1
Short Bio
Jovi C. Dacanay graduated BS Statistics, MS Industrial Economics and MA Economics and
is currently pursuing her PhD Economics in the Ateneo de Manila University. She lectures
in Mathematical Statistics, Social Economics and Research Seminar in the University of Asia
and the Pacific. Her research includes industrial economics, industrial organization of
health care markets, economics of film and microfinance. She is currently involved in
empirical work on the microfinance industry of the Philippines and has presented papers in
international conferences and published in conference proceedings.
Jovi C. Dacanay
Instructor and Senior Economist
School of Economics
University of Asia and the Pacific
Business Address
Pearl Drive corner St. Josemaría Escrivá Drive
Ortigas Business Center, Pasig City (1605), Philippines
(063) 637-0912 to 0926
Home Address
54 Examiner St., Barangay West Triangle Diliman (1104), Quezon City, Philippines
Direct Line: (063) 372-4008 to 4010 or 414-9383
Cellphone Number: (063) 09274942714
E-mail Address
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Explaining Growth and Consolidation in the Microfinance Industry
of the Philippines
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 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 do not have
easy access to credit. This study attempts to tackle the following problem. Will an
understanding of the life cycle of firms in the microfinance industry explain their performance?
This problem shall be answered through the following objectives: First, describe the
performance of microfinance firms in the Philippines, through a growth trajectory, also termed as
life cycle, by using relevant financial indicators; Second, determine the factors affecting the
performance of microfinance firms by age group, making use of indicators which indicate
portfolio quality, efficiency, sustainability and outreach.
The methodology of the study is based on an analysis of the life cycle and growth
trajectory of small firms (Reid 2003). With the social performance preference of the industry,
i.e. poverty alleviation via entrepreneurship, micro entrepreneurs usually have priority over
loans. 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 46 regularly
reporting MFIs all over the Philippines, a panel regression correcting for heteroskedasticity was
done. The life cycle phases of the MFIs can be explained using the performance standard
indicators for MFIs: portfolio quality, efficiency, sustainability and outreach. The results are
consistent with the expected outcomes from the life cycle model of Reid (2003). The results
show that even with a composition of micro borrowers forming part of the clientele, the
performance of MFIs can be monitored and their behavior follows the behavior of market
competitive small firms.
Keywords: life cycle, financial viability, social performance, microfinance
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Table of Contents
1. Introduction 4
2. Literature Review 5
3. Theoretical Framework 11
4. Empirical Methodology 23
5. Results 28
6. Conclusion 34
References 36
Appendix 1. P.E.S.O. Standards 38
Appendix 2. Database 40
Appendix 3. Description of Firms 58
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Explaining Growth and Consolidation in the
Microfinance Industry of the Philippines
1. Introduction
Background
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. 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 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.
1 http://www.themix.org/about-mix/about-mix#ixzz1UUlL62Qg
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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 performance?
This problem shall be answered through the following objectives:
First, describe the performance of microfinance firms in the Philippines, through a
growth trajectory, also termed as life cycle, by using relevant financial indicators;
Second, determine the factors affecting the profitability of microfinance firms by age
group, making use of indicators which indicate portfolio quality, efficiency, sustainability and
outreach.
2. 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 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
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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.
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
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.
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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.
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.
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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 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,
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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 is common among
MFIs. 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
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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. (See Table 1).
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 2009
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
4 http://www.themix.org/about-mix/about-mix#ixzz1UUlw3jXI
5 Ibid.
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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 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).
Table 1. Main Types of Institutions Providing Microfinance Services in RP (2003)
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In the Philippines, the more financially viable microfinance firms are located in urban
areas, whose borrowers are into the business of retail (sari-sari stores), transport (tricycle and
jeepney) service, laundry, dress shops and personal services. Rural based micro-enterprises are
still perceived to be high risk and fewer families are able to access loans. (See Table 2). Rural
banks have to engage themselves into intense monitoring of rural and family-based micro-firms
who possess assets such as land and a house made of concrete. In fact, more viable micro-firms
or family enterprises, do not belong to the lowest income class, and have family incomes which
are above the poverty threshold.
Rather than focusing on the design of peer monitoring groups and the financial structure
of micro-enterprises into group lending, 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.
