1. How
Does
the
Intensity
of
Commercial
Microfinance
Lending
Affect
Inequality?
Term
Paper,
Economics
435
Matthew
Bonshor
Sumon
Majumdar
December
12th,
2014

2. Introduction
Microfinance
lending
has
received
much
attention
in
recent
years
as
a
potential
way
to
reduce
income
inequality
by
providing
access
to
loans
and
other
financial
services
for
the
poor.
Today,
microfinance
services
are
provided
by
a
variety
of
lenders
that
range
from
charities
and
non-­‐governmental
organizations
to
registered
financial
institutions
and
banks.
One
important
question
about
microfinance
surrounds
the
best
method
of
delivery.
Namely,
are
for
profit,
self-­‐sustainable
and
large
scale
financial
institutions
more
effective
than
non-­‐profit
organizations
in
reducing
income
inequality
among
the
users
of
microfinance
services?
Should
microcredit
be
delivered
by
organizations
and
institutions
solely
focused
on
reducing
poverty
and
not
on
making
profits?
This
paper
expands
on
a
growing
body
of
literature
that
utilizes
cross-­‐country
data
on
microfinance
lending
to
examine
the
relationship
between
commercial
and
for-­‐profit
microfinance
lending
intensity
and
the
rural
poverty
gap.
My
analysis
suggests
that
the
percentage
of
microfinance
loans
issued
in
a
country
by
commercial
microfinance
institutions
is
negatively,
though
not
significantly,
associated
with
the
rural
poverty
gap
in
that
country.
This
provides
some
evidence
against
the
critics
of
commercial
microfinance,
who
claim
commercial
microfinance
lending
may
contribute
to
problems
of
inequality.
The
outline
of
the
paper
is
as
follows:
Section
I
provides
a
brief
history
of
microfinance,
the
role
that
microfinance
may
play
in
reducing
poverty
and
the
theoretical
debate
surrounding
the
delivery
methods
of
microcredit.
Section
II
presents
my
empirical
framework.
Section
III
discusses
the
results,
weaknesses
and
conclusions.

3. Section
I
In
its
simplest
form,
microfinance
is
the
“provision
of
non-­‐exploitative,
small-­‐scale
financial
services
to
low
income
clients”
(Ledgerwood,
1999).
It
provides
financial
services
to
those
with
little
or
no
collateral
who
have
been
deemed
too
risky
to
receive
credit
by
the
traditional
financial
sector.
Modern
microfinance
originated
with
Dr.
Muhammed
Yunus’s
Grameen
Bank,
which
provided
microcredit
to
a
select
few
villages
in
India
(Grameen
Bank).
There
was
an
observed
boost
in
the
income
of
people
who
had
access
to
this
credit.
Since
its
formal
introduction
in
1983
this
model
of
small,
collateral
free
loans
has
expanded
across
the
world.
In
the
1970’s,
Grameen
Bank
and
others,
such
as
ACCION
international
began
to
institutionalize
their
services
(Robinson,
2001).
Throughout
the
1990’s
and
2000’s
this
process
was
taken
even
further,
as
microfinance
lending
started
to
gain
traction
as
a
profitable
and
viable
business
model.
Today
microfinance
has
evolved
to
be
an
important
component
in
many
developing
countries’
financial
sectors.
In
2011
there
were
195
million
microcredit
borrowers
(Microcredit
Summit).
It
is
also
useful
to
step
back
and
examine
why
we
believe
that
microfinance
is
a
useful
tool
to
reduce
poverty.
Put
most
simply,
microfinance
can
induce
pro-­‐poor
growth,
where
pro-­‐poor
growth
refers
to
the
incomes
of
poor
rising
faster
than
the
incomes
of
the
wealthy.
Since
many
poor
people
do
not
have
access
to
financial
services
of
any
kind
(credit,
savings
etc.)
anything
that
develops
the
financial
sector
and
provides
those
services
to
the
poor
is
likely
to
disproportionately
benefit
poor
people.
If
microfinance
can
be
seen
as
a
way
to
develop
the
financial
sector,
and
induce
pro-­‐poor
growth,
then
perhaps
it
is
an
effective
tool
in
bridging
the
gap

