System Dynamics for Marginality Analysis Workshop organized at BRAC Center, RED, Dhaka, Bangladesh Feb. 20, 22, 23, 2011 Franz Gatzweiler, Center for Development Research, ZEF Bonn Summary The workshop was part of the Bill and Melinda Gates Foundation funded planning project on Marginality. It aimed at testing a system dynamics tool for analyzing the root causes of extreme poverty and its suitability to capture the perspectives of the poorest themselves. It was carried out at BRAC headquarters in Dhaka, Bangladesh by going through each of the steps with a group of junior and senior level researchers from BRAC‐RED, based on a case study of an extreme poor rikshaw puller in a semi‐urban slum who has migrated from the countryside. The method was found to be complimentary to the methods used by BRAC to identify the extreme poor. It is suitable to identify key variables of the life systems of the poorest, explain the variable’s behavior within the system and analyze the impacts of interventions by changing system variables. The method can be applied in the field by making use of various PRA techniques. Apart from familiarizing participants with system thinking and the specific system dynamics tool, it became clear to the workshop participants, that from a systems perspective, interventions which are efficient or effective from the viewpoint of the intervening organization are not necessarily desired or enduring from the viewpoint of the poorest themselves. Slight differences in the attributes and aspirations of the target group can lead to very different types of interventions. As a next step it was decided to apply the method in the field and in different country contexts. Background Although globally there has been progress in reducing the number of poor, the poorest have not been reached and left behind. Their numbers are stagnating or increasing. They do not have a voice, they are decoupled from the market economy and poverty reduction and development programs are poorly designed to reach them or respond to their needs. This group thereby is a product of multiple failures of markets, institutions and policies. As human lives are at stake, such a situation is extremely inefficient in macro‐economic, and unjust from a human rights perspective. Being excluded, not only from growth, but also from other dimensions of development and progress, is an indication for the extreme poor being at the margin of society. Marginality is frequently cited as a root cause of poverty. It refers to an involuntary position and condition of an individual or group at the margins of social, political, economic, ecological and biophysical systems, preventing them from access to resources, assets, services, restraining freedom of choice and developing capabilities, and eventually causing extreme poverty. The 1 conceptt of marginality refers to t the roott causes of extreeme povertty which can be described in terms of complexes of proximaate and underlying causes, feedback loops, stocks and flows within the lifee systems of the poorestt. Becausee of the complex c
nature of marginality, the nvesting benefitss from in
into thee lives of th
he marginaalized poor can often only be secured in th
he medium or long term. ntext of ZEFF‐BRAC collaaboration fo
or a projectt funded The workshop takees place witthin the con
by the Bill and Me
elinda Gatees Foundatiion and con
nducted byy the Centeer for Development Researcch (ZEF) at the Universsity of Bonn, which aims at identifying the marginalizeed poor, understtanding the root causees for extrem
me poverty and identiffying suitab
ble intervention and develop
pment oppo
ortunities fo
or the margginalized poor. One of tthe objectivves of the p
project is to identtify method
ds which help understaand the roott causes of extreme po
overty, with
h and for the poo
orest themselves. The Vesster Sensitivvity Analysiss© belongss to the group of Systeem Dynamiccs methods. System Dynamics “is a com
mputer‐aideed approach
h to policy aanalysis and
d design. It applies to d
dynamic ms arising in
n complex social, man
nagerial, economic, orr ecological systems ‐‐ literally problem
any dyn
namic syste
ems characcterized by interdepen
ndence, mu
utual interaaction, info
ormation feedbacck, and circu
ular causality.“ (System
m Dynamicss Society, w
www.system
mdynamics.o
