Insight Maker(S) To Support The Management Of Protected
Areas And Related Ecosystem Services: Example For
Recreational Value
Rocco Scolozzi', Uta Schirpke’
1. -skopia Anticipation Services
Salita dei Molini, 2 - 38123 Villazzano di Trento (TN) Italy
2. Institute for Alpine Environment, EURAC research
Viale Druso 1, 39100 Bolzano, Italy
Abstract
The management of ecosystems and the resulting services requires exploring and understanding
the complexity of both ecological and socio-economic processes. Participatory modelling
approaches that involve the local stakeholders provide an opportunity for managers of protected
areas to promote a better comprehension of biodiversity conservation and to improve the
modelling itself. In this study, we developed three generalised models of the cultural ecosystem
service “recreational value” for small protected areas, such as those of the Natura 2000 network,
considering three different contexts with an increasing number of “management variables”. The
models are presented to } ial users and stakeholders in the web-based interactive learning
environment Insight Maker, Although further steps would be needed tc to translate the formed
insights into decision support, such as q ive analysis, 1p verification, model
validation and calibration, these simplified models have an educational utility concerning the
complexity of ecosystem services and may work as tools guiding towards social learning within
social-ecological systems.
Keywords: Protected areas, ecosystem services, group model building, dynamics model library,
Natura 2000, recreational value
1. Introduction
The achievement of conservation targets of protected areas depends on many interconnected
ecological, societal, and economic processes, all these interacting at different temporal and spatial
scales (Scolozzi et al., 2014a). The management of protected areas requires the involvement of
local stakeholders to be effective (Antunes et al., 2009; Hare et al., 2003), however, managing
authorities are usually faced on the one hand with environmental issues, such as degradation of
habitats, fragmentation, and climate change. On the other hand, they are confronted with
constraints of funding, conflicts with local stakeholders, and increasing demand of recreational
activities of nature-based tourism (Brandon et al., 2005; Europare Federation, 1995). Moreover,
they are often overwhelmed by external pressures (between institutional obligations and duties,
social demands, and economic drivers) and limited by time and human resources to tackle with
the increasing complexity of natural resources (Scolozzi et al., 2014b). Several experimental
studies about the performance of people confronted with complex dynamic systems revealed that
they were unable to correctly infer how these systems will behave or how they should be managed
(Sweeney and Sterman, 2000).
Page 1 of 13
Management strategies that are unaware of system complexity may cause negative cumulative
impacts such as unexpected ecosystem degradation and unsustainable use of habitat, affecting the
provision of ecosystem services. Ecosystem services are defined as the benefits people obtain
from ecosystems and are classified into four categories, including provisioning, regulating,
cultural and supporting services (Millennium Ecosystem A 2003). The of
ecosystem services has advanced during the past decades (Egoh et al., 2012; Staub et al., 2011),
and the recreational value was assessed based on (spatial) indicators (Nahuelhual et al., 2013;
Paracchini et al., 2014; Sziics et al., 2015) or surveys (Plieninger et al., 2013; van Riper et al.,
2012). However, these assessments usually do not provide information about the relationships
between different ecological, social and economic variables and may be not enough to anticipate
possible side effects of management actions, because, in almost all cases, ecosystem services
depend on complex links (including circular feedbacks) among ecological and socio-economic
processes (Abelairas-Etxebarria and Astorkiza, 2012; DeFries et al., 2010). The final users of
such assessment tools, like decision makers and managers, may remain unaware about the full
complexity of ecosystem services provision and about potential consequences of management
choices.
2. SD modelling for understanding ecosystem services in protected areas
The approach of system dynamics (SD) supports the understanding and management of complex
systems, such as environmental and social systems, providing an effective contribution to
environmental decision-making (Antunes et al., 2006). SD models have been already developed
and used for the assessment of ecosystem services (Batker, 2010; Costanza et al., 2007; Costanza
and Voinov, 2001), and dynamic modelling has been used to collect data, synthetize knowledge
and communicate key issues of environmental problems (Costanza and Ruth, 1998). In particular,
building dynamic models through stakeholder participation, named “group model building”
(GMB) (Vennix, 1999) or “mediated modelling” (Antunes et al., 2006), revealed to be effective
to improve the understanding of complex environmental problems (Rouwette et al., 2002). In this
approach, stakeholders collaborate to the construction and application of dynamic models and
simulations, creating an helpful context for consensus building and sharing of visions (Gaddis et
al., 2010; van den Belt et al., 1998). In GMB, SD modelling promotes elicitation of participants’
knowledge and mental models, helping to articulate and reframe perceptions, and create maps of
the feedback structure of a problem from those perceptions (Forrester, 1987). SD simulations
allow to assess the dynamics of those maps and test new policies (Chen et al., 2014).
