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22nd International System Dynamics Conference
Oxford, July 25-29, 2004
Processes and determinants of rural development
in Switzerland
Birgit Kopainsky, Peter Rieder
Agricultural Economics, ETH Zentrum, CH-8092 Zurich, Switzerland
phone: +41-1-632 53 28, fax: +41-1-632 10 86
e-mail: birgit.kopainsky@iaw.agrl.ethz.ch
Abstract
In many countries, lagging rural areas face the challenge of adaptation and structural ad-
justment to changing economic and social conditions. In peripheral micro-regions in Switzer-
land, population decline, demographic change and a narrowing economic base constrain fu-
ture development perspectives and threaten the fulfillment of the national policy goal of a
decentralized settlement. At the same time, Swiss regional policy is undergoing fundamental
changes. Instead of distributive measures aimed at attenuating regional socio-economic dis-
parities, emphasis is given to the competitiveness of rural localities and to local initiatives.
This implies an increasing need for policy concepts and analyses based on an integrated view
of the processes and actors affecting rural development.
The paper focuses on the local dimension of employment and population dynamics in rural
Switzerland and on an ex-ante analysis of development perspectives. The simulation model
developed for this purpose is based on the literature in regional economics and rural studies
and combined with insights from related fields such as urban dynamics and innovation man-
agement. Preliminary model analysis emphasizes the need for national and regional policy
concepts that focus on the support of local actors to bring about new development routines.
1 Introduction
In the process of the ongoing economic structural change as well as of social and political
changes rural Switzerland faces a differentiation in economic and social conditions. Those
locations confronted with a lagging development see their viability become at risk. Popula-
tion decline and a narrowing economic base not only affect future development perspectives,
they also put under threat the fulfillment of the national goal of a decentralized settlement
of the country. Coupled to a decentralized settlement is the provision of a series of public
goods such as socio-cultural diversity and the maintenance of a cultivated landscape (ERRING-
TON 1997: 207-211).
The mosaic of rural regions with winners, in-betweens, and losers raises the question about
driving forces behind this pattern. This question has often been posted in the economic lit-
erature on the driving factors behind economic performance of countries or regions. In re-
gional economics, for example, the causal interrelationship of forces leading to changes in
population numbers, migration and regional income have become examined more intensely
(ISARD ET AL. 1998: 3).
Insight into the driving factors behind economic performance of rural regions is not only sci-
entifically of interest, but it is also relevant from a public policy point of view. Swiss regional
policy is currently undergoing fundamental changes. Instead of distributive measures aimed
at attenuating regional socio-economic disparities, emphasis is given to the competitiveness
of rural localities and to local initiatives (see EvD 2004). In structural policy terms this reform
is a shift from policies concerned with the maintenance of economic structures toward poli-
cies that help to actively adapt structures to changes in economic conditions (see PETERS
1996). Such policies affect actors in different ways. Their conflicting interests are important
determinants of the success of any policy.
Two important aspects concerning employment and population decline in lagging rural areas
should therefore be considered. On the one hand, these locations are small economies that
depend heavily on the economic and political development on the national and international
level. It is reasonable to assume that these developments will put more pressure on lagging
rural areas in the future. On the other hand and at the same time, development strategies
focus more and more on entrepreneurship and innovation capacity in these locations to
boost the competitiveness of the local economy. It is equally reasonable to assume, however,
that entrepreneurship and innovation capacity of a municipality's population decrease as a
consequence of employment decline and out-migration.
The literature suggests that the factors behind the different economic performance of rural
regions are related to an interplay of local and global forces, in which territorial, population
and globalization processes are thought to be the main determinants (TERLUIN 2003). Rural
development therefore emerges from an interaction of effects produced by global forces and
local responses. The discussion, however, does not provide a sufficient answer concerning the
dynamic nature of this interaction.
The purpose of this paper is therefore to develop an integrated dynamic theory of employ-
ment and population development in lagging rural locations in Switzerland. The theory ex-
plicitly takes into account socio-political processes affecting the development of local com-
petitiveness. Based on the observed processes in lagging rural areas (section2) and building
on the existing literature we synthesize a model describing the dynamics of employment and
population development in section 3. We then translate this framework into a simulation
model (section 4). The formal model allows the detailed analysis of the dynamic behavior cre-
ated by the structures common to the relevant theory (section 5). Implications for the design
of effective regional policy measures from model formulation and analysis are described in
section 6.
2 Processes in lagging rural areas in Switzerland
The aim of this section is to analyze the main socio-economic trends in lagging rural areas in
Switzerland. It serves as a general introduction to the opportunities and threats faced by rural
regions and lays the ground for model development in subsequent sections.
There are many ways to define rurality, ranging from spatial classifications to social represen-
tations (TERLUIN 2001: 21). For the purpose of this paper, lagging rural areas are defined as ar-
eas that have either experienced population decline in the past or that are threatened by it in
the near future because the development of their 20 to 64 years old population shows expo-
nential or linear decline (see BUCHLI ET AL. 2004). The level of analysis is the local level and de-
notes municipalities of an average size of some 250 inhabitants. Of the nearly 3’000 munici-
palities in Switzerland, almost 10% fall into the category of lagging rural areas according to
this definition.
