Towards analysing social norms in microfinance groups Pablo Lucas by fdjerue7eeu


Towards analysing social norms in microfinance groups Pablo Lucas

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									                         Towards analysing social norms in microfinance groups

                    Pablo Lucas dos Anjos1, Federico Morales2, Ignacio Garcia3
                        Centre for Policy Modelling, Manchester Metropolitan University
                            PROIMMSE-IIA-National Autonomous University of Mexico
                          Facultad de Filosofía y Letras, Universidad de Buenos Aires

Abstract: This research focuses on the social organisation and commitment dynamics among borrower
groups at a microfinance institution (MFI) in Mexico. Due to our publishing agreement, their identity is
omitted. The MFI manages micro-credit loans given to geographically distributed groups in the southern
state of Chiapas, each with 3 to 7 women only. This non-governmental organisation has adapted in
1998 the Grameen Foundation methodology and use guidelines from the Consultive Group to Assist
the Poor to implement their own solidarity lending, life insurance in cooperation with Zurich Financial
Services, educational and nutritional programs that prioritise the local rural community [1]. Technical
advisers are trained to facilitate, using Spanish or one of the 8 regional Mayan languages, the process
of managing quota repayments that are periodically expected from individuals in every group. As
financial techniques are employed to support a social mission, lending does not rely on traditional
assets required by private and public banks in order to consider a credit application. Instead, social
collateral is assessed according to socio-economic situation of every applicant and a reference poverty
line. Although there is vast collection of published academic and third sector literature on MFI good
practices, little is known about social norms that influence social collateral among borrowers at MFIs.
Given this clear gap of dedicated studies analysing the internal structure and supporting mechanisms of
such micro-credit groups, our 2008 research project is analysing data from 5 MFI financial databases,
collected interview data from technical advisers and clients in order to both better understand their
social context and guide the development of an agent-based computer simulation. In addition to
contribute with a clear sociological and socio-economical analysis, the computational modelling
approach is being informed by available data to describe and simulate (1) the evolution –not
necessarily optimisation– of commitment to quota repayments, (2) which aspects in a group can
contribute, or deteriorate, the individual reliance on the social collateral, and (3) assess if this evidence-
driven simulation has potential to relevant stakeholders.
The proposed simulation is focused on exploring the dynamics of social collateral among groups of
borrowers participating in microfinance. In this sense, it is essential to guide the individual agent
development with reliable and statistically significant data extracted from questionnaires about the
behaviour of the MFI clientele and numerical evidence from their financial databases. Apart from the
initial socio-economical assessment and technical advisors throughout their loan period, there is very
scarce understanding of how social networks and trust mechanisms are structured within those
microfinance groups. Their financial data have detailed information tracking every individual payment
according to the interest rate associated with MFI approved loans, but no register is made on social
behaviours thay influence individuals to pay or cover quotas. Existing software such as Microfin [2] and
Symbanc [2] are only suitable to manage or analyse MFI financial processes, but offer no feature to
analyse data regarding the internal mechanisms that can influence the social collateral of groups and
cooperative behaviour of its members. Agent behaviour and structure of social networks among
microfinance groups are being implemented according to the available MFI evidence. That is, two
online questionnaires administered to 35 credit officers, one form to 600 borrowers and a semi-
structured interviews carried during a fieldwork visit in May 2008. The model is being tested using these
retrospective datasets in comparison to outcomes from what-if simulated scenarios.

[1] AlSol Chiapas AC, Background, Client profiles and monthly Operation Report: Grameen Foundation,
USA, June 2007.
[2] Anthony Sheldon, Chuck Waterfield, Business planning and financial modeling for MFI: a Microfin
handbook, CGAP, 1998.
[3] G. Hirsch,J. Rosengard, G. Stuart, D. Johnston, A Simulator for Microfinance Institutions. ADB
Finance for the Poor 6.4, Dec 2005.

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