While GMB applications for envirs 1 are i ing, those concerning small
protected areas are still rare, especially related to the European-wide Natura 2000 network which
includes a large number of relatively small areas (the smallest areas cover only few hectares).
Specific administrators or organisations, belonging to local administrations (e.g.
icipalities, consortium of ipalities, or provinces) are responsible for these sites. In most
cases, the different authorities generate overlapping and complicated institutional settings with
many actors and levels of governance, sometimes disagreeing in perspective and objectives.
Additionally, the results of Scolozzi et al. (2014a) revealed for most sites in Italy that they were
subject to strong pressures from urbanization and intensification of land use in the surroundings.
Protected areas can be considered as social-ecological systems (Olsson et al., 2006), in which
ecosystems are evolving with the local community; promoting the comprehension of ecosystems
services provision and sharing management scenarios might support better decisions and
consensus building on management options (Resilience Alliance, 2008). In many cases, this could
be more salient or urgent than the exact quantification or mapping of ecosystem services. Building
Page 2 of 13
models of itself, the local community becomes an "anticipatory system" (Scolozzi and Poli, 2015),
capable to move towards an “anticipatory governance” (Boyd et al., 2015), able to anticipate and
manage current and future changes rather than endure them. In this paper, we propose the use of
Insight Maker (Fortmann-Roe, 2014) for ecosystem services modelling to improve the
understanding and to promote the social learning about Natura 2000 sites. We developed three
different models for the recreational value considering three different contexts with an increasing
number of “management variables” to demonstrate the potential of SD modelling in context with
the management of the Natura 2000 network.
3. Basic SD models of recreational value (as cultural ecosystem service) of Natura 2000
sites
The recreational value of an area exists only if the area is accessible and can be visited, i.e. only
if the visitor or tourist can enjoy the landscape and its features, for example scenic views, or can
practice recreational activities such as hiking, cycling, bird-watching etc.. Access depends not
only on geography but also on dedicated infrastructures (e.g. hiking trails, mountain huts) that
facilitate the visit or enable recreational activities. This ecosystem service, therefore, partly
depends on the natural component (ecosystems that offer recreational spaces and opportunities)
and partly on the work of humans (enabling access and enjoyment of said spaces).
The recreational value of Natura 2000 sites in particular depends on many factors according to
the context, and various levels of human intervention and/or naturalness can be distinguished. For
example, in remote areas (e.g. high altitude), human intervention is generally limited to the
opening and maintenance of access tracks. In flat areas, which are naturally easier accessible, the
recreational value may depend more on artificial structures (e.g. bird-watching towers) that make
one site more attractive than another in the same area.
In the relation between the recreational value of a site and its biodiversity, here for brevity called
“environmental quality”, there is a recurrent dynamic typical of nature tourism: the number of
visitors/tourists increases (decreases) with increasing (decreasing) environmental quality, but
their increase eventually affects environmental quality. In SD terms, the process includes a
negative feedback cycle that reduces environmental quality with respect to its initial value before
the arrival of visitors (Figure 1), until reaching an equilibrium.
Environmental
stress
Environmental M
je Visitors
quality
‘Attractiveness
Figure 1 Elementary model of recreational value service: negative feedback reduces
Environmental quality and number of Visitors.
Ina basic model, the variables that link visitors and environmental quality may be attractiveness
and the level of environmental stress, namely the set of negative impacts on the functionality of
the area to host its biodiversity. While attractiveness may in some cases increase through
Page 3 of 13
marketing (left, Figure 2); the number of visitors can locally provide resources for investments
for improvement or maintenance of environmental quality (right, Figure 2). This creates two
opposite feedback loops: a negative one tending to stabilise the system and a positive one tending
to promote exponential growth; the two can strike a balance that can result in sustainability of the
recreational service.
Environmental
stress
Jo my
Easel Environmental is Visitors
ores quality +
- is
Environmental Visitors ‘ss
‘Attractiveness
x
Marketing +
Tnvestments
quality es
+ ,
"Attractiveness
Marketing
Figure 2 Causal diagram with a negative loop (left, model 1), and with two opposite loops
(right, model 2).