Figure 1 to Figure 5 show some reference modes. The values in the graphs are the average
value of the lagging municipalities and show the typical behavior of these entities in the past.
The main symptom of the problems in lagging rural areas is population development (Figure
1). Figure 2 and Figure 3 sketch the direct determinants of population, migration, births and
deaths.
Figure 1: Long term population development
290
270
250
2 230
: 210
a
190
Beer ec ace rrr
150
1970 1980 1990 2000
Source: Bundesamt fiir Statistik, Volkszahlung 1970-2000
Figure 2: Development of in-migration and out-migration
—e In-migration
—s—Out-migration
0
PS Po?
wr wg %
Source: Bundesamt fiir Statistik, Bilanz der standigen Wohnbevélkerung 1985-2000
Figure 3: Development of births and deaths
3.5
3.0
g
2 25
* 2.0
—e— Births
—s—Deaths
Source: Bundesamt fiir Statistik, Bilanz der standigen Wohnbevélkerung 1985-2000
While the trend in births and deaths is less clear, in-migration has steadily fallen below the
rate of out-migration since the beginning of the 1990s. The overall population decline has
affected age cohorts differently. Figure 4 shows that while the number of younger people (0-
65) has fallen, there has been a slight increase in the retired population.
Figure 4: Population development — age cohorts
300
250
200 01970
a
1980
Fue 2
8 01990
100 12000
50
0
0-19 20-65 >=65 Total
Source: Bundesamt fiir Statistik, Volkszahlung 1970-2000
One important determinant of migration is the number of available jobs. Figure 5 shows the
development of jobs in the primary sector and jobs in the secondary and tertiary sector.
Whereas employment in agriculture has declined steadily, employment in the manufacturing
and service industry reached a maximum at the beginning of the 1990s and has since de-
creased, too.
Figure 5: Development of employment in different economic sectors
BOcpeeana bora oererarancenceenoun
B Agriculture
@ Manufacturing&s enice
20 + = .--- 9 --- 3 ----- industry
1985 1990 1995 1998 2001
Source: Bundesamt fiir Statistik, Eidgendssische Betriebszahlung, Strukturerhebung Landwirtschaft
3 Conceptual framework on employment and population
dynamics in lagging rural areas
Theories that conceptualize the driving forces behind economic development in rural regions
of advanced countries can be found in various disciplines. Regional economics and rural stud-
ies offer promising prospects as the former focuses on regional economic development and
the latter concerns rural development (TERLUIN 2003: 328).
The large number of theories in the regional economics debate all focus on explaining the
growth of a region’s output. They do so by including different factors in the production func-
tion describing a region’s output. Theories in rural studies are concerned with the more or-
ganizational aspects of the rural economy. TERLUIN (2003) elaborated a systematic framework
for the comparison of these theories and subsequently analyzed in an international, empirical
study which theories are supported by empirical evidence in rural regions (see also TERLUIN
AND PosT 2000). Theories that are capable of explaining employment and population devel-
opment in the past relate economic development — given the availability of labor and capital
— to a high capacity of local actors and strong internal and external networks. Their implicit
dynamic properties are analyzed in this section.
3.1 Basic mechanisms of a regional economy
The key notion of theories on economic development is the growth of a region’s output (ARM-
STRONG AND TAYLOR 2000: 1). While economic growth clearly cannot be equaled to development
it is nevertheless an important component of it.
Rural development policy is not only concerned with an increase in output but with providing
employment opportunities as well. The relation between output and employment in a region
can be captured by the demand for labor necessary to produce the output. The output of a
region’s economy is itself determined by the region’s own demand for goods and services and
the demand from other regions. Employment growth leads to in-migration, thus adding
more population and labor supply to the region. These linkages are displayed in Figure 6. For
further explanations of this basic scheme of the regional economy see ARMSTRONG AND TAYLOR
(2000).
In order to distill the dynamic processes that cause population, the product market, and the
labor market to co-evolve over time we add the two bold links to the diagram in Figure 6. The
first relates population to the local demand for goods and services thus creating the positive
feedback loop reinforcement population — economy. It represents the logic contained in re-
gional multiplier analysis (e.g. ARMSTRONG AND TAYLOR 2000: 18-20). The second link connects
labor supply to employment via the labor gap. It states that an increase in labor supply due to
an increase in population closes the gap between necessary and available labor for the pro-
duction of the region’s output. As a result, it depicts the negative feedback loop balancing
labor supply and demand by migration.