4. between
rich
and
poor
(Beck
et
al,
2007).
Li
et
al’s
1998
study
demonstrated
that
capital
market
imperfections
explain
many
of
the
differences
in
income
inequality
between
rich
and
poor
countries.
Clark
et
al
(2006)
showed
that
an
increase
of
1%
by
financial
development
reduces
the
level
of
income
inequality
by
0.31%.
Microfinance
is
thus
argued
to
have
a
beneficial
impact
on
poverty
and
income
inequality
by
further
developing
the
financial
sector.
Poverty
Lending
vs.
Financial
Systems
Approach
“Comparison
is
made
of
the
two
main
approaches
to
financing
the
poor:
the
poverty
lending
approach,
which
promotes
donor-­‐funded
credit
for
the
poor,
especially
the
poorest
of
the
poor;
and
the
financial
systems
approach,
which
advocates
commercial
microfinance
for
the
economically
active
poor
and
other,
subsidized
and
charitable
nonfinancial
methods
of
reducing
poverty
and
creating
jobs
for
the
extremely
poor.
The
primary
goal
of
the
two
approaches
to
microfinance
is
similar—widespread
financial
services
for
the
poor.
The
debate
is
on
the
means”
(Robinson,
2001).
Drawn
from
papers
by
Otero
and
Rhyne
(1994),
Ledgerwood
(1999)
and
Robinson
(2001),
Chasmar
(2009)
provides
a
succinct
and
simple
overview
of
the
competing
visions
and
in
this
section
I
draw
largely
from
her
summary.
The
first,
and
“…best
known
of
the
early
microcredit
models
is
the
poverty
lending
approach,
pioneered
at
Bangladesh’s
Grameen
Bank
and
elsewhere”
(Robinson
2001).
This
school
of
thought
focuses
on
microfinance
as
a
tool
to
lift
people
out
of
abject
poverty
and
develop
communities.
Loans
are
structured
in
an

5. attempt
to
maximize
repayment.
Often
accompanying
microcredit
services
are
education
programs
for
families
of
borrowers
or
other
initiatives
that
attempt
to
promote
better
quality
of
living
for
poor
people
such
as
clean
water
pumps
(Grameen
Bank).
The
focus
of
poverty
lending
is
a
more
holistic
approach
to
poverty
itself.
Their
focus
is
on
the
depth
of
outreach
–
serving
the
poorest
of
the
poor.
Loans
are
sometimes
offered
at
below
market
interest
rates.
To
provide
such
services,
the
institutions
that
offer
these
loans
are
often
subsidized
by
donors,
charities
or
governments.
This
paradigm
sees
heavy
commercialization
and
profit
incentives
displacing
the
social
mission
that
created
the
original
mandate
for
microcredit.
The
other
school
of
thought
is
the
financial
systems
approach.
Put
simply,
proponents
of
the
financial
systems
approach
believe
that
the
only
way
to
effectively
meet
the
vast
credit
needs
of
the
poor
that
MFI’s
must
integrate
into
the
formal
financial
sector.
Their
focus
on
the
breadth
of
their
outreach
–
that
is,
reaching
as
many
people
as
possible.
Less
emphasis
is
placed
on
other
measures
of
inequality
such
as
education
or
other
social
programs.
The
goal
of
MFI’s
that
participate
in
this
sphere
are
to
be
self-­‐sufficient
and
not
rely
on
any
donor
or
charity
money
to
remain
operational.
Most
of
the
institutions
that
exist
in
this
sphere
are
also
for
profit.
They
believe
that
self-­‐sufficiency
and
profit
motives
drive
efficiency,
and
most
importantly,
integrating
into
the
formal
financial
sector
allows
access
to
large
capital
markets
that
can
ultimately
meet
the
growing
demand
for
microcredit.

