Microfinance Impact

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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

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• 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)

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• 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

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T2 T1 compared with C2 C1 difference-in-diffe
rence approach
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• 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

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• 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

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• 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 .

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• 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

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• 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)

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• 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

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• 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

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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
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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

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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

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Basic insight
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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

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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

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• 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 ?

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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

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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

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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
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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!!!