Figure 6: Basic scheme of the regional economy
external demand for
regional goods&services Se
+4
7 regionaloutput
Y \
Idemand A
regional eman labor demand
goods&services
+
f: (R) reinforcement v
labor gap
population population - economy
(demand-supply)
i °
Ky n
\in-migration hiring
vt
os
employment
(8) balancing labor&and
supply by migration
+
~™ labor supply
With this first basic scheme the interactions between the labor market and population are
captured. Built into a series of feedback loops, the theory so far describes a cumulative causa-
tion process that is summarized in the positive feedback loop reinforcement population —
economy. It states that once regional disparities come into existence, a self-reinforcing proc-
ess starts that, in absence of other events, maintains the status of growing areas and drains
lagging areas in a success to the successful archetype (see MYRDAL 1957).
The existence of this loop is supported by the literature on new growth theory (for an over-
view, see NIJKAMP AND Poot 1998) and new economic geography (KRUGMAN 1995, FUJITA ET AL.
1999). At the core of these theories is the notion of increasing returns to economic activities
in a region that set the positive feedback loop reinforcement population — economy into mo-
tion.
New growth theory also emphasizes the importance of innovation in economic growth. Inno-
vations can act as a development impulse and provide the region with a competitive edge.
They are therefore able to shift loop direction in the reinforcement population — economy
loop. This aspect is further explored in the next section.
3.2. Dynamics of initiatives
Since the works of SCHUMPETER (1934), innovation has been considered as one of the most im-
portant drivers behind economic growth. Relative differences in innovation capacity are seen
as the main reason for unequal regional economic development as the capacity to innovate
in the realms of products, processes and organization crucially affects the competitiveness of
a firm. The same applies to a region as a set of firms (MAIER AND TODTLING 1996: 119).
There is, however, also a policy resistance aspect involved in the innovation process. On the
one hand, innovations, in addition to their beneficial effect on competitiveness, also imply
difficult sectoral, social and regional restructuring (MAIER AND TODTLING 1996: 120). On the
other hand, the production of technological change via innovation is characterized by strong
external effects (MAIER AND TODTLING 1996: 103). These two aspects taken together lower the
incentives for firms to innovate.
Given the volume and density of economic activities in lagging regions, it seems more appro-
priate to focus on innovation imitation and adoption, and the exploitation of market niches.
There are neither clusters of firms in lagging rural areas nor sufficient infrastructure such as
universities to enable the existence of big companies with research and development de-
partments so that the region’s firms could offensively engage in innovation activities (e.g.
MAIER AND TODTLING 1996: 142).
While rural regions clearly cannot catch up rapidly in the production of new technologies,
they can and must catch up rapidly in the utilization of these technologies (CAMAGNI 1992: 15).
For this purpose we use the term of taking initiatives with which we characterize the process
of deciding to adopt an existing innovation and implement it in the region under considera-
tion by blending the best technologies with traditional and local organization practices. Ini-
tiatives in this sense encompass a series of activities designed to improve employment condi-
tions in the region.
The feedback loop depicted in Figure 7 reflects the two aspects concerning initiatives dis-
cussed in this section. The stock initiatives accounts for the potential of initiatives to trigger
the reinforcement population — economy loop from Figure 6. The balancing feedback loop
initiatives only under pressure represents the policy resistance aspect involved in the innova-
tion process. It takes its legitimization from the microeconomic concept of public goods
which states that public goods are only provided if the marginal benefit for the individual
exceeds the marginal costs (e.g. VARIAN 1993: 583). Creating employment opportunities to
maintain population and regional output has a public good character. Actor goups are there-
fore more likely to take the initiative if the pressure is so high (i.e. the employment gap so big)
that new employment opportunities, created to meet the external demand for regional
goods and services, will benefit them directly.
Figure 7: Initiatives as a trigger of economic growth
regional output~.
+
a external demand for
labor demand
/ regional goods&services
yt *|
labor ga
BaP initiatives
(demand-supply) (8) initiatives only
unde pressure +A
+
A necessity to take
hiring
\ initiatives
\o +4
4 J necessary employment to
employment} employment maintain popuation and
= gap ™ output
3.3 Self-help capacity linking population, economy and initiatives
Innovation is generated by entrepreneurs and induces a process of economic growth (NUKAMP
2003: 396). Whether the necessity to take initiatives in Figure 7 really leads to more initiatives
depends on a series of additional factors in the local milieu. Skills of the labor force, technical
and organizational know-how, and social and institutional structures affect the revenues
from the input of labor and the diffusion of innovation. The factors that were identified to be
significant in this context (TERLUIN 2003: 341) are all related to networks. An active role of local
actors in internal and external networks seems to increase the self-help capacity of munici-
palities and to stimulate employment growth. Initiatives arise mainly from leaders in these
networks, leaders being newcomers, the young population, political decision makers, or en-
trepreneurs (TERLUIN AND Post 2000: 186).
Figure 8 captures these ideas by relating the self-help capacity of a municipality to its popula-
tion. Self-help capacity itself enables actors to take initiatives. The capacity then determines
whether the necessity to take initiatives in Figure 7 can be translated into actual initiatives.