Unsustainable dynamics may occur if i are aimed at i ing attractiveness through
marketing and structures for recreational activity, without proportionally increasing or
maintaining environmental quality. In these conditions, two feedback loops, decoupled from
environmental quality (Figure 3) may destabilise the system: decreasing (or annulling) the
stabilising function of the feedback between environmental quality and visitors, and leading to a
rapid increase in environmental stress (no longer controlled internally by the system). Dynamics,
even worse for environmental quality, may occur when investments and structures attract new
human settlement and population.
at
Pa
Environmental
. stress
<. of
Environmental
a. Environmental { \._ Visitors
quality +
Environmental S) Visitors + A + Lecal popul
quality + * attractiveness mae a
investments
z t 2)
Attractiveness Marketing siryctures
bh. + —*
la
Male Sractugs Investments AL
4) /
LD) -
Figure 3 Model 3 (left) and model 4 (right) with feedbacks potentially independent of
environmental quality.
Page 4 of 13
Considering these dynamics, different types of social-ecological system, in which ecological and
human variables are interdepend can be di ished, as shown in Table 1. Each model is
accessible in the web platform Insight Maker (searching in “Explore insights” the tag “SD
Conference 2016”). In the following sections, many details of the models are not reported, for
those we invite the reader to explore the models in the associated web pages
(https://insightmaker.com).
Table 1. Three models of increasing complexity (according to different socio-ecological systems)
for recreational services.
Model Type of system/protected Key variable Management variables
area
Sl Remote areas with reduced Environmental Marketing
human presence quality
Visitors
S2 Natural areas with margin for Environmental Marketing
environmental improvement quality Investments on environment quality
Visitors
S3 Areas where infrastructure Environmental Marketing
could be developed quality Investments on environment quality
Visitors “Artificial” attractivity
Infrastructure
3.1. The simulation model M1
In the proposed model (Figure 4), the stock variables are environmental quality and visitors; the
first is qualitative (more is better) with values measured in an ordinal scale; the second one
represents a value in an interval scale but without a real reference (the range could be scaled with
real data of a site). The variable environmental stress associates the number of visitors and the
level of environmental quality; in other words, the environmental stress caused by a single visitor
is greater in a site with the maximum environmental quality than in a degraded site. The number
of visitors is initially set at 0 and oscillates around a level of equilibrium depending upon
marketing success and environmental quality, realizing the negative (stabilizing) feedback loop
in the Figure 2.
In general, all variable values of this and subsequent models are not realistic numbers, the interest
of modelling here is the understanding and making evident the dynamics between variables; thus,
comparison between variables is more relevant than their absolute values. Some variables could
be scaled with real data and calibrated, in following steps of modelling and simulation.
Page 5 of 13
&> Degradation rate SS
Figure 4 Model S1 (hexagon: s variables”).
Useful considerations from model S1
Some of the model variables could be modified through hypothetical management actions: the
biodiversity could be enhanced through specific actions on habitat restoration or enlargement
(Figure 5); marketing effort can be increased or measures could be taken to improve the return of
visitors (Figure 6). Such variables can be changed in simulations with stakeholders within Insight
maker©.
Relevant dynamics to be shared with stakeholders concern the equilibrium between visitor
number and level of envi 1 quality. Depending on the negative feedback loops between
key variables, the system stabilises to a lower value of environmental quality and a “sustainable”
number of visitors in relation to the regeneration rate and environmental degradation.
With doubling of the degradation rate (Figure 5, left), environmental quality and visitors reach
equilibrium values that are almost half those of initial conditions (blue and red lines). With the
hypothesis of +20% increase in the regeneration rate (Figure 5, right), these values would increase
almost proportionally (green line).
Environmental quality Visitors
2 4 6 68 0 2 & 1 18
Tine (Year)
Visitors :S1
32 Visors :Degrad. rate x2; $$$
gen. ate +20% Vistors : Regen rate +209 $$$
Figure 5 Dynamics of Environmental quality and Visitors in scenarios with different
regeneration rate (+20%, green line) or degradation rates (doubled, red line).
The dynamics of environmental quality and number of visitors oscillates to reach an equilibrium,
which is also related to management variables. If marketing is doubled (Figure 6, left),
environmental quality decreases (by about 50%), but the number of visitors does not increase
proportionally (Figure 6, right), rather the dynamic shows a larger oscillation, finally reaching
close values.
