Using microfinance for flood mitigation and cli ptation in Bangl
Naveen Srivatsav, Marc Jaxa-Rozen & Rick van Staveren
Faculty of Technology, Policy and Management, Delft University of Technology
Jaffalaan 5, 2628 BX, Delft, The Netherlands
N.Srivatsav@student.tudelft.nl
Draft paper for the 32nd International Conference of the System Dynamics Society
Delft, The Netherlands — July 20-24, 2014
ABSTRACT
This draft paper describes the preliminary outcomes of a model-based investigation of long-
term strategies to reduce the impacts of coastal flooding in Bangladesh. Specifically, a system
dynamics model was constructed to simulate the effect of flood mitigation methods on the
population, rural economy, housing and welfare of Bangladeshis in the coastal district of
Cox’s Bazar, if they were to be implemented. The model shows how microfinance-based
investments could endogenously contribute to such infrastructures, in the context of policies
for climate adaptation and rural development. Additional policy insights were observed
by analyzing overall system behavior using techniques for Exploratory System Dynamics
Modeling and Analysis (ESDMA).
1 Background
The People’s Republic of Bangladesh is situated on a low-lying delta formed by the con-
fluence of the Ganges, Brahmaputra and Meghna rivers. In addition, its proximity to the
Himalayan range, as well as the local climate’s pronounced monsoon seasons, all combine to
(Alexander, 1993). For exam-
ple, extreme monsoonal flooding inundated more than 60% of Bangladesh’s land area in 1988,
make Bangladesh prone to severe flooding on a yearly basis
killing close to 45,000 people and displacing another 28 million (Dartmouth Flood Observatory,
2008).
While the immediate death toll due to these natural disasters is of obvious concern, damage
to shelters and source
s of livelihood, especially agriculture and aquaculture, yield further
long-term implications that exacerbate the causes of poverty in this developing country.
However, the tropical climate is also of benefit to the Bangladeshi people; regular monsoons
and fertile floodplains help rank the country among the top ten rice producers in the world,
among other crops (Papademetriou, 1999). In fact, the dependence of agricultural output
on the local climate was demonstrated in recent years when effects of El Nifo dramatically
reduced production levels across Asia (FAO, 2010).
The tenuous relationship of both prosperity and adversity with the Bangladeshi climate has
not been good for the economic growth and development of Bangladesh. Indeed, Bangladesh
GDP per capita is ranked 166th by the World Bank (2013). Nearly 50% of the population
is below the national poverty line, which corresponds closely with the fact that 45% of the
population is employed primarily in the rural agriculture sector (CIA, 2013).
The Bangladeshi predicament arising from its geographic characteristics is made worse by the
threat of climate change (UNDP, 2007). Not only would the associated sea level rise inundate a
disproportionate landmass within the country, the rising temperatures could potentially result
in more frequent and more severe cyclones. Moreover, the encroaching salinity of soil with
rising sea level would also reduce the productivity of nearby agricultural land, in addition to
damaging fields inundated with seawater (Hossain & Hossain, 2012).
One World Bank project (2000) calculated an estimated 10 cm, 25 cm and 1 m increase in
sea level by 2020, 2050 and 2100 respectively. These scenarios are projected to result in
the effective loss of up to 17.5% of Bangladeshi landmass
imminent priority to be dealt with, even alongside shorter-term development efforts.
by 2100, making climate change a
The challenge for Bangladesh is thus to take advantage of its climactic suitability for agricul-
ture while minimizing the devastation caused by extreme weather, in order to foster robust
development. In the coastal regions of southern Bangladesh, for example, the major economic
drivers are agriculture, aquaculture, salt cultivation and tourism (Hossain & Hossain, 2012),
all of which are threatened by seasonal disasters and well as the long-term changes expected
from climate change. As such, adaptation and the mitigation of local impacts are the most
practical options available to populations in this region.
While Bangladesh has evaluated policy frameworks involving coastal flood mitigation in the
past, these initiatives are considered to have failed to significantly address the problem (Sid-
diqui & Hossain, 2006). Debate is ongoing as to why these initiatives failed; the lack of national
funds for the continued maintenance of the measures is considered to be the primary reason
(Siddiqui & Hossain, 2006). The Bangladesh Flood Action Plan (FAP), for instance, was an
initiative from 1989 to attempt to control flooding with sluice gates and secondary embank-
ments. However, the policies and plans were not implemented, as the economic returns from
such actions were predicted to be insufficient (Brammer, 2010).
The success of microfinance in Bangladesh
In 1983, Professor Muhammad Yunus initiated the Grameen Bank Project (GBP) to accelerate
the uplifting of the poor in Bangladesh (Mahjabeen, 2008). Specifically, the GBP tackles abject
poverty by employing microcredit schemes, empowering poor entrepreneurs to set up micro-
entreprises. The GBP model is considered a success, with close to 140 billion Takas (1.77
billion USD) in total assets and close to 8.5 million members (loanees), who collectively own
95% of the bank (Grameen Bank, 2012).
