Growth and Financial Performance of MFIs using Survival Analysis

Growth, Financial Performance and Survival Analysis
in the Microfinance Industry of the Philippines
Jovi C. Dacanay, Ph.D. Economics,1
Viory Yvonne Janeo, MSIE2
& Mary Grace R. Agner, MSIE3
1
Professor and Senior Economist, University of Asia and the Pacific
2
Instructor and Junior Economist, University of Asia and the Pacific
3
Instructor and Junior Economist, University of Asia and the Pacific
Abstract
Microfinance industries have tried to mitigate the risks inherent among micro-enterprises as borrowers
through a system or combination of group and individual lending. Microfinance institutions supply loans,
savings and other financial services to the poor. This study attempts to tackle the following problem. Will an
understanding of the life cycle of firms in the microfinance industry explain their financial performance? This
problem shall be answered through the following objectives: First, describe the performance of microfinance
firms in the Philippines, through their life cycle, by using relevant financial indicators. Second, determine the
sources of poor financial performance using a scoring method.
The methodology of the study is based on an analysis of the life cycle and growth trajectory of small
firms (Reid 2003). The growth stage of small firms are usually not phases of high profitability, debt is resorted
to, yields on loans, in the case of the microfinance industry, has to increase through a better quality of loans.
Thus, microfinance industries go through a next stage wherein borrowers are closely monitored. Once borrower
quality of assured, the firm enters into a second growth phase wherein the firm resorts to equity financing in order
to achieve its expansion phase. In this stage, the firm can pay dividends to investors and the firm resorts to
decreasing its own borrowings.
With the use of reported financial indicators in the MIX Portal from 42 regularly reporting MFIs all over
the Philippines, a probability of failure (60% to 78%) was determined from selected financial indicators using
survival analysis, a statistical tool which calculates the hazard rates, also termed as failure rates, of each financial
indicator. These rates are obtained from the estimated probability distribution of each of the selected financial
indicators. Using this criteria, a financial performance scoring was done, thereby determining those MFIs who
have poor financial performance.
Keywords: Microfinance, Growth, Survival Analysis, Life Cycle of Firms
Introduction
An effective financial sector serves as a link
or mediator in order to allow a steady flow of funds to
finance business operations and investments. Firms
considered as high risk do not have access to such
funds. Among such firms are micro-enterprises who
only have access to funds for loans made available by
the microfinance industry, if the proprietors of the
business resort to commercial financing schemes.
The experience across countries shows that the
microfinance industry is considered a high risk
industry. Firms engaged in micro-enterprise
lending, hereby termed as microfinance, do not have
the same access as other private enterprises to the
funds provided by the commercial financial sector.
Even if some firms lending to micro-enterprises are
registered as non-government organizations, they
operate as commercially established microfinance
firms.
Microfinance industries have tried to mitigate
Contact Author: Jovi C. Dacanay, Professor and Senior
Economist, University of Asia and the Pacific
Address: School of Economics, University of Asia and the
Pacific, Pearl Drive, Ortigas Center, Pasig City, Manila,
Philippines, 1605
Tel: (632) 631-1305 Fax: 634-2822
e-mail: jovi.dacanay@uap.asia

the risks inherent among micro-enterprises as
borrowers through a system or combination of group
and individual lending. Microfinance is the supply
of loans, savings and other financial services to the
poor. The poor throughout the developing world
frequently are not part of the formal employment
sector. They may operate small businesses, work on
small farms or work for themselves or others in a
variety of businesses. Many start their own “micro”
businesses, or small businesses, out of necessity,
because of the lack of jobs available.1
The more
stable microfinance enterprises have operated for less
than 15 years. Analysts of the sector claim that the
stability of a microfinance enterprise will be seen only
when it is able to survive more than two decades.
Due to the greater number of firms who have been
operating for less than 15 years, the view that the
microfinance industry is a high risk sector lingers and
limits the amount of funds made available for loans
and credit.
Problem and Objectives of the Study
This study attempts to tackle the following
problem. Will an understanding of the life cycle of
firms in the microfinance industry explain their
financial performance?
This problem shall be answered through the
following objectives:
First, describe the performance of
microfinance firms in the Philippines, through their
life cycle, and by using relevant financial indicators;
Second, determine the sources of poor
financial performance through a scoring method based
on the results of survival analysis.
Literature Review
Several studies on the microfinance industry
have used finance theory to explain the operations of a
micro-lender. These studies, however, usually rely
on empirical investigations and results as a main
source to explain the basic relationship between firm
performance to growth and financing. Reid (1996,
2003) provide the theoretical underpinnings to relate
the operations of small business enterprises (SBEs)
with the financial needs. His theoretical approach uses
the basic neo-classical economic assumptions on the
behavior of a profit-maximizing small firm.
According to Reid (1996), Vickers (1970)
was the first writer to integrate the production aspect
1
http://www.themix.org/about-mix/about-mix#ixzz1UUlL62Qg
of the firm with the financial. The firm needs
financial capital to hire inputs and to produce and to
sell outputs. It acquires outside financial capital either
in the form of debt, for which it pays a rate of interest,
or in the form of equity, which has a required rate of
return, to be interpreted as the cost of equity. The
value maximization problem which the firm solves
involves both the production function constraint, and
also the financial capital constraint. Thus the solution
of this problem not only determines what will be sold
and how much will be hired of various factors, but
also how much financial capital will be used, and in
what ways.
Subsequent studies such as Leland (1972)
first combined production and finance in a dynamic
theory of the firm (Reid, 1996). In his case, the theory
of the firm adopted was based on so-called
‘managerial’ principles. Therefore the goal of his firm
was to maximize the total discounted value of sales
(over a finite planning horizon) plus the final value of
the equity. However, though this model started an
important new line of enquiry, in itself it contained
several flaws and inconsistencies. For instance, it
required that the discount rate be equal to the
borrowing rate, but yet that there was a decreasing
efficiency of debt compared to a constant efficiency of
retained earnings (Reid, 1996). More rigorous
treatments of how a small firm combines production
and financing in a dynamic theory of a firm had to be
done.
A synthesis of these approaches is provided
by Hilten, Kort and Loon (1993).2
The type of firm
being considered is a familiar one to small firms
specialists. It has no access to the stock exchange, has
limited access to debt finance, and its technology is
subject to decreasing returns. It is assumed that
production is a proportional function of capital, and
sales are a concave function of output. In terms of its
balance sheet, the value of capital assets is equal to the
sum of debt and equity.3
Equity can be raised by the
retention of earnings, and there is assumed to be
a maximum debt to equity (i.e. gearing) ratio
determined by the risk class of the enterprise. It is
assumed that there is a linear depreciation rate on
capital.
2
See Reid (1996).
3
Hence, also, the rate of change of capital assets equals the rate of
change of equity plus the rate of change of debt.

