Practice What you Preach - Benchmarking Microfinance Institutions with Heterogeneous Preferences

Introduction
Benchmarking
Results
Conclusions
Practice What You Preach
Benchmarking Microﬁnance Institutions with Heterogeneous
Preferences
Jaap Bos, Maastricht University
Matteo Millone, Maastricht University
ECCE Webinar, 17 April, 2013
Jaap Bos & Matteo Millone Practice What You Preach

Introduction
Benchmarking
Results
Conclusions
Motivation and Contribution
Research Questions
Findings
What we do
Benchmark the efficiency of microfinance institutions, while taking
into account differences in business models.

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
”No longer can microfinance investment be viewed as an
exclusively do-good, low-risk, relative safe haven. [...] equity
investors are reassessing the social and financial performance
of the asset class.” J. P. Morgan, Global Microfinance
Valuation Survey 2011

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
”No longer can microfinance investment be viewed as an
exclusively do-good, low-risk, relative safe haven. [...] equity
investors are reassessing the social and financial performance
of the asset class.” J. P. Morgan, Global Microfinance
Valuation Survey 2011
”Everyone involved in microfinance shares a basic goal: to
provide credit and savings services to thousands or millions of
poor people in a sustainable way ... a problem of dual
maximisation” Rhyne, The Microbanking Bulletin

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Investing in microﬁnance
More than one criterium guides the decision to invest in
microﬁnance:
1 Financial risk and return
2 Outreach and impact (social and political risk)

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
microﬁnance:
Large variation across microﬁnance institutions (MFIs)

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
microﬁnance:
Relative weight of criteria depends on preferences of investor

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
microﬁnance:
Relative weight of criteria depends on preferences of investor
Unclear relationship between two sets of criteria

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Benchmarking Paradox
Benchmarking makes sense only if we compare apples to apples

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Benchmarking Paradox
Benchmarking makes sense only if we compare apples to apples
Focus on efficiency
Relevant for microfinance, given scarcity of inputs!
Calculate efficiency by taking into account different
dimensions of output

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Research questions
Are MFIs really that different?
Is there a trade-off between number of clients served, mission
drift and price of the loans?
Are MFIs that lend to richer borrowers more efficient?
Is it possible to offer small and affordable loans?
Is there such a thing as an excessive focus on women
borrowers?

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Benchmarking and Engaging
Our methodology provides a useful tool to:
Benchmark Pick the most eﬃcient institutions compared to a
peer group.
Advantage:Ranking valid across output measures.
Target:Passive investors with predeﬁned preferences,
looking at investing in the best practice MFIs

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Benchmarking and Engaging
Our methodology provides a useful tool to:
Benchmark Pick the most efficient institutions compared to a
peer group.
Advantage:Ranking valid across output measures.
Target:Passive investors with predefined preferences,
looking at investing in the best practice MFIs
Engage Identifies common traits of under performing MFIs.
Advantage:Indicates size and direction of possible
efficiency gain.
Target:Investors looking at opportunities to generate
social impact through engagement.

Introduction
Benchmarking
Results
Conclusions
Research Questions
Findings
Preview of the results
MFIs differ strongly given differences in their legal status
There is indeed a non-linear trade off between mission
drift and number of clients served
Targeting richer borrowers reduces efficiency
Lending to the very poor is efficient, but not affordable
(for the clients): MFIs with higher outreach charge higher
interest rates
Lending exclusively to women reduces efficiency

Introduction
Benchmarking
Results
Conclusions
Approach
Measuring preferences
Theory
Advantages
Benchmarking firms with heterogeneous preferences
Population of MFIs
Non-for profit Microfinance Institutions (MFIs) with high
outreach and dependent on subsidies
Profitable MFIs with lower outreach, high interest rate and
private funding
... and a whole lot in between
How can we compare the performance of institutions with different
objectives and incentives?

Introduction
Benchmarking
Results
Conclusions
Approach
Theory
Advantages
MFIs have heterogenous preferences (goals), that need to be taken
into account when assessing performance.
Decompose output into 3 measures:
Yield - Price of the Loans
Average Loan Size - Mission Drift(Depth of Outreach)
Number of Loans - Outreach(Breadth of Outreach)
Value Added of Gross Loan Portfolio (Total Output) =
Yield x Average Loan Size x Number of Loans

Introduction
Benchmarking
Results
Conclusions
Approach
Theory
Advantages
Accounting for multiple dimensions
Given the same inputs:
The cost per dollar lent of a small loan will always be larger
than for a bigger loans...
...MFIs that serve the very poor will have higher costs and
poorer ﬁnancial performance...
... which will lead to a lower added value of gross loan
portfolio in the future.

Introduction
Benchmarking
Results
Conclusions
Approach
Theory
Advantages
Accounting for multiple dimensions
Given the same inputs:
The cost per dollar lent of a small loan will always be larger
than for a bigger loans...
...MFIs that serve the very poor will have higher costs and
poorer ﬁnancial performance...
... which will lead to a lower added value of gross loan
portfolio in the future.
Find a measure of performance that does not depend on
preferences:
Eﬃciency

Introduction
Benchmarking
Results
Conclusions
Approach
Theory
Advantages
Average Loan Size
Numberofloansoutstanding
C
A
B
D
Gross loan portfolio
E
"Poorest of the poor" type MFI
"Mission drift" type MFI
Ineﬃ
ciency
Ineﬃciency