The successes of the GBP and other microfinance institutions (MFIs) are laudable. However.
micro-lending schemes typically address the symptom of dire poverty whereas one of the
temic causes ~ social and economic instability due to flood damage ~ is not directly addressed.
In parallel, microfinance and community-based initiatives have increasingly been studied as a
possible approach for funding climate adaptation in developing countries (Agrawala & Car-
raro, 2010; Francisco, 2008). As such, there remains significant potential for the further
development and refinement of microfinance schemes in the specific context of Bangladesh.
2 Model objectives
The system dynamics approach applies principles of control systems to diverse si
ts of problems.
Many real-world processes are driven by complex systems, characterized by time dependence
and intricate feedback mechanisms (Pruyt, 2010). As such, the SD approach can highlight the
true impact (or lack thereof) of proposed solutions. Good models also help decision-makers
visualize and clarify the relationship between the system, its underlying structures and time.
In this paper, the model serves an additional purpose as well. As a preliminary approach to
the problem, it is used to evaluate the potential of investments in flood mitigation and their
long-term impacts, so as to identify important causal drivers and areas for further investi-
gation. This aspect is vital in the Bangladeshi context, as the country has limited reserves
and resources to tackle multiple crises such as flooding, poverty, economic development and
agricultural productivity, among others. As such, presently, actions that yield the greatest
benefit in the least time using least resources are prioritized. However, demonstrating the
complex interplay of flooding on all other sub-systems may highlight the benefits of strategic
long-term policies.
While there are existing studies that evaluate the success of the Grameen Bank (and other
MFIs), the literature review did not yield any exploratory studies that have investigated
the overall relation between the MFIs, agriculture, population, micro-enterprises, welfare and
development and housing. Moreover, the systemic effects of flooding and flood-related damages
are not adequately addressed in the financial evaluation of MFI sustainability.
The goal of this paper is to explore the possibility of utilising the collective wealth of regional
MFIs to sustainably finance long-term flood mitigation infrastructure, such as coastal break-
waters and extensive drainage. Specifically, the paper focuses on the economically significant
coastal district of Cox’s Bazar in the Chittagong Division in south-east Bangladesh.
A system dynamics approach to this issue is a practical way of exploring the impact of flood
mitigation on the inter-related interactions between the relevant sub-systems. This is par-
ticularly important because constructions like coastal breakwaters are significant investments
with long construction delays. Furthermore, these constructions themselves may be damaged
intermittently by the yearly flooding during the construction period, adding extra time and
costs to construction.
The authors hypothesize that investment in long-term flood prevention would indeed positively
impact the Cox’s Bazar region, both economically and socially, while still allowing the MFIs
to perform sustainably (i.e. without compromising their core mission of funding conventional
micro-loans).
The results of the analysis may be particularly useful in a Bangladeshi context; large-scale
infrastructure projects are normally initiated and overseen by national or regional governments.
However, in the case of Bangladesh, the low levels of economic development as well as more
immediate priorities such as eliminating abject poverty may not be favourable for such sizable
and therefore risky investments.
On the other hand, if the local MFIs, of which a majority stake is owned by the loanees/members,
were to invest a portion of their assets into such a project, there is potential for catalyzing in-
vestment in large-scale infrastructure for public benefit. It would therefore crucial to evaluate
the potential benefits as well as the risks of this approach, so as to establish its feasibility and
promote further analysis.
3 The model
3.1. Conceptual model
The following figure is a high-level causal loop diagram for the system dynamics model, which
shows the interplay between the different sub-systems. The model boundary is limited to
the Cox’s Bazar District in South-Eastern Bangladesh. Cox’s Bazar is chosen as it is one of
the coastal areas that are significantly affected by annual flooding; furthermore, there was
sufficient empirical data available to model the sub-systems appropriately.
- Profits in MFI
sector
Agricultural Landlessness
PE a ra j 3 i
[° Deaths — | | I
¥ “y +N
2% ‘ MEI . Available
| . Adult applicants > ail loans 4 ___. MFI funds
Human and
economic
development
Figure 1: Aggregated causal loop diagram of the model
Cox’s Bazar as a whole is modelled as operating primarily on an agrarian economy, with heavy
emphasis on seasonal harvest of rice. The working population are initially unskilled adults
who will typically end up in farming-related employment.
Within this context, the existence of the MFIs allows some enterprising individuals (partic-
ularly women, as per the GBP’s credit policies) to take loans to start micro-enterprises and
augment the families’ basic income from agriculture. The micro-enterprise ecosystem results
in a new segment of the local economy, which complements the agricultural economy. The
MFIs also help reliable borrowers with additional loans to build their own houses, which they
are otherwise not able to, since materials for more permanent constructions are relatively
expensive. The houses are themselves at risk during extreme flooding events, in addition to
natural degradation over time, contributing to landlessness. MFI loans are thus prioritized
for applicants who do not own houses (i.e. landless), since their needs are more urgent.