The existence of this link establishes the reinforcing positive loop reinforcement capacity —
population. Self-help capacity is fed by two factors. The link coming from in-migration cap-
tures the role of newcomers as potential leaders. It also contains the notion of newcomers’
involvement in external networks. The link from population to self-help capacity accounts for
the role of the younger population as potential leaders. An increasing number of people in a
municipality not only raises the average level of know-how and skills (see BRETSCHGER 1999 for
the role of knowledge diffusion in the development of regions), it also implies a higher variety
of both internal and external networks.
Figure 8: Self-help capacity and initiatives
regional output~+
7 +
+ external demand for
labor demand
y regional goods&services
/ *
| Aa
labor gap tat
initiatives
(demand-supply)
| +h
(8) reinforcement |
A ‘i capacity - population capacity totale
irin,
\ 8 initiatives
+ +4
A J
self-help
employment capacity
X x +44 .
in-migration
\
ts population
With the introduction of self-help capacity that links population, initiatives and employment
the model describes a dynamic hypothesis about employment and population dynamics in
lagging rural areas that encompasses the feedback loops displayed in Figure 6 to Figure 8.
4 Analytical framework on employment and population
dynamics in lagging rural areas
In this section, the dynamic hypothesis is translated into a quantitative simulation model.
The purpose of the model is to
e Trace the basic processes that influence employment and population development.
e Analyze the socio-political processes affecting the development of local competitiveness.
e Assess the impact of future development trends and policy measures on employment and
population.
The modeling effort is concerned with theory building in the first place. The model is, how-
ever, also used for communication with key decision makers in regional rural policy. The time
horizon covers a period of twenty years into the past and 50 years into the future. The period
since the beginning of the 1980s is considered as the period in which rural regions have com-
pleted their transition from an agrarian economy to a modern industrial or services economy
(TERLUIN 2003: 328). The values for the parameters and lookup functions in the model are
based on different statistical databases obtained from the Swiss Federal Statistical Office and
on expert knowledge about regional rural development.
Model formulation follows the same logic as the development of the conceptual framework
in section 3. The following sections focus on the formulation of the quantitative model. In
order to capture the dynamic complexity implicitly stated in the regional economics and rural
studies literature, generic structures from related disciplines have to be added to the analysis.
Model equations are listed in the appendix.
4.1 Basic mechanisms of a regional economy
The basic mechanisms of a regional economy follow the same rules in rural and urban areas.
Their formulation is therefore related to existing work on urban dynamics (e.g. FORRESTER
1969, ALFELD AND GRAHAM 1976). With respect to the problem under study the following points
deserve attention:
e In order to analyze to which extent the decline in agriculture is paralleled by non-
agricultural employment growth, we distinguish two employment stocks.
e Population is also divided in two stocks as the distinction between economically active
and retired population has important implications for the initiatives and self-help sector
of the model.
Figure 9: Basic scheme of the regional economy in stock-flow forma
t
FRACTIONAL DEATH TIME INACTIVE
RATE POPULATION FRACTIONAL DEATH
FRACTIONAL BIRTH | y, eaTeeetineD
RATE sul iB
Retired
population
net birth rate aging dying
» »
FRACTIONALJOB NORMAL ale Nona
LOSS RATE IN-MIGRATION. CO) =P population : ead OUT-MIGRATION
% —pin-migrating 27 poutmigrating
v oad we
Established Jobs},
LABOR FORCE FRACTION
job loss rate
a | (@)adjusting by OF POPULATION
Re in-migration s \
P total jobs labor (8) adjusting by
| utsmigection)
~
effect of L/ condition current labor to job effect of L!) condition on
4 on inmigraton ratio past Uipario ut igration
Agricultural |__ pny pe
Jobs | a pricultural ob \, TIMETO PERCEIVE Labor tojob
Sas" pesiate CONDITION condition
4 .
FRACTIONALJOB LOSS
perceived U7} +
RATE AGRICULTURE -
condition
4.2 Dynamics of initiatives
The literature on the dynamics of innovation is abundant (e.g. ABRAHAMSON AND ROSENKOPF
1997, MILLING 2002, STERMAN 2000). It differentiates between several stages in the innovation
process. For the purpose of this paper that is concerned with how and under which circum-
stances actors in a municipality start an employment-related initiative, two stages are distin-
guished (see Figure 10). Different socio-political processes influence the decision whether an
initiative moves one stage further ahead or is dismissed.
Push- and pull-factors determine whether a potential initiative is taken up for planning
(normal fraction; pressure to plan resulting from population development). The success of an
initiative depends on the commitment of the actors involved in the initiative and on the sup-
port these actors experience. How these processes relate with each other is investigated in
section 4.3.
Figure 10: Dynamics of initiatives in stock-flow format
SURVIVAL TIME OF TIMETO EVALUATE
POTENTIAL INITIATIVES INITIATIVES
— 2° A
INTIATIVE ’ . -4— outflow from
CREATION RATE ifjismissing pot [| !osing unsuccessful
implemented in.
initiatives ” '
in.