6. Section
II
Many
of
the
studies
that
have
been
conducted
to
measure
the
effectiveness
of
microfinance
lending
programs
are
randomized
control
trials
that
measure
the
incomes
of
a
specific
group,
village
or
city.
For
example,
Banerjee
et
al.
(2010b),
“report
on
a
randomized
control
trial
of
the
classic
microcredit
model...they
evaluated
Spandana’s
microlending
program
in
Hyderabad
city.
The
program
was
characterized
by
minimal
screening
of
applicants,
group-­‐based
lending,
21
small
loans
(approximately
$250),
exclusively
female
borrowers,
and
relatively
low
interest
rates
(24%).”
18
months
after
the
loans
were
disbursed
they
compared
the
incomes
of
those
who
had
received
loans
versus
those
who
had
not.
They
do
not
find
a
significant
impact
on
total
consumption
of
food
or
durable
goods
in
either
the
short
or
long
run
(Banjerjee
et
al,
2013).
Conclusions
are
then
drawn
from
these
results
in
an
attempt
to
provide
empirical
proof
that
microfinance
lending
is
effective.
Hermes
(2014)
argues
that
these
studies
are
highly
context
specific
and
that
it
is
risky
to
draw
global
conclusions
from
a
small
trial
in
such
a
small
place.
Until
recently,
few
studies
existed
that
examine
country
level
data.
As
the
scale
of
microfinance
lending
has
grown
and
both
the
poverty
lending
institutions
and
financial
systems
institutions
have
become
more
formal,
more
quality
data
has
become
available.
One
such
country-­‐level
study
was
done
by
Niels
Hermes,
who
analyzed
participation
in
microfinance
programs
(as
measured
by
the
total
microcredit
loan
portfolio
in
a
country
divided
by
that
countries
GDP
or
as
measured
by
the
number
of
active
borrowers
in
a
country
divided
by
that
countries
population)
and
the
effect

7. of
participation
on
income
inequality.
After
controlling
for
a
number
of
factors,
and
introducing
instrumental
variables
for
his
measures
of
microfinance
participation,
Hermes
showed
that
participation
in
microfinance
was
significantly
associated
with
a
decrease
in
income
inequality
(as
measured
by
the
GINI
coefficient)
but
that
the
magnitude
of
this
was
small,
only
a
fraction
of
a
percentage
(Hermes,
2014).
He
speculates
that
the
small
impact
is
likely
due
to
the
small
size
of
the
loans
relative
to
the
GDP
of
the
country
and
thus
concludes
that
microfinance
should
not
be
“seen
as
a
panacea
for
bringing
down
income
inequality
in
a
significant
way”
(Hermes,
2014).
My
methodology
uses
a
similar
cross-­‐country
analysis
as
that
of
Niels
Hermes.
Where
Hermes
focuses
solely
on
the
total
microfinance
loan
portfolio
in
a
given
country,
I
break
this
down
into
the
percentage
of
loans
issued
in
a
country
by
commercial
or
non-­‐commercial
microfinance
institutions
(I
will
expand
on
my
definition
of
commercial/non-­‐commercial
in
the
next
section).
I
then
add
another
independent
variable
that
I
hope
will
shed
some
light
on
this
debate,
which
is
the
percentage
of
loans
issued
in
a
country
by
for-­‐profit
microfinance
institutions.
What
I
hope
to
gain
from
this
is
some
insight
into
the
debate
surrounding
the
poverty
lending
approach
versus
the
financial
systems
approach.
If
we
believe
that
microfinance
institutions
can
develop
financial
markets
and
reduce
income
inequality,
I
believe
that
country-­‐level
analysis
of
the
effectiveness
of
commercial
vs.
non-­‐commercial
and
profit
vs.
not
for
profit
MFI’s
is
highly
relevant
as
it
may
help
to
determine
the
most
effective
delivery
model
for
microfinance
services
going
forward.

