Individual lending versus group lending: An evaluation with Kenya's microfinance data

Abstract

Group micro-lending has been used successfully in some parts of the world to expand the reach of microcredit programs. However, our study shows that microfinance institutions in Kenya prefer individual lending which is associated with higher default rates compared to group lending. The study also shows that high interest rates increase the odds of client delinquency while loan size is inversely related to delinquency. Given these findings, policymakers need to work for stability in the macro-environment to ensure interest rates charged by microfinance institutions (MFIs) remain stable and affordable. Alternatively, MFIs can develop a graduated scale for charging interest rates in which credit is extended to groups at first to hedge the firm against repayment risk; following this, the firm identifies individuals within the groups whose credit risk has improved and issue progressive individual loans to them. Such individual loans would fetch higher returns in form of interest for MFI and boost their outreach, reduce delinquency, and enhance self-sufficiency.

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Review
of
Development
Finance
3
(2013)
99–108
Individual
lending
versus
group
lending:
An
evaluation
with
Kenya’s
microfinance
data
Odongo
Kodongo a,∗,
Lilian
G.
Kendi b
aGraduate
School
of
Business
Administration,
University
of
the
Witwatersrand,
Johannesburg,
South
Africa
bStrathmore
University,
Nairobi,
Kenya
Abstract
Group
micro-lending
has
been
used
successfully
in
some
parts
of
the
world
to
expand
the
reach
of
microcredit
programs.
However,
our
study
shows
that
microfinance
institutions
in
Kenya
prefer
individual
lending
which
is
associated
with
higher
default
rates
compared
to
group
lending.
The
study
also
shows
that
high
interest
rates
increase
the
odds
of
client
delinquency
while
loan
size
is
inversely
related
to
delinquency.
Given
these
findings,
policymakers
need
to
work
for
stability
in
the
macro-environment
to
ensure
interest
rates
charged
by
microfinance
institutions
(MFIs)
remain
stable
and
affordable.
Alternatively,
MFIs
can
develop
a
graduated
scale
for
charging
interest
rates
in
which
credit
is
extended
to
groups
at
first
to
hedge
the
firm
against
repayment
risk;
following
this,
the
firm
identifies
individuals
within
the
groups
whose
credit
risk
has
improved
and
issue
progressive
individual
loans
to
them.
Such
individual
loans
would
fetch
higher
returns
in
form
of
interest
for
MFI
and
boost
their
outreach,
reduce
delinquency,
and
enhance
self-sufficiency.
©
2013
Africagrowth
Institute.
Production
and
hosting
by
Elsevier
B.V.
All
rights
reserved.
JEL
classification:
G21;
G23;
O16
Keywords:
Loan
delinquency;
Microfinance;
Microcredit;
Group
loans;
Individual
loans
1.
Introduction
The
operations
of
microfinance
institutions1in
Kenya
are
governed
by
the
Microfinance
Act
of
2006.
According
to
the
Act,
microfinance
institutions
(MFIs
hereinafter)
are
classified
into
and
registered
in
three
different
tiers:
deposit-taking
institutions
such
as
commercial
banks,
credit-only
non-deposit
taking
insti-
tutions,
and
informal
organizations.
The
latter
category
includes
∗Corresponding
author
at:
2,
St.
David
Street,
Johannesburg
2050,
South
Africa.
E-mail
addresses:
kodongo03@gmail.com,
kodongoc@yahoo.com
(O.
Kodongo),
liliankendi73@yahoo.com
(L.G.
Kendi).
1Johanna
(1999)
describes
a
microfinance
institution
as
a
financial
institution
whose
major
activities
include
provision
of
small
loans
typically
for
working
capital,
informal
appraisal
of
borrowers
and
investors,
provision
of
collateral
substitutes
(such
as
group
loans),
the
provision
of
social
intermediation
via
group
formation,
and
training
in
financial
literacy
and
management
capabilities.
Peer
review
under
responsibility
of
Africagrowth
Institute.
1879-9337
©
2013
Africagrowth
Institute.
Production
and
hosting
by
Elsevier
B.V.
All
rights
reserved.
http://dx.doi.org/10.1016/j.rdf.2013.05.001
rotating
savings
societies,
club
pools
and
financial
services
asso-
ciations.
The
52
MFIs,
registered
in
Kenya
with
the
objective
of
facilitating
access
to
financial
services
among
the
unbanked
poor,
currently
serve
about
6.5
million
clients
with
an
outstand-
ing
loan
portfolio
in
excess
of
US$
310
million.2Despite
the
enactment
of
the
Microfinance
Act
in
2006
and
the
subsequent
proliferation
of
MFIs,
available
statistics
show
that
35.2%
of
Kenyans
are
still
unable
to
access
formal
financial
services
and
another
30.2%
are
entirely
excluded
from
accessing
any
form
of
financial
service.3
Worldwide,
the
microfinance
sub-sector
has
had
to
contend
with
numerous
challenges.
One
of
the
major
challenges
faced,
especially
by
personal
loan
programs
of
MFIs,
is
that
borrowers
are
highly
risky
since
they
are
typically
low
net-worth
indi-
viduals
with
little
or
no
collateral
that
can
be
acquired
by
the
MFI
in
the
event
of
default.