The MFIs are tied to the success of these micro-enterprises in order to remain financially
sustainable. The MFIs sustain themselves primarily from the repayments (with interest) and
compulsory savings by the loanees. Additionally, as the most organized financial institu-
tions closest to the population, they receive some portion of the financial aid and disaster
relief funds that reach Bangladesh yearly. This financial aid is also vital for the establish-
ment and functioning of NGOs and welfare institutions such as schools, clinics and other such
development-related agencies. The number of welfare and development institutions then influ-
ences the community acutely. For example, better education and widespread family planning
information can be assumed to decrease the birth rate (Banerjee & Duflo, 2011).
The floods in Cox’s Bazar are either monsoonal or coastal floods (Hossain & Hossain, 2012),
and these occur seasonally. However, the severity of the flooding can vary from year to
year. While the typical loss of lives due to flooding is relatively small compared to the total
population, the flood-related damage has a great impact on the local economy, agriculture
and housing. For example, flooding not only damages crops on the field, but also spoils grains
in storage. Without a long-term strategy to directly tackle the flooding problem, the system
is chronically unstable. As such, the proposed flood mitigation policy is primarily aimed at
reducing the severity of flooding. In addition, the policy must minimize the damage caused
by flooding to individual sub-systems by various means, e.g. reinforcing and maintaining
structures to reduce susceptibility to flood-related damage.
3.2 Simulation model
s of 8
sub-systems: Housing, Welfare & Development, Agriculture, Flooding, Population, Micro-
Appendix I presents the stock-flow model as implemented in Vensim 6.0. It con:
enterprises, Flood Mitigation and MFI Resources.
3.3. Model testing and analysis
The figures below depict several key performance indicators for a baseline scenario, without
MFI-funded investments in flood mitigation.
1e9 MFI funds for lending - baseline jeg _ Agricultural production - baseline
6
45
5
4.0
4
3.5
80: 23
25
2
20
j
15)
"Soi0 2080 2080 5040 5050 2060 2070 5080 F090 Soi0 3020 5030 2040 2050 5060 2070 2080 2090
Houses - baseline Fraction of landless families - baseline
450000 0.55
400000 0.50
350000 0.45
300000 0.40
250000 0.35
010 2020 2030 2040 2050 2060 2070 2080 2090 S10 2020 2080 2040 5050 2060 2070 2080 2090
Number of microenterprises - baseline Parr} Number of MFI borrowers - baseline
900000
150000
800000
100000 eee
700000
50000
600000
010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090
Figure 2: Baseline model results
Although the microfinance subsystem maintains its profitability and consistently generates new
investments, the output of the agricultural subsystem tends to decline over time due to the
increasing impacts of climate change and coastal flooding on productivity. Furthermore, the
fraction of landless famil: stable; in addition to direct flooding damage,
essentially remains
this can be explained by observing that flooding severely reduces the area of cultivable land,
with significant implications to harvest volumes. As such, the resulting profits from agriculture
tend to decline in the long term. Since a significant portion of the profits are used to prepare
cultivable land for the next harvest, the resulting feedback loop tends over time to decrease
overall harvest volumes and profits, and increase landlessness.
4 Policy analysis
The results obtained from the model illustrate the significant impacts of seasonal flooding:
although MFIs contribute to development in the short term, the lack of long-term investment
in flood mitigation eventually leads to a significant decrease in agricultural production. This
section presents a proposed policy in which three different mitigation measures would be
initiated, starting from 2025:
e Yearly inv
stments to reinforce the structural resilience of weakened buildings in coastal
communities, thus increasing their lifespan;
Better drainage (e.g. sluice gates and secondary embankments) to drain flood water more
efficiently and reduce damage;
Construction of floating /under-surface breakwaters at sea, reducing the severity of coastal
flooding.
The investments are assumed to be partially funded by MFIs, in a proportion varying from
10% to 40% depending on the type of infrastructure. The figures below present preliminary
results from the policy case:
5.018 MFI funds for lending - policy 4e9 Agricultural production - policy
6
45 Baseline Baseline
Flood mitigation policies 5 Flood mitigation policies
4.0
4
3.5
3.0 23
25
2
20
1
15)
"Soi0 2080 2080 5040 5050 2060 2070 5080 F090 Soi0 3020 5030 2040 2050 5060 2070 2080 2090
Houses - policy az Fraction of landless families - policy
Baseline 065 Baseline
450000 Flood mitigation policies Flood mitigation policies
0.60
400000
0.55
350000 0.50
0.45
300000
0.40
250000
0.35
010 2020 2030 2040 2050 2060 2070 2080 2090 S10 2020 2080 2040 5050 2060 2070 2080 2090
Number of microenterprises - policy Parr} Number of MFI borrowers - policy
Baseline — Baseline
s00000 Flood mitigation policies — Flood mitigation policies
150000
800000
= eee
700000
50000
600000
010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090
Figure
Model results with flood mitigation polic:
The flood mitigation policies appear to have significant benefits for the agricultural sector, st:
bilizing long-term production despite the increasing severity of climate change-driven flooding.
