability thus they will make their challenge harder the policy initiative by
making tougher their contradiction perspective. In other words, if the opposition perceived that
the successful implementation of the proposed policy will give the benefit to the incumbent
party’s electability in the next round of the general election then they are eagerly to block the
policy as possible as they can. Specifically, the more IPE, the more change to the contradiction
perspective, the tougher opposition standing. On the other hand, the more opposing stand; the
lesser addition to it since practically there is no opposing views forever (B2).
As perception on policy can go very differently between the incumbent and opposition party if
the opposing party perceived that the policy will only give its benefit to the incumbent party,
they begin to lobby to against the policy implementation.
The more lobby to against the policy implementation, the more opposing rate which has
implications to the lesser supportive legislative member (B3) and the more legislative members
who oppose the policy (R3). The development behavior of the legislative members who are
supportive or against the policy implementation can be seen on the Figure 23:
Figure 23: Simulated legislative’s member on the policy initiative
person
90
60
— Supportive Legislative member
— Againts Legislative member
30
Jan01 Jul. Jan. Jul. Jan 01
2011 | 2012 |
As can be seen on Figure 23, it has been set for initial simulation that there is no majority
figure in the legislative body (50-50, with the total legislative member is 100 person) concerning
to the confidence of the policy until the MSC is established. MSC starts to make lobbies as the
preparation for the proposed policy in order to reach a majority figure in the parliament which is
at least 51% is in favor to support the policy. Thus, if the ‘favor’ majority cannot be reached then
for sure government cannot implement the policy.
IMPLEMENTATION STAGE
Outline of the implementation stage can be drawn as in the following Table 8 for the system
mapping and Table 9 for the variables are included in the simulation with the initial value,
expected value, and the final value after the simulation.
21
Table 8: Implementation stage system mapping
The Actors Resource | Intermediate Final Final Indicator
Strategic Control Result
Government: Delivery of | Delivery Policy _|. Delivery GAP
Implementation Team policy adjustment | delivered |. Expected and Budget
(included local branch of department) rate 100% Spent Ratio
Table 9: Initial, expected, and final value of the implementation stage
No Variable Type Initial Value Expected | Final Unit
Value Value
1 Delivery Stock 0 100 99.66 | %
2 Normal Time to adjust delivery Constant 4 : : mo
3 Department Staff Capability Stock it 1 9928 | dmnl
4 Normal average budget spent per Constant 5,000,000 - - euro
1% delivery
5 Detail Policy Operational Stock 0 1 1 dmnl
6 Delivery GAP Auxiliary | 100 0 34 %
(at the
implementation
starting date)
Ki Budget Spent Ratio Auxiliary | 1.14 1 1 dmnl
(at the
implementation
starting date)
The fourth step in the policy cycle model requires firm confidence from the former stage. As
can be seen on the Figure 24 below, the implementation team’s capability in doing the required
job in this stage and the detail of policy operationalization are dominating the process.
Figure 24: Policy implementation stocks and flows diagram
oO x Detaness Potcy
Policy Operational |_Operational
in Detail +
7 dy Effect of DPO to
Bl Expected Delivery
m Capabii Allocated F —
et Espected Davery Nonval Tine to
zs Delivery GAP sei deste
Nommal Expected
Delivery BI
Expected Curent ti
| Phase Budget Spent } —— Aca tine to
7 | by adjust delivery
\ ‘Adjustient Rate #
a ae A onsale 6
‘spent per delivery Expected and Spent \ ‘Adjustment rate
‘Budget Ratio Tine allocated ‘
sl
*Bhdget Spent
ae fmplementation]
team Capabiliy
Effect of TC to Average ~~
~ Budget spent per delivery“
22
However, two loops are introduced in this stage; first, the confidence on the detail policy
information somehow has an effect to ‘expected delivery’ which is the more confidence on the
policy operationalization leads to the more expected delivery. Quantification on this effect can be
seen on the Figure 25:
Figure 25: Effect of policy operationalization on the expected policy delivery
100%
80%
60%
40%
20% 2 =|
0%
Expected Policy
Implementation
Delivery
0 0.2 0.4 0.6 0.8 1
Confidence on Policy Operationalization
On the above figure, there can be seen a high expectation on policy implementation. The
quantification follows the sense of an organization frequently likes to put their expectation as
high as possible regardless the resource it has is able to support or no. In this concern, the sense
implies the government still puts the high expectation to have 100% policy implementation
delivery even though the confidence on policy operationalization is not perfect on the highest
level which is 1 (one). In the paper simulation, the government expectation is set on the level of
1 (one) if the level of confidence on policy operationalization is equal or more than 0.5. Below
that level of confidence, the government expectation will decrease nonlinearly to 0% if there is
no confidence at all on the policy operationalization which is in theory might be happened.