The mathematical development used to
explain a dynamic theory of the firm led to the study
of financial structure to the stages of development of a
firm to risk. Modigliani and Miller (1958, 1963)
highlighted the important issues involved in financial
structure decisions namely: the cheaper cost of debt
compared to equity; the increase in risk and in the cost
of equity as debt increases; and the benefit of the tax
deductibility of debt. They argued that the cost of
capital remained constant as the benefits of using
cheaper debt were exactly offset by the increase in the
cost of equity due to the increase in risk. This left a
net tax advantage with the conclusion that firms
should use as much debt as possible. In practice firms
do not follow this policy (Chittenden et al, 1996).
Access to capital markets is not frictionless and
influences capital structure.
These findings lead one to look at the
micro-enterprise in terms of its stage of development,
hereby termed as life cycle. However, the life cycle
of a firm would have to be related to its financial
structure in order to finance production. Lastly, the
friction which happens within firms, i.e. the choice to
use more or less debt to finance production and
expansion leads to agency problems. Agency
problems arise due to the relationship between
ownership and management, as is observed in the
contractual arrangements which firms would
undertake in order to access external financing.
These key issues would provide the main areas of
literature used in the study.
Life Cycle Approach to Analyzing Financial
Structure
Reid (1996, 2003) explains the maximization
problem in a dynamic setting. The maximand is the
shareholders' value of the firm, under the assumption
of a finite time horizon on the dividend-stream
integral. The constraints of this maximization problem
have been largely covered in the previous paragraph
with the addition of initializing values of variables,
and non-negativity constraints on capital and
dividends (i.e. a zero dividend policy is possible). This
problem can be solved by the Pontryagin Maximum
Principle. The state variables, representing the state of
the firm at a point in time, are equity and capital. The
control variables are debt, investment and dividend.
The results give a trajectory for the life cycle of the
firm given a debt-equity or financing source choice on
the part of the firm’s owner. Each small firm goes
through a stage of growth, consolidation, further
growth or expansion, and stationarity. Due to the
small scale of the firm, positive marginal returns to
capital will stay positive if the firm decides to expand
or grow. The stages a firm goes through in the
trajectory will depend on the level of debt versus
equity which the firm owner chooses as a financing
source or instrument. It can either borrow, therefore
rely on debt financing, or, rely on its internally
generated profits or equity financing. Reid (1996
and 2003) provides a theoretical explanation of the
firm’s trajectory in its life cycle for each choice.
Specifically, his model enables the firm to predict the
relationship between performance to capital growth
and financing source. Agarwal and Gort (2002),
with the use of survival analysis and hazard models,
have operationalized the factors which affect the life
cycle of firms, and have provided a methodology to
predict failure.
Statistical business failure prediction (BFP)
models attempt to predict the failure or success of a
business. Discriminant analysis (DA) and logit
analyses (LA) have been the most popular approaches,
but there are also a large number of alternative
techniques available. A comparatively new technique
known as survival analysis (SA) has been used for
business failure prediction. Studies on business
failure have shown that survival analysis techniques
provide more information that can be used to further
the understanding of the business failure process.
Survival analysis techniques are more
sophisticated than the traditional popular techniques
of discriminant analysis (DA) and logit analysis (LA).
Survival analysis does not assume that the failure
process remains stable over time. All other cross
sectional models are only valid if the underlying
failure process remains stable over time, which is a
problem as the steady failure process assumption is
usually violated in the real world (Laitinen and
Luoma, 1991). The built in time factor in survival
analysis models allows them to model time dependent
explanatory variables. Laitinen and Luoma (1991)
went further and added that the values of the
coefficients may also change relative to time before
failure. Thus, an advantage of survival is the
capability to model these changes, which cannot be
done with cross sectional models.
Almost all well-known approaches assume
that the data (businesses) comes from two distinct

populations, which are those either going to succeed
or fail. Survival analysis models do not make this
assumption, but rather assume that all businesses
come from the same population distribution. In
survival analysis models, the successful businesses are
distinguished by treating them as censored data, which
indicates that their time of failure is not yet known.
This assumption more accurately models the real
world (Laitinen and Luoma, 1991).
An important disadvantage of survival
analysis models is that they are designed to focus on
determining the effects of explanatory variables on the
life of businesses, rather than being designed to
predict outcomes such as the failure of businesses.
The ramification of this is that obtaining predictions
from SA models is more difficult than anticipated.
The pioneering paper on survival analysis
applied to business failure prediction is by Lane et al.
(1986), who used the Cox model to predict bank
failure. Lane et al. created their model based on a
selection of 334 successful and 130 failed banks from
the period 1979 to 1983. The model was then tested
on a hold-out sample with one and two year
predictions, in which the cut-off value was set at the
proportion of failed banks in the sample. The
prediction accuracy of the Cox model was found to be
comparable with discriminant analysis and logit
analysis on the initial and hold-out data, but the Cox
model produced lower Type I Errors. Although the
techniques were comparable, discriminant analysis
and logit analysis were found to be slightly superior
predictors to the Cox model. Nevertheless, Laitinen
and Luoma (1991) argued that the survival analysis
approach was more natural, appropriate and flexible,
and used more information.
Survival analysis, which forms part of
reliability modelling, is also used for credit scoring.
It has been shown previously by Thomas (2000) that
survival analysis can be applied to estimate the time to
default or to early repayment. Survival analysis is the
area of statistics that deals with analysis of lifetime
data. Examples of lifetime data can be found in
medical or reliability studies, for example, when a
deteriorating system is monitored and the time until
event of interest is recorded. Thomas and Stepanova
(2002) have used survival analysis in credit scoring
personal loan data in the UK and the results show that
the prediction capacity of models using survival
analysis is better than the traditional logit models. In
credit scoring one looks for differences in application
characteristics for customers with different survival
times. Also, it is possible that there are two or more
types of failure outcome. In consumer credit one
would be interested, in several possible outcomes
when concerned with profitability: early repayment,
default, closure, etc.(Thomas and Stepanova (2002)).
Thus, survival analysis can be used to predict these
events when regression analysis is applied using age
as an explanatory variable, and, other financial
variables which explain default, repayment or even
closure.
Pecking Order Framework
The empirics provide strong support for a
pecking order view of financial structure, explaining
well the tendency of small business enterprises to rely
heavily on internal funds as proposed by Myers
(1984). The pecking order framework (POF) suggests
that firms finance their needs in a hierarchical fashion,
first using internally available funds, followed by
debt, and finally external equity. This preference
reflects the relative costs of the various sources of
finance. This approach is particularly relevant to small
firms since the cost to them of external equity, stock
market flotation, may be even higher than for large
firms for a number of reasons. As a consequence,
small firms avoid the use of external equity.
According to Myers (1984), contrasting the
static tradeoff theory of Modigliani and Miller (1958,
1963) based on a financing pecking order: First, firms
prefer internal finance; second, they adapt their target
dividend payout ratios to their investment
opportunities, although dividends are sticky and target
payout ratios are only gradually adjusted to shifts in
the extent of valuable investment opportunities; third,
sticky dividend policies, plus unpredictable
fluctuations in profitability and investment
opportunities, mean that internally-generated cash
flow may be more or less than investment outlays. If it
is less, the firm first draws down its cash balance or
marketable securities portfolio; fourth, if external
finance is required, firms issue the safest security first.
That is, they start with debt, then possibly hybrid
securities such as convertible bonds, then perhaps
equity as a last resort. In this story, there is no
well-defined target debt-equity mix, because there are
two kinds of equity, internal and external, one at the
top of the pecking order and one at the bottom. Each
firm's observed debt ratio reflects its cumulative