In parallel, the policy measures have a direct impact on the available housing stock and the
fraction of landless families. Although the investments financed by MFIs decrease available
resources in the short term, these investments then generate increased output over the long
term, which is reflected in the funds available for lending. This outcome demonstrates that,
with proper financing and control, partial community-based funding for large-scale infras-
tructure development may have significant potential. This is very encouraging and can be
a good basis to begin serious consideration of flood mitigation policies in collaboration with
community-owned MFIs.
The policy mitigation case was further analyzed by evaluating the system’s behavior over a
range of parametric uncertainties, summarized in the table below:
Variable Unit | Min. value | Max. value
‘Average basic loan amount Tk 6000 14000
‘Average basic loan repayment period Year 05 2
‘Average housing loan amount Tk 15000 35000
‘Average housing loan interest rate % 0.06 0.12
Average housing loan repayment period | Year 4 8
‘Average loan interest rate % 0.08 0.18
Average size of loan group : 3 6
e yearly wages per employee _| Tk/year 7000 12000
Breakwater efficiency per km 1/Year 04 08
Cost of coastal protection per m Tk/m 180000 320000
Cost of drainage per m Tk/m 3000 6000
Drainage efficiency per km| 1/Year 0.003 0.01
Loans per group : 1 3
Percentage basic loan set aside % 0.01 0.08
Table 1: Summary of parameter values for uncertainty analysis
vity analysis was performed for 1000 runs with Latin Hypercube sam-
A multivariate sens’
pling. The figures below present the resulting uncertainty envelopes for agricultural production
and available MFI funds, in the baseline and policy scenarios; the panel to the right of each
line graph presents the Gaussian kernel density estimator (KDE) of both scenarios, at the
final time of the s The KDE for the poli enario over the full duration of the
simulation is further detailed in the waterfall plots below.
A Simulation Model of Katouzian’s Theory of Arbitrary State and
Society*t
Saeed P. Langarudi*
Email: slangarudi@wpi.edu
Michael J. Radzicki*
Email: mjradz@wpi.edu
Abstract: This paper represents an initial effort to model the volatile behavior of Iran’s socio-political-economic system.
More specifically, Home Katouzian’ s theory of Iranian political economy—a well-established descriptive theory of Iran’s
unstable i into a system dynamics model, tested for internal consistency, and used for
policy analysis. Simulation results re Katouzian’s claim that periodic episodes of significant arbitrary power are key to
understanding the historically less-than-optimal behavior of the Iranian socioeconomic system. They also confirm the
significance of oil revenue, economic sanctions, and civil resi on Iranian i Of note is that
experimentation with the model reveals that educational policies that generate increased respect for the law by Iranian citizens
can significantly improve the behavior of the Iranian socioeconomic system. The paper concludes with suggestions for future
research.
Keywords: Iran, Katouzian, Social Chaos, Arbitrary State, System Dynamics
* The authors would like to thank Homa Katouzian, the participants of the Collective Learning Meetings at Worcester Polytechnic
Institute, and four anonymous referees for their thoughtful comments on earlier drafts of this paper. Any errors of omission and/or
commission in this version of the paper are the sole responsibility of the authors.
"This article is an abbreviated version. The full version of this paper will be published in a special issue of Forum for Social Economics
(Langarudi and Radzicki 2015). The interested reader is encouraged to read the full article there.
* Social Science & Policy Studies, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, USA.
109 Agricultural production 109
Baseline
Flood mitigation policies
abi0 20202030 2040 2050 2060 2070 2080 2 426-10
Figure 4: Comparison of uncertainty ranges for agricultural production with and without flood mitigation
KDE for agricultural production over time
x10”
Kernel density estimate
2040
4
2
x10° Production (kg)
Figure 5: Kernel density estimator over time for agricultural production in the flood mitigation scenario
1e9 Funds available for lending 109
| [— Baseline
—_ Flood mitigation policies
7
6
5
x
4
3
2
4
2010 2020-2030 «2040-2050 «2060: «2070 «2080-2090 2.7610
Figure 6: Comparison of uncertainty ranges for available MFI funds with and without flood mitigation
KDE for available funds over time
Kemel density estimate
15 Available funds (Tk)
Figure 7: Kernel density estimator over time for available MFI funds in the flood mitigation sc:
These results appear to support the insights gained from the initial model runs: the distribu-
tion of outcomes for agricultural production is significantly different for both scenarios, with
values being clustered higher for the policy case, yet the distribution for available funds re-
mains comparable in both cases. As modeled, the level of infrastructural investment provided
by MFIs would therefore not detract from their core functions for micro-loans and housing
credit.
a
5 Discussion and recommendations
In building the model, and clarifying the complex relationships between the relevant sub-
systems, the systemic damage caused by seasonal flooding in Cox’s Bazar has been illustrated.