Table 2. Regional Distribution of Active Microfinance Loans, 2007
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3. Theoretical Framework
In a neoclassical theory of the small firm, generalized to incorporate money capital
(Vickers,1987), the conditions for maximizing profit will determine an optimal asset structure for
the small firm, along with the familiar marginal conditions for production optimality. It requires
that the full marginal cost of debt should equal the full marginal cost of equity, which in turn
should equal the discount factor on the marginal income stream. Thus optimal amounts of debt
and equity (and hence gearing) are determined, along with optimal hiring of factors of
production. Previous evidence (Reid, 1991) has suggested that this optimality requirement has
been reflected in a strong measured association between gearing and survival of the small firm.
In particular, lower gearing significantly raised survival prospects for the small firm over a three
year time horizon. It is likely that this arises because of both the lower risk exposure and the
lower debt servicing associated with lower gearing. (Reid, 1999).
Microfinance firms, on the other hand, would be composed of a large number of such
types of borrowers. Their ability to manage funds will depend on the depth of exposure and
intensity of monitoring in handling the debt obligations of micro-enterprise creditors. The neo-
classical model of analyzing small firms can also be applied to small to medium-sized enterprises
whose main source of business is lending. Optimal amounts of debt and equity are chosen based
on their ability to gain profits and internally source capital. These decisions are made according
to the stage each firm has in its life cycle.
The general finding is that financial structure is not a major determinant of performance
in this, the very earliest, phase of the life-cycle of the micro-firm. While it is possible to identify
specific financial features which may favor survival (i.e. the availability of trade credit) or may
threaten survival (i.e. the use of extended purchase commitments), conventional features of
financial structure (i.e. assets, gearing) do not play a significant role. However, other (non-
financial) explanations of early-stage survival are available, including the use of advertising and
business planning, and the avoidance of precipitate product innovation. This suggests that market
features and internal organization of the micro-firm may dominate financial structure as
determinants of survival in the very earliest phase of the life-cycle. A subsidiary finding favors
the view that high efficiency entrepreneurs tend to form larger firms which attract higher
efficiency and higher paid labor. (Reid, 1999).
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Formally, the dynamic view of the firm can be modeled as follows (Reid, 2003). Reid
(1996, 1999, 2003) uses a dynamic financial model of the small, owner-managed enterprise.
The emphasis is upon debt and equity relationships, and their modification, as the small firm
goes through various stages of growth. The basis of this modeling is the extant literature on the
dynamics of the firm.
It is assumed that the owner-manager engages in maximizing the value of his or her firm
according to:
where D > 0 is the dividend stream, and i is the owner-manager's rate of time preference. E
denotes equity, τ is the planning time horizon, I is gross investment and B is debt. For this
model, the state variables are the amount of equity (E) and the capital stock (K); with the control
variables being debt (B), investment (I) and dividend (D). It is assumed that the owner-manager
pursues the goal of maximization of value as in (1) by its dividend, investment and debt policy,
subject to the following constraint upon policy, and therefore upon the state of the firm and its
performance:
where (2) is the state equation for equity, with operating profit, r the interest rate on debt, and
δ the depreciation rate on capital goods, γ is the maximum gearing ratio permitted for the risk
class of debt to which an interest rate r is attached. Notice that what drives this maximum on
gearing is a limit on desired risk exposure, not a limit on outside finance (which could be
expressed as a credit rationing argument). In fact, limits on gearing depend on the debt-equity
ratio not the level to which equity or debt are provided by investors or lenders. It is also notable
15
that small firms often have gearing ratios well in excess of unity, in the early stages of the life
cycle, casting doubt on the credit-rationing argument of debt finance.
