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Microfinance Impact

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Successful borrowers were buying land may explain why no impact on household consumption ... For every 100 taka lent to a man consumption 11 taka ... – PowerPoint PPT presentation

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Title: Microfinance Impact


1
Microfinance Impact What are we trying to
measure? How can we accurately evaluate the
impact of microfinance? Attempts to measure
impact thus far?
2
  • Microfinance may impact households in various
    ways via income for example
  • Income effect
  • ? ?demand for children
  • ? ?health
  • ? ?childrens education
  • ? ?leisure
  • Substitution effect
  • ??demand for children (i.e., womens opportunity
    cost increases)
  • ??childrens education
  • ??leisure

3
  • Other channels
  • Womens bargaining power
  • Social capital
  • And, more direct interventions via services added
    to financial services
  • Education and training (i.e., Freedom for Hunger,
    Pro Mujer, BRAC)
  • Health (i.e., Health Banks)

4
  • Lets Narrow- down our search impact on income
  • Attributes
  • Measurable age, education, experience..
  • Non-measurable entrepreneurial organizational
    skills, valuable
  • networks.
  • Challenge disentangling the role of
    microfinance from measurable
  • and non-measurable attributes
  • Challenge even greater when the decision to
    participate in a
  • microfinance program depend on those same
    attributes

5
T2 T1 compared with C2 C1 difference-in-diffe
rence approach
6
  • Problems
  • Make sure that control groups are comparable to
    treatment groups
  • ? need to consider who joins the microfinance
    program that cannot be
  • compared to those who do not
  • Why?
  • Unmeasured attributes (i.e., entrepreneurial
    ability of those who join)
  • ? selection bias
  • Potential solution consider a similar village
    without microfinance
  • Problem again, unmeasured attributes of
    villagers that have not yet self
  • selected themselves
  • ?selection bias

7
  • Well-known attempts
  • Bret Coleman (1999) (2002) on Thailand
  • Tries to address the selection bias by
    identifying potential borrowers in villages where
    microfinance is not yet present
  • In particular
  • He gathers data in 1995 from 14 villages, 8 of
    which already have a microfinance program in
    operation, and the other 6 do not but would-be
    borrowers have already been identified
  • Estimates

8
  • Findings
  • After controlling for selection and program
    placement
  • - Impact not significantly different from zero
  • - Some impact for the wealthier participants
  • However Thailand is relatively wealthy, village
    members have
  • multiple sources of credit .

9
  • Karlan (2001) on Peru
  • Comparing old borrowers with new borrowers
    using cross sectional
  • Data
  • ?selection bias due to the timing of entry (early
    entrants may differ from late
  • entrants, i.e., more entrepreneurial, more
    motivated..)
  • And Karlans experience in FINCA Peru, points out
    two additional biases,
  • both due to dropouts
  • 1)Dropouts may be the failures ? impact is
    overstated, or vice versa
  • 2) Non random attrition If dropouts are
    failures ? pool of borrowers are
  • richer on average ? impact overstated, and vice
    versa
  • Potential solutions Hunt down the drop outs
    which is expensive, or estimate
  • predictors which has a problem in that the
    reweighing scheme does not
  • take into account the size of the impact

10
  • USAID AIMS on India, Peru, and Zimbabwe
  • Use data collected at several points in time
    allowing for before versus after
  • comparisons
  • Control for nonrandom participation and
    nonrandom placement
  • However, approach subject to biases due to
    unobservable attributes that change over time
  • Nevertheless
  • Data collected from a random sample of
    participant households in several programs that
    were resurveyed two years later
  • As for the control groups random sample from
    nonparticipants ( India and Peru)
  • Or
  • A random walk (Zimbabwe)

11
  • Researchers followed dropouts to avoid attrition
    biases
  • However
  • Researchers decided against analyzing difference-
    in differences
  • ? Biases due to omitted variables that do not
    change over time
  • In particular, researchers should have estimated
  • Yijt Xijt a Vj ß? Mij ? Cijt d ?ijt,
    (8.2)
  • ? Problem Potential bias due to omission of
    unobservable variables
  • that do not change over time