org). The Vester SSensitivity Analysis© A
c
consists of computerizzed system tools for the manageement of complexx problem situations – – such as those t
of thee marginalized poor. An A elderly extreme poor refflects this situation weell by sayingg: “Wee are caugh
ht up in a co
omplex knott – other poor people a
also get caug
ght up from
m time to time iin a knot, bu
ut their kno
ots are simp
pler… you ca
an easily detect the sou
urce of the kknot and do so
omething ab
bout it… our knots have many sou
urces… often
n pulling on
n one carelessly only m
makes the kn
not more co
omplex.” Source: M
Matin, I., Munsh
hi Sulaiman, M. and Rabbani, M
M. 2008. Workiing Paper 109, Chronic Poverty Research Cen
nter, March 2008
8, Crafting a Graaduation Pathw
way for the Ultraa Poor: Lessonss and Evidence from a BRAC programme. Unlike o
other System Dynamiccs tools, likee VenSim o
or Stella, the Vester Seensitivity to
ool has a strong mediation capacity an
nd has feattures which
h illustrate complex issues in a way w that they can be understood easiily. It involvves stakeho
olders in th
he modelingg of their ssystems, 2 helps revealing the underlying causality patterns of the marginalized poor and by means of sensitivity analysis allows for policy tests and the identification of leverage points for action. The workshop aims at introducing the method to ZEF partners in Bangladesh, especially members of the BRAC Research and Evaluation Division and to receive feedback on the strengths and weaknesses of the method for marginality assessment in poor rural environments and for designing intervention mechanisms with and for the marginalized poorest. Workshop schedule Su, Feb 20 0900‐1200 Introduction of participants Introduction of: Marginality project at ZEF Vester Sensitivity Analysis as example for SD tool 1300‐1700 System description, definition of system variables Mo, Feb 21 Field trip to nearby slum
Tu, Feb 22 Impact matrix (group excercise) We, Feb 23 0900‐1300 Identification of the role of each variable within the system Building the effect system, Scenarios, Simulation 1400‐1600 Feedback and discussion: Suitability of Vester Method for Marginality analysis in the second phase of the project and for usefulness of the method for BRAC Workshop summary Workshop participants 1 2 3 4 5 6 7 8 9 10 11 12 Name Dr. Franz Gatzweiler Dr. Md Abdul Malek Ms. Aniqa Tasnim Hossain Ms. Raniya Shams Mr. Md Enamul Huq Mr. Kairul Hasan Dr. Shyamal C. Ghosh Ms. Nusrat Zaitun Hossain Mr. Sanjit Roy Mr. Narayan Chandra Das Jörg Volkmann Dr. Rezaul Karim Organization Center for Development Research (ZEF Bonn) BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division BRAC‐Research and Evaluation Division Evolve – Consulting for Development BRAC‐Research and Evaluation Division 3 Worksshop report Day 1 During the introdu
uction to th
he worksho
op, Franz G
Gatzweiler from f
ZEF explained e
ZEF‐BRAC he frame off the MARG
GIP projectt, the goals and purpo
ose of the MARGIP collaborration in th
project,, and why (aamong otheers) system dynamics p
plays an imp
portant metthodologicaal role. Discussion: The group found
d that upon first sigh
ht, the tooll seems to be useful to plan interven
ntions. Thee usual app
proach to developmen
d
nt interven
ntions alwayys has uneexpected side‐efffects. Also, interventio
ons can bee better taailored to the t
needs of the poo
orest by applyingg the tool. A senior BRAC reseearcher (Mrr. Narayan) explained, tthat many vvillages in w
which BRAC is active port, but often o
the institutional gap whicch BRAC fills when are orgganized by BRAC supp
workingg with the vvillage, is no
ot filled with
h self‐organ
nization by tthe village, after BRAC
C has left the areea. Similar examples e
f
from differeent develop
pment aid project in other parts of the were mentio
oned. world w
Jörg Volkmann, wh
ho was conssulted to introduce thee Vester too
ol, explained how it is possible nd exact to understand essential sysstem functiionality witthout complete inforrmation an
dge. Floodss of informaation can bee effectivelyy reduced tto grasp the
e main components knowled
and functionalities of a system
m. Marginaality can bee modeled as life sysstems of in
ndividuals or o groups who w belongg to the marginaalized poorr. The SD tool is a pre‐interveention metthod. It alllows plann