requirements for external finance. Simply, the
pecking order framework states that small firms prefer
to use internally generated funds to finance debt, and
in the event that if this is not enough do they resort to
external sources.
The combination of rapid growth and lack of
access to the stock market are hypothesized to force
small firms to make excessive use of short-term funds
thereby increase their overall debt levels and reduce
their liquidity. (Chittenden et al, 1996). But the lack
of exposure to such financial activities makes MFIs
less risky, given their current size (Krauss and Walter,
2008). Empirical studies (Karlan 2005, 2009, 2010)
show that during expansion, MFIs resort to internally
generated funds
Agency Theory
The literature on financial structure of
established firms argues that firms with high
information asymmetry should have more external
debt than external equity. On the other hand, those
with high asset specificity should have a funding
structure favoring internal equity first, followed by
external equity, and then external debt. Small
entrepreneurial ventures have attributes of both high
information asymmetry and high asset specificity.
How will the two forces identified by the established
firm literature play out in terms of financial structure,
particularly given that small firms can access
somewhat different funding sources compared with
established firms. In particular, the start-up has the
funding source of ‘internal debt’ (e.g. loans from
owner, friends, and family) but also has the potential
external equity funding source of venture capital and
angel finance instead of shareholder equity. (Sanyal
and Mann, 2010)
The use of external finance by small firms is
also amenable to a transaction cost/contracting/agency
theory analysis. The fixed cost element of
transactions inevitably puts small firms at a
disadvantage in raising external finance. Agency
theory provides valuable insights into small firm
finance since it focuses on the key issue of the extent
of the interrelationship between ownership and
management. Agency problems in the form of
information asymmetry, moral hazard and adverse
selection are likely to arise in contractual
arrangements between small firms and external
providers of capital. These problems may be more
severe, and the costs of dealing with them, by means
of monitoring and bonding, greater, for small firms.
Monitoring could be more difficult and expensive for
small firms because they may not be required to
disclose much, if any, information and, therefore, will
incur significant costs in providing such information
to outsiders for the first time. Moral hazard and
adverse selection problems may well be greater for
small firms because of their closely held nature.
Bonding methods such as incentive schemes could be
more difficult to implement for such firms. The
existence of these problems for small firms may
explain the greater use of collateral in lending to small
firms as a way of dealing with agency problems.
(Chittenden et al, 1996; Reid, 1996).
The lack of financial disclosure and their
owner-managed nature. This leads to the hypothesis
that lenders will be unwilling to lend long-term to
such firms particularly because of the danger of asset
substitution. Consequently, the smallest firms will
have to rely on short-term finance to the detriment of
their liquidity. Alternatively, in order to induce
lenders to provide long-term funds in the face of
agency problems, the small firm could provide
collateral. This would be a suitable approach for small
firms with a high proportion of fixed assets and so
asset structure is included as an independent variable.
Multilateral agencies and commercial,
investment banks are willing to expand their outreach
to microcreditors, but a deeper study has to be done,
i.e. randomized trials. In RP, such trials have been
done for Green Bank and First Macro Bank (Karlan
2009, 2010).
Microfinance Industry in the Philippines
A period of cheap capital made available to
micro-firms in the Philippines lasted briefly due to the
occurrence of the Asian Financial Crisis, as the
commercial financial sector opted to regulate the
financial system through market discipline, i.e. strict
monitoring of debts, loans and risks. This period
translated to a relatively rapid commercialization of
the microfinance industry, enabling small
entrepreneurs to operate their business and manage
their limited financial assets under the purview of
strict market discipline. (Charitonenko, 2003;
Meagher et al, 2006). It also led to the closure of
credit cooperatives with a high percentage of
non-performing loans due to the large number of
members/borrowers who invested heavily on
non-productive fixed assets such as building

construction, trucks, etc. Eventually some rural
banks have extended loans to the microfinance
industry, non-government organizations (NGOs) have
established themselves to form part of the
non-financial institutions offering credit to
micro-enterprises, and, more financially sustainable
credit cooperatives have survived the financial crisis.
Thus, firms from the commercial financial sector who
are involved in micro-lending can be grouped into
three: banks (thrift, rural and cooperative banks),
non-government organizations and savings/credit
cooperatives, credit unions.
In 2013, there were 182 banks with
microfinance operations, comprised of 137 rural
banks, 18 cooperative banks, 25 thrift banks, 1
commercial bank and 1 universal bank, serving 1.05
million borrowers with loans outstanding amounting
to P8.7 billion. Compared with 2012, the number of
microfinance banks and clients slightly decreased in
2013. Despite this, the microfinance loan portfolio
managed to expand by 3%. (See Table 1)
Table 1. Microfinance in the Banking Industry
(2012-2013)
2012 2013
Number of Banks with
Microfinance Operations
187 182
Number of Microfinance
Borrowers
1,137,813 1,049,988
Amount of the
Microfinance Loan
Portfolio (in Billion Pesos)
8.4 8.7
Source: Bangko Sentral ng Pilipinas
A financial performance monitoring system
for cooperatives and the microfinance industry was
set-up under the supervision of the World Bank and
implemented by the Microfinance Council of the
Philippines, Inc. and the Cooperatives Development
Authority (for data collection), and the Bangko
Sentral ng Pilipinas. Regulation in the industry
allows a flexible system which would allow the
industry to grow and mature (Meagher et al, 2006).
As a consequence financial performance indicators are
used as a monitoring tool to gauge satisfactory
financial performance, growth and outreach,
efficiency and sustainability through the Philippine
Microfinance Performance Standards (Performance,
Efficiency, Sustainability and Outreach (P.E.S.O.)),
which were defined by the National Credit Council.
Currently, financial information is made available
through the Microfinance Information Exchange
(MIX) Market platform. The Microfinance
Information Exchange (MIX), incorporated in 2002, is
a non-profit organization headquartered in
Washington, DC with regional offices in Azerbaijan,
India, Morocco, and Peru. 4
MIX collects and
validates financial, operational, product, client, and
social performance data from MFIs in all regions of
the developing world, standardizing the data for
comparability. This information is made available on
MIX Market (www.mixmarket.org), a global,
web-based, microfinance information platform, which
features financial and social performance information
for approximately 2000 MFIs as well as information
about funders, networks, and service providers.5
This
portal shall be the source of valuable information on
the international microfinance industry, including
annual financial data for the Philippines, from which
the 2013 data listed more than a hundred firms
involved in micro-enterprise lending.
Micro-enterprises operating in poor and/or
developing countries lack access to bank credit,
especially in rural areas, where a large majority of
individuals do not have adequate collateral to secure a
loan. These individuals, largely as a result of the
inability of formal. credit institutions to monitor and
enforce loan repayments, are forced either to borrow
from the informal-sector and moneylenders at
usurious interest rates, or are simply denied access
to credit and therefore investment. A potential
solution to the above problem is the implementation
of peer-monitoring contracts by formal credit
institutions such as savings/credit cooperatives. In
contrast to the standard bilateral creditor–borrower
debt contracts, such agreements involve, on a
collective basis, a group of borrowers without
collateral who are linked by a joint-responsibility
default clause, that is, if any member of the group
defaults, other members have to repay her share of the
debt, or else the entire group loses access to future
refinancing. (De Aghion, 1999). Collective credit
agreements with joint responsibility have the property
of inducing peer monitoring among group members,
thereby transferring part of the costly monitoring
effort normally incurred by credit institutions onto the
4
http://www.themix.org/about-mix/about-mix#ixzz1UUlw3jXI
5
Ibid.