A
popular
remedy
to
this
problem
2The
52
MFIs
are
those
affiliated
to
the
Association
of
Microfinance
Institu-
tions
of
Kenya
(AMFI),
an
organization
registered
in
1999
under
the
Societies
Act
to
build
capacity
of
the
microfinance
industry
in
Kenya.
The
statistics
were
accessed
February
11,
2013,
from
the
AMFI
website:
http://www.amfikenya.
com/pages.php?p=1.
3Information
accessed
December
9,
2012,
from
Central
Bank
of
Kenya
website:
http://www.centralbank.go.ke/financialsystem/microfinance/
Introduction.aspx.

100
O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
involves
requiring
borrowers
to
apply
for
credit
in
voluntarily
formed
groups:
since
such
borrowers
know
each
other,
safe
bor-
rowers
will
likely
form
their
own
groups,
avoiding
those
with
higher
risk
profiles
–
this
mitigates
the
adverse
selection
problem
(Armendariz
and
Morduch,
2007).
The
group
lending
model,
first
used
in
Bangladesh,
may
not
be
exactly
replicable
in
the
Kenyan
context:
Bangladesh
has
an
area
of
147,600
km2with
130
million
people
while
Kenya
has
an
area
of
580,400
km2with
43
million
people.
This
implies
that
the
information
network
in
Kenya
could
be
much
weaker
than
that
of
Bangladesh
where
group
lending
model
has
operated
effi-
ciently;
members
of
a
group
in
Kenya
may
not
be
able
to
as
fully
monitor
how
funds
borrowed
from
MFI
are
used
by
their
peers
as
members
of
a
Bangladeshi
group.
Nevertheless,
the
micro-
finance
sector
in
Kenya
has
largely
adopted
the
Bangladeshi
model
and
runs
two
broad
microcredit
programs:
personal
lend-
ing
and
group
lending.4Credit
is
typically
granted
to
finance
business/entrepreneurial
activities
under
both
programs
but
it
is
believed
that
significant
unfulfilled
market
demand
also
exists
for
personal
loans
to
finance
consumption
and
emergency
needs
(see
also
Woller,
2002).5The
two
credit
programs
(personal
and
group
lending)
exhibit
different
characteristics
defined
by,
among
others,
the
rapidity
of
loan
approval,
repayment
periods
(defines
as
weeks
or
months),
interest
rates,
and
other
program
specific
terms.
Dellien
et
al.
(2005)
discusses
key
differences
between
the
group
lending
and
individual
lending
programs.
First,
because
time
and
effort
is
invested
in
building
social
networks
that
enable
groups
to
select
members
who
are
creditworthy
under
group
lending,
the
role
of
loan
officers
is
to
provide
structure,
training
on
loan
processes
and
administrative
support.
Under
individual
lending,
loan
officers
bear
principle
responsibility
for
loan
deci-
sions;
they
screen,
and
monitor
their
clients
as
well
as
come
up
with
mechanisms
of
enforcing
repayment.
Second,
the
prin-
ciple
incentives
for
repayment
of
group
loans
is
joint
liability,
group
reputation,
credit
rating
and
future
access
to
credit
for
each
member,
all
of
which
are
directly
contingent
on
each
mem-
ber
upholding
their
obligations.
On
the
other
hand,
individual
lending
programs
use
a
variety
of
incentives
such
as
collateral
requirements,
co-signers
and
guarantors
to
promote
repayment
and
repayment
discipline
is
created
by
strict
enforcement
of
contracts.
Each
of
the
two
lending
programs
has
its
strengths
and
weak-
nesses.
Armendáriz
and
Morduch
(2000)
observe
that
group
meetings
facilitate
education
and
training
useful
for
clients
with
small
experience
and
improve
financial
performance
of
their
businesses.
Other
researchers
(Godquin,
2004;
Madajewicz,
2011)
argue
that
group
lending
helps
mitigate
the
risks
asso-
ciated
with
information
asymmetry:
for
instance,
because
group
4In
Kenya,
as
in
Bangladesh,
personal
lending
involves
extending
credit
to
an
individual
borrower
while
group
lending
involves
extending
credit
to
two
or
more
people
who
are
held
liable
for
each
other’s
credit
(Maria,
2009).
5In
the
context
of
microfinance
credit,
a
business
loan
is
a
working
capital
loan
designed
to
facilitate
growth,
expansion
and
upgrade
of
a
business
while
a
personal
loan
is
an
unsecured
salary
advance
for
customers
to
meet
emergency
needs
(Faulu
Kenya,
2012).
borrowers
are
linked
by
joint
liability,
if
one
of
them
switches
from
safe
to
risky
project
(moral
hazard),
the
probability
that
her
partner
will
have
to
pay
the
liability
rises.
This
gives
group
members
the
incentive
to
monitor
each
other.
The
reduction
in
group
members’
default
through
peer
pressure
and
social
ties
has
also
been
discussed
(Guttman,
2007;
Dixon
et
al.,
2007;
Al-Azzam
et
al.,
2011).
However,
Maria
(2009)
points
out
that
group
monitoring
may
be
rendered
ineffective
where
social
ties
are
loose,
and
the
cost
of
monitoring
each
other
high.
Group
lending
is
not
without
setbacks.
Savita
(2007)
argues
that
group
lending
is
associated
with
additional
costs
including
group
formation
costs,
training
borrowers
on
group
procedures,
higher
degree
of
supervision
and
a
higher
frequency
of
install-
ment
payments.