Like dividends, debt and capital are subject to non-negativity constraints; and the
initializing values of equity and capital are e0 and k0, respectively. Operating profit () is defined
as the difference between sales (S) and production costs, given that capital is the only factor
input. It is assumed that the output rate of the firm (Q) is proportionately related to the capital
input by the capital productivity parameter K. Thus operating profit may be written:
assuming a unit price of capital goods. Finally, the firm's sales are defined by the function S(Q)
which is monotonically increasing and concave in Q, with sales being positive for positive
outputs. Thus:
In effect, this small firm is subject to decreasing returns to scale, the source of which may be an
imperfect goods market and/or unspecified non- production costs which raise the marginal costs
of organizing the production plan of the firm as it grows. The evidence for decreasing returns in
small firms has been established by the author Reid (1993). Parameter restrictions for the model
are that:
Further restrictions, which make the model more tractable, are that there are constant unit
(and hence marginal) costs of finance, which are denoted cE, cD or cED depending on whether the
financial structure of the firm is equity (E) or debt (D) dominated, or some mixture of the two
(ED). It is assumed that marginal revenue close to zero output exceeds the greatest of these
costs, implying the small firm has a motivation to at least start investing and producing. Finally,
it is assumed that operating profit cannot be negative ( > 0) (Reid, 2003), that the prices of debt
and equity differ (r ≠ i) and that equity at time zero is positive (E(0) > 0). These last restrictions
follow from the assumptions: (a) that making non- negative profit is a survival criterion; (b) that
debt and equity markets are distinct; (c) that holding equity in itself may engender utility (e.g.
from owner-management and the control it implies) that makes equity-holders willing to accept
16
less than the return relevant to the investment risk class; and (d) that the owner-manager has at
least a certain amount of equity at inception of the business, e0 > 0.
The Hamiltonian for the system is
The derivation of feasible paths for the above model is omitted, but the implications can
be summarized as follows (Reid, 1996). If debt is cheap (i > r) maximum debt finance is used,
and no dividend is paid until a stationary state is reached (See Figure 1). Then there is no further
growth in output, debt or capital stock, and a positive dividend is paid. Whilst growth occurs,
marginal revenue from sales exceeds the marginal cost of debt, that is S' > cD. This implies that
the marginal return to equity exceeds the owner manager's time preference, so all earnings will
be re-invested. When this inequality ceases, because of decreasing returns, the optimal output
( ) has been reached.
Figure 1. Trajectories if debt is cheap (i > r)
When equity is cheap (i < r) then, assuming that the owner-manager has at least some
equity at start-up, the firm will increase its borrowing to start with (until t1) (See Figure 2),
because the marginal revenue of sales exceeds the marginal cost of debt finance, or S' > cED.
17
Figure 2. Trajectories if equity is cheap (i < r)
Each additional unit of capital which is bought with debt finance generates a greater
increment in sales than the increment in cost of debt incurred. Once S' > cED debt will start to be
paid back out of retained earnings (during the consolidation phase), until it is completely paid
back (t2), at which stage further growth occurs (after t2) because S' > cE where cE is the marginal
cost of capital goods which are financed entirely by equity. This will cease (in the stationary
phase) once marginal returns from sales fall to i. The optimal output ( ) will then have been
reached, only replacement investment will occur, and the remaining dividend will go to
shareholders (Tse-Wei Fu et al, 2002). In this study, it is important to gauge at what stage of
the life cycle is the firm undergoing. Once the stage where the firm is in the life cycle is
achieved, one can predict how it will behave as regards the choice on either resorting to debt or
equity to finance investments.
Under the pecking order framework (POF), the use of external funds is very much related
to profitability and so this is included as an independent variable with the hypothesis being that,
since small firms in particular will make use of internally generated funds as a first resort, those
which make use of external funds will be those with a lower level of profit. The corollary of this
for liquidity is that firms with higher profits will have more internal funds available and will,
therefore, need to borrow less in the way of short-term funds thereby improving liquidity. It can
also be hypothesized from the POF, given the importance of retained funds, that older firms will
18
make less use of external finance and have higher liquidity. Reid’s (2003) growth trajectory
model follows the pecking order framework on decisions made by firms when deciding on the
optimal combination of debt and equity. Thereby, the behavior of firms using the life cycle
model of Reid (2003) is also consistent with the pecking order framework.
For microfinance institutions, in general, external financing is resorted but only through
commercial banks, commercial investors and multilateral institutions granting loans to
microfinance. The stock market is not resorted as the firms are not open to public ownership,
thereby reducing its sensitivity to market signals and dependency on capital markets.
Internationally, MFIs display virtually no correlation with global capital markets (Krauss and
Walter (2008)).