borrowers. In practice, the use of peer monitoring
arrangements has been extensive, particularly in
developing countries. However, results as measured
by repayment rates, have been mixed, according to a
large number of descriptive and empirical articles on
the subject. (De Aghion, 1999).
In spite of the high interest rates charged to
borrowers of microfinance institutions, MFIs in the
Philippines are able to achieve high repayment rates
and, on the average, are operating at sustainable levels,
measured by an operational self-sufficiency index of
greater than 100. (See Figure 1)
New rules issued by the BSP and effective
July 1, 2012 outlaw the use of flat interest rate
calculation methods for regulated institutions.
Unregulated NGO-MFIs and cooperatives are
encouraged to follow suit but the BSP lacks the
authority to require them to do so. This makes the
calculation of an effective interest rate difficult.
Through the flat balance calculation method, the
interest rate is applied to the initial loan amount
throughout the entire loan term. Through this method
the borrower pays interest on the full loan amount
even though the amount they have over the loan term
is less and less as they repay the loan. Interest rates
calculated using the flat balance appear much cheaper
than declining balance rates, but are in fact nearly
twice as expensive. For example, an annual interest
rate of 15% charged on a flat balance results in almost
the same amount in interest payments as an annual
interest rate of 30% charged on a declining balance.
This can make comparison between the prices of loans
difficult, posing a serious obstacle to MFIs in terms of
their ability to make informed price-setting decisions
and to clients in terms of comparing the prices of the
loan products available to them. Through the
declining, or reducing, balance interest rate
calculation method, the lender charges interest on the
loan balance that the borrower has not yet repaid. This
amount declines over time as the borrower repays the
loan, so that interest is only charged on money that the
borrower is in possession of. This regulation is
intended to enable micro entrepreneurs repay their
loans at a more effective level of interest rates.
Figure 1. Profit Margin, Operational
Self-Sufficiency (OSS) and Return on Assets
(ROA) for Rural Banks and NGOs (2003-2013)
Source: MIX Market Data
Banks extending loans to microfinance
institutions have diversified their loan products, to
meet the needs of their clientele. The number of
banks offering microfinance loans to microenterprises
decreased to 168 banks in 2013 from 171 banks in
2012, although the amount of microenterprise loans
still expanded by 7% to P7.4 billion in 2013 from P6.9
billion in 2012. The types of microfinance loan
products have diversified to products such as
microfinance plus and housing microfinance loans
which experienced growth in 2013. Housing
microfinance loans increased by 9% to P263 million
in 2013 from P242 million in 2012. Microfinance Plus
loans, which are loans amounting to P150,001 –
P300,000 specifically designed for growing
microenterprises, climbed by 34% to P111 million in
2013 from P83 million in 2012. (See Figure 2)
This study shall dwell on the organization of
each lender involved in microfinance. An
appropriate framework shall now be established based
on the three main issues tackled in the industrial
organization literature as regards the relationship
between growth, choice of financing source and risk
among small firms.

Figure 2. Microfinance Loan Products
Source: Bangko Sentral ng Pilipinas
Empirical Methodology
The variables suggested by Reid (2003)
were operationalized using the performance standards
of the National Credit Council and MicroRate.
These standards aim to benchmark microfinance
institutions with one another so that performance
standards would be gathered. The standards of
performance are: portfolio quality, efficiency,
self-sufficiency and outreach for P.E.S.O and
MicroRate considers portfolio quality, efficiency and
productivity, financial management, profitability and
social outreach. These standards are matched with the
corresponding accounting variables which can be
found in the MIX Portal.
The evaluation of the financial performance
of the microfinance industry of the Philippines shall
be based on the following hypothesis:
For the microfinance industry,
financially well-managed and mature
NGOs and rural banks sustain their
good financial performance by
following the performance standards
set by the rating agencies.
This hypothesis shall be tested using the
results of the survival analysis of each of the financial
indicators used by MicroRate to evaluate the financial
performance of MFIs. Survival analysis allows one to
determine the probability of failure at each historical
level of the financial indicator. The behavior of the
probability distributions of the financial indicators
shall be done from the selected MFIs listed in the
Microfinance Information Exchange Portal (MIX).
The MFIs to be selected should have data from at least
2005 to 2015. The study only uses the financial data
reported by 21 NGOs and 20 Rural Banks, and, 1
Credit Union/Cooperative.
To be able to answer objective 1, averages for
the indicators of portfolio quality, efficiency and
productivity, financial management, profitability, and,
social performance shall be obtained for NGOs and
also computed for Rural Banks and Credit
Unions/Cooperatives, or a total of 2 groups. These
averages shall be computed from 2006 to 2015 as
most MFIs have data in the MIX during these years.
Also, using the probability, hazard and survival plots
of each indicator, the specific level of each of the
financial indicators where a 60%-70% probability of
failure will occur, shall be obtained. This is the level
to be used in the evaluation of the performance of the
MFIs. The data reported by each MFI shall be
compared with level at which a 60%-70% failure rate
is likely to happen. Any data point that falls above a
70% failure would mean that the MFI has not operated
according to the set performance standard.
For objective 2, each MFI shall be given a
score from 1 (lowest) to 4 (highest), based on the
number of years they have reported a greater than
70% failure rate from 2006 to 2015. For all the
financial indicators listed in Table 3, except
Debt-Equity and Social Performance, a score of 4
means that the firm reported less than 60%-70%
failure (or passed) across all the years. A score of 3
means that the firm reported at most a third of the
years failed the standard. A score of 2 means that
greater than a third to half failed. And a score of 1
means that the firm reported more than half of the
years from 2006 to 2015 with a failing mark.
For the debt-equity indicator, a score of 4
indicates that the firm passed the survival analysis
scoring, i.e. less than 60%-70% failure rate for all the
years, 2006 to 2015. A score of 3 is given if the firm
has a failing score for less than a third of the years.
A score of t is given if the firm reported to more than a
third to one half of the years with a failing mark.
And a score of 1 if the firm reported failing for more
than half of the years, 2006 to 2015.
The social performance indicators were not
subjected to a survival analysis as most firms did not
report complete data.
The above-mentioned hypothesis shall be