12
  • Addressing the problem
  • Yijt1 Xijt1 a Vj ß? Mij ? Cijt1 d
    ?ijt1 (8.3)
  • And, subtract (8.2) from (8.3) to obtain
  • ? Yij ? Xij a ? Cij d ? ?ij, (8.4)
  • ? a consistent estimate of the impact of d
  • Additional problem reverse causality

13
BANGLADESH

Population 143.8 million Urban 23.9
million HDI Rank 138 Adult illiteracy
58.9 Population lt 1 36.0 million Largest
Microfinance Programs 98 Grameen, BRAC,
RD-12 Serving the landless rural poor
14
Pitt and Khandker (1998)
  • Attempt to measure the impact of microfinance
    participation, by gender on
  • - boys and girls schooling
  • - household expenditures (consumption)
  • - accumulation on non land assets
  • - womens and mens labor supply

15
Cross Section Data
  • 1,798 households in 87 villages were surveyed in
    1992
  • 905 households were under a microfinance program?
    treatment
  • 893 households were not ? control
  • Results
  • Relative to credit provided to men, credit
    provided to women
  • (a) ?Schooling (both boys and girls)
  • (b) ?Household expenditures (consumption)
  • (c) ?Non-land assets held by women
  • (d) ?Labor supply of men and women

16
Basic insight
17
Problem
  • How to address the biases?
  • Find an IV a variable that explains levels of
    credit received but has no direct relationship
    with the outcomes of interest
  • In this case Schooling, Household Expenditures,
    Non Land Assets,
  • Labor supply
  • An eligibility rule only functionally
    landless households (with lt ½ of land) can have
    access to microfinance
  • The fact that there ineligible households (260)
    within villages with programs ? there is another
    control group which helps to alleviate the bias

18
An improved estimation strategy
  • Compare
  • Treatment with ineligible households living in
    the same village
  • Ineligible with would be eligible
  • ? households with access to microfinance are
    doing better than their ineligible neighbors
    relative to the difference in outcomes between
    functionally landless households in control
    villages versus their ineligible neighbors

19
  • Yij Xij a Vj ß? Eij ? (Tij Eij) d
    ?ij, (8.5)
  • Disappointing results w/r to impact on household
    consumption
  • But
  • Microfinance helps to diversify income streams so
    that consumption is less variable across seasons
  • Also
  • Landholdings may not be exogenous
  • On the other hand
  • Successful borrowers were buying land ? may
    explain why no impact on household consumption ?
  • Moreover, debate over ineligible households that
    participated (25). But Pitt-Khandker (1999)
    acknowledged the problem, made robustness checks
    and show that their results change very little ?

20
Note that
  • Yij Xij a Vj ß? Eij ? Cij d
    ?ij, (8.6)
  • Where
  • d captures credit access
  • Now, by expanding the set of instruments to Xij
    Tij Eij
  • ? there are as many instruments as there are X
    (education.)
  • ? d takes advantage of variation of how much
    credit households receive

21
Now, when comparing groups of men with groups of
women
  • Pitt-Khandker (1998) most cited result
  • For every 100 taka lent to a woman consumption ?
    18 taka
  • For every 100 taka lent to a man consumption ?11
    taka
  • Now, another round of data was collected in 1998
    1999
  • And Khandker (2003) look at some trends

22
20 per cent poverty decline both participants and
nonparticipants Pessimists decline would have
happened even without microfinance Optimists
impact of microfinance has had positive
spillovers to nonparticipants
23
Khandkers (2003) econometric estimates show that
  • Microfinance contributed to roughly ½ of the 20
    percentage points decline in poverty
  • For every 100 taka lent to a woman consumption ?8
    taka
  • Ideally, another round of data collection should
    help
  • Problem microfinance in Bangladesh has spread
    far and wide
  • ? No more control groups!!!
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