4
Z
Potential
= sg J
Graton of [_ntatves planing me
potential in ~—> initiatives established jobs
i
NORMAL FRACTION TAKEN
UP FOR PLANNING pressure to take a risk seas ie crash
and plan Ini
population
w condition \
MINIMUM ACTIVE a
POPULATION FOR LOCAL recent active
population
SCHOOL st 7
i TIMETO PERCEIVE
Populatio POPULATION TREND
4.3 Self-help capacity linking population, economy and initiatives
Self-help capacity describes an aggregate of capacity of policy makers and entrepreneurs to
act effectively in formulating and delivering policies as a response to market changes, in sup-
porting local initiatives and in attracting funds and investments (TERLUIN 2003: 335). It deter-
mines whether the growth-generating effects of initiatives can be developed or whether they
are inhibited by unfavorable combinations of socio-political processes.
The formulations for this part of the model are based on the literature about innovation im-
plementation (see REPENNING 2002). The key concept in innovation implementation is the
commitment of the involved actors. This commitment is part of a reinforcing feedback loop
(reinforcement success and commitment) containing the success resulting from commitment
and feeding back into commitment. It is also part of a balancing feedback loop (commitment
through motivation) that determines the direction of the reinforcing loop. The idea of the
balancing loop is that the gap in commitment is closed by entrepreneurs’ effort to motivate
actors. These ideas are sketched in Figure 1.
Repenning’s paper refers to the situation in private enterprises. The logic described there
consequently has to be adapted to local economies as a whole, especially to the fact that
there are no such actors as a company’s managers that have the competence to induce the
necessary commitment. We therefore add a decision structure that determines whether the
necessary effort to motivate can at all be made by the entrepreneurs, given their capacity to
inspire and mobilize (possible support or support adequacy, respectively). Possible support
arises from entrepreneurial capacity. It is determined by a variety of factors and can be en-
couraged by structural changes in industrial composition and organization, shifts in the labor
11
market, or socio-demographic changes (BAUMOL 1990). These are all related to the population
development with its consequences for the average skills and networks of entrepreneurs.
In addition to Repenning’s paper, a drain on commitment is added. The drain reflects the fact
that commitment has a limited half-life and needs constant and active renewal.
Figure 11: Self-help capacity and initiatives in stock-flow format
“ ~
TIMETO LOSE necessary
# QO / possible support
y COMMITMENT support ~
losing \
THRESHOLD COMMITMENT “commitment a?
FOR SUCCESS / _ ‘Actors ‘ support
_~ - Commitment to jd —Be adequacy
~ commitment Initiative change in
adequacy Se commitment |
/ TIMETO ADAPT A ,
vt COMMITMENT commitment from
effect of commitment indicated aipport
on success commitment <
nai
\ (a) reinforcement success +
+ and commitment
fraction successful commitment from
initiatives success
/
recent suecess
+ ~~
success | ___“- condition ~ THRESHOLD SUCCESS
TIMETO CHANGE =
SUCCESS PERCEPTION
FOR COMMITMENT
5 Model analysis
Model analysis is divided in two parts. Section 5.1 analyzes model sectors initialized to equilib-
rium and their reactions to step inputs. Section 5.2 sets the agenda for backcasting and fore-
casting experiments.
5.1 Model sectors initialized to equilibrium
The main symptoms of the problems in lagging rural areas are population and employment.
Figure 12 and Figure 13 show the reactions of these two variables to parameter changes. The
parameters varied for the simulations refer to the main influencing factors of population and
employment development. Changes in in-migration, job loss rate, initiative creation rate and
the fraction of initiatives taken up for planning are analyzed. The simulations apply to the
two model sectors basic mechanisms of a regional economy and dynamics of initiatives. The
dynamics of the self-help capacity sector are not integrated in these analyses but are investi-
gated separately in Figure 14 and Figure 15.
12
Figure 12:
Reaction of active population to changes in basic mechanisms in the economy
and to changes in the dynamics of initiatives
170 4 nel ica
say ——# ‘
i itt
150 cs sa 3 5 5
130
fo) 5 100 1520 28530 38H OS 5
Active Population -base run people
Active Population :step increase jobs people
Active Population :step increase planning people
Active Population :step increase initiatives + cs + + people
‘Active Population
step increase in-migration
Figure 13: Reaction of total employment to changes in basic mechanisms in the economy
and to changes in the dynamics of initiatives
60
= Ls aT
50 + 3 =
B = 4
y en 2
2 re = t 3
3 % + #
40
fo) 5 oO 0615) 2025 30's 35's Osis“
total jobs : base run jobs
total jobs - step increase jobs jobs
total jobs : step increase planning jobs
total jobs : step increase initiatives + + + a 4 + jobs
total jobs : step increase in-migration jobs
Changes in exogenous forces clearly affect employment and population development. From a
policy point of view it is interesting to note that the reactions to a change in the fractions
that govern the flows in the initiatives aging chain are much bigger than the reactions to
changes in variables at the boundary of the model. This effect even exceeds changes in the
overall economy (job creation or job loss rate).