The expected delivery will then determine how much the budget will be allocated precisely.
Thus, the more expectation on the policy deliveries means the higher budget will be allocated.
And through some connecting variables as can be seen on former Figure 24, the total budget has
implications on the effort to detail the policy operationalization to boost its confidence. These
series of causality create reinforcing loop (R1) in this stage.
On the other side, Implementation Team Capability (ITC) has been assumed to have two
effects; first on the time to adjust delivery (TAD) and second on the budget spent (BS) which are
quantified as the following Figure 26a,b:
Figure 26a: Effect of ITC toTAD || Figure 26b: Effect of ITC to BS
2
15
2 &
gels fy 18
E2u+ ae,2%
A
2809 2g 514
fg beg12
rt as =e
$305 == SS ae ae
Se
Eo a2 04 «08 S08 g
Implementation Team Capability
Implementation Team Capability
As can be observed on above two graphs, the effect of ITC on the variable of time to adjust
the delivery and average budget spent per 1% deliveries are in nonlinear pattern with a negative
slope. The difference is on its elasticity, specifically the effect of ITC on Figure 26a is inelastic
and to the other Figure 26b the effect is elastic.
The second loop is created from series of causality as follows; the more gaps on the policy
implementation delivery will lead to force the implementation team to boost their effort to
minimize the gap and then the faster adjustment rate implies the more delivery is delivered. The
more delivered delivery will reduce the delivery gap (B1). Thus, the behavior of the delivery is
expected to be a goal seeking.
Given the time to implement the policy a year, the behavior delivery of the policy
implementation is seen on the Figure 27 below:
Figure 27: Simulated policy delivery within the given time schedule
10
08
—Time allocated
— Delivery
02 |
stat Tae | ard gt | 4th gt | Astgt | ami ard gt | 4th gt | Ast at hava Ind gt 4th gt
It seems on the above figure that the simulation result supports the expectation on the
delivery behavior which is ‘goal seeking’. It tells that on the first quarter the delivery grows very
fast as the delivery gap is very wide in that time and then along with the delivery gap reduction,
the delivery increment becomes lesser and lesser.
There are two variables, which are considered as the positive indicator for the policy
implementation in the paper that is ‘delivery gap’ and ratio of expected budget and budget spent.
The delivery gap is expected to be 0 (zero) while the ratio is expected to reach 1 (one) at the end
of the implementation time. The behaviors of the two indicators are shown on the Figure 28:
Figure 28: Simulated expected budget to budget spent ratio
——
—Time allocated
— Budget Spent Ratio
— Delivery GAP
05
0.0
istat [and at | a at | ath a] istat and at 3a | ath at | astat [2nd aaa | ath at
24
As can be seen on the Figure 28, the expectations on the two indicators more or less are
reached. In the end of simulation for this stage, it gives the value of 1(one) for the ratio and of
0.34% for the delivery gap. These two values also can be seen on the former Table 9.
EVALUATION STAGE
Outline of the evaluation stage can be drawn as in the following Table 10 for the system
mapping and Table 11 for the variables are included in the simulation with the initial value,
expected value, and the final value after the simulation.