ning for interven
ntions before actually implementting them and testing llikely outco
omes by sim
mulation. Interven
ntions can b
be adjusted
d to the speccific needs o
of the poorest (target group). The meethod allow
ws telling no
ot only whether, e.g. income is important or not. Inttuitively, mportant income is always im
f the poorest. Thatt is why for t
the variable “income” is chosen as a variablee which defines thee system. But the question w
would be wh
hich role “income” has h as partt of the system to improve th
he living o the poorrest. For condition of example: for f some poor p
HH an additio
onal goat changes everything,, for otherss a goat may be beetter than nothing and as theyy formally qualify to 4 receive a goat, they take it. For this family, access to food or to land may be much more effective to improve their lives, than a goat. BRAC researchers reported that this experience is actually relevant for their work with the ultra‐poor. BRAC researchers also reported that they often face the situation that there is a survey fatigue among the ultra poor. They do not see the benefits from carrying out yet another survey. Trust needs to be built. There is no quick fix to such situation, but the situation of “lacking trust” can be built into the model to explain why an innovation does not take place or why well‐being is not improved (or the opposite). BRAC researchers often need to deal with the problem of having to collect information fast and efficient. Much information gets lost if sufficient time is not spent with understanding the life systems of the poorest. Time efficient information extraction therefore can be problematic. Also the poorest learn which criteria they need to fulfill in order to fall into the group which receives support and they provide information accordingly. The SD‐tool can counteract, because the system needs to be built by those affected themselves. During course of building the system “false information” will eventually be revealed because it does not fit to the other system components and causes inconsistencies. Day 2 The second day of the workshop started with the aggregation of system variables from the variables which have been mentioned during the first step of system description. Steps of the Vester Sensitivity approach: 1. System description – Interview of stakeholder in a group or individually, in order to get a complete picture of the life system. During the workshop one person represented the target group, in this case a rikshaw puller living in a semi‐urban slum. The person tells his life story and is additionally questioned by others (researchers). a. In a field research context individuals or a group can be interviewed. By means of a questionnaire, questions are asked which relate to all aspects of a living system. These aspects are outlined as criteria for system description by the Vester Sensitivity tool. 2. Identification of system variables based on the system description. Each variable is described. a. 21 System variables were defined: 1. Stability of family relations 2. Size of family household 3. HHs food security 4. Family decision‐making 5. Life security 6. Self‐awareness 7. HH education 8. Accessto lending 5 9. Access to energy 10. Legality of income sources 11. Gender balance in income generation 12. Income diversification strategy 13. Costs of living 14. Social capital 15. HH income 16. Access to land 17. Asset holding 18. Need for migration 19. Health and sanitation condition 20. Satisfaction with lifestyle 21. Recreation and leisure time 3. Aggregation and selection of most important variables which still capture 4. Check the system variables against criteria which describe living systems (criteria matrix) 5. Impact matrix: Assess the impact of each variable on every other in both ways (impact matrix). In this step the question is which potential direct impact a change of var1 has on var2,3,4,… and how each variable is impacted by each of the other variables. Only direct impacts are captured. The quality of the impact is: 0=no impact, 1=under proportional impact, 2=proportional impact, 3=over proportional impact. a.
Proportional=1; 1 b.
Over proportional=a one step change of var1 results in a higher change in var2, e.g. a change in family size results in a change of food security to a higher extent. 1 c.