These
costs
increase
interest
rates
of
such
microcredit
loans
leading
to
enhanced
repayment
risk.
Other
researchers
argue
that
joint
liability
in
group
lending
penalizes
good
credit
risk
customers
(Giné
and
Karlan,
2010),
could
hinder
optimal
utilization
of
borrowed
funds
by
clients
(Madajewicz,
2003)
and
might
even
jeopardize
repayment
since
the
incentive
of
future
credit
is
no
longer
present
in
the
event
that
one
member
fails
to
pay
(Besley
and
Coate,
1995).
Individual
lending
programs
also
present
several
benefits.
For
instance,
Armendáriz
and
Morduch
(2000)
find
that
the
guarantor
exerts
sufficient
social
pressure
on
the
client
to
repay
MFI
loans
in
Russia
and
Eastern
Europe.
However,
Laure
and
Baptiste
(2007)
argue
that
the
guarantee
mechanism,
especially
personal
guarantees,
is
only
meaningful
if
the
borrower
has
assets
that
can
be
pledged
as
surety,
if
the
institutional
frame-
work
permits
the
actual
transfer
of
ownership
of
the
pledge
from
the
borrower
to
the
creditor
easily
and
if
the
pledged
assets
are
not
very
liquid.
The
duo
contends
that
these
three
conditions
are
not
met
in
many
developing
countries.
In
particular,
Kenya
has
a
rigid
judicial
system
with
a
large
number
of
pending
cases
which
may
hinder
timely
transfer
of
pledge
and
most
MFI
borrow-
ers
may
not
even
have
“that
small
collateral”.
Another
benefit
of
individual
lending
is
that
it
spares
borrowers
the
negative
effects
such
as
time
spent
in
group
meetings
and
loss
of
pri-
vacy
when
they
discuss
their
financial
situation
and
investment
projects
with
the
peers
who
could
oppose
such
projects
(Maria,
2009)
in
the
process
impeding
their
individual
growth
(Giné
and
Karlan,
2010).
Given
the
strong
arguments
advanced
in
favor
of
both
individ-
ual
and
group
lending,
MFIs
find
it
confusing
making
a
choice
between
the
two
lending
programs.
We
believe
that
the
choice
should
be
informed,
in
principle,
by
each
firm’s
philosophi-
cal
orientation.
The
provision
of
microcredit
services
has
been
explained
by
three
philosophical
arguments
(Armendáriz
and
Morduch,
2000).
First
is
the
institutional
approach,
which
argues
that
institutional
sustainability
is
paramount
so
that
MFIs
should
be
able
to
cover
their
operating
and
financing
costs
with
program
revenue.
The
opposing
view
is
the
welfare
approach,
which
argues
that
MFIs
can
attain
sustainability
without
achieving
financial
self-sufficiency.6Then
there
is
the
middle
ground
view,
6The
institutionalists
argue
that
large
scale
outreach
to
the
poor
on
a
long-
term
basis
cannot
be
guaranteed
if
MFIs
are
not
financially
sustainable
while

O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
101
known
as
the
win-win
approach,
which
argues
for
balancing
the
goals
of
poverty
alleviation
and
financial
self-sustainability.
Our
thesis
is
that
microfinance
institutions
with
high
aversion
to
risk
ascribe
to
the
institutional
approach
and
tend
to
prefer
group
lending
while
those
with
lower
aversion
to
risk
tend
to
identify
with
the
welfarists’
approach
and
prefer
individual
lending.
However,
Hermes
and
Lensink
(2009)
have
observed
that
a
majority
of
MFIs
are
now
focusing
on
financial
sustainability
and
efficiency
(the
institutional
approach)
due
to
increasing
competition.
Given
this
observation,
it
is
our
view
that
the
risk
of
delinquency
should
play
a
key
role
in
informing
the
pref-
erence
for
either
group
lending
or
personal
lending
by
MFIs.
Empirical
investigations
have
pointed
out
a
number
of
factors
that
may
affect
the
likelihood
of
delinquency
on
microcre-
dit
obligations.
Mokhtar
et
al.
(2009)
find
that
training
to
an
MFI
borrower,
the
loan
amount
advanced
and
age
are
sig-
nificant
factors
affecting
loan
default
in
Malaysia.
Similarly,
Laure
and
Baptiste
(2007)
find
loan
amount
a
significant
vari-
able
affecting
default
in
microcredit
programs.7The
interest
rate
has
also
been
found
to
be
an
important
factor
affecting
microcredit
loan
delinquency
(Warui,
2012;
Pereira
and
Mourao,
2012).8
A
key
feature
of
MFIs
that
is
often
linked
to
delinquency
risk
is
the
frequent
collection
of
loan
installments.
According
to
Field
and
Pande
(2008),
frequent
repayments
provide
clients
with
a
commitment
device
that
helps
them
form
a
habit
of
sav-
ing
(this
facilitates
loan
repayment),
and
improves
their
trust
in
loan
officers
and
their
willingness
to
stay
on
track
with
repay-
ments.
However,
frequent
repayments
increase
transaction
costs
and
increase
default
risk
when
clients
graduate
to
larger
loans
since
this
increases
the
amount
of
their
cash
outlays.
Default
risk
has
also
been
found
to
increase
when
loan
officers
fail
to
undertake
their
key
roles
–
screening
and
encouraging
clients,
and
training
them
on
financial
discipline
–
properly
(Dixon
et
al.,
2007).