Page 6 of 13
Environmental quality Visitors
25 Xt
o 2 4 6 8 2 MW 16 18 20 @ 0 12
Tine ex ‘Time (Y ear)
o
rt
Figure 6 Dynamics of Environmental quality and Visitors in scenarios with different marketing levels.
3.2. The simulation model S2
A second model (Figure 7) introduces further realism with the variable investment to support an
active improvement of environmental quality (e.g. through active maintenance), standing for an
environmental remediation or compensation of tourism impacts. Such investment would depend
on the partial reinvestment of the revenues obtained from the expenditure of the tourists/visitors
in the site. Another new element is the variable attractiveness, dependent on the environmental
quality and limited by the number of visitors according to a threshold of congestion (the number
of other visitors tolerated by each visitor).
Among the management variables, which could be changed in simulation and hypothetical
scenarios, there are expenditures per visitor and re-invested fraction. Again, the values are purely
fictitious and could be integrated and calibrated, for instance in supplementary field surveys.
GC -xpenditures per
= ite
Congestion limit _ ) . visitor
rn
.
ot EE Hoe
stop eat n CCarottin n> CCarottin n>
Investment
Figure 7. Model S2 (hexagons represent “management variables”).
efficacy
Useful considerations from model S2
Page 7 of 13
In such model, three feedback loops emerge (Figure 7), creating a balancing dynamics between
the positive feedbacks on visitors through investment and attractiveness and the negative feedback
/ stress. The in in the envirc | improvement
partially compensate the negative impact of visitors on environmental quality (Figure 8), which
reaches an equilibrium with higher value than in the previous model (S1).
The model simulations allow exploring interesting scenarios, for instance: doubling the marketing
efforts, environmental quality decreases, but less than in the previous model (Figure 9, left),
because it is compensated by improvements induced by higher investments (funded by higher
incomes, due to higher number of visitors).
Environmental quality
SI
82
Figure 8 Dynamic of the environmental quality in the model S1 (blue line) and the model S2
(red line).
This model includes several r variables (expendi per visitor, fraction reinvested,
and marketing) that can guide management and/or local development strategies. Considering
possible changes in investments, cost per visitor and marketing, the model provides answers to
questions such as: what strategy most increases the volume of business and at what price for
environmental quality?
Environmental quality Visitors
; Saul -
as i
25 os a a ee re oe a ot
0
Te eat Tne ea)
2 aia a i ak
ere weg ee
Figure 9 Dynamics of environmental quality in Model S2 with changes in marketing (x2, green
line) and investment (x2, grey line).
In the model S2, the best strategy appears to be to increase spending per visitor (Figure 10): it
increases the environmental quality (with an impact identical to that derived from the option of
doubling the rate of re-investment of revenues) and increase revenues (on condition to keep other
variables unchanged, e.g. the rate of re-investment in environmental quality).
Page 8 of 13
Environmental quality Tumover
1 “
78, 30 c
is SSS] cae
& fi
‘ ‘
== Tan tn
. —
eee
‘ment
Tumover: $2. Marketing x2
Tk Expendi
52 Maroungs2
ipendurex2
Figure 10 Dynamics of environmental quality and turnover with changes in marketing,
rei rate, and expendi per visitor.
3.3. The simulation model S3
As mentioned above, in certain situations the number of visitors may depend only on external
inputs (Figure 11), such as investment in artificial attractions (or structures) and in marketing,
which builds “artificial” instead of “natural” attractiveness. When visitors and environmental
quality are decoupled, the environmental quality of the system becomes eroded (Figure 12).
<4» -———
,
2)
z
:
5
structures
npocts per 2 :
4
)
¢ eae
Figure 11. Model S3: the variable Visitors and Structures are connected by positive feedback
loop, but not influenced by the Environmental quality.
The model S3 includes the variable structures, measured in terms of number of beds, recreational
spaces or other equipment for visitors, measured as “equivalent visitors”. The variable
attractiveness is simplified as free spaces/beds, or the difference between the number of available
places and the current number of visitors, assuming that a greater number of free spaces is more
attractive. In such model, each visitor and each structure determines its own specific impact that
sum in generating environmental stress.