Under proportional=a one step change of var1 results in a lower change of var2, e.g. change in family size results in a change in food security to a lower extent. 1 Later on, when running simulations, the extent to which a change of var1 impacts on var2 in each time period, can be adjusted. The basic structure of a formal system dynamics model is a system of coupled, nonlinear, first‐order differential equations. Simulation of such systems is easily accomplished by partitioning simulated time into discrete intervals of length dt. 6. Effect system 7. Partial scenarios and policy tests 8. Simulation 6 Snapshot of discussions for filling the impact matrix Only direct and immediate relations/impacts are taken into account 1. Stability of family relations – Access to energy a. The stability of a family has no direct impact on access to energy b. Value: 0 2. Stability of family relations – Legality of income sources a. There is an over proportional relationship because the less stable a family, the (over proportionally) higher the family would depend on illegal income. Family members will engage in diverse income generating activities, also and increasingly, illegal sources. b. Value: 3 3. Stability of family relations – Income diversification strategies a. The impact is proportional b. Value: 2 4. Stability of family relations – social capital a. A change in the stability of family relationships changes social capital to the same extent b. Value: 2 5. Stability of family relations – health and sanitation condition a. There is no direct impact b. Value: 0 6. Stability of family relations – satisfaction with lifestyle a. With each improvement of the stability of family relations the satisfaction with lifestyle changes to a higher extent (over proportional). b. Value: 3 For reasons of saving time, filling the impact matrix was delegated to two groups. The completed matrix was discussed and the consensus matrix was built with the entire group. Consensus does not necessarily mean that it is a compromise under the condition of incomplete knowledge. The group members will discuss and exchange additional views, experiences and information which are reflected in the consensus matrix. Note: For the application in the field one system and set of variables for a similar setting can be used and adjusted. The 5 criteria for identifying the ultra poor from BRAC can be used to identify the target group. Table 1 illustrates the criteria for selection of hardcore poor and poor used by BRAC. After identifying the group, causalities can be analyzed and interventions can be planned by the use of the SD tool. In the field context PRA tools need to be applied to visualize how each variable impacts on another and what the strength of the impact is. 7 Tablee 1: BRAC crriteria for th
he selection
n of hardcorre poor and
d poor Hardcore
e poor Poor Non‐p
poor Landlesss household
d Homelesss householld Less than
n 10 decimaal (40.47 m
m2) agricultu
ural land No fixed source of income Disabled
d or 65+ yeaars old female h
headed househo
old Up to 1
100 decimalls (404.70
0 m2) of lan
nd (agricultural and homesttead) Sells manual laborr ng for livin
Houseehold that d
do not fall in any of the otther catego
ory Figure 1: The
e impact maatrix which impact each system vaariable has on all otherrs. 8 The imp
pact matrix calculates 4 types of vvalues: 1) th
he active, 2
2) the passivve values, 3
3) the Q‐
values aand 3) the P
P‐values. A high aactive sum (AS) indicaates that the variable has an average high aactive impact on all other vaalues. A higgh passive ssum (PS) sayys that the variable is strongly beeing impacteed by all other vaariables. Q‐valuees (quotientt value) show AS/PS an
nd inform ab
bout the ovverall degree of activen
ness and passiven
ness of thee variable within w
the syystem. A high Q‐value
e indicates that the vaariable is highly active. Neu
utral variab
bles are around 1. A A highly passive valu
ue is, in o
our case “satisfaction with lifestyle” with a Q‐value of 0.39. ne the systeemic charactter of each variable. Figure 2: P‐ and Q‐vvalues defin
P‐valuess (producct values) are calcculated byy AS*PS and indicate the overall interwo
ovenness/integratedneess of the variable into the entire sysstem. The highest interwo
oveness is considered
d a “criticaal variable” and low interwoven
ness is con
nsidered “bufferiing” within the system
m. A highlyy critical variable is “H
HH income”” with a P‐vvalue of 1584. Sllightly buffe
ering variab
bles are variables like “access to laand” (319) aand “recreation and leisure”” (312). Varriables with
h low P‐values and low
w Q‐valuess, like “acce
ess to enerrgy”, are likely to
o be less imp
portant for the specificc system. 9 Day 3 The effeect system is being bu
uilt by identtifying causal loops between variaables. Whereas the impact matrix show
ws the maggnitude of aall potential impacts frrom all variables on all others, builds causaal loops onlyy between tthose variab
bles which are relevant for the the effeect system b
specific life system
m of the individual or ggroup; in ou