Without the self-help capacity sector, model behavior is restricted to first or higher order de-
lays. The system is driven by a series of balancing feedback loops that lead to goal-seeking
behavior as a reaction to parameter changes. Figure 14 and Figure 15 therefore show some
behavioral patterns for the self-help capacity sector.
The self-help capacity sector is driven by a reinforcing and a balancing feedback loop. The
simulations show that this model sector shows the characteristics of an unstable equilib-
rium. Once the system is pushed out of its initial equilibrium it seeks a new equilibrium at the
extreme ends, either at full commitment or zero commitment. The drivers for these changes
are changes in the threshold values that actors apply for their decisions and that determine
the direction of the positive feedback loop.
Figure 14: Reaction of actors’ commitment to changes in the self-help capacity sector
1 Pn Cee
37
ert
ax
= ae A
=f 7
+
05 a
2
2
2
ae
2
fe}
Actors' Commitment to Initiative : base run Dmnl
Actors' Commitment to Initiative : loss in capacity |§©=—-2—2——22 22. Dn
Actors' Commitment to Initiative :step commitment |=—3——3——3— 33. Dn
4
Figure 15: Reaction of the fraction of successful initiatives to changes in the self-help capac-
ity sector
0.5 st 3 tt tot
fraction successful initiatives : base run Dmnl
fraction successful initiatives :loss incapacity |9—2——s——2—222 2 Dn
fraction successful initiatives :step commitment =3——3——3—— 333 Dn
5.2 Planned policy analysis
Based on the understanding of model behavior from the previous section, model analyses
will be conducted with parameters and initial values that represent the situation in the mu-
nicipalities as captured by statistical data or estimated by expert knowledge. Backcasting ex-
periments evaluate the ability of the model to reproduce the reference modes shown in sec-
tion 2 and will be complemented by additional model validation tests. Forecasting experi-
ments test a series of policies that address either current discussions in regional policy and
the regional science literature or issues that proved to be insightful in model analyses in the
previous section.
6 Discussion
In this paper an integrated dynamic theory of employment and population dynamics in lag-
ging rural areas in Switzerland was developed. The conceptual framework was based on lit-
erature in the fields of regional economics and rural studies. The resulting dynamic hypothe-
sis was translated into a formal simulation model by recurring to the literature in related
fields such as urban dynamics and innovation implementation and management.
One major reason for the interest in employment and population dynamics stems from the
current reform in regional policy in Switzerland. While there is consensus about a shift from
top-down to bottom-up development approaches little is know about how to effectively sup-
port the latter. Partial model analysis confirmed the vulnerable nature of these small econo-
mies to trends in national and international market forces. However, it also showed that in-
ternal mechanisms have higher leverage potential. Local policy makers and entrepreneurs are
15
the main actors in designing and implementing development strategies to counteract ex-
ogenous forces. This does, however, not imply that regional policy as a public policy loses its
significance. In many cases, local actors will not or only partially manage to bring about these
new developing routines. Therefore, encouragement from upper administrative levels or
other external actors will be required. It is only by these policies that the leverage points iden-
tified in partial model analysis can effectively be influenced. Partial model analysis so far sug-
gests that special attention be paid to policies that affect the threshold values in the self-help
capacity sector.
7 References
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MA.
ARMSTRONG H., TAYLOR J. 1993. Regional economics and policy, 3" edition. Blackwell Publishers,
Oxford.
BAUMOL W.]. 1990. Entrepreneurship. Journal of Political Economy 98 (5): 893-921.
BRETSCHGER L. 1999. Knowledge diffusion and the development of regions. The Annals of Re-
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BUCHLI S., KOPAINSKY B., MENET S., RIEDER P. 2004. Erfilllung des Verfassungsauftrages durch die
Landwirtschaft unter besonderer Berticksichtigung ihres Beitrages zur dezentralen Be-
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Appendix: Model equations
"population & labor market"
(02) Active Population= INTEG (net birth
rate+"in-migrating"-"out-migrating"-aging, 282)
Units: people
(03) aging=
Active Population/TIME IN ACTIVE POPU-
LATION
Units: people/Year
(04) agricultural job loss rate=
Agricultural Jobs*FRACTIONAL JOB LOSS
RATE AGRICULTURE
Units: jobs/Year
(05) Agricultural Jobs= INTEG (-agricultural job
loss rate, 5)
Units: jobs
(06) becoming established jobs=
"outflow from implemented in."*fraction
successful initiatives
Units: initiatives/Year
(07) births step=
°
Units: Dmnl/Year
(08) CARRYING CAPACITY POPULATION=
600
Units: people
(09) current labor to job ratio=
labor/total jobs
Units: people/jobs
(10) deaths step=
°
Units: Dmnl/Year
(n) dying=
Retired Population*FRACTIONAL DEATH
RATE RETIRED.