Table 10: Evaluation stage system mapping
The Actors Resource Strategic | Intermediate Final Final
Control Result Indicator
Government Review on_ the | Facts finding | Report Public
Ministerial related to the policy Implemented. perception
Non-Government Organization Policy
Academicians
Legislative body
The last stage of the policy cycle finally is reached. According to Edwards, the evaluation
policy stage can lead to the policy revision, and then she said that the objective of the stage is to
assess the extent to which the policy objectives originally set were actually met and met
efficiently (Edwards, 2001, p. 6-7). In the paper has been introduced the delivery gap and the
budget spent and expected ratio as the final indicators. Successively, those indicators
accommodate ‘the policy objective originally set were actually met’ is meant 100% delivered
and ‘met efficiently’ is meant delivered with exact expected budget (the ratio budget spent to
expected ratio is 1 (one)).
Table 11: Initial, expected, and final value of the evaluation stage
No Variable Type Initial | Expect | Final | Unit
Value ed Value
Value
1 Potential Positive Policy Stock 1 0 001 | dmnl
Evaluation Report
2 Positive Policy Evaluation Stock 0 1 999 | dmnl
Report
3 Public Perception Auxiliary | 0 1 .87 dmnl
About the evaluation stage, Edwards has given important remarks in her book, which is the
evaluation stage is not necessarily a neutral, technical exercise but can be as politically charged
as any of the other policy development phases. To understand the evaluation stage, it is
therefore important to consider also who initiates the evaluation, why, and how it is organized
(Edwards, 2001, p. 7).
25
However, all policy reviews that have been posted either the negative or positive review on
the policy cycle from the initial stage to the final stage will be perceived by the policy’s object
itself which is the public. Specifically, as can be seen on Figure 29: System Mapping for
Evaluation Stage, the more positive outcome that public can take the benefit from the policy
implementation will for sure lead the higher positive public perception.
On the Figure 29, public perceived the positive with some time lags, but eventually public
will fully perceive what have been reported positively from the policy implementation after it has
felt the policy’s outcome.
The more positive public perception makes the better incumbent party electability projection
(IPE) in the next round general election. With involving causality series from the former stage
i.e. Figure 22; the more IPE means the higher expected delivery and the higher delivery gap.
Figure 29: Evaluation stage’s stocks and flows diagram
Budget Spent to
effect of RBS to postr +
Submission on Policy Haperte Naty (RES)
Evaluation
effect of Delivery GAP to
Evaluation Time positive submission on aN
Evaluation -
Yo as v\
Facts on Positive
Public discourse Policy Evaluation
intensity “I Party
Bl Contradiction
Positive Policy RI 5
Evaluation
Report
Tcumbent Party
Electabilty
+
+
Public fei
Then, the lesser delivery gap leads to the higher effect on the facts finding (positive review) of
the policy and the last will make the positive policy evaluation report is higher to the maximum
normative evaluation i.e. 1 (one). These series of causality will form balancing loop (B1).The
simulation result concerning to the public’s perception can be seen on the following Figure 30:
26
Figure 30: Simulated positive policy valuation report and the public perception
0.0- +
Tnabecbaalmalnabsrcseddiahaaheabelnchstaba declan ahineboe dba ale al
The quantification effect of the delivery gap to the submission of positive facts finding can be
seen on the Figure 31 below:
Figure 31: Effect of delivery gap on | } Figure 32: Effect of RBS deviation
the positive report on the policy from 1 to positive report on policy
1 1 -_ —
ac ec i
& +
6 $508 2 $2 oss
Cae
tte BEZ 09
23504 ess
oe 30 2 oss
By = 02 on
sid ood, Pusat
gee ° gee
fe 0 os 1 $e 0506070809 1 1112131415
£36 °
Delivery Gap Budget Spent to Expected Ratio (RBS)
As it can be observed on above figure, the more delivery gap will lead to the lesser positive
submission on the policy review. However, it also can be seen that the lowest effect is 0.2 when
the delivery gap is 1 or 100%. The sense behind such quantification is that even though there are
100% gap of delivery which the policy is failed to be implemented; there will be still a positive
report concerning to the ‘lesson learned’ at least.
On the other hand, the better IPE will mean the closer budget spent to expected ratio to 1
(one) then the closer this ratio to 1 (one) leads to the much more positive evaluation reports
through the quantification of its effect to the submission of positive facts finding, which can be
seen on the Figure 32. These series of causality form second reinforcing loop (R2).