tested using survival analysis which has the following
steps:
1. Determine the best fit probability
distributions of each of the financial
indicators used by MicroRate.
2. Plot the standards set by the rating agencies
into the hazard and survival functions and
determine the probability of failure when the
industry sets these standards for the MFIs.
3. Plot the computed mean from the probability
distributions of each of the financial
indicators, into the hazard and survival
functions and determine the probability of
failure when the industry sets these standards
for the MFIs.
4. Compare the probabilities of failure using the
standards of the rating agencies and the mean
of the distributions. A 60%-70% probability
of failure shall be determined for each of the
indicators. This is the probability used
because for most of the probability
distributions, the mean of the distribution is
accounted for my 60-70% of the data. The
corresponding data-point for this probability
of failure also corresponds either to the
maximum point in the hazard plot if the
hazard plot has a hump, or the steepest (or
approaching the steepest) portion of the
hazard plot if the hazard plot is an S-curve.
5. Provide a score from 1 to 4 for each of the 16
financial performance indicators: portfolio
quality (portfolio at risk 30 days, portfolio at
risk 90 days, risk coverage, write-off,
impairment expense ratio), financial
management (debt-equity ratio), efficiency
and productivity (cost per borrower, cost per
loan, personnel productivity ratio, loan officer
productivity ratio, operating expense ratio,
administrative efficiency ratio), profitability
(return on assets, return on equity, yield on
gross loan portfolio (nominal), yield on gross
loan portfolio (real)). Perfect score of 64
points.
6. Provide a score for social performance based
on the standard set by MicroRate.
7. A firm shall obtain a good to high evaluation
if the score is from 60-90%. A firm shall
obtain a poor evaluation if the score is below
60%
Results
Objective 1. Evaluation of the Microfinance
Industry Based on the Survival Analysis
The results for NGOs (See Table 4) show
that the industry suffered poor portfolio quality as the
years 2007-2015 show an average which puts the
industry at a >70% probability of failure, given a
portfolio at risk for 30 days and 90 days of greater
than 8.89% and 7.22%, respectively. In fact, the
industry standard of 5% is much stricter, if when
followed would put the entire industry at a poorer
rating. From the survival analysis, a portfolio at risk
level of 5% for either 30 or 90 days would only put
the industry at a 51%-62% probability of failure (See
Appendix A).
The poor quality of the portfolio of NGOs
has a negative influence on profitability, i.e. return on
assets and equity. Follow-up on these loans
increases operational costs. Due to the poor quality
of the industry’s portfolio, most NGOs resorted to a
high risk coverage, which on the average, makes them
provide a risk coverage more than double the actual
amount of their loans. It can be observed, though,
that the average financial indicators for 2015 have
improved significantly for portfolio quality, except for
the write-off ratio. Efficiency and productivity have
also improved. This is a good sign because based on
the hazard plots for portfolio quality for NGOs, the
tail-end portion of the hazard plots actually referred to
earlier years, i.e. when the NGOs were still being
established. Learning from the experience of other
NGOs has allowed the industry to obtain lower
portfolio at risk ratios. Their current level of
portfolio quality shows that they are at the competitive
stage of their life cycle, that is, when they are trying
ways and means to monitor borrowers better (See
Appendix B).
Rural Banks, Credit Unions/Cooperatives,
engaged in microfinance lending, also suffered poor
portfolio quality and this is carried over in 2015. (See
Table 5) The industry standard of less than 5%
portfolio at risk for 30 or 90 days seems to be a
stricter standard as it gives the industry only a
31%-45% probability of failure. Using this standard
would give a poorer rating for the industry. Cost per
borrower and cost per loan are also much higher than
the expected level of US$ 120. A higher cost per
borrower and cost per loan is expected to be higher for

banks in comparison to NGOs due to their practice of
individual lending. The profitability ratios, though,
are sound and are consistently positive. The survival
analysis, however, calculated that a return on equity of
7% would only give the industry a 68%-79%
probability of failure, as opposed to a higher
probability of failure if the standard were set at 5%.
It is imperative for rural banks to intensely
monitor their portfolio quality because based on the
behavior of their hazard plots, any further increase in
their portfolio at risk would put them in the danger of
continuously increasing their hazard rates, thus even
further increasing their probability of failure. This is
so because the hazard plot for portfolio at risk of rural
banks has an S-shaped curve. This means that if
rural banks do not monitor their portfolio at risk, there
will be a strong tendency for their portfolio at risk to
grow even faster since the hazard plot is increasing,
especially after the point where their probability of
failure of 78% is situated.
From the over-all results of objective 1, it
could be observed that the standards obtained using
survival analysis could be regarded as the least strict
financial performance standard, i.e. probability of
failure from 68% to 78%. The industry standards are
stricter, as the probability of failure is less than 60%.
The industry standards for portfolio at risk, for
example, has a lower probability of failure level, if
based on the obtained hazard and survival plots of the
probability distribution of portfolio at risk.
Following the practice of industry standards for MFI
performance, therefore, allows a margin for MFIs to
adjust their operations so as to achieve the industry
standards, which are also the international standards,
i.e. MicroRate, of evaluating the financial
performance f MFIs.
Objective 2. Scoring of Firms Based on the
Survival Analysis (or Reliability Analysis)
Standards.
The survival analysis-based financial
performance evaluation of MFIs has allowed a scoring
method to determine those firms which are least
probable to fail. From the results obtained and listed
in Appendix C, it can be seen that those firms which
have a poor evaluation are also those suffering from
portfolio quality, management inefficiency and
unproductivity. These are also happening
coincidently with losses as reported from their returns
to assets and equity.
Poor portfolio quality and management
inefficiency and unproductivity can also be observed
among the firms which scored poor in the survival
analysis evaluation method.
From the results of the survival analysis, it
can be observed that low profitability is linked to poor
portfolio quality. In most instances, poor portfolio
quality also increases the cost per borrower and cost
per loan.
Conclusion
The life cycle model is useful in explaining
variations in the decision of small firms as regards
their financial and operational performance.
Microfinance Institutions are not micro in size, but
they deal with micro firms. Their financial decisions
are closely related to the financial decisions and
behavior of small firms. The decisions of MFIs can
be known through their reported financial variables.
The MFIs studied manage their assets and
debt through a close monitoring of the financial
performance indicators: portfolio quality, financial
management, efficiency and productivity indicators,
and, profitability. The study showed that the
standards set by the rating agencies follow economic
theory and prevents the MFIs from falling into a spiral
of bad debt and poor quality loan portfolios. Once
MFIs fail to prudently follow these guidelines, they
run the risk of easily accelerating their
non-performing loans thereby further increasing their
portfolio at risk, resulting to losses. Such a situation
makes them prone to default.
Commercial investors can therefore predict
the failure rate of MFIs through a close monitoring of
their reported financial indicators, using survival
analysis. Micro firms and microfinance institutions
definitely seem to follow the behavior of
profit-oriented small businesses, and, when managed
properly or sustainably, would allow investment
inflows into their firms.
References
1) Agarwal, Rajshree and Michael Gort 2002. Firm and
Product Life Cycles and Firm Survival. American Economic
Review, 92(2):184-190
2) Charitonenko, Stephanie 2003. Commercialization of
Microfinance: The Philippines, Asian Development Bank:
Manila.
3) Chittenden, Francis, Graham Hall, Patrick Hutchinson 1996.
Small Firm Growth, Access to Capital Markets and
Financial Structure: Review of Issues and an Empirical