Another
factor
that
influences
delinquency
risk
is
gender.
Studies
have
shown
that
women
often
demonstrate
stronger
willingness
to
pay
than
men
(Armendariz
and
Morduch,
2007;
Phillips
and
Bhatia-Panthaki,
2007)
largely
because
they
have
lower
credit
opportunities
than
men
and
hence
must
repay
their
loans
to
ensure
continued
access
to
credit
and
are
easier
to
monitor
since
they
tend
to
stay
closer
to
their
homes
than
men.
welfarists
argue
that
the
poor
cannot
afford
higher
interest
rates,
therefore,
aim-
ing
at
financial
sustainability
ultimately
goes
against
the
goal
of
serving
large
groups
of
poor
borrowers.
7The
two
studies
contradict
each
other
on
the
role
of
loan
amount
on
client
default.
Mokhtar
et
al.
(2009)
find
lower
the
loan
amounts
to
be
associated
with
higher
chance
of
default
since
low
loan
amounts
are
mostly
extended
to
business
beginners
who
lack
experience
on
strategies
of
running
a
profitable
venture.
However,
the
findings
of
Laure
and
Baptiste
(2007)
associate
higher
loan
amounts
with
higher
likelihood
of
borrowers
experiencing
repayment
prob-
lems
because
it
becomes
difficult
for
an
individual
MFI
borrower
to
reimburse
excessively
high
amounts
if
their
ability
to
pay
has
not
substantially
changed
since
their
first
appraisal.
8The
two
studies
provide
evidence
suggesting
that
low
interest
rates,
by
reduc-
ing
borrower
cash
outflows,
result
in
low
default
risk
under
both
individual
and
group
lending.
Our
study
sought
to
examine
the
two
key
microfinance
programs
with
a
view
to
evaluating
their
effectiveness
in
addressing
the
financial
needs
of
the
target
beneficiaries
(bor-
rowers).
We
also
sought
to
attribute
loan
delinquency
under
each
program.
The
study
proceeds
on
the
premise
that
bor-
rowers’
likelihood
of
default
diminishes
if
their
financial
needs
are
satisfactorily
addressed.
Consequently,
we
evaluate
the
effectiveness
of
a
lending
program
through
an
analysis
of
its
propensity
to
loan
delinquencies.
Thus,
microfinance
programs
that
exhibit
high
tendencies
for
loan
delinquency
are
deemed
not
to
effectively
address
borrowers’
financial
needs.
Warui
(2012)
documents
an
increasing
trend
in
level
of
loan
delinquency
among
MFIs
in
Kenya.
This
may
be
a
pointer
to
increased
ineffectiveness
of
the
institutions’
various
lending
programs.
Although
many
studies
(e.g.,
Guttman,
2007;
Dixon
et
al.,
2007;
Aniket,
2011;
Al-Azzam
et
al.,
2011)
have
analyzed
the
pros
and
cons
of
group
and
individual
lending,
data
sets
are
often
insufficient
to
draw
meaningful
inferences
about
the
most
suitable
microcredit
program.
As
we
have
shown
through
a
survey
of
the
literature,
researchers
have
advanced
conflicting
arguments
about
the
two
lending
programs.
Such
conflicting
arguments
about
the
approach
to
use
in
delivering
credit
services
have
left
a
gap
and
uncertainty
as
to
which
is
the
appropri-
ate
credit
program,
particularly
where
default
risk
mitigation
is
concerned.
A
recent
study
almost
similar
to
ours
is
Pereira
and
Mourao
(2012).
However,
their
study
focuses
on
how
MFIs
can
over-
come
credit
defaults.
For
the
purposes
of
their
analysis,
the
duo
categorizes
the
world
into
regions,
which
include
the
Middle
East
and
North
Africa
(MENA).
In
their
conclusions,
however,
they
warn
about
the
danger
of
generalizing
default
risk
of
MFI
credit
since
MFIs
operate
in
places
that
are
geographically
iso-
lated
and
hence
their
borrowers
exhibit
varying
characteristics.
In
the
Kenyan
MFI
context,
there
has
never
been
a
detailed
com-
parative
evaluation
of
the
two
microcredit
programs.
Therefore,
participants
in
Kenya’s
microfinance
industry
have
no
scien-
tific
rationale
for
preferring
one
of
the
two
lending
programs
over
the
other.
And
as
our
results
show,
sub-optimal
decisions
have
been
made
by
microfinance
credit
providers
in
as
far
as
making
the
appropriate
choice
of
a
suitable
lending
program
is
concerned.
The
key
finding
of
this
study
is
that
group
lending
is
bet-
ter
able
to
mitigate
loan
delinquency
than
personal
lending.
The
study
proffers
several
policy
suggestions.
Among
others,
we
rec-
ommend
that
the
threshold
for
individual
lending
must
include
demonstrable
ability
of
a
borrower
to
pay
interest
of
at
least
1.8%
per
month.
Group
loans
should
be
issued
where
this
con-
dition
is
not
met.
Secondly,
MFIs
are
advised
to
extend
high
loan
amounts
(amounts
in
excess
of
KES
100,000)
largely
to
group
borrowers,
which
the
study
finds
to
show
tendency
for
low
default
risk.
The
rest
of
this
article
is
organized
as
follows.