In parallel, the preliminary results from the model show that microfinance-supported invest-
ments in flood mitigation methods may benefit coastal districts without compromising the
long-term financial sustainability of MFIs. In extension, it is intended to highlight the poten-
tial benefits for long-term flood mitigation for Bangladesh.
While microfinance has over the years at least overcome initial skepticism, the model tends to
reinforce the hypothesis that MFIs may be unable to eradicate poverty on their own due to the
systemic perturbations induced by natural disasters in the region. However, the strategic use of
MFI funds for infrastructural investment, combined with comprehensive strategies to directly
mitigate disasters and the threats posed by climate change, may enable regional microfinance
to play an increasingly significant role in the long-term development of Bangladesh’s coastal
districts.
The links between development rate and flooding, development and birth rate, flooding and
agricultural productivity, MFI loans and landlessness, among others have also been demon-
strated. These relations clearly highlight the complex interplaying dynamics of the system,
and the extent to which flooding does more than just short-term property damage: floods
significantly affect the rate of development of Bangladesh. This further justifies long-term
investments in flood mitigation.
le infrastructure
In particular, the proposition of community-based investments for large-
projects has shown promise in the model, as opposed to the traditional reliance on private or
public s
ector investments. While the MFIs contribute only a fraction of the required finances
for such projects, the potential of such initiatives is put into perspective by keeping in mind
that the MFI pool represents the collective wealth of some of the poorest communities in a
single district in Bangladesh. As such, the model suggests that microfinance could reliably
contribute to long-term, high-investment development projects.
As mentioned earlier, the model used in this paper is preliminary, and is intended as a first
approach to illustrate the long-term negative effects of chronic flooding, and the potential
of mitigation policies to alleviate the situation. Future work will focus on testing a more
detailed representation of MFI funding mechanisms against empirical data, as well as on the
regional economy of Cox’s Bazar. Addtional research should investigate region-specific models,
with additional factors like urbanisation, river flooding and the relationship between saltwater
intrusion and agricultural productivity, among others. In building such region-specific models,
an exploratory modelling analysis is recommended to evaluate the robustness of policies under
deep uncertainty (Pruyt et al., 2011).
In summary, the current model justifies the investigation of strategic flood mitigation poli-
cies, while highlighting the potential contribution of community-based initiatives for climate
adaptation. With more detailed, region-specific models that can more accurately correspond
with statistical behaviour, the true impact of flood mitigation measures can be elucidated.
Hopefully, these feasibility studies will further justify the call for long-term solutions.
12
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4
Appendix 1: Model specification
The following sub-sections detail each sub-system individually.
The core of the population sub-system is a typical aging chain, from children to young adults, working
adults and finally to retirees. Young adults mostly end up in the unskilled labour force, of which a
majority are employed in the agriculture industry with some unemployed individuals. The existence
of the MFIs grants entrepreneurial female young adults an alternative to becoming employed in the
local agriculture industry - namely to become micro-entreprencurs. The restriction to only females is
justified by GBP’s prevailing policy where 97% of loances/members are women (Grameen Bank, 2012).
MFI loan
are approved when potential loances meet certain condition: .
is the primary factor to get a loan from the MFIs. Landless MFI applicants may become approved
in this model, landles
MFI borrowers, while the rest are unsuccessful and end up in the unskilled labour force. A small
percentage of MFI borrowers may default their payments even after the grace period of 12 months (a
total of 2 years). In this case, the defaulters are also removed from the pool of MFI borrowers. On
the other hand, loyal borrowers who pay back their loans within the allotted time are given the option
of borrowing another time (returning borrowers). Only returning borrowers are trusted with housing
se the bas The side-effect
is that these micro-enterprises also create a secondary avenue of employment for some in the unskilled
loans. MFI borrowers c loans from the MFIs to set up micro-enterp!
labour force - namely as employees.
Each year, in Bangladesh, there is a serious hunger season right after the sowing of the future crops,
locally referred to as “monga”. While monga is very pronounced in northern Bangladesh, there are
still spillover effects in the south, resulting in a significant scasonal problem. This yearly crisis results
in a significant number deaths due to malnourishment or starvation. In the model, the hunger
season
is assumed to claim only landless, unemployed unskilled labourers who may be unable to afford food
during the lean season.
The other variables of interest in this sub-model are family planning factor and medical advances factor,
which influence the birth rate, infant mortality rate and the (natural) death rate. These variables are
themselves influenced by the extent of development in the region, dictated by the number of welfare
and development institutions in the region.