Page 9 of 13
Useful considerations from model S3
The system represented by the model 3 has two positive feedback loops, as shown in Figure 11,
that are not offset by any negative feedback; from the simulation, we can see how visitors and
structures can grow indefinitely until destroy environmental quality (Figure 12). Specifically, the
presented model shows the unsustainable dynamics of recreational use in the absence of
in for envir 1 mai and/or Pp ion, in other words, in absence of
negative feedback loops stabilizing the system.
In general, some of negative loops cannot be totally controlled, such as congestion effect (see
model S2); others are crucial issue for management, such as investment in environment
conservation or maintenance; both these types should be at least considered and possibly
investigated at site level for effective local policies.
Environmental quality Visitors
@ 9 1
Time (Year) 2 4 6 € 0
s2 Time (Year)
83 Vistors $2 Visitors :$3
Figure 12. Dynamics of environmental quality and visitors in model S2 (blue line) and model
S3 (red line).
4. Discussion and conclusion
In this study, we stress the importance of the modelling itself, rather than of final models.
Modelling together with local stakeholders may be a way to bridge local knowledge (hold by land
managers, owners, beneficiaries in general) and science knowledge (by experts, academics, and
researchers). Hence, we use a general-purpose tool for web-based modelling and simulation
(Fortmann-Roe, 2014) to present specific models of ecosystem services which are open to an
interaction by users. The ideal outcomes of the use of such web-based interactive environment is
to start a library of models dedicated to ecosystem services, in which experts can take materials
to develop own models and stakeholders can easily interact with the complexity of “own” socio-
ecological systems.
Although most of the presented trends related to the recreational value are intuitive and well
known when they are considered separately, the diagrams and semi-quantitative scenarios,
resulting from simulations, provide clear and relevant information (insights) that can be shared
with stakeholders and actors in the tourism sector for planning purposes. The process of SD model
building, if shared and enriched by stakeholder involvement, may support a more informed and
robust ecosystem management.
The proposed SD models have various aims and potentials. They can be used to represent the
main variables involved in site management and in the process of reproduction of the ecosystem
service, and they facilitate the understanding by visualising feedbacks between possible
management measures and ecosystem services. The models allow to reveal implicit assumptions
Page 10 of 13
that would be deleterious for the aims of enduring development and biodiversity protection in
Natura 2000 sites, and they may sustain collaborations for promoting ideas, knowledge, interests
and positions, based on i di ion among stakeholders. Furthermore,
problems and strategies can be defined from new and possibly varied perspectives (e.g. tourism,
landscape ecology, biodiversity conservation) to develop more specific models for the sites and
to simulate management scenarios.
Nevertheless, the presented models have several limitations because they are hypothetical and
generic, based on theories and assumptions derived from general notions of ecology and
environmental economy and not from local data. They are incomplete as they do not include all
variables involved but mainly those linked to possible management measures (e.g. “productive
area” rather than “vegetation growth”). The variables have dummy values with an essentially
qualitative meaning (often zero stands for minimum quantity or value, 1 or 10 stand for maximum
quantity or value). For an operational use, as the definition of local actions, these models are not
sufficient and could even be misleading, since they require validation and verification with real
data and, probably, reformulation with new variables.
In conclusion, the simulation of scenarios and immediate viewing of the possible consequences
can help effective communication and facilitate an informed discussion among stakeholders. The
simplified models are good starting points to develop more operative dynamic models of
ecosystem services. Attention to the time variable, typical of dynamic models, can help to spread
among the same decision-makers and stakeholders a medium-to-long term perspective (Hjorth
and Bagheri, 2006) that is necessary for a wise of ecosystems and landscapes. Ideal
developments include participatory modelling that is specific for each interest area (included or
not in protected areas) and for each ecosystem service by mixed communities of experts (from
different research fields) and stakeholders, including citizens as well as public or private managers
of ecosystem and landscapes.
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4 Preliminary Results
The simulation model can be used to study the dynamic behaviour of the system of
an individual employee saving for their retirement. Various scenarios can be
investigated, and the results of some scenarios are presented below.
4.1 Could the normal contributions be sufficient?
The system models the additional contributions that an employee makes. A pertinent
question is whether these additional contributions are required at all. If the employee
belongs to a retirement fund that requires sufficient “normal” contributions, then it
is quite possible that no additional contribution is required. The first set of results
therefore investigates this possibility by studying the behaviour of the system for
different “normal” contribution rates.