Section
2
dis-
cusses
the
data;
Section
3
presents
the
theoretical
model
and
describes
the
study’s
methodology;
Section
4
presents
and
dis-
cusses
our
findings;
conclusions
and
policy
implications
are
in
Section
5.

102
O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
Table
1
Summary
statistics
of
some
microcredit
lending
terms.
Mean
Standard
deviation
Minimum
Maximum
Skewness
Interest
rate
(%,
monthly)
1.75
0.01
0.025
2.89
−0.957
Age
(years) 37.13 0.36 24
62
0.930
Loan
amount
(KES) 33,446.30 1487.56 5,000
250,000
3.301
Repayment
(no.
of
weeks)
14.61
0.25
4
40
2.138
2.
Data
A
structured
questionnaire
was
used
to
gather
data
from
loan
officers
and
credit
controllers
at
the
head
offices
of
the
microfinance
institutions
registered
by
the
Association
of
Micro-
finance
Institutions
of
Kenya
(AMFI).
AMFI
has
a
registered
membership
of
52
firms
of
which
48
have
their
head
offices
in
Kenya’s
capital,
Nairobi.
We
surveyed
all
the
48
firms.
How-
ever,
only
35
firms
returned
filled
questionnaires,
of
which
three
were
incomplete
or
had
missing
information
and
were
therefore
discarded.
Over
a
period
of
three
years,
through
November
30,
2012,
and
for
each
loanee,
data
was
gathered
in
respect
of
age,
loan
amount
granted,
repayment
intervals,
interest
rates
charged,
whether
loan
was
granted
to
a
loanee
as
part
of
a
group
or
as
an
individ-
ual,
whether
the
loanee
was
given
financial
training,
and
whether
the
account
was
delinquent.
For
the
purposes
of
this
analysis,
a
customer’s
account
is
deemed
delinquent
if
it
is
classified
as
past
due
or
has
been
declared
to
be
in
default
by
the
concerned
institution.
Many
MFIs
in
Kenya
consider
a
loan
past
due
if
a
period
of
four
weeks
or
more
has
elapsed
after
the
loan’s
due
date.
The
loan
is
considered
in
default
if
it
is
eight
or
more
weeks
past
its
due
date
or
if
at
least
48
weeks
have
elapsed
after
the
first
payment
and
the
customer
is
yet
to
settle
the
entire
obliga-
tion.
The
maximum
amount
of
time
given
to
loanees
to
pay
up
is
typically
40
weeks
and
clients
are
generally
expected
to
make
the
same
payment
at
each
interval.
Thus,
the
two
delinquency
measures
given
above
exclude
customers
who
are
taking
long
to
repay
not
because
they
are
defaulting,
but
because
perhaps
the
interest
rate
is
high
and
they
need
a
longer
period
than
the
40
weeks
to
fully
settle
their
obligations
to
the
MFI.
We
use
the
actual
records
kept
by
loan
officers
on
defaulted
and
past
due
accounts.
The
survey
was
conducted
in
December
2012/January
2013.
Table
1
displays
the
summary
statistics
for
some
“lending
terms
variables”
used
in
the
empirical
analysis.
MFIs
compute
the
interest
payment
to
make
it
simple
for
the
client
to
deci-
pher.
Thus,
once
the
interest
rate
is
agreed
on
between
the
client
and
the
loan
officer,
the
interest
payment
for
the
year
(or
loan
period
if
shorter)
is
computed
and
loaded
on
to
the
principal.
A
repayment
schedule
is
constructed
by
dividing
the
resulting
figure
over
the
number
of
repayment
weeks.
This
number
is
then
adjusted
up
or
down
to
reach
a
round
weekly
payment.
The
table
shows
that
the
mean
monthly
nominal
interest
rate
is
1.75%,
or
about
21%
per
year,
with
negative
skewness.
Since
the
standard
deviation
is
a
paltry
0.01%
per
month,
the
bulk
of
the
loans
given
attract
interest
rates
clustered
around
1.75%;
how-
ever,
there
are
a
few
cases
when
interest
rates
exceed
the
mean
value.
The
mean
interest
rate
appears
high
but
the
(annualized)
monthly
inflation
rate
over
the
study
period
averaged
11.10%.9
Thus,
the
real
annualized
interest
rate
on
the
microloans
aver-
aged
only
9.90%,
which
appears
appropriate
for
the
high
risk
levels
generally
exhibited
by
microcredit
applicants.
The
mean
age
of
a
loanee
is
37
years
with
a
positive
skew-
ness,
implying
that
MFIs
generally
tend
to
avoid
loaning
to
very
young
clients.
A
more
in-depth
analysis
of
the
data
indicates
that
youthful
applicants
aged
below
30
only
represent
26%
of
the
total
number
of
loanees.
This
may
be
explained
by
the
fact
that
the
majority
of
the
loans
are
given
for
business/entrepreneurial
purposes
and
younger
clients
are
most
likely
avoided
due
to
their
relative
business
inexperience.
Notably
by
the
time
the
borrow-
ers
have
attained
the
age
of
30–43
(64%
of
borrowers),
they
will
have
acquired
adequate
business
knowledge
hence
should
present
lower
default
risk.
The
maximum
amount
of
loan
issued
by
a
microcredit
lender
is
KES
250,000
to
be
repaid
within
40
weeks.