8. I
use
an
OLS
regression
for
my
estimates.
The
dependent
variable
is
the
rural
poverty
gap.
I
chose
the
rural
poverty
gap
as
a
measure
of
inequality
as
I
believed
it
would
be
illustrative
of
the
debate
discussed
in
the
previous
section.
If
the
financial
systems
approach
has
experienced
significant
mission
drift
(that
is,
focusing
entirely
on
profits
at
the
expense
of
poverty
alleviation)
we
might
expect
to
see
a
higher
concentration
of
commercial
microfinance
lending
associated
with
higher
levels
of
poverty.
I
believed
this
would
be
especially
relevant
to
the
rural
poverty
gap,
since
commercial
microfinance
institutions
focus
not
on
depth
of
outreach
but
on
breadth,
and
in
many
developing
countries
the
most
acutely
poor
are
rural
citizens.
I
also
include
the
percentage
of
loans
in
a
country
issued
by
for-­‐profit
MFI’s,
which
again
may
shed
light
on
the
poverty
lending
vs.
financial
systems
approach.
My
independent
variables
of
interest
are
the
percentage
of
loans
issued
by
commercial
microfinance
institutions
in
a
country,
and
the
percentage
of
loans
issued
by
for
profit
MFI’s
in
a
country.
Borrowing
from
the
literature
I
add
a
number
of
controls
in
my
regression
including:
inflation,
education,
arable
land
and
state
fragility
Data
My
analysis
focuses
on
microcredit.
I
collect
data
from
the
MIX
market
on
over
70
countries.
The
MIX
market
is
the
most
comprehensive
public
database
for
microfinance
institutions.
Each
MFI
is
ranked
on
the
quality
of
their
reports,
they
in
a
scale
of
‘diamonds’
where
1
diamond
is
the
least
transparent
and
5
diamonds
is
the
most
transparent
(5
diamonds
contains
audited
financial
statements
and
other
aspects).
I
only
included
in
my
results
the
reports
of
MFI’s
with
4
or
5
diamonds.

9. Because
data
is
not
available
from
every
microfinance
institution
in
every
year,
I
took
the
most
recent
observation
of
each
institution
from
the
years
2007-­‐2009.
Each
MFI
is
classified
as
a
bank,
credit
union/cooperative,
non-­‐banking
financial
institution,
NGO
or
other.
To
calculate
the
percentage
of
commercial
loans
I
divided
the
total
loans
in
each
country
issued
by
banks
and
credit
union/cooperatives
by
the
total
outstanding
loans.
The
data
on
GDP,
arable
land,
rural
poverty
gap,
education,
and
GINI
coefficients
are
taken
from
the
World
Bank
indicators.
In
an
attempt
to
control
for
short-­‐term
fluctuations
and
exogenous
effects,
as
well
as
missing
data
points
in
some
countries,
I
took
the
average
of
each
variable
from
the
years
2007-­‐
2009.
The
measure
of
state
fragility
is
taken
from
the
Polity
IV
data
set
and
I
again
took
the
average
of
the
years
2007-­‐2009
in
an
attempt
to
control
for
any
short-­‐term
fluctuations.
Section
III
Table
1
Table
1
presents
the
correlation
matrix
of
the
variables
included
in
my
regression.
The
control
variables
are
all
highly
correlated,
in
the
direction
one
would
expect,

10. with
our
dependent
variables
(for
example,
school
enrolment
is
negatively
correlated
with
the
rural
poverty
gap,
inflation
is
positively
correlated
with
the
rural
poverty
gap
etc.).
Interestingly,
the
percentage
of
loans
issued
by
commercial
microfinance
institutions
is
negatively
correlated
with
the
rural
poverty
gap,
whereas
the
percentage
issued
by
for
profit
microfinance
institutions
is
positively
correlated
with
the
rural
poverty
gap.
Tables
2
and
3
present
the
results
of
the
regressions.
Table
2
regresses
the
percentage
of
loans
issued
by
commercial
microfinance
institutions.
Table
3
does
the
same
exercise
with
the
percentage
of
loans
issued
by
‘for
profit’
microfinance
institutions
as
the
independent
variable
of
interest.
Column
6
of
Table
3
adds
all
variables
in
the
same
regression.
Table
2