Agency theory suggests that information asymmetry and moral hazard will be greatest for
the smallest firms because of the lack of financial disclosure and their owner-managed nature.
Agency theory can be applied to MFIs from the point of view of lack of observable information
when monitoring outreach, i.e. reasons for default on loans, efficiency of group lending versus
individual lending, etc. Output can only be observed when the MFIs reports financial accounts
on a regular basis. Otherwise, there is no possibility for operations to be monitored.
With this in mind, the study focuses its analysis only on firms who have regularly made
their financial data available through the Microfinance Information Exchange (MIX) Portal.
Some of these firms may have been established in the 80s and 90s but data can only be made
available as of 1996 and 1997, and only for four firms. The rest have data from 2002 onwards.
Applicability of the Framework to MFIs in the Philippines
It can be observed that the life cycle trajectory proposed by Reid is applicable to
microfinance institutions when their total assets (indicator for output), total capital and total
equity (indicators for capital) are plotted with respect to time (See Figure 3). Below is a time
series for Alalay sa Kaunlaran, Inc. (ASKI), an MFI and NGO located in Cabanatuan, Nueva
Ecija, whose data is available only from 2002 to 2010. The NGO was established in 1987, and
91-100% of its clients are micro firms. Even if ASKI started commercial operations in 1987,
their financial information made available to the public only from 2002. The MFI seems to be
serving micro firms for only a few years due to its growth trajectory.
19
The expected behavior for cost of borrowings, r, is positive. However, return from
equity, the indicator for the cost of investments, i, in the model of Reid (2003), is decreasing
when the MFI is embarking on an expansion. This means that for an MFI, expansion is financed
via debt, at the initial growth phase. More active borrowers are sought, there is an increase in
assets, the loan portfolio is expanded, but equity does not grow as much. In this phase, dividends
cannot be paid yet, the firm re-invests earnings, that is, yield from loans of creditors, or yield on
loans is decreasing. This phase ends until the growth in the number of active borrowers slows
down. From Figure 4, we can observe the above-mentioned relationship among the variables, as
explained by Reid (2003).
Such a trend happens for firms which are still in the first growth phase until the period of
stagnant growth or consolidation. The early growth phase is an attempt to increase the number
of active borrowers among micro firms, the consolidation phase is an attempt to improve the
quality of loans, thereby, increasing efforts to monitor clients.
Figure 3.
Source: MIX Portal (www.mixmarket.org)
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
20000000
15000000
10000000
5000000
0
in U
S D
olla
rs
ASKIGLP
ASKITotAs
ASKITotEq
Variable
Gross Loan Portfolio, Total Assets and Total EquityAlalay sa Kaunlaran, Inc. (ASKI)
20
Figure 4.
806040 60.00%50.00%40.00%
10000000
5000000
020000000
10000000
012000000
8000000
4000000
500003500020000
3000000
2000000
1000000
30.00%15.00%0.00%
Gro
ss L
oan P
ort
folio
Tota
l Ass
ets
Tota
l Borr
ow
ings
Number of Active Borrowers
Tota
l Equity
Cost per Borrower Return on Equity Yield on Gross Loan Portfolio
Matrix Plot of Gross Loan Portfolio, Total Assets, Total Borrowers, Total Equity
versus Number of Active Borrowers, Cost of Borrowing, Return on Equity,Yield on Loans
MFI: ASKI
Source: MIX Portal (www.mixmarket.org)
A similar trend can be observed for Banco Santiago de Libon, a rural bank, located in
Libon, Albay, whose clients of micro firms only comprises 11-20% of total clients (See Figures
5 and 6). It was established in 1973. Data made available to the public for the bank’s operations
only started in 2003. For a relatively young MFI, both Banco Santiago de Libon and ASKI, the
growth trajectory as modeled by Reid (2003) consistently explains how growth and expansion is
financed via debt. Yield on loans are re-invested, and no dividends are paid.