Figure 3 shows the additional contributions required with normal contributions rates
of 15, 20 and 25 percent. The additional contribution is expressed as a percentage of
the living expenses of the employee. Figure 4 shows the development of the
retirement income for the same scenarios.
14
Additional contributions as a proportion of living expenses
2
15
zo
05
0
18 30 41 53 64
Time (Y ear)
Additional contributions as a proportion of living expenses : Normal ion 15 percent
Additional contributions as a proportion of living expenses : Normal ion 20 percent
Additional contributions as a proportion of living expenses : Normal ion 25 percent
Figure 3. Additional contributions as a proportion of the living expenses with
different levels of normal contributions (contributions start at age 25, the
standard of living continuation ratio is 0.75, the contributions can be adjusted
by up to 5 per cent of living expenses per year, adjustment takes place in 1
year)
It is evident from Figure 3 that all the contribution rates considered here are
insufficient to yield enough retirement capital so that a gap does not develop along
the way which the model then tries to close with additional contributions. The model
detects a retirement income gap and calls for a steep increase in additional
contributions to close that gap. In about year 40 there is another increase in
contributions because the specific scenarios modelled here showed a decline in
investment returns in that period. When the anticipated investment returns do not
materialise the gap widens and the model calls for further additional contributions.
The gap is closed in about year 50, and then additional contributions are no longer
required.
The different scenarios show the results for different normal contribution rates. As
can be expected, higher normal contributions mean that less additional contributions
are required.
15
Projected retirement income gap
70,000
35,000
8
: 0
-35,000
-70,000
18 30 41 53 64
Time (Y ear)
Projected retirement income gap : Normal 15 percent
Projected retirement income gap : Normal 20 percent
Projected retirement income gap : Normal 25 percent
Figure 4. Projected retirement income gap with different levels of normal
contributions (contributions start at age 25, the standard of living continuation
ratio is 0.75, the contributions can be adjusted by up to 5 per cent of living
expenses per year, adjustment takes place in 1 year)
Figure 4 shows the projected retirement gap for the three normal contribution rates.
It is clear that a large gap develops initially. This gap is closed through additional
savings. The gap is close to zero until the age of 40 years. The savings then
overshoots because high investment income is attained in the later years, and the
model does not allow for a reduction in the rate of normal contributions. The extent
of overshooting is much larger for the higher contribution rate than for the lower
contribution rate.
4.2. What happ when start ing for retir t when
they are older?
The example above looks at employees starting to work and starting to make
contributions to retirement savings at the age of 25. The example below will consider
what happens if employees start to work at a higher age, or if they withdraw their
retirement savings and then have to start afresh saving for retirement when they are
older. Figure 5 shows the additional contributions required for employees starting at
16
different times when they are older, and with the normal retirement contribution set
at 20 percent in all cases.
Additional contributions as a proportion of living expenses
3
225
075
0
18 30 41 53 64
Time (Y ear)
A dditional contributions as a proportion of living expenses : Starting ions at 35
Additional contributions as a proportion of living expenses : Starting ions at 30
Additional contributions as a proportion of living expenses : Starting ions at 25
Figure 5. Additional contributions as a proportion of the living expenses with
different starting ages (level of normal contributions is 20 per cent throughout,
the standard of living continuation ratio is 0.75, the contributions can be
adjusted by up to 5 per cent of living expenses per year, adjustment takes place
in 1 year)
Figure 5 shows the importance of starting to save for retirement early. Much larger
additional contributions are required for someone starting to save later. If the
starting point of saving for retirement is delayed by only ten years from the age of 25
to the age of 35, this requires additional savings of more than 20 percent of the living
expenses to try and make up the backlog. Even with that high level of additional
expenses, the backlog is not completely reduced when the employee retires, as an
additional payment is still required right up to retirement.
This is also evident from Figure 6 below, which shows how the retirement income
gap develops over the employee’s working life. The savings plan no longer overshoots
for someone starting to work at age 35, and the additional payments are only
sufficient to keep the income gap at close to zero.
17
Projected retirement income gap
70,000
35,000
: N
; 0
-35,000
-70,000
18 30 41 53 64
Time (Y ear)
Projected retirement income gap : Starting contri at 35
Projected retirement income gap : Starting conti at 30
Projected retirement income gap : Starting contri at 25
Projected retirement income gap : Normal 20 percent
Figure 6. Projected retirement income gap with different starting ages (level of
normal contributions is 20 per cent throughout, the standard of living
continuation ratio is 0.75, the contributions can be adjusted by up to 5 per cent
of living expenses per year, adjustment takes place in 1 year)
4.3 What happ when employ respond more slowly?
The model can also be used to study different response behaviours in trying to close
the retirement income gap. As an example, Figure 7 presents the pattern of
additional contributions if the adjustment is slower. Up till now, the model assumed
that the adjustment will take place in a year. The simulation runs in Figure 7 also
show the results when the adjustment takes place over five years and over ten years.