11. Table
3
Results
From
the
results
in
Table
2
and
Table
3,
neither
the
‘pctcomm’
or
‘pctprofit’
variable
is
significant.
My
regressions
show
that
the
percentage
of
loans
issued
in
a
country
by
commercial
MFI’s
or
the
percentage
of
loans
issued
by
for-­‐profit
MFI’s
have
a
significant
impact
on
the
rural
poverty
gap.
While
not
significant,
I
find
it
interesting
that
the
sign
of
the
coefficient
is
negative.
It
would
seem
to
work
against
the
proponents
poverty
lending
approach
as
we
did
not
observe
a
positive
effect.
We
can
observe
that
some
of
the
control
variables,
namely
school
enrolment
and
arable
land,
are
significantly
associated
with
the
rural
poverty
gap.

12. Methodological
Weaknesses
The
first
weakness
in
this
approach
is
with
the
quality
of
my
data
collected
on
the
MFI’s.
Country
level
data
on
MFI’s
has
only
become
widely
available
recently.
The
MIX
market
makes
an
attempt
to
audit
and
rank
data
on
the
basis
of
quality
but
there
is
no
consistent
reporting
standard
across
countries
to
begin
with.
I
did
my
best
to
address
this
problem
by
only
using
data
points
that
came
from
institutions
with
audited
financial
statements
that
are
published
for
the
year
and
rating
or
due
diligence
reports
that
are
published
for
the
year,
as
defined
by
the
MIX
market’s
diamond
rating
system.
A
second
weakness
concerns
reverse
causality.
This
paper
does
not
prove
a
causal
relationship
between
commercial
or
for-­‐profit
microfinance
intensity
and
the
rural
poverty
gap.
A
logical
argument
may
be
that
the
a
negative
relationship
exists
between
the
rural
poverty
gap
and
commercial
microfinance
intensity
because
countries
that
have
a
lower
rural
poverty
gap
to
begin
with
were
more
receptive
to
the
commercial
microfinance
lending
model.
I
believe
that
by
including
state
fragility
I
do
take
some
steps
towards
controlling
for
this
problem.
A
paper
published
by
Ault
and
Spicer
(2013)
found
that
the
state
fragility
of
a
country
was
a
significant
predictor
of
the
structure
of
microfinance
lending
in
that
country.
They
found
that
the
financial
systems
approach
in
a
country
“experienced
greater
difficulty
than
nonprofit
lenders
in
growing
their
client
base
in
more
fragile
state
settings”
(Ault
and
Spicer,
2013).
Third,
the
rural
poverty
gap
may
not
be
as
illustrative
a
dependent
variable
as
I
initially
thought.
One
possible
reason
for
this
is
that
rural
poor
may
not
be

13. served
in
any
significant
way
by
either
commercial
or
non-­‐commercial
microfinance
institutions.
There
is
some
evidence
of
this
in
my
regressions.
In
column
6
of
Table
3
I
include
Niels
Hermes’
indicator
of
microfinance
intensity
(the
total
outstanding
loans
divided
by
the
country’s
GDP)
which
indicates
that
there
is
no
significant
relationship
between
microfinance
participation
and
the
rural
poverty
gap.
Fourth,
if
I
was
to
find
any
significant
relationship
at
the
country
level,
it
might
be
better
to
choose
a
dependent
variable
that
could
be
affected
by
microfinance
lending
in
the
short
term.
One
such
variable
could
be
the
number
of
new
business
registered
in
the
country,
which
is
reported
on
the
World
Bank
Indicators
data
set.
Intuitively,
more
access
to
credit
in
a
country
could
facilitate
more
entrepreneurship
and
I
think
this
would
be
an
interesting
area
for
future
research.
Conclusion
A
final
note
is
that
inequality
can
be
entrenched
in
a
country
and
highly
static
over
time.
While
the
provision
of
microcredit
is
not
new,
it
has
only
reached
a
large
global
scale
in
the
last
decade.
In
a
paper
by
Arestis
et
al.
they
explore
the
persistence
of
inequality
and
its
static
nature.
They
show
“…powerful
evidence
that
substantial
income,
poverty
and
welfare
inequalities
exist
between
and
within
countries
across
the
glob”
but
have
difficulty
demonstrating
“whether
these
disparities
have
been
narrowing
or
increasing
over
the
past
50
years
or
so”
(Arestis
et
al,
2011).
Thus
given
that
microfinance
lending
has
only
been
on
a
global
and
significant
scale
in
the
last
decade,
its
effects
on
such
an
entrenched
phenomenon