Figure 32 shows the effect of deviation from the expected RBS which is 1. Such
quantification tells that the more deviation from 1, the lesser positive submission reviews on the
policy implementation.
The other regard from the Figure 29 is that the higher IPE will be perceived ‘bad news’ by the
opposing party, that is why the higher IPE will make the opposition party put more contradiction
(challenge) to the government. Also through a long series of causality from the former system
mapping stage i.e. from Figure 1; 14; 22; and 24, the higher challenge from the opposition party
will lead to the higher delivery gap. These series will create the first reinforcing loop in the
system mapping (R1) and the higher deviation of budget spent to expected ratio from 1 (one) and
the series create the second balancing loop (B2). For the result of the simulation of the current
stage can be seen on the following Figure 33:
27
Figure 33: Simulated positive report on the policy
Policy Eauaton Report
1
[aa lawalivalers [ie fee lseelon tne lowelweloe| na feel orelae|
Policy implementation’ dynamics can be seen on above Figure 33 where it is reflected by the
delivery delivered time to time beginning from the start of policy implementation. The delivery
of the policy is grown increasingly as the delivery gap is still wide. The growth is starting to
decrease after the first quarter is reached and then keep on decreasing until it reaches the desired
delivery which is 100% delivery (goal seeking phenomenon).
CONCLUSION AND IMPLICATION FOR FURTHER RESEARCH
This paper has presented stage by stage of the policy cycle model as the basis framework to
explore and understand complexities in the public policy-making process. The policy cycle
model has been observed on some cases from Australia written by Edward (Edward, 2001) and a
case from Indonesia written by Pramusinto (Pramusinto, 2006).
From the first stage, complexities arise when among the government and the pressure groups
have a different perception, a different point of view on an issue. Each actor has different
agendas with a variety background of interest, mainly for political power reason. The perception
gap is then publicized by the press to create public opinion since indeed public has the need of
information to be fulfilled. This is creating another pressure to either government (issue related
ministerial office) or pressure groups to align the perception until the sense of broad agreement is
reached.
The second stage of the policy cycle model has other complexities that can be understood.
Complexities now come from the new actors who come into the stage system. When the
ministerial steering committee is established with some members coming from different
background and experiences, the committee per se will have its credibility. The credibility of the
committee will have some important roles to put the committee on a strong command and
determination to make the interdepartmental team works with enthusiast. Credibility of the
committee and the solid interdepartmental team will be very helpful to attract submission of
ideas. On the other way, the committee should be able to find the key policy questions and
clarify the findings to the top level of government, i.e. the prime minister and his cabinet since
the clear objective is needed to filter the idea from the submission.
The main complexity in the third stage is coming from the vested interest of the actors in the
house of legislative; either incumbent party or opposition party. Each side will as much as
possible to support policy that could raise their own prospect to win the next round general
election to have a parliament majority. However, ministerial steering committee still has
28
important roles in this concern. The committee could be a boundary spanner to alter the
departmental staff capability to work on the policy. On the other hand, the committee should be
able to convince and to make the cabinet meeting within a tight fiscal condition choose a solid
and robust selection on the available policy options. Law makers lobbying is become another
role that the committee should do to secure the legislative approval, failed to accomplish this role
will block all effort to implement the policy. It implies that the committee should consider the
interest of the opposition party in the parliament.
The fourth and fifth cycle discussed about implementation and evaluation. The
implementation stage starts from how detail the policy operationalization is, the more detail
could make the implementer team’s work is easier. The implementation success indicators are
‘the delivery gap’ and ‘the budget spending to the expected budget ratio’ that depend also on the
capability of the implementer team. In evaluation stage, perception from public on the different
between what are the institutional reports on their review on the policy implementation and what
is public receive and perceive from the policy implementation will determine the next general
election winner among the incumbent party or the opposition party. The complexities that have
been explored in the paper is hoped to be able to enlarge a widely open discussion further in the
field of public policy field from the point of view system dynamics and the paper also support
the use of system dynamics for learning tools to build pitfall awareness in the policy making for
public among the public policy makers.
REFERENCES
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