ur case the rikshaw puller. The diffference between both is: 1. TThe impactt matrix only shows direct impactts from onee variable to
o another, w
whereas t
the effect s
ws all types of impacts,, also indire
ect ones. ystem show
2. The T impactt matrix sho
ows all possible impaccts each variable can have h
on thee others a represents the general poteential of a rikshaw pu
and uller’s life system. Th
he effect s
system will hich are high
hly interwoven into reduce thee variables, especially tto those wh
t
the system (variables w
with high P‐‐values). Figurre 3: The syystemic rolee diagram sh
hows how tthe variablees are distrib
buted acrosss the different syystemic characteristics. 10 In the eeffect system
m one can d
draw reinfo
orcing and aadverse relaations betw
ween variablles, both negativee and positive. A reinfforcing relattionship meeans: “if varr1 increasess or decreasses then var2 inccreases or d
decreases”. If this relationship is th
he same fro
om var1 to vvar2 and vicce versa, then it iis a positivee feedback. Positive feedback loops are self‐reinforcing and will evventually lead to system collapse. hips have the oppositee effect and mean: “if var1 increeases or deecreases Adversee relationsh
then vaar2 decreasees or increaases”. A ne
egative feed
dback loopss is one witth oppositee effects, keepingg the changes betweeen two variables in a balance and thereb
by stabilizing their relation
nship. The relationships refer to th
he actual life system of the individual or group. g
In tthe field d be intervieewed to ideentify the qu
uality of each relationsship, e.g. byy asking: context they could
ncreases/deecreases, what happen
ns to the fo
ood securitty situation of your “If yourr income in
family?”” or: “If foo
od securityy improves, which imp
pact does th
hat have on the educcation of househo
old membeers?” Figure 4
4: The effecct system sh
hows causal loops and relationships between
n relevant variables After building the effect systtem the Ve
ester tool provides so
ome functio
ons to analyze the numberr and type o
of feedbackk loops, which each variable is paart of. In our case theree are no negativee feedbackk loops. Th
hat means the system
m is highly instable or o vulnerable. Such 11 systemss can be easily changed by intervention to the better o
or to the wo
orse. Small changes from ou
utside are su
ufficient to bring about large systeem changess or even syystem collap
pse. Tab
ble 2: Numb
ber and type of feedbaack loops for each variaable T
Table 3: Typ
pe and length of feedback loops in
n the system
m 12 This resu
ult needs to
o be interp
preted in an apprropriate waay. Most probably p
the syste
em is desiggned to be instable because the poor rikshaw puller p
is d with being b
a always unsatisfied
m. In this rikshaw puller living in a slum
h no intention to specific case, he has n a way to stabilize adjust his system in
emain rikshaaw puller fo
or a long it and re
period of o time. As A we havee learnt from intterviewing the rikshaw
w puller, his amb
bition is to leave the slum as soon ass possible and return tto his villagee. From a ssystems perrspective th
his means h
he would leave his urban rikkshaw pulleer system and a becomee part of a different system s
in the t rural setting. Interventio
ons which lead to a fundamenttall change of his life
e system would w
be preferreed in this caase. Such in
ntervention
ns would bee those, e.g. which increase his m
monetary savings to a degreee which allo
ows him to leave the slum and return to the vvillage. “Improvved access to lendingg (mircro‐crredits)” wo
ould be an effective but b not neccessarily desired interventio
on as it wo
ould keep the rikshaw
w puller insside his current systeem for a period of tim
me. He wou
uld need to keep his job to pay back the loan although h
his actual longer p
aim is to leave thee slum as so
oon as he h
has sufficien
nt savings. Ergo, takingg the aspiraations of ount is imp
portant in order not improve th
heir well‐b
being, as the exttreme poorr into acco
measured also against their asspirations. uation and the appropriate interrventions may m be com
mpletely diffferent for rikshaw The situ
pullers from the city: c
they may m have been born in
n the slum and intend
d to becom
me more p
buy their own rikshaw an
nd maybe rent r
rikshaw
ws to otherrs in the wealthyy rikshaw pullers, future, or advancee within thee system byy other meaans. They may not ha
m
ve the inteention to heir system but to stab
bilize it and iimprove theeir position inside the system. leave th
Improviing well‐being can therefore be brougght about b
by an exit straategy (leaving one sysstem and enttering a new one) or by a graduattion strate
egy (impro
oving well‐being by advvancing within me system). the sam
13 Interven
ntions In addittion to interviews on
ne can takee the P‐ an
nd Q‐value
es as an indication fo
or which variablees are mostt relevant as a leverage variables. Variables with w high P‐ P and Q‐vaalues are suitablee as leveragge variabless as they haave a salient p