However,
on
the
average,
customers
are
extended
loans
with
a
repayment
period
of
only
15
weeks,
with
a
standard
devia-
tion
of
0.25
weeks.
Clearly,
lending
terms
appear
pretty
stringent
in
Kenya’s
microcredit
market,
making
it
very
likely
that
clients
generally
strain
to
make
good
their
obligations
to
the
lending
firms.
3.
Empirical
strategy
We
use
a
structured
questionnaire
to
gather
data
from
loan
officers
and
credit
controllers
of
MFIs.
The
first
part
of
the
ques-
tionnaire
uses
a
Likert-type
scale
in
which
each
of
the
provided
choice
of
answers
is
assigned
an
ordinal
value,
generating
quan-
titative
data
that
we
interpret
and
present
in
frequency
tables
and
charts.
The
first
set
of
data
therefore
provides
general
informa-
tion,
particularly
pertaining
to
factors
considered
by
MFI
loan
officers
when
screening
credit
applicants.
The
second
part
of
the
questionnaire
provides
data
that
can
explain
loan
delinquency
among
group
and
individual
lenders.
All
the
key
potential
factors
from
the
literature
are
incorporated
in
the
structured
question-
naire
and
loan
officers
are
requested
merely
to
indicate
the
appropriate
response
in
respect
of
each
client.
The
data
are
pooled
and
analyzed
using
logistic
regres-
sion.
Suppose
that
the
dependent
variable,
y,
can
be
explained
9The
data
is
computed
from
the
inflation
figures
obtained
on
February
15,
2013,
from
the
Kenya
National
Bureau
of
Statistics
website:
http://www.knbs.or.ke/news/lei122012.pdf.
The
inflation
rate
for
the
year
November
2010
through
November
2011
was
19.72%
and
the
inflation
rate
for
the
year
November
2011
through
November
2012
was
3.25%.

O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
103
by
a
vector
of
r
independent
variables
(or
factors),
X.
Thus,
y
=
f(X)(1)
The
logistic
distribution
function
is
expressed
in
the
form
(see,
e.g.,
Gujarati,
2004):
P
=
E(y
=
1|X)
=1
1
+
e−βX(2)
where
β
is
the
vector
of
coefficients
and
P
represents
the
odds
of
“success”
for
the
dependent
variable,
y.
Now
suppose
z
=
β’X.
The
distribution
function
in
Eq.
(2)
can
now
be
expressed
in
a
simpler
form
as
follows:
P
=1
1
+
e−z=ez
1
+
ez(3)
The
parameter
z
has
values
ranging
from
−∞
to
∞
while
P
ranges
from
−1
to
+1.
It
is
important
to
note
that
P
is
a
nonlinear
function
of
z
and
hence
nonlinear
in
X
and
in
β.
Thus,
the
OLS
regression
procedure
cannot
be
used
to
estimate
the
parameters,
β.
However,
Eq.
(3)
can
be
linearized,
first
by
expressing
it
as
a
function
of
1
−
P,
the
probability
of
“failure”:
1
−
P
=
1
−1
1
+
w−z=1
1
+
ez(4)
Dividing
Eq.
(3)
by
Eq.
(4)
yields
P
1
−
P=1
+
ez
1
+
e−z=
ez(5)
The
quantity
P/1
−
P
is
the
odds
in
favor
of
“success”.
Taking
logarithms
on
both
sides
gives
L
=
ln P
1
−
P=
z
=
βX(6)
L
is
known
as
the
logit,
hence
the
term
logit
(or
logistic)
regression.
Parameter
estimates
are
typically
interpreted
in
their
antilogarithm
form
(Eq.
(5)),
which
gives
the
odds
in
favor
of
the
dependent
variable,
y.
Finally,
we
gather
data
on
the
number
of
delinquent
accounts
as
a
proportion
of
the
total
number
of
loans
given
for
each
of
the
two
loan
programs
–
group
loans
and
individual
loans.
We
use
this
data
to
test
the
null
hypothesis
that
the
two
proportions
are
equal.
This
is
tested
against
the
alternative
that
the
proportion
of
delinquency
is
higher
under
individual
lending
programs.
4.
Results
4.1.
The
preference
for
group
or
individual
lending
among
MFIs
We
first
sought
to
establish
the
philosophical
orientation
of
Kenya’s
microcredit
firms.
Consistent
with
the
observations
of
Hermes
and
Lensink
(2009),
our
data
show
that
69%
of
MFIs
pursue
institutional
sustainability
(or
financial
self-sufficiency)
in
their
lending
policy.
Only
6%
of
the
surveyed
firms
lend
with
the
objective
of
poverty
alleviation,
or
outreach
to
the
poor,
while
Savings
15%
Credit
55%
Insurance
6%
Deposits
15%
Other
9%
Fig.
1.
Services
offered
by
MFIs
in
Kenya.
the
rest
(25%)
seek
both
financial
stability
and
poverty
allevi-
ation
(win–win
approach).
Next,
we
establish
which
services
are
offered
by
the
MFIs
in
the
country.
Fig.
1
presents
our
find-
ings.
The
figure
shows
that
credit
provision
comprise
of
55%
of
services
offered
by
microfinance
institutions
while
savings
and
deposits
constitute
15%
each.
The
remaining
services
provided
by
these
institutions
include
insurance
(6%)
and
“other”
services
(9%),
which
include
money
transfer
and
financial
consultancy,
among
others.