Investigation, Small Business Economics, 8(1): 59-67,
Special Issue on Financing and Small Firm Dynamics
(http://www.jstor.org/stable/40228760. Accessed:
01/08/2011 at 23:30)
4) Dacanay, Jovi C. 2016. “Explaining Growth and
Consolidation in the Microfinance Industry of the
Philippines,” Research Paper Submitted to the Faculty of
the Department of Economics, Ateneo de Manila University,
Quezon City.
5) Aghion, Beatriz Armendáriz 1999. On the design of a
credit agreement with peer monitoring, Journal of
Development Economics, 60:79–104.
(http://www.economics.harvard.edu/faculty/armendariz/files
/design.pdf. Accessed: 07/08/2011 at 17:01)
6) Gepp, Adrian and Kuldeep Kumar 2008. The Role of
Survival Analysis in Financial Distress Prediction,
International Research Journal of Finance and Economics,
ISSN 1450-2887, 16:13-34
(http://www.eurojournals.com/finance.htm)
7) Karlan, Dean S. and Jonathan Morduch 2009. “Access to
Finance,” Chapter 2. Handbook of Development Economics.
Dani Rodrik and Mark Rosenzweig, (eds). Elsevier: North
Holland. Volume 5.
8) Karlan, Dean S. and Jonathan Zinman 2010. Expanding
Credit Access: Using Randomized Supply Decisions to
Estimate the Impacts. Review of Financial Studies
23(1): 433-464
9) Karlan, Dean S. and Nathaniel Goldberg 2011.
Microfinance Evaluation Strategies: Notes on
Methodology and Findings, Beatriz Armedáriz and Marc
Labie, (eds) The Handbook of Microfinance. World
Scientific Publishing, Co: Singapore. pp. 17-59
10) Laitinen, E. K. and Luoma, M. 1991. Survival analysis as a
tool for company failure
prediction. Omega, 19(6):673-678.
11) Lane, W. R., Looney, S. W., and Wansley, J. W. 1986. An
application of the Cox proportional hazards model to bank
failure. Journal of Banking and Finance, 10(4):511-531.
12) Krauss, Nicolas and Ingo Walter 2009. Can Microfinance
Reduce Portfolio Volatility. Economic Development and
Cultural Change, 58(1) (October)
13) Meagher, Patrick, Pilar Campos, Robert Peck Christen, Kate
Druschel, Joselito Gallardo, and Sumantoro Martowijoyo
2006. “Microfinance Regulation in Seven Countries: A
Comparative Study," Report submitted to Sa-Dhan, New
Delhi, IRIS Center, University of Maryland.
(http://www.microfinancegateway.org/gm/document-1.9.24
382/26.pdf. Accessed: 07/08/2011 at 17:12)
14) MicroRate 2014. Technical Guide: Performance and
Social Indicators for Microfinance Institutions. MicroRate
Incorporated (www.microrate.com) Accessed July 2014.
15) Modigliani, Franco and Merton H. Miller 1963. Corporate
Income Taxes and the Cost of Capital: A Correction,
American Economic Review, 53(3): 433-443.
(http://www.jstor.org/stable/1809167. Accessed: 02/08/2011
at 11:14)
16) Modigliani, Franco and Merton H. Miller 1958. The Cost of
Capital, Corporation Finance and the Theory of Investment,
American Economic Review, 48(3):261-297
(http://www.jstor.org/stable/1809766. Accessed: 02/08/2011
at 11:14)
17) Myers, Stewart C. 1984. The Capital Structure
Puzzle,”Journal of Finance, 39(3): 575-592, Papers and
Proceedings, Forty-Second Annual Meeting, American
Finance Association, San Francisco, CA, December 28-30,
1983 (http://www.jstor.org/stable/2327916. Accessed:
02/08/2011 at 00:07)
18) Reid, Gavin C. 1991. Staying in Business, International
Journal of Industrial Organization, 9:545-556.
19) Reid, Gavin C. 1996. Financial Structure and the Growing
Small Firm: Theoretical Underpinning and Current
Evidence, Small Business Economics, 8(1):1-7, Special
Issue on Financing and Small Firm Dynamics
(http://www.jstor.org/stable/40228754. Accessed:
31/07/2011 at 00:19).
20) Reid, Gavin C. 1999. “Capital Structure at Inception and the
Short-Run Performance of Micro-Firms,” Chapter 7, in
Entrepreneurship, Small and Medium-Sized Enterprises and
the Macroeconomy, Zoltan J. Acs, Bo Carlsson and Charlie
Karlsson, editors., Cambridge, United Kingdom:
Cambridge University Press, pp. 186-205
(http://www.google.com/books?hl=en&lr=&id=un1JmcFa4
QIC&oi=fnd&pg=PA186&ots=ZyPAWEo4cC&sig=B6EH8
S72S58T8fh8flO8rfX_F1w#v=onepage&q&f=false.
Accessed: 09/08/2011 at 12:06)
21) Reid, Gavin C. 2003. Trajectories of Small Business
Financial Structure, Small Business Economics,
20(4):273-285 (http://www.jstor.org/stable/40229267.
Accessed: 31/07/2011 at 00:20)
22) Sanyal, P. and Mann, C., 2010. “Asset specificity,
information asymmetry and new firm financing.” Working
paper, Department of Economics, Brandeis University.
23) Stepanova, M. and Thomas, L.C., 2002. Survival analysis
methods for personal loan data. Operations Research,
50(2):277-289. (http://teaching.ust.hk/~ismt253w/cox.pdf.
Accessed March 2016)
24) Thomas, L.C., 2000. A survey of credit and behavioural
scoring: forecasting financial risk of lending to consumers.
International
25) Journal of Forecasting, 16(2):149-172.
(http://www.yaroslavvb.com/papers/thomas-survey.pdf.
Accessed May 18, 2016)
26) Tze-Wei Fu, Mei-Chu Ke, Yen-Sheng Huang 2002.
Capital Growth, Financing Source and Profitability of Small
Businesses: Evidence from Taiwan Small Enterprises,
Small Business Economics, 18(4):257-267
(http://www.jstor.org/stable/40229208). Accessed:
31/07/2011 at 00:17)
27) Vickers, D. 1987. Money Capital in the Theory of the Firm.
Cambridge: Cambridge University Press.