Additional contributions as a proportion of living expenses
09
.0675
EB 045
0225
0
18 30 41 53 64
Time (Y ear)
Additional contributions as a proportion of living expenses : A djust living standard in 1 year
Additional contributions as a proportion of living expenses : Adjust living standard in 5 years
Additional contributions as a proportion of living expenses : Adjust living standard in 10 years
Figure 7. Additional contributions as a proportion of the living expenses with
different adjustment rates (contributions start at age 25, level of normal
contributions is 20 per cent throughout, the standard of living continuation
ratio is 0.75, the contributions can be adjusted by up to 5 per cent of living
expenses per year)
Figure 7 shows that the adjustment is slower, reaching a lower proportion of living
expenses. From Figure 7 it would also appear as if the total level of additional
contributions required is lower with the slower adjustment rate. This is so for the
particular set of parameters assumed here. With the slower rate of adjustment the
savings do not overshoot as much as with a quicker adjustment rate.
This is also evident from Figure 8 below, which plots the retirement income gap for
different adjustment rates. With a slower adjustment rate the projected income gap
does not close so quickly, and then does not overshoot to the same extent as
experienced with the quicker adjustment rate. The additional amount saved with the
quick adjustment rate is not lost, though. The retiree can enjoy a higher retirement
income which is created because the process overshoots the target.
Projected retirement income gap
50,000
25,000
g
: 0
-25,000
-50,000
18 30 41 53 64
Time (Y ear)
Projected retirement income gap : A djust living standard in 1 year
Projected retirement income gap : A djust living standard in 5 years
Projected retirement income gap : A djust living standard in 10 years
Figure 8. Projected retirement income gap with different adjustment rates
(contributions start at age 25, level of normal contributions is 20 per cent
throughout, the standard of living continuation ratio is 0.75, the contributions
can be adjusted by up to 5 per cent of living expenses per year)
4.4 What happ when empl start late and have high retirement
y
income expectations?
The importance of starting early to save for retirement is clear from the simulations
presented above. If an employee starts to save later in life, it becomes necessary to
make large additional savings in order to reach a retirement income goal. To
illustrate this point, the next set of simulations considers an employee starting at the
age of 45, aiming to maintain their standard of living in retirement.
The additional contributions this employee makes to retirement savings is
constrained by the amount by which the employee is prepared to adjust their
standard of living. The set of simulations consider an employee that will adjust in a
year, and can adjust by three different amounts in that year (one per cent of living
expenses, two per cent of living expenses, and five per cent of living expenses
respectively).
20
Additional contributions as a proportion of living expenses
2
15
ol
5
0
18 30 41 53 64
Time (Y ear)
Additional contributions as a proportion of living expenses : Starting at 45 increase adjust by 5 percent peryear
Additional contributions as a proportion of living expenses : Starting at 45 increase adjust by 2 per cent peryear
Additional contributions as a proportion of living expenses : Starting at 45 increase adjust by 1 percent peryear
Figure 9. Additional contributions as a proportion of the living expenses for an
employee starting contributions at the age of 45 and aiming for a standard of
living continuation ratio of 1 and with different adjustment responses (level of
normal contributions is 20 per cent thr t the at takes
place over 1 year)
Figure 9 shows that the additional contributions required in this instance ramps up
against this adjustment constraint for close to the full 20 year working period. Only
in the case of a five per cent adjustment per year do we reach a point where the
calculations do not show that an even higher level of additional contributions is
required. At that point, the additional contributions required are larger than the
amount that the employee retains for living expenses.
This result shows that people who start saving for retirement late in their careers
cannot rely on a slow or a weak response if they want to reach a realistic retirement
income target.