15
stioctaene
‘apricot
mage
petal atv
tues
sspgurenn pe
wha ‘esi pep
roca wanicg
sBercfgonl
ro)
soul
cine
_ conartie yee _
avg wea of agricutturd! land ‘harvest time “ _
pee
rie pt Getibce ine
soonest Redstation
Besant
As mentioned carlier, the majority of the unskilled adults are assumed to be employed in local agricul-
ture. This sub-system keeps stock of the agricultural productivity of the system. The total agricultural
produce is governed by the inflow from harvests and the outflows of consumption by farmers and
oilt due to the floods.
produce for sale, Some of the produce is also
The quality of the harvests are determined by the area of tilled land available as well as the fertility of
the land. The latter can be improved with some investments from the profits of the sales, e.g. fertilizers
Since the main food crop is rice, which has a distinct harvest period, the productivity is highly
Of the accumulated produce, the model assumes the farmer population (including their families) covers
their own consumption, with a portion of the rest being put for sale for the local population. Here
there counter-intuitive behaviour between the price of ric
and the average consumption per farmer
per year. It has been observed that when the market value of crops is higher, the average consumption
by the farmers goes down; this is because the poor farmers prefer to capitalize and make more profits
than eat healthily. On the other hand, when the market value is lower, they cat more healthily.
While there are profits from the sales of the produce, this money does not end up in the MFI circulation
because there is no incentive nor obligation for farmers to put it in the bank. Instead, these profits
are used to propagate better agriculture, i.c. irrigation and tilling of cultivable land and increasing
farming efficiency.
16
(ee Sey
soghouigione Sy apie eae
aamectnoeengicne “4S
yeauceategnses
In this sub-model, the MFIs present in Cos
represent the aggregate role of gencric institutions. The major inflows are financi
s Bazar are simplistically lumped together, in order to
1 aid, di
joans and housing loans),
ter relief
funds, loan repayments and savings. The outflows are loans disbursed (b:
operational expenses and flood mitigation expenditures (policy).
Financial aid comes in on a yearly basis whereas disaster relief funds are immediately following floods
and proportional to the severity of the flood damage. The number of basic loans disbursed is propor-
tional (but not equal) to the number of approved loance:
s, as the MFI model works on the principle
of shared liability (Grameen Bank, 2012). A portion of the loan amount (roughly 5%) is mandatorily
set aside in a savings account; this functions as both a savings deposit for the borrowers as well as in-
surance against defaulters. Repayments comprise of loan amounts returned by loyal borrowers (within
one year) and others (within two years). Housing loans on the other hand, are given exclusively to
loyal borrowers, to be repaid in five years. While there are defaulters, the net amounts being lost
are negligible enough to assume these debts are written off. This corresponds to the GBP practice of
not litigating against defaulters (Grameen Bank, 2012). However, defaulters are permanently removed
from the pool of eligible loane
The operational expenses of the bank are relatively high due to the particular operating conditions
of MFIs, e.g. representatives personally visit villages on a weekly basis to collect repayments as well
as to recruit new members. An additional assumption in this sub-model is that only loanees go on
to become micro-entrepreneurs, and that only the profits from micro-enterprises contribute to the
repayments. This implies that the sustainability of the MFIs is primarily dependent on the suce
This
operations, there was significant poverty and a minimal number of micro-enterpris
arted
suggesting a
of local micro-enterpris umption may be justified by the fact that before MFIs s
significant portion of new cash flows are the direct result of investments from microfinance loans.
Finally, an outflow from MFI funds finances the flood mitigation measures (as described in the pol-
icy r dations). As ioned earlier, large-scale infrastructure developments like these are
normally financed by government funds. However, this is practically not possible in the Bangladeshi
context. On the other hand, MFIs are not purely private institutions, and could possibly use member
funds to finance local infrastructure development.
17
epi peinteasicetenions
i es
This sub-model consists of two structures; the structure on the right is the stock of the number of
active micro-enterprises and the structure on the left determines the monetary flows in and out of
these enterprises. The primary assumption is that cach basic loan corresponds to the start-up of a
new micro-enterprise. There are also some enterprises which are destroyed due to the floods. From
the number of enterprises, the number of jobs created can be calculated; the number of employees
combined with the number of farmers yields the employed fraction of the unskilled labour force.
ltipli
The structure on the left represents the effect of investments going into micro-enterp
this way, net revenue is calculated. A fraction of the net revenue is designated as net profits from all
the micro-enterprises. Two competing influences affect micro-enterprise productivity: below a certain
threshold, the more micro-enterprises there are, the higher the profitability — because the local economy
functions like an interdependent eco-system. On the other hand, if there are too many micro-enterprises
within a small area, there is over-competition with net losses.
=
‘institutions: J
tecelapment cenaeet
<afeatcteal
damage
development ate
Welfare and development institutions refer to every form of social welfare, including NGOs, schools and
hospitals, among others. These constitute the development backbone of the ity. The rate of
1
de is primaril on the inve s in development, i.c. proportional to incoming
financial aid. In turn, the level of development influences birth rate, death rate, infant mortality rate
as well as food distribution.
18
a:
‘houses (re)built
resilience of
_
lle
Low-cost housing is assumed to have a natural lifetime of ten years after which they must be demolished.