Introduction
Benchmarking
Results
Conclusions
Approach
Theory
Advantages
Only one measure: Eﬃciency
The position on the curve (preferences) does not matter, only
relevant factor is the distance from the curve
Intuitive interpretation
80% eﬃciency: given inputs output can be increased by 20%
Circumvents limitations of rating agencies (CGAP, 2010):
Diversity of products and lack of clarity
Duality between assessment for the MFI and service for the
investor
Emphasis placed by MFIs on professional and interpersonal
relationships with rating agencies may jeopardize neutrality of
agencies
Cheap, fast, standardized

Introduction
Benchmarking
Results
Conclusions
Data
Heterogeneity of MFIs
Output trade-off
Efficiency and Mission Drift
Exogenous Determinants of Efficiency
Microfinance Information Exchange market (MIX)
Widely used in the literature
Self reported balance sheet information
Unbalanced panel data
1,146 MFIs
2003-2010
3,890 total observations

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-oﬀ
MFIs diﬀer strongly depending on legal status
Bank NGO
Outputs
Average Loan Size 1627.5 650.4
Number of Loans 78132 45154
Yield 0.24 0.28
Yield(n) 0.33 0.35
GLP (millions) 88.5 12.9
Costs
Costs per loan 283.9 115.1
Cost per dollar 0.23 0.26
Other
Portfolio at risk 30 0.05 0.05
% women borrowers 53.0 74.4

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Implications of MFI heterogeneity
Depending on legal status MFIs have very different business
models
Size of loans, cost per loan and % of women show largest
variation
Difficult to identify ”better” institutions
Warrants the use of a multi output performance tool

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Output trade-off
Mean Elasticity
of Average Loan Size
Elasticity to number of loans -0.68***
Elasticity to yield on gross loan portfolio -0.18***
Total input price elasticity 0.94***
How to read the coefficients
Negative elasticity implies a negative relationship (trade-off)
The larger the elasticity the stronger the trade-off
Total input elasticity <1 implies no economies of scale

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Implication of output trade-off
Breadth and depth of outreach are positively correlated
Offering larger loans (mission drift) does not help to increase
the number of clients
Smaller loans are more expensive
Increasing the size of the institutions does not lead to
economies of scale

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-oﬀ
Is the trade oﬀ constant?
.2
.4
.6
.8
1
−ElasticityofALStoNumberofLoans
0 5000 10000 15000 20000
Average Loan Size
−.2
0
.2
.4
.6
−ElasticityofALStoYield
0 5000 10000 15000 20000
Average Loan Size

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-oﬀ
Implications of non-linear trade-oﬀ
Increasing strength of elasticity to loan size
Number of loans and size of the loan are always negatively
correlated
Larger loans are more expensive in terms of depth of outreach
No evidence that mission drift increases breadth of
outreach
Decreasing strength of elasticity to yield on gross portfolio
As average loans size increases the negative relationship with
yield tends to disappear
For a number of MFIs there is no need to charge higher
interest rates for smaller loans
No evidence that mission drift is always correlated with
a reduction in interest rates

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Does mission drift increase efficiency?
.2
.4
.6
.8
1
TechnicalEfficiency
Small Loans Large Loans
Correlation between average loans size and efficiency = -0.20***

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Implications of mission drift and efficiency
No evidence of MFIs offering larger loans being more efficient
Mission drift does not lead to better use of resources
Smaller loans are more efficient
Challenges of moving upmarket
Lower quality of residual borrowers
Different managerial skill set
Increased competition with banks

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-oﬀ
Can the poor be served eﬃciently?
.65
.7
.75
.8
Efficiency
Cheap Loans Expensive Loans
Small Large Small Large Small Large Small Large

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Implications of price and size of loans
Average efficiency is higher for MFIs that offer more expensive
loans
Easy way to cover costs
... but requires low price elasticity of borrowers
Positive effect on efficiency erased by large loan size
The poor could be served cheaply
The most efficient MFIs offer small expensive loans
... but MFIs that offer relatively small and cheap loan can still
operate above average efficiency

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
How can efficiency be improved without changing
preferences?
Variables Effect
PAR30 · GrossLoanPortfolio -
%Women · Borrowers -***
Loans/Borrowers -***

Introduction
Benchmarking
Results
Conclusions
Data
Output trade-off
Implications of exogenous determinants of efficiency
Benefits of screening and monitoring do not outweigh the
costs in terms of efficiency
Focus on women, reduces efficiency as MFIs need to deviate
from equilibrium
... but has positive social impact
Multiple loans should be avoided

Introduction
Benchmarking
Results
Conclusions
Main Findings
Discussion
Main Findings
Which MFI can make the most out of its limited resources?

Introduction
Benchmarking
Results
Conclusions
Main Findings
Discussion
Main Findings
Which MFI can make the most out of its limited resources?
Mission drift decreases both breadth and depth of outreach
Serving richer borrowers does not increase efficiency
Cheap small loans are not common, but are possible (and
efficient!)
Lending to women and over lending reduce efficiency

Introduction
Benchmarking
Results
Conclusions
Main Findings
Discussion
Discussion
MFIs are efficient when they stick to what they know best
Mission drift (loan size creep) does not increase efficiency:
Insufficient economies of scale
Lack of managerial skills
Loss of subsides
Increased competition

Introduction
Benchmarking
Results
Conclusions
Main Findings
Discussion
Limitations
Imperfect measure of prices
Take into account only lending
Possible applications to other industries with mixed social and
ﬁnancial returns

Practice What you Preach - Benchmarking Microfinance Institutions with Heterogeneous Preferences

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