This effect is also evident from the projected retirement income gap as presented in
Figure 10. With a weak response (which would increase the additional contributions
by one or two per cent of living expenses per year) the income gap never closes. It is
only with the aggressive five per cent adjustment that the income gap is eliminated
21
shortly before retirement. The results show that it may have been preferable to adjust
the contributions even quicker. The five percent per year increase in contributions
with a two per cent per year salary increase means a constant three per cent per year
living standard decline over close to twenty years. A large initial adjustment effecting
a quantum jump (“biting the bullet once”) might have been preferred by an employee
instead of the slow decline in living standards modelled here.
Projected retirement income gap
200,000
150,000
g
: 99,990
49,980
-30
18 30 41 53 64
Time (Y ear)
Projected retirement income gap : Starting at 45 increase adjust by 5 percent peryear
Projected retirement income gap : Starting at 45 increase adjust by 2 percent per year
Projected retirement income gap : Starting at 45 increase adjust by 1 percent per year
Figure 10. Projected retirement income gap for an employee starting contributions at
the age of 45 and aiming for a Jard of living conti ion ratio of 1 and
with different adjustment responses (level of normal contributions is 20 per
cent throughout, the adjustment response takes place over 1 year)
5 Conclusions
This paper analysed the dynamic behaviour of the decisions of a salaried employee
saving for retirement. In a preliminary and simplified model of this decision, various
scenarios were considered and presented. There are four conclusions that can be
drawn from the simulation results and the various scenarios that were considered:
22
The results show the importance of starting the process of saving early if a
reasonable retirement goal is to be met.
The results also show that it is important to evaluate the projected retirement
income periodically and to make strong and swift adjustments to ensure a
reasonable retirement income. Any additional contribution to retirement
income has to be financed by a cut in living expenses. This will come with
some pain. But if an employee has fallen behind it becomes more and more
difficult to catch up again. It may be better to make a quantum adjustment
once than to experience a slow decline in living standards as more and more
funds are required to meet the retirement targets.
The alternative, sadly, is to be forced to take a drop in living standards during
retirement. This is a predicament into which many retirees are eventually
forced if they are not pro-active in saving for their retirement.
The model also shows how the savings plan can overshoot. This usually
happens when the realised investment income is higher than the expected
investment income on which the savings plan is based. This result was
experienced in many of the simulations present here. But the opposite is also
possible. If the expected investment income is not realised this could lead to
an unexpected shortfall. An extended model that considers investment as a
stochastic variable could help to devise an optimal response which will ensure
that sufficient funds are accumulated while limiting the extent to which the
plan overshoots with the varying investment returns.
The model used in this study is a very simplified model of the real decision, and the
research could be expanded to investigate other aspects of the system.
The first aspect already referred to is the stochastic nature of the market returns. An
interesting dynamic develops when the expected investment returns used to estimate
the retirement income gap are not realised. What was considered a surplus in one
year could, after very bad investment returns, turn into a shortfall. It is also possible
that good investment returns can turn a shortfall into a surplus. It is possible that
23
this dynamic could encourage a slower response than we have advocated here. It may
be optimal to wait longer to see how things pan out before making adjustments. To
study this would require a better understanding of the stochastic process generating
investment returns, how the decision maker could respond, and what the decision
criterion would be to select an optimal strategy.
A similar dynamic exists in the capital conversion rate that is used to convert the
retirement capital into retirement income. This conversion rate depends very
strongly on long term interest rates, especially if life annuity rates are used as the
basis for this conversion. The model can therefore be extended to include a
consideration of the stochastic nature of the conversion rate so that the influence of
this variation on the dynamic behaviour of the system can be observed.
The present model can also be extended by allowing an individual to bring their
retirement date forward or to postpone retirement. An interesting dynamic will likely
develop between the capital conversion rate and the retirement age, and an extension
of the model can be used to observe how this influences the behaviour of the rest of
the model.
Future extension of the model will include investigation of the dynamics between the
investment returns and the capital conversion rate. The latter depends on long term
interest rates, so it is really the relationship between equity returns and interest rates
that will be underlying this relationship. The system as defined at present potentially
suffers from what Chu, Strand and Fjelland (2003) refer to as radical openness. The
investment returns and the interest rates underlying the capital conversion rates
from part of the broader investment environment of the system. But these two
variables may be influencing each other in a broader system, causing a dynamic
relationship between the two. This may increase or reduce the risk of the system we
are studying.
Without any extensions to the simplified model used here, the importance of the
central message emerging from the interactions is evident. Pro-active employees
should position them for their retirement, should periodically determine their
24
retirement income gap, and should take the necessary steps to close this gap to
ensure an acceptable income in retirement.
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