Appendices
Appendix A. Financial Indicators for NGOs
Fin
Mgt
Portfolioatrisk>30
days
Portfolioatrisk>90
days
Riskcoverage
Write-offratio
ImpairmentExpenseRatio
Operatingexp/loan
portfolio
Costperborrower
Costperloan
PersonnelProductivity
Ratio
LoanOfficerProductivity
Ratio
AdministrativeEfficiency
Ratio(AdmExp/GLP)
Debttoequityratio
Returnonassets
Returnonequity
Yieldongrossportfolio
(nom)
(real)
Survival
Analysis
Standard
<8.89% <7.22% <100% <3% <5% <45% <57 <54 114 200 17% 4 0.5% 0% -1% >42% >39%
2006 7.3% 5.2% 177.8% 2.3% 5.0% 41.4% 36.3 34.3 143.9 224.6 16.7% 3.54 4.4% 21.0% 56.7% 47.5%
2007 9.3% 6.8% 146.7% 3.4% 5.2% 38.7% 43.1 44.7 141.0 233.6 15.4% 2.51 1.6% -13.9% 51.4% 46.8%
2008 9.2% 7.8% 506.8% 3.1% 4.3% 39.9% 53.2 46.8 133.7 229.6 15.1% 2.71 2.2% 31.2% 50.9% 39.0%
2009 9.0% 8.5% 9361.8% 3.8% 2.7% 41.0% 55.8 52.0 147.1 251.3 21.3% 2.21 3.3% 11.3% 47.9% 43.3%
2010 6.5% 5.4% 2214.8% 6.4% 2.4% 39.1% 50.8 47.4 143.8 247.4 15.2% 1.13 3.9% 17.1% 49.3% 43.7%
2011 8.9% 6.7% 1512.9% 1.9% 3.6% 40.3% 56.0 51.5 148.1 254.8 15.7% 2.99 4.5% 18.9% 51.5% 45.0%
2012 8.3% 7.3% 1431.7% 5.7% 3.9% 36.3% 52.0 48.8 130.0 262.8 14.1% 3.34 6.0% 25.6% 50.0% 44.7%
2012 8.3% 8.3% 5770.7% 0.9% 4.8% 42.4% 61.8 53.8 151.8 264.1 15.7% 2.87 0.1% 2.9% 51.8% 46.8%
2014 8.2% 8.0% 2991.7% 5.2% 2.7% 40.6% 73.1 63.7 140.7 244.1 15.9% 2.54 2.0% -5.6% 49.4% 45.1%
2015 3.9% 3.9% 2578.6% 22.2% 1.9% 44.2% 56.4 51.0 148.7 270.3 16.5% (1.93) 7.4% 25.3% 57.0% 52.5%
Table 4. Financial Indicators for NGOs
Years
Portfolio Quality Efficiency and Management Profitability
Note: Shaded values have a failure rate that is greater than 70%; Source: www.mixmarket.org
Appendix B. Financial Indicators for Rural Bank, Cooperatives
Fin
Mgt
Portfolioatrisk>30
days
Portfolioatrisk>90
days
Riskcoverage
Write-offratio
ImpairmentExpenseRatio
Operatingexp/loan
portfolio
Costperborrower
Costperloan
PersonnelProductivity
Ratio
LoanOfficerProductivity
Ratio
AdministrativeEfficiency
Ratio(AdmExp/GLP)
Debttoequityratio
Returnonassets
Returnonequity
(nom)
(real)
Survival
Analysis
Standard
<9.5% <8% <100% <1.5% <2.6% <27.2% 120 120 69 160 <14% >6.4 >1% >7% >26.25% >18%
2006 10.1% 10.1% 109.8% 0.7% 1.3% 23.2% 92.1 90.4 128.4 186.5 12.2% 5.8 1.6% 13.0% 30.7% 23.1%
2007 10.1% 8.2% 4376.6% 3.7% 0.7% 22.4% 111.4 105.5 94.2 217.3 12.0% 6.0 2.0% 12.9% 29.4% 25.9%
2008 3.7% 3.1% 2032.4% 1.2% 1.8% 23.7% 116.5 112.8 113.5 327.8 12.0% 5.9 1.9% 12.5% 32.6% 21.6%
2009 7.6% 5.8% 90.9% 1.6% 2.5% 22.6% 107.5 102.8 154.5 539.4 12.3% 5.7 2.4% 15.2% 31.7% 27.8%
2010 7.8% 5.8% 88.4% 1.9% 1.5% 21.8% 122.4 125.1 92.7 525.4 11.2% 5.9 2.1% 13.4% 29.6% 24.8%
2011 8.4% 7.2% 65.3% 1.3% 2.3% 21.5% 154.2 145.6 86.7 261.3 10.5% 5.7 2.0% 12.9% 29.9% 24.2%
2012 5.0% 3.6% 82.1% 0.9% 1.4% 21.6% 143.7 124.3 126.5 476.0 12.1% 5.2 3.8% 22.7% 34.6% 29.3%
2013 8.2% 6.4% 77.7% 2.0% 1.7% 22.0% 203.7 174.0 97.2 373.1 11.9% 5.7 2.7% 14.1% 30.0% 25.7%
2014 11.9% 10.3% 62.6% 1.1% 1.5% 21.4% 209.0 171.1 90.9 569.9 11.0% 5.3 1.9% 10.5% 28.9% 25.2%
2015 10.0% 9.5% 45.7% 3.5% 2.3% 23.8% 324.5 222.3 117.6 576.6 12.4% 4.4 1.4% 5.0% 27.9% 24.2%
Table 5. Financial Indicators for Rural Banks, Cooperatives
Portfolio Quality Efficiency and Management Profitability
Years
Note: Shaded values have a failure rate that is greater than 70%; Source: www.mixmarket.org