On top of this, yearly flooding also damages a large number of houses leading to increased landlessness
among the population. The assumption is that the houses are too expensive to rebuild without a
housing loan from the MFIs.
used in the calculation of ratio of landless families, which in turn is the prime
The number of houses
driver of loan approvals by the MFIs.
agricultural tand
damage
flood switch
yerly cyclone
Sequency
,
noone,
Sequency of
cyclonas
In this sub-model there are no explicit stocks and flows. Instead there are seasonal pulses of monsoons
19
Over the last century Iran’s economic growth has been fairly unstable, primarily due to the dynamics of its political
atmosphere (Issawi 1971; Bharier 1971; Floor 1998). The unsteadiness of Iranian economic growth can be seen in the time
series data presented in Figure 1. In this figure Iranian GNP data is divided into two periods—1900-1960 and 1960-2010—so
that the instabilities in the Iranian economy can be clearly identified.
Iranian GNP (1900-1960) Iranian GNP (1960-2010)
350 700,000
300 600,000
Abdication of Reza Shah (1941) Revolution (1979)
250 500,000
400,000
300,000
200,000
100,000
SIS/CIA coup d'état against Mossadegh (1953)
0 0
1900 1910 1920 1930 1940 1950 1960 1960 1970 1980 1990 2000 2010
Figure 1: Real Iranian Gross National Product—in Billion Rials at constant prices*
An inspection of the figure reveals that the same qualitative pattern of behavior exists in both time periods — i.e., GNP
initially grows exponentially until a political disruption takes place. During the 1900-1960 period Reza Shah was forced to
abdicate during the Anglo-Soviet invasion of Iran in 1941, which was followed by a coup d'état against the democratic
governance of Mohammad Mossadegh in 1953. In the 1960-2010 period the political system endured a crisis in the mid-
1970s that precipitated the 1979 revolution. In both cases the Iranian economy collapsed after a period of political instability
and it took a while for it to return to its previous pattern of growth.
In terms of a more generic and simplified pattern of behavior, the dynamics inherent in Figure 1 can be portrayed by the
growth-stagnation-growth time shape presented in Figure 2. Arguably, a useful theory of Iranian socio-economic
development should be able to replicate this qualitative mode of behavior.*
4 Figure 1 was created from a combination of two datasets. The first source of data, shown in the left-side diagram, comes from the
work of Bharier (1971, 59), who provides a realistic estimate of Iran’s real GNP from 1900 to 1960 in constant 1959 prices. The second
source, shown in the right-side diagram, comes from the online portal of Iran’s Central Bank (CBI 2014), which provides data on Iran's real
GNP from 1959 to 2010 in constant 1997 prices.
5 Saeed (1992) argues that complex dynamic behavior modes should be “sliced” into simpler qualitative time shapes (i.e., reference
modes) so that a system dynamics modeling effort can be directed toward capturing the feedback processes that generate them.
and cyclones combined with randomizers yielding variable severity over the years. In turn, severity is
used to calculate the specific damage to agriculture and infrastructure.
pelo
deg onc pe
a
poems 2
egeensanen
ge i pn
‘stractura! resilience ee ‘food murtgatin |
‘pethods
Biciency ofbresiwat
~~ ate
‘ost ofceuntal protection
policy sltch 3
In the final sub-model of the system, three different mitigation measures are initiated starting from
year 10 of the simulation. The first measure is yearly investments in reinforcing the structural resilience
of weakened buildings in the ity, thus i ing the lifespan. The second measure is better
drainage (e.g. sluice gates and secondary embankments) to drain flood water more efficiently, in order to
minimize damage. The third and most expensive measure is the construction of floating/under-surface
breakwaters at sea, to reduce the severity of coastal flooding.
In combination, these measures are designed to nullify between 25 to 50 percent of flood related damage.
The funds to finance these measures are partially drawn from the local MFIs.
20
Figure 2: mode the jitative behavior of Iranian GNP
Since the 1970s there have been many attempts to explain the distinctive dynamics of Iran’s macro economy.® The
literature on Iranian economic development is vast and can be broadly divided into two major groups: quantitative analyses
and qualitative (descriptive) studies. Quantitative analyses’, mostly econometric models, are highly dependent on numerical
data and thus intrinsically unable to explain Iran’s long-term economic dynamics because most Iranian time series data only
goes back to 1959. The reliability of these data is also suspect (Amuzegar 1997). Moreover, the effect of political factors such
as revolution and war are normally represented as exogenous inputs into these econometric models, which implies that these
phenomena are created by external forces. In fact, the very nature of the methods employed in these studies prevents a
modeler from integrating Iran’s socio-political system into a model of its economic system. As a consequence, most of the
modeling studies undertaken by mainstream Iranian economists have been unable to incorporate those features of Iran’s
socioeconomic system that are key to understanding its dynamics. Stated differently, most quantitative analyses have utilized
factors that are merely the result of the complex interrelationships that comprise the Iranian socioeconomic system, rather
than the root causes that define the system’s complex interrelationships and that generate its dynamics.