14. like
the
rural
poverty
gap
may
not
be
felt
for
years.
Future
research
in
cross
country
data
may
find
it
useful
to
use
a
lagged
measure
of
microfinance
intensity,
for
example,
which
could
account
for
the
delayed
effects.
I
believe
this
paper
has
contributed
a
useful
framework
for
future
study
in
an
area
that
is
only
in
its
infancy.
As
the
microfinance
industry
continues
to
develop
and
formalize
more
quality
country
level
data
will
become
available.
Cross
sectional
and
country-­‐level
analysis
may
yield
more
insight
into
the
benefits
or
potential
harms
of
microfinance,
as
the
potential
lagged
effects
of
lending
are
felt,
or
as
the
gross
amount
of
loans
grows
relative
to
the
size
of
a
country’s
GDP
and
important
conclusions
about
the
most
effective
delivery
of
microfinance
services
may
emerge.

15. References
Arestis,
Philip,
Martin,
Ron
and
Tyler,
Peter.
2011.
“The
Persistence
of
Inequality?”
Cambridge
Journal
of
Regions,
Economy
and
Society,
4,
3–11
Ault,
Joshua
and
Spicer,
Andrew.
2014.
“The
Institutional
Context
of
Poverty:
State
Fragility
as
a
Predictor
of
Cross-­‐National
Variation
In
Commercial
Microfinance
Lending.”
Strategic
Management
Journal,
34:
1818-­‐1838.
Banerjee,
Abhijit.
2013.
“Microcredit
under
the
Microscope:
What
have
We
Learned
in
the
Past
Two
Decades,
and
What
Do
We
Need
to
Know?”
Annual
Review
of
Economics,
5:487-­‐519.
Beck,
T.,
Demirgüç-­‐Kunt,
A.
and
Levine,
R.
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Inequality
and
the
Poor.”
Journal
of
Economic
Growth,
12,
27–49.
Chasmar,
Katherine.
2009.
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of
Microfinance
in
Latin
America.
“
Kingston,
Ontario.
Queen’s
University,
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Grameen
Bank.
http://www.grameenfoundation.org/
Clarke,
G,
Colin
Xu,
L.
and
Zou,
H.
Fu.
2006.
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and
income
inequality:
what
do
the
data
tell
us?”
Southern
Economic
Journal,
72,
578–96.

16. Hermes,
Niels.
2014.
“Does
Microfinance
Affect
Income
Inequality?”
Applied
Economics,
Vol.
46,
No.
9,
1021-­‐1034.
Ledgerwood,
Joanna.
1999.
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Handbook:
An
Institutional
and
Financial
Perspective”
World
Bank.
http://dx.doi.org.proxy.queensu.ca/10.1596/978-­‐0-­‐8213-­‐
4306-­‐7
Li,
H.,
Squire,
L.
and
Zou,
H.-­‐fu.
1998.
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international
and
inter-­‐temporal
variations
in
income
inequality.”
Economic
Journal,
108,
26–43.
Microcredit
Summit,
2011.
http://www.microcreditsummit.org
Microfinance
Information
Exchange.
The
Mix
Market.
http://www.mixmarket.org/
Robinson,
Margeurite.
2001.
“The
Microfinance
Revolution:
Sustainable
Microfinance
for
the
Poor.”
World
Bank.
http://dx.doi.org.proxy.queensu.ca/10.1596/0-­‐8213-­‐4524-­‐9