position witthin the system. Those vvariables which w
have low P‐ and Q‐
values are less reelevant for overall sysstem ning. Thosee are the variables v
w
which function
are reaactive and not much integrated into ore relevan
nt for the systtem. They ccould be mo
other syystems. Adversely, if a a variable has h a high P‐vvalue but a low Q‐valu
ue, it should
d not necessaarily be omiitted becau
use it is stro
ongly interwo
oven and has a bufferring functio
on in the systtem. Implicattions for interventionss to reduce
e extreme poverty: p
The life systems of the extreme poor, w
we have anaalyzed in this case, are dominated
d by positivve feedbackk loops. Thaat means the systtem is extreemely vulnerable and susceptiblee to change
e or even to
o collapse. ‘System collapsee’ is not necessarily a negative fe
eature if the poor aim
m at an exit strategy. Different D
interven
ntions need
d to be chossen for diffeerent exit sttrategies. Escaping povverty traps within a life system and graaduating witthin the sysstem, requirre different interventio
ons than leaaving life nes. The advverse featu
ures of the systems in which the poorest systemss and building new on
live are not viable ssystems oveer long periiods of timee. A simulation would h
have confirm
med this ot be perforrmed because of lacking time. result but could no
n of the metthod in the field, the field team sh
hould be It was suggested, tthat for the application
multidissciplinary. Final d
discussion The Vesster metho
od is a pow
werful tool which w
helps understand the causalities of why w the poorestt are trapped in poveerty, which variables their t
life syystems are composed
d of and which intervention
ns potentially lead to improved living cond
dition. It en
nables to taailor the interven
ntion packaages for th
he poorestt to their specific s
needs. Also it can facilitate in identifyying additional criteria for targeting the exxtreme poor (beyond the 5 criteria from BRAC). One of tthe outcom
mes of our exxercise also
o shows that improvingg access to m
money lend
ding, e.g. by micrro credits, could be an a importan
nt leveragee for improving the livves of the poor. It basicallyy confirms the approaach taken b
by BRAC. Ho
owever effe
ective this lever might be, it is necessaary to checck the inteervention(s)) against th
he understtanding of the entire system behavio
or and the aaspirations of those livving in the system and
d not just aggainst few features 14 of the p
poor which qualify them
m for specific meaasures, likke he micro‐credits. Oveerlooking th
p
doees fact thaat a poor person not want to im
mprove his n inside thee system bu
ut position
aims att leaving the system
m, could leead to the selection o
of wrong interventio
on nisms. Acceess to micro
o‐
mechan
credits ccould impro
ove his livin
ng conditio
on within the system
m but it may m also tie t him to a system he actually intends to leave. The imp
proved und
derstanding of the systtem of an eextreme po
oor also led to the insiight that the miccro systemss of the po
oorest, at leeast in thiss case, can be extremely vulneraable and instablee. Small changes intervventions from outside) can have laarge effectss on the system and can theerefore be regarded r
as efficient: large impaacts at low cost. In our o case “A
Access to lending””, for example, will havve an imme
ediate effecct on the lifee system off the rikshaw
w puller. This does however not necesssarily implyy a positivee long term
m effect of the t interveention. It w
pulls the poor back b
into could be a short term impactt which doees not last long and which ding does no
ot enable the rikshaw puller to eescape his system. In that case povertyy if the lend
the inteervention would w
keep the rikshaw puller in
n the life syystem from
m which he actually aims to
o escape. Thus T
intervventions wh
hich do no
ot show im
mmediate reesults may still be interven
ntions with effective and a long‐laasting positive impactss. Also, inte
erventions, such as “access to lending” can makee the pooreest better off o but keep
p them insiide a system
m which nother poveerty trap on
n a differentt level of they acttually wish to leave – tthereby creaating yet an
well‐being. n for the persistence p
of extremee poverty in general. The life This could be an explanation
oorest, if ch
haracterized
d by vulnerrable and in
nstable systems, reactt fast to systemss of the po
certain interventio
ons, such as improved
d access to credit butt they can fall back in
nto their he optimal ccombination of interveention mecchanisms original condition jjust as fast. Finding th
me, is the ch
hallenge, and orgganizing them in a way that makess them supeerfluous afteer some tim
which can c be better mastereed after a better b
understanding of o the life system s
dynaamics of the poo
orest, by thee help of thee Vester too
ol. Next steeps: some of the participants wo
ould like to have accesss to the software and apply it to theirr work in th
he field and
d for research. Next, in
n the context of the M
MARGIP projject, the method
d should be applied in tthe field. 15