Now,
credit
services
offered
by
MFIs
can
be
in
the
form
of
individual
or
group
loans.
Fig.
2
presents
the
factors
motivating
the
preference
for
either
of
the
two
credit
programs.
The
figure
indicates
that
individual
lending
is
preferred
by
microfinance
institutions
whose
goals
are
to
reach
out
to
the
poor,
to
minimize
transaction
costs
and
to
maintain
market
share
(by
pursuing
low
client
dropouts).
Con-
trarily,
MFIs
prefer
group
lending
if
their
goal
is
to
expand
in
size
or
to
lower
delinquency
and
therefore
increase
their
chance
of
financial
sustainability
and
long-run
survival.
MFIs
preferring
group
lending
also
point
out
the
crucial
roles
of
group
meet-
ings
in
screening
repayment
ability
of
the
members,
facilitating
member
training
on
business
skills,
and
monitoring
loan
use.
Despite
the
clear
benefits
associated
with
group
meetings
and
lower
delinquency
levels
in
group
loans,
we
find
that
75%
of
microcredit
issued
in
Kenya
goes
to
individual
borrowers
while
only
25%
of
credit
is
extended
to
group
borrowers.
So,
why
do
Kenyan
MFIs
prefer
individual
lending?
86%
of
our
respondents
attribute
this
preference
to
poor
information
networks
among
group
members
that
affect
monitoring
of
loan
usage.
Since
it
has
relatively
higher
default
risk
in
general,
individual
lending
tends
to
derail
the
objective
of
financial
sustainability
and
threatens
firms’
long-run
survival.
However,
MFIs
try
to
“hedge”
their
exposure
to
the
higher
default
risk
by
imposing
Fig.
2.
Microcredit
approaches
and
reasons
for
their
adoption.

104
O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
Table
2
Importance
attached
to
factors
considered
in
approving
a
loan
application.
Factor Important
Moderately
important
Very
important
Extremely
important
Panel
A:
individual
loans
Weekly
loan
repayment
ability 14% 36% 25% 25%
Interest
rate
above
1.8%
pm 19% 19% 29%
33%
No
collateral
3%
21%
21%
55%
Age
of
applicant
18%
32%
36%
14%
Business
loan
8%
27%
23%
42%
Consumption
loan
14%
38%
34%
14%
Loan
amount
>
KShs.100,000
–
–
45%
55%
Panel
B:
group
loans
Weekly
loan
repayment
ability
25%
19%
38%
18%
Interest
rate
above
1.8%
pm
23%
23%
27%
27%
No
collateral
15%
26%
33%
26%
Age
of
applicant
21%
29%
33%
17%
Business
loan
29%
25%
25%
21%
Consumption
loan
37%
33%
20%
10%
Loan
amount
>
KShs.100,000
11%
21%
21%
46%
“security”
requirements
on
individual
loan
applicants.
Security
refers
to
method
of
enforcing
discipline
in
loan
repayment.
Our
findings
suggest
that
a
large
proportion
(37%)
of
MFIs
secure
their
individual
loans
through
a
third
party
guarantor,
underly-
ing
the
suggestion
that
guarantors
have
the
potential
to
exert
sufficient
pressure
on
borrowers
to
pay
because
they
are
held
personally
liable
in
the
event
that
the
guaranteed
loanee
defaults
(Armendáriz
and
Morduch,
2000).
The
remaining
firms
secure
their
individual
loans
either
through
the
specific
pledge
of
col-
lateral
(34%)
or
through
cosigners
(20%)
or
by
legal
action
(9%).
4.2.
Factors
affecting
the
preference
for
group
and
individual
lending
To
mitigate
adverse
selection
problems,
microfinance
insti-
tutions,
like
most
conventional
credit
providers,
take
their
loan
applicants
through
an
elaborate
screening
procedure
before
granting
a
loan.
The
key
factors
considered
in
the
screen-
ing
process
are
displayed
in
Table
2.
As
evident
from
the
two
panels
in
the
table,
the
degree
of
importance
attached
to
various
factors
in
approving
a
loan,
depends
on
the
lending
pro-
gram.
It
is
generally
important
for
the
MFI
to
check
clients’
repayment
ability
under
both
programs;
this
is
because
under
either
of
the
lending
programs,
borrowers
are
expected
to
repay
the
loans
in
frequent
installments,
typically
weekly.
This
is
because,
as
well
as
protecting
micro-lenders
from
huge
cash
out-
flows,
frequent
repayments
provide
clients
with
a
commitment
device
that
helps
them
form
a
habit
of
saving,
hence
facilitating
loan
repayment
(Field
and
Pande,
2008;
Pereira
and
Mourao,
2012).
Closer
check
of
the
purpose
of
the
loan
(business
use
or
consumption)
by
MFI
loan
officers
is
done
under
individual
lending
(panel
A)
while
this
factor
appears
to
be
of
less
impor-
tance
for
group
borrowers
(panel
B);
this
implies
that
groups
are
assumed
to
be
responsible
for
monitoring
their
members
and
it
does
not
really
matter
what
use
each
individual
member
of
the
group
declares
to
the
MFI.
Where
loan
amounts
greater
than
KES
100,000
are
to
be
disbursed,
microfinance
lenders
vet
individual
borrowers
more
heavily
(55%
extremely
impor-
tant,
panel
A)
than
group
borrowers
(46%
extremely
important,
panel
B).