Units: people/Year
(12) "effect of L/J condition on in-migration"=
WITH LOOKUP (
"perceived L/J condition",
({(0,0)-
(2,2)],(0,2),(0.2,1.95), (0.4,1.8),(0.6,1.6), (0.8,1.35),(1,1), (1.2
,0.5),(1.4,0.3),(1.6,0.2),(1.8,0.15),(2,0.1) ))
Units: Dmnl
(13) "effect of L/J condition on out-migration"=
WITH LOOKUP (
“perceived L/J condition",
({(0,0)-
(2,2)],(0,0.1),(0.2,0.15),(0.4,0.2),(0.6,0.3),(0.8,0.5),(1,1),(
1.2
1.35),(1.4,1.6), (1.6,1.8), (1.8,1.95), (2,2)))
Units: Dmnl
(14) “effect of population density on in-
migration"= WITH LOOKUP (
population density,
({(0,0)-
(,1)],(0,1),(0.1,1), (0.2,1), (0.3,1),(0.4,1),(0.5,1),(0.6,1),(0.7
,0.95),(0.8,0.8),(0.9,0.5),(1,0.1) ))
Units: Dmnl
(is) Established jobs= INTEG (job creation rate-
job loss rate,
job creation rate/FRACTIONAL JOB LOSS)
Units: jobs
(16) FRACTIONAL BIRTH RATE=
0.03*(1+STEP (births step,10))
Units: Dmnl/Year
(7) FRACTIONAL DEATH RATE=
0.03*(1+STEP(deaths step, 10))
Units: Dmnl/Year
(18) FRACTIONAL DEATH RATE RETIRED=
0.03
Units: Dmnl/Year
(19) FRACTIONALJOB LOSS=
0.01+STEP(job step,10)
Units: Dmnl/Year
(20) FRACTIONAL JOB LOSS RATE AGRICUL-
O+STEP(step loss agriculture,10)
Units: Dmnl/Year
(21) Implemented initiatives= INTEG (+planning
initiatives-becoming established —_jobs-
18
losing unsuccessful initiatives,planning ini-
tiatives*TIME TO EVALUATE INITIATIVES)
Units: initiatives
(22) "in-migrating"=
Active Population*"NORMAL IN-
MIGRATION" "effect of L/J condition on in-
migration"*"effect of population density on
in-migration"
Units: people/Year
(23) "in-migration step"=
°
Units: Dmnl/Year
(24) job creation rate=
JOBS PER INITIATIVE*becoming established
Units: jobs/Year
(25) job loss rate=
Established jobs*FRACTIONAL JOB LOSS
Units: jobs/Year
(26) job step=
°
Units: Dmnl/Year
(27) JOBS PER INITIATIVE=
2
Units: jobs/initiative
(28) labor=
Active Population*LABOR FORCE FRACTION
OF POPULATION
Units: people
(29) LABOR FORCE FRACTION OF POPULATION=
03
Units: Dmnl
(30) labor to job condition=
current labor to job ratio/"PAST L/J RATIO"
Units: Dmnl
(31) net birth rate=
Active Population*(1/65+FRACTIONAL BIRTH
RATE-FRACTIONAL DEATH RATE)
Units: people/Year
(32) "NORMALIN-MIGRATION"=
0.056" (1+STEP("in-migration step”, 10))
Units: Dmnl/Year
(33) "NORMAL OUT-MIGRATION"=
0.056" (1+STEP("out-migration step", 10))
Units: Dmnl/Year
(34) “out-migrating"=
Active Population*"NORMAL OUT-
MIGRATION"*"effect of L/J condition on
out-migration"
Units: people/Year
(35) “out-migration step"=
°
Units: Dmnl/Year
(36) "PAST LJ RATIO"=
1.025
Units: people/jobs
(37) “perceived L/J condition"=
SMOOTH (labor to job condition,"TIME TO
PERCEIVE L/J CONDITION")
Units: Dmnl
(38) population density=
total population/CARRYING — CAPACITY
POPULATION
Units: Dmnl
(39) Retired Population= INTEG (aging-dying,
Active Population/(TIME IN ACTIVE POPU-
LATION* FRACTIONAL DEATH RATE))
Units: people
(40) step loss agriculture=
°
Units: Dmnl/Year
(41) TIME IN ACTIVE POPULATION=
65
Units: Year
(42) "TIME TO PERCEIVE L/J CONDITION"=
2
Units: Year
(43) total jobs=
Agricultural Jobs+Established
jobs+(Implemented initiatives*JOBS PER INITIATIVE)
Units: jobs
(44) total population=
Retired Population+Active Population
Units: people
"self-help capacity"
(46) — Actors' Commitment to Initiative= INTEG
(change in commitment-losing commit-
ment, 0.5)
Units: Dmnl
(47) change in commitment=
(indicated commitment-Actors’ Commit-
ment to Initiative)/TIME TO ADAPT
COMMITMENT
Units: Dmnl/Year
(48) | commitment adequacy=