Appendix C. Reliability Analysis Scoring of Microfinance NGO’s (Based on 2005-2010 data)
FinMgt
Portfolio
atrisk
>30
days
Portfolio
atrisk
>90
days
Risk
coverage
Write-
off
ratio
Impairment
Expense
Ratio
Operating
exp/loan
portfolio
Costper
borrower
Costper
loan
Personnel
Product-
ivityRatio
Loan
Officer
Producti-
vityRatio
(careofJovi)
Efficiency
Ratio(Adm
Exp/GLP)
Debtto
equity
ratio
Return
on
assets
Return
on
equity
Yieldon
gross
portfolio
(nom)
Yieldon
gross
portfolio
(real)
Aveloanbal
/borr/
GNIpercap
Borrower
retention
rate
Staff
turnover
rate
<5%<5%>100%<1.5%
0.5%to
2.7%
<30%
Global2012
Averageis
US$156;
EAP:US$80-
US$85
Global2012
Averageis
US$156;EAP:
US$80-US$85
150-192
GL:400;
IL:300
<30%
Regis
5:1;
Unregis
3:1
Stabilizing
from2%
to4%
>11%-
20%
Stabilizing
to30%for
mature
markets
Stabilizing
to25%for
mature
markets
<8.89%<7.22%<100%<3%<5%<45%<57%<54%>114>200<17%4%>0.5%>0%-1%>42%>39%
444444444444444464/64=100%33373/73=100%
1ASAPhilippines444423443323334484.38%12382.19%
2ASHI441444111134333267.19%13368.49%
3ASKI231113211132233248.44%12249.32%
4CARDNGO343444444414444390.63%13287.67%
5CEVI331121323332113354.69%13357.53%
6CMEDFI*334411111113434460.94%13363.01%
7DSPI*111134444441331162.50%13nodata62.86%
8ECLOF-PHL111134111133211140.63%13345.21%
9FCBFI*343134443344442179.69%11376.71%
10HSPFI*443nodata44444432331180.00%14nodata75.71%
11JVOFI341232111133234459.38%12360.27%
12Kasagana-Ka333331233314444475.00%12272.60%
13KazamaGrameen*221233344424333168.75%13369.86%
14KCCDFI332121113112344456.25%12256.16%
15KMBI*332331334413334473.44%11168.49%
16MILAMDEC343133443331332270.31%12369.86%
17NWTF443344444343343185.94%13384.93%
18PALFSI*111224443341111153.13%1nodatanodata52.24%
19RSPI441221324413444473.44%11371.23%
20TSKI441123321111334459.38%11nodata57.14%
21TSPI*333443323134444379.69%11275.34%
Table6.ReliabilityAnalysisScoringofMicrofinanceNGOs(Basedon2005-2015data)
TotalScore
onFinancial
Indicators
andSocial
Perfor-
mance
National
LoanSize
Ratio.Lower
Market
Nicheis50%
Goalis
higher.
Regis
71%;
Unregis
63%
<10-30%;
EAP:<10-
15%
PortfolioQualityIndicatorsEfficiencyandProductivityIndicatorsProfitabilitySocialPerformance
Note:*(Starred)MFIsareunregulated
TotalScore
onFinancial
Indicators
Source:MicrofinanceInformationExchangePortal(www.mixmarket.org)assourceofbasicdata.ScoringbasedonEmpiricalMethodology
FinancialIndicators,
GoalsandCriteriafor
theScoring,Firms
Goal(Basedon
Microrate)
ValuesusedasCriteria
forScoring
PerfectScore

Appendix D. Reliability Analysis Scoring of Rural Banks, Credit Unions and Cooperatives into Microfinance
(Based on 2005 -2010 data)
FinMgt
Portfolio
atrisk
>30
days
Portfolio
atrisk
>90
days
Risk
coverage
Write-
off
ratio
Impairment
Expense
Ratio
Operating
exp/loan
portfolio
Costper
borrower
Costper
loan
Personnel
Product-
ivityRatio
Loan
Officer
Producti-
vityRatio
(careofJovi)
Efficiency
Ratio(Adm
Exp/GLP)
Debtto
equity
ratio
Return
on
assets
Return
on
equity
Yieldon
gross
portfolio
(nom)
Yieldon
gross
portfolio
(real)
Aveloanbal
/borr/
GNIpercap
Borrower
retention
rate
Staff
turnover
rate
<5%<5%>100%<1.5%
0.5%to
2.7%
<30%
Global2012
Averageis
US$156;
EAP:US$80-
US$85
Global2012
Averageis
US$156;EAP:
US$80-US$85
150-192
GL:400;
IL:300
<30%
Regis
5:1;
Unregis
3:1
Stabilizing
from2%
to4%
>11%-
20%
Stabilizing
to30%for
mature
markets
Stabilizing
to25%for
mature
markets
<8.89%<7.22%<100%<3%<5%<45%<57%<54%>114>200<17%4%>0.5%>0%-1%>42%>39%
444444444444444464/64=100%33373/73=100%
11stValleyBank131344224443441271.88%33375.34%
2
BancoSantiagode
Libon
111111443314444464.06%1nodatanodata62.69%
3BangkoKabayan441434111334441167.19%3nodata370.00%
4BangkoMabuhay231423111342332257.81%33261.64%
5CantilanBank331233222342443470.31%23271.23%
6CARDBank333131444413434476.56%13275.34%
7CBMO121414443344442375.00%1nodata374.29%
8FICO441424113233443473.44%3nodata375.71%
9FirstMacroBank321434111441331157.81%3nodata260.00%
10GMBankofLuzon111433111331111142.19%3nodatanodata44.78%
11GreenBank333443324312224473.44%1nodata170.00%
12OKBank111111343314114453.13%11nodata51.43%
13
PAGLAUMMULTI-
PURPOSE
CCOPERATIVE
111411443314334465.63%13265.75%
14PBC333444444443334490.63%11284.93%
15ProgressiveBank443333444443114482.81%1nodatanodata80.60%
16RBCamalig441434344434331381.25%13279.45%
17RBDigos441444124341441273.44%1nodatanodata68.57%
18RBGuinobatan442214444441443482.81%13382.19%
19RBMontevista443nodata34444441114476.56%1nodatanodata74.63%
20RBTalisayan111114444443334370.31%1nodata268.57%
21ValiantRB121444342241121157.81%3nodata260.00%
Source:MicrofinanceInformationExchangePortal(www.mixmarket.org)assourceofbasicdata.ScoringbasedonEmpiricalMethodology
Table7.ReliabilityAnalysisScoringofRuralBanks,CreditUnionsandCooperativesintoMicrofinance(Basedon2005-2015data)
PerfectScore
ValuesusedasCriteria
forScoring
Goal(Basedon
Microrate)
FinancialIndicators,
GoalsandCriteriafor
theScoring,FirmsTotalScore
onFinancial
Indicators
andSocial
Perfor-
mance
National
LoanSize
Ratio.Lower
Market
Nicheis50%
Goalis
higher.
Regis
71%;
Unregis
63%
<10-30%;
EAP:<10-
15%
PortfolioQualityIndicatorsEfficiencyandProductivityIndicatorsProfitabilitySocialPerformance
TotalScore
onFinancial
Indicators

Growth and Financial Performance of MFIs using Survival Analysis

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Growth and Financial Performance of MFIs using Survival Analysis

Similar a Growth and Financial Performance of MFIs using Survival Analysis (20)

Último

Último (20)

Growth and Financial Performance of MFIs using Survival Analysis