This
is
a
pointer
to
the
fact
that
individual
bor-
rowers
are
generally
regarded
to
be
of
higher
risk
than
group
borrowers.
In
both
cases,
however,
the
finding
that
extreme
importance
is
attached
to
this
factor
appears
to
suggest
that
MFIs
would
be
hesitant
to
extend
huge
amounts
of
credit
to
their
borrowers
in
general.
The
age
of
loan
applicant
is
also
generally
regarded
as
a
key
variable
in
the
screening
exer-
cise.
Because
of
their
higher
default
probabilities,
individual
bor-
rowers
are
generally
charged
a
higher
default
premium.
Thus,
MFIs
place
extreme
importance
on
individual
borrowers’
abil-
ity
to
pay
interest
rate
above
1.8%
per
month
and
to
provide
collateral
(panel
A).
The
interest
rates
charged
under
the
two
programs
are
reported
in
Table
3.
The
table
shows
that
group
lending
attracts
interest
rates
in
the
range
1.2–1.8%
per
month
while
under
individual
lending,
borrowers
are
mostly
charged
above
1.8%
per
month.
This
finding
is
consistent
with
the
earlier
explanation
that
group
lending
would
be
preferred
because
of
lower
average
delinquency
levels
and
social
collateral.
From
another
perspec-
tive,
the
higher
interest
rates
on
individual
lending
translate
into
potentially
higher
average
returns
which
may
make
indi-
vidual
lending
more
attractive
to
MFIs
than
group
lending.
This
may
explain
the
preference
for
individual
lending
among
MFIs.
Table
3
Average
interest
rates
under
the
two
microcredit
programs.
Interest
rate
1.2–1.8%
pm
Above
1.8%
pm
Panel
A:
group
lending
Percentage
of
MFIs
80%
20%
Panel
B:
Individual
lending
Percentage
of
MFIs
39%
61%

O.
Kodongo,
L.G.
Kendi
/
Review
of
Development
Finance
3
(2013)
99–108
105
Table
4
Logistic
regression
estimation
results.
Variables LR/
Constant
INT
AGE
LAMT
RPT
CAT
Wald
Eq.
(1)
4.68**
1.44***
−0.02
−0.56***
−0.03
0.83***
33.19
(2.49)
(2.80)
(−1.63) (−3.30) (−1.28) (2.84)
[0.00]
Eq.
(2) –
1.87***
−0.02
−0.23**
−0.03
1.12***
70.37
(3.40)
(−1.21)
(2.46)
(−1.19)
(4.09)
[0.00]
The
sample
consists
of
420
clients
about
whom
we
obtained
data
from
the
surveyed
MFIs.
The
LR/Wald
is
the
chi-square
statistic
(with
five
degrees
of
freedom),
respectively
for
the
models
with
constant
and
with
constant
restricted
to
zero,
for
the
hypothesis
that
all
the
five
variables
are
zero.
INT,
AGE,
LAMT,
RPT
and
CAT
respectively
are
interest
rates,
age
of
client
in
years,
natural
log
of
loan
amount,
repayment
period
in
weeks
and
loan
category
(group
or
individual).
The
values
in
brackets
are
the
z-statistics
(calculated
with
robust
standard
errors)
of
the
reported
coefficients;
p-values
of
the
reported
chi-square
statistics
are
in
square
brackets.
4.3.
Causes
of
delinquency
in
microfinance
lending
programs
The
potential
factors
determining
loan
delinquency
among
microfinance
loanees
have
been
alluded
to
in
our
findings
reported
in
Section
4.2
as
well
as
in
the
literature
(Adongo
and
Stock,
2005;
Field
and
Pande,
2008;
Field
et
al.,
2010).
These
factors
include
interest
rates,
age,
loan
amount,
repayment
period
and
loan
category
(group
or
individual).
We
examine
the
relative
importance
of
these
five
factors
on
the
probabil-
ity
of
default
on
microcredit.
To
achieve
this,
we
run
a
logistic
regression
with
these
factors
explaining
loan
delinquency
(the
dependent
variable).
We
estimate
the
following
model:
DELi=
β0+
β1INTij +
β2AGEij +
β3AMTij
+
β4RPTij +
β5CATij +
εi
where
for
each
client
i
and
lender
j,
INTij is
the
interest
rate
charged;
AGEij is
the
age
of
client;
AMTij is
the
amount
of
credit
advanced;
RPTij is
the
number
of
weeks
after
which
loan
must
have
been
fully
cleared;
and
CATij ,
the
category
of
loan
granted,
takes
the
value
“0”
if
the
client
was
a
member
of
a
group
and
“1”
if
the
loan
was
given
to
the
client
as
an
individ-
ual.
Following
Field
and
Pande
(2008),
the
dependent
variable
(delinquency
rate,
DELi)
is
a
dummy
which
takes
on
a
value
of
“1”
if
a
client’s
loan
account
was
delinquent
and
“0”
otherwise.
Coefficients
estimates
are
evaluated
for
significance
against
het-
eroskedasticity
robust
standard
errors.
We
estimate
two
equations,
one
with
an
intercept
term,
the
other
with
the
intercept
restricted
to
zero.
Our
results,
presented
in
Table
4,
show
that
coefficient
estimates
are
qualitatively
sim-
ilar
for
the
two
equations.10 However,
the
test
for
the
restriction
that
all