Actors’ Commitment to Initia-
tive/THRESHOLD COMMITMENT FOR SUCCESS
Units: Dmnl
(49) | commitment from success= WITH LOOKUP
(
success condition,
({(0,0)-
(1,2)],(0,0.1),(0.2,0.2),(0.4,0.3),(0.6,0.5),(0.8,0.9), (1,1) ))
Units: Dmnl
(50) | commitment from support= WITH LOOKUP
(
support adequacy,
((-4,0)-(,0.5)),(-
1,0),(0,0),(0.2,0.1),(0.4,0.2),(0.6,0.3),(0.8,0.4),(
1,0.5) ))
Units: Dmnl
(51) effect of commitment on success= WITH
LOOKUP (
commitment adequacy,
({(0,0)-
(2,1)],(0,0.025),(0.3,0.05),(0.75,0.3), (1,0.5),(1.25,0.7),(1.7,
0.95
)(21)))
Units: Dmnl
(52) indicated commitment=
commitment from success+commitment
from support
Units: Dmnl
(53) losing commitment=
Actors' Commitment to Initiative/TIME TO
LOSE COMMITMENT
(54)
(55)
(56)
Units: Dmnl/Year
necessary support=
1-commitment adequacy
Units: Dmnl
possible support=
1+STEP(step support,10)
Units: Dmnl
recent success=
SMOOTH(fraction successful _ initiatives,
TIME TO CHANGE SUCCESS PERCEPTION)
(s7)
(58)
(59)
(60)
Units: Dmnl
step commitment=
°
Units: Dmnl
step support=
°
Units: Dmnl
step threshold success=
°
Units: Dmnl
success condition=
recent success/THRESHOLD SUCCESS FOR
COMMITMENT
(61)
(62)
Units: Dmnl
support adequacy=
necessary support*possible support
Units: Dmnl
THRESHOLD COMMITMENT FOR SUCCESS=
O.5+STEP(step commitment, 10)
Units: Dmnl
THRESHOLD SUCCESS FOR COMMITMENT=
0.5+STEP(step threshold success,10)
Units: Dmnl
TIME TO ADAPT COMMITMENT=
5
Units: Year
TIME TO CHANGE SUCCESS PERCEPTION=
10
Units: Year
TIME TO LOSE COMMITMENT=_ 5,
Units: Year
20
Control
(69) FINALTIME =50
Units: Year
(70) INITIALTIME =0
Units: Year
(71) SAVEPER =
TIME STEP
Units: Year [0,?]
(72) TIME STEP = 0.125
Units: Year [0,?]
initiatives aging chain
(74) "creation of potential in."=
INITIATIVE CREATION RATE
Units: initiatives/Year
(75) “dismissing pot. in.
Potential initiatives/SURVIVAL TIME OF
POTENTIAL INITIATIVES
Units: initiatives/Year
(76) fraction successful initiatives=
effect of commitment on success
Units: Dmnl
INITIATIVE CREATION RATE=
1+STEP(step initiatives, 20)
Units: initiatives/Year
(7)
(78) losing unsuccessful initiatives=
"outflow from implemented in."*(1-fraction
successful initiatives)
Units: initiatives/Year
(79) MINIMUM ACTIVE POPULATION FOR LOCAL
SCHOOL=
65
Units: people
(80) NORMAL FRACTION TAKEN UP FOR PLAN-
NING=
0.2
Units: Dmnl/Year
(81) "outflow from implemented in."=
Implemented initiatives/TIME TO EVALU-
ATE INITIATIVES
Units: initiatives/Year
(82) planning initiatives=
Potential initiativessNORMAL FRACTION
TAKEN UP FOR PLANNING*pressure to take
arisk and plan
Units: initiatives/Year
(83) population condition=
recent active population/MINIMUM ACTIVE
POPULATION FOR LOCAL SCHOOL
Units: Dmnl
Potential initiatives= INTEG ("creation of
potential in."-"dismissing pot. in."-planning
(84)
initiatives, "creation of potential
in."/(Q/SURVIVAL TIME OF POTENTIAL
INITIATIVES)+
(NORMAL FRACTION TAKEN UP FOR PLAN-
NING* pressure to take a risk and plan)))
Units: initiatives
(85) pressure to take a risk and plan= WITH
LOOKUP (population condition,
([(0,0)-(2,2)],(0,2),(0.5,1.8), (1,1), (1.5,0.6), (2,0.5)
)
Units: Dmnl
recent active population=
SMOOTH{(Active Population, TIME TO PER-
CEIVE POPULATION TREND)
Units: people
step initiatives=
°
(86)
(87)
Units: initiatives/Year
SURVIVAL TIME OF POTENTIAL INITIATIVES=
20
(88)
Units: Year
TIME TO EVALUATE INITIATIVES=
10
Units: Year
TIME TO PERCEIVE POPULATION TREND=
5
Units: Year
21
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