Qualitative studies of the Iranian economy, on the other hand, go far deeper into the very complicated and interrelated
feedback structures that define the Iranian socio-political system. Some of these studies are more general and try to explain
the causes of relative economic underdevelopment in eastern societies’, while others are case studies that specifically focus
‘on Iran’s socio-economic system and explain “why Iran lagged behind while the west moved forward.”?"° Although these
studies provide more detailed—and hence more realistic—explanations for the system’s behavior, they lack two important
features that are crucial for rigorous scientific work. First, they cannot generate synthetic data that can be formally compared
to numerical data from the actual system. Second, rigorous policy analysis is not possible because they cannot be used to run
controlled experiments.
The purpose of this paper is to provide a rigorous explanation for Iran’s pattern of unstable economic growth. The system
dynamics model put forth in this paper is based on the work of Homa Katouzian (1978; 1981; 1997; 2003; 2004; 2009; 2010;
2011), an economist and historian who created a well-known socio-political-economic theory of Iranian economic
development. The approach taken in this paper is to retain the richness of a qualitative study of the Iranian socio-political-
economic system and combine it with the rigor of a quantitative analysis.
System dynamics has already been used to test complex, nonlinear, and feedback-rich descriptive economic theories.’
In the case of Iran the first, and arguably most important application of system dynamics to economics was put forth by
Mashayekhi (1978). Mashayekhi developed a system dynamics model to analyze Iran’s long-term economic development
options made possible by its oil revenue. Since the focus of this model was oil revenue and its use in economic development,
and not the more general issues associated with Iranian political economy, it cannot be used to explain Iran’s long-run
a
5 See Esfahani et al. (2012) for a comprehensive review of Iranian macroeconomic modeling efforts.
7 See for example Habib-Agahi (1971), Bharier (1973), Heiat (1987), Valadkhani (1997), and Becker (1999).
8 Some of the most well-known theories in this area are “the Asiatic mode of production” of Karl Marx (Shiozawa 1966), Max Weber's
“theory of social and economic organization” (1947), and Wittfogel’s “oriental despotism” (Wittfogel 1957).
° This question is the title of a popular book in Iran written by Kazem Alamdari (2010).
29 See for example (Katouzian 1978; 1981; 1997; 2003; Ashraf 1980; Tabatabaei 2001; Arianpour 2003; Peyman 2003; Piran 2005;
Alamdari 2010).
11 Radzicki (2009) reports some of these efforts in his paper.
socioeconomic dynamics.” That said, beyond Mashayekhi’s work there has been no serious system dynamics modeling effort
aimed at analyzing the dynamics of the Iranian socio-political-economic system.
This paper represents an initial effort to model the dynamics inherent in Iran’s socio-political-economic system. More
specifically, Homa Katouzian’s theory of arbitrary state and society'—a very well-established descriptive theory of Iran’s
unstable economic development—is translated into a system dynamics model,” tested for internal consistency, and used for
policy analysis.** Initially, the model’s ability to mimic the irregular dynamics of the Iranian economy is presented. Then, the
model is used to test different scenarios and policy prescriptions aimed at improving the behavior of the Iranian
socioeconomic system.
In terms of building confidence in the Katouzian model, validation tests show that its dynamic behavior is consistent with
the qualitative behavior of both Iranian historical data and Iran’s socio-political-economic dynamics as described by Katouzian
in his theory.
In terms of simulation experiments the effects of both oil revenue and the citizenry’s respect for the rule of law on Iranian
economic development were examined. It is shown in this paper that periodic episodes of significant arbitrary power are key
to understanding the historically less-than-optimal behavior of the Iranian socioeconomic system. Simulation results indicate
that if Iran was a less arbitrary system it could experience a greater pattern of economic, social, and political development.
The results also show that although oil revenue has had a substantial impact on the economy it has had little effect on the
overall behavior of the Iranian socio-political system. Oil revenue helps the state to accumulate more power but doesn’t
change the generic cycle of “arbitrary rule-chaos-arbitrary rule.” Additional simulation experiments examined the impact of
economic sanctions and civil resistance on the political economy of Iran. From simulations of the Katouzian model it was
possible to generate some insight into the types of policies that might be effective in improving the dynamics of Iran’s socio-
political-economic system.
The purpose of this paper was to shed some light on the issue of the underdevelopment of a nation with an unstable
socio-political environment using Katouzian’s theory of Iranian political economy. Therefore, the boundary of the model was
limited to Katouzian’s theory of Arbitrary State and Society. The analytical capabilities of the model are thoroughly explored
and reported in this paper. In particular, it is shown that the model—if customized and elaborated appropriately—can be
applied to address the impact of socio-political-economic factors such as resource abundance, economic sanctions, civil
resistance, cultural transformation, etc., on the system as a whole.
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