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Assessing the Impact of Microfinance: A Methodological Study Using Evidence from India

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Title: Assessing the Impact of Microfinance: A Methodological Study Using Evidence from India


1
Assessing the Impact of Microfinance A
Methodological Study Using Evidence from India
  • Maren Duvendack
  • Procedural Paper Presentation
  • 23 May 2008
  • Supervisors
  • Arjan Verschoor Nitya Rao

2
Introduction to Microfinance
  • What is microfinance?
  • Provision of financial and non-financial services
    to low-income households
  • Microfinance important strategy in
    the fight against poverty
  • Importance of microfinance recognised by United
    Nations and Nobel Prize Committee
  • No clear empirical evidence yet that microfinance
    has positive impacts
  • Impact assessments crucial for donors and
    microfinance institutions

3
Introduction of Research Project
  • Challenge of every impact assessment
  • Measurement of counterfactual
  • Elimination of biases (i.e. selection attrition
    bias)
  • Limited number of rigorous impact studies exist
  • Study intends to focus on methodological
    challenges of microfinance impact assessment
    studies
  • Suggest solutions to bias problem

4
Research Questions
  • What is the impact of microfinance on the
    households economic and social well-being?
  • How are microfinance assessment studies measuring
    the impact of microfinance?
  • What are the methodological challenges of
    microfinance impact assessments?
  • How can a rigorous treatment of biases, in
    particular drop-outs, improve the accuracy of
    impact assessment studies?

5
Research Context
  • Financial exclusion of Indias poor recurring
    problem for almost 100 years
  • Access to finance poverty reduction,
    thus Indian government launched various policy
    initiatives aimed at financial inclusion
  • BUT Most government-run subsidised credit
    programmes had negative effects
  • Emergence of microfinance in India mainly due to
    lack of effective government policies

6
Research Context
  • Emergence of microfinance in India in the 1990s
  • Tremendous growth of Indian microfinance in terms
    of outreach and loan disbursements
  • BUT Only 8 impact assessment studies conducted
    in India
  • Studies vary significantly in terms of scope and
    approach
  • They investigate one or more of the following
    impacts
  • Poverty reduction
  • Financial services
  • Womens empowerment
  • Studies provide conflicting results, ? impact of
    microfinance unclear
  • Thus, more systematic approach to impact
    assessments needed

7
Conceptual Approaches
  • Core elements of conceptual frameworks in impact
    assessments
  • The impact chain model
  • The units of assessment
  • The impact type

The impact chain model
Source Hulme, 2000.
8
Conceptual Approaches
  • Units of assessment
  • Individual, enterprise, household, community and
    institutional level
  • Majority of studies examine impact at multiple
    levels
  • Identification of impact type
  • Economic, social or socio-political impacts
  • Early impact studies mainly investigated economic
    impacts, using indicators such as income, assets
    and expenditure
  • In the 1980s, focus on social impacts, using
    indicators such as education, health, housing and
    sanitation
  • More recently, shift towards socio-political
    indicators such as womens empowerment

9
Paradigms of Impact Assessments
  • Attribution additional challenge of impact
    assessments
  • Two main paradigms can be extracted commonly used
    to demonstrate attribution

Scientific Method
Humanities Tradition
10
Scientific Method
  • Typically attempts to attribute effects of an
    intervention to its causes by utilising either

11
Humanities Tradition
  • Humanities tradition seeks to explain interpret
    the underlying processes of an intervention
  • Dual function
  • Triangulation to crosscheck quantitative data
  • Provides understanding of changes in social
    relationships
  • Difficulties in demonstrating attribution due to
    lack of control group approach
  • Causality inferred by collecting data on causal
    chain by interviewing programme participants,
    then comparison to data from areas which did not
    have access to programme

12
Methodological Challenges Biases
  • Biases common occurrence in impact evaluations
    adversely effect impact results, thus
    solution crucial
  • Typically the following biases occur in the
    context of microfinance
  • Selection bias self-selection non-random
    programme placement
  • Attrition bias
  • Only handful of rigorous impact studies exist
    that control for biases
  • Hulme and Mosley (1996)
  • Coleman (1999)
  • Pitt and Khandker (1998)
  • Alexander and Karlan (2007)

13
Selection Bias Hulme Mosley, Coleman
  • Hulme and Mosely (1996) study of microfinance
    programmes in seven different countries
  • Controlled for self-selection bias but not
    non-random programme placement bias
  • Novelty sampling of prospective clients as a
    control group
  • Mixed results, depending on programme design and
    country context
  • Coleman (1999) study on Thailand, uses
    village-level fixed-effects to control for
    non-random programme placement bias
  • Also, he uses Hulme Mosleys (1996) approach of
    sampling prospective clients as a control group
  • Difference-in-difference approach employed
  • Little impact found, more importantly
    microfinance led to vicious circle of bad debts

14
Selection Bias Pitt Khandker
  • Until today, most rigorous attempt at controlling
    for selection bias
  • Quasi-experiment eligibility requirements used
    to measure programme impact
  • Primary eligibility criterion landownership

Treatment Village
Control Village
Source Armendáriz de Aghion and Morduch, 2005.
  • Overall findings microcredit has positive
    impacts
  • BUT accuracy of results disputed due to lax
    enforcement of eligibility criteria
  • Econometric debate between Pitt Khandker and
    Morduch, not resolved until today

15
Selection Bias Solution?
  • Propensity score matching (PSM) popular method
    used to eliminate selection bias
  • Works by matching participants to
    non-participants based on predicted probability
    of programme participation or the propensity
    score
  • Basis for matching observable characteristics ?
    drawback
  • Underlying assumption no selection bias due to
    unobservables
  • Combine PSM with difference-in-difference, picks
    up on unobservables but baseline data set
    required
  • PSM results good approximation to those obtained
    under experimental approach

16
Attrition Bias
  • Drop-out rates estimated to be between 3.5 to
    60 in microfinance programmes worldwide
  • Two different types of clients exiting
  • Graduates
  • Drop-outs
  • Attrition bias neglected by majority of studies,
    Alexander and Karlan (2007) one of the few
    recognising its importance
  • Solution to attrition bias
  • Better sampling
  • Systematic interviews with drop-outs

17
Methodology Research Design
  • Mainly a quantitative study with selected
    qualitative elements
  • Questionnaire survey of 500 households
  • Semi-structured interviews with selected key
    borrowers, in particular drop-outs
  • Study proposes to employ propensity score
    matching (PSM) as a means to control for
    selection bias as well as attrition bias ?
    novelty in the context of microfinance
  • Requirement sampling of participants and
    non-participants as well as drop-outs

18
Methodology Overview (1)
19
Methodology Overview (2)
20
Methodology Sampling Procedure
  • Study proposes to employ multistage cluster
    sampling, as illustrated by figure
  • Sampling in stages first, identify large areas,
    then narrow them down by selecting smaller areas
    within those larger ones
  • Research location Andhra Pradesh
  • Sample selection criteria
  • Mature microfinance programmes preferred, at
    least 5 years of operation
  • Participants 4-5 loan cycles required

21
Methodology Ethics
  • Oral and/or written consent of research
    participants shall be obtained before embarking
    on data collection
  • Data collected shall be kept confidential and
    will be anonymised
  • Reliance on research assistant and translators is
    expected, they shall be treated with the utmost
    respect and their expenses shall be covered by
    the researcher

22
Timeline
23
  • Q A Session
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