For firms which have served micro firms for longer years, such as CARD NGO and
CARD Bank, an NGO and rural bank, respectively, located in San Pablo, Laguna and has served
micro firms since 1986, its year of establishment, and micro firms comprising 91-100% of total
clients, a second growth phase can be observed. For both MFIs, a period of consolidation
happens right before the period of expansion. During the consolidation phase, after having
improved the quality of loans, and monitoring active borrowers, the firm is increasing its yield
on loans and its returns to equity. The second growth phase is now financed via equity, no
longer debt. The MFI pays dividends and attracts more borrowers and depositors, thereby
21
increasing its outreach. Reid (1991) model also seems to explain the growth trajectory of bigger
and older firms such as CARD NGO. (See Figures 7-10)
In summary, the life cycle of MFIs seem to be related to the loan cycle: period of
growth to consolidation to the second growth phase seem to last for 10 years. The variable
which consistently shows this is total equity, as compared to total assets, total borrowing and
gross loan portfolio.
Figure 5.
Source: MIX Portal (www.mixmarket.org)
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
4000000
3000000
2000000
1000000
0
in U
S D
olla
rs
BSLGLP
BSLTotAs
BSLTotEq
Variable
Gross Loan Portfolio, Total Assets and Total EquityBanco Santiago de Libon
22
Figure 6.
504540 50.00%45.00%40.00%
3000000
2000000
1000000
4000000
3000000
2000000
500000
300000
100000
1000075005000
900000
600000
300000
32.00%24.00%16.00%
Gro
ss L
oan P
ort
folio
Tota
l Ass
ets
Tota
l Borr
ow
ings
Number of Active Borrowers
Tota
l Equity
Cost per Borrower Return on Equity Yield on Gross Loan Portfolio
Matrix Plot of Gross Loan Portfolio, Total Assets, Total Borrowers, Total Equity
versus Number of Active Borrowers, Cost of Borrowing, Return on Equity,Yield on Loans
MFI: Banco Santiago de Libon
Source: MIX Portal (www.mixmarket.org)
Figure 7.
Source: MIX Portal (www.mixmarket.org)
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
70000000
60000000
50000000
40000000
30000000
20000000
10000000
0
in U
S D
olla
rs
CNGOGLP
CNGOTotAs
CNGOTotEq
Variable
Gross Loan Portfolio, Total Assets and Total EquityCARDNGO
23
Figure 8.
403530 50.00%45.00%40.00%
50000000
25000000
0
50000000
25000000
0
20000000
10000000
0
5000002500000
16000000
8000000
0
20.00%10.00%0.00%
Gro
ss L
oan P
ort
folio
Tota
l Ass
ets
Tota
l Borr
ow
ings
Number of Active Borrowers
Tota
l Equity
Cost per Borrower Return on Equity Yield on Gross Loan Portfolio
Matrix Plot of Gross Loan Portfolio, Total Assets, Total Borrowers, Total Equity
versus Number of Active Borrowers, Cost of Borrowing, Return on Equity,Yield on Loans
MFI: Center for Agricultural and Rural Development (CARDNGO)
Source: MIX Portal (www.mixmarket.org)
Figure 9.
Source: MIX Portal (www.mixmarket.org)
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
70000000
60000000
50000000
40000000
30000000
20000000
10000000
0
in U
S D
olla
rs
CBKGLP
CBKTotAs
CBKTotEq
Variable
Gross Loan Portfolio, Total Assets and Total EquityCARD Bank
24
Figure 10.
645648 50.00%45.00%40.00%
40000000
20000000
0
50000000
25000000
0
16000000
8000000
0
2000001000000
8000000
4000000
0
40.00%20.00%0.00%
Gro
ss L
oan P
ort
folio
Tota
l Ass
ets
Tota
l Borr
ow
ings
Number of Active Borrowers
Tota
l Equity
Cost per Borrower Return on Equity Yield on Gross Loan Portfolio
Matrix Plot of Gross Loan Portfolio, Total Assets, Total Borrowers, Total Equity
versus Number of Active Borrowers, Cost of Borrowing, Return on Equity,Yield on Loans
MFI: CARD Bank
Source: MIX Portal (www.mixmarket.org)
A look at the descriptive statistics of the old and young MFIs shows that the assumptions
imposed by Reid (2003) on the market structure of the firms, to ensure that the firms are small
and are perfectly, competitive seem to be verified in the descriptive statistics below. These
assumptions are: (a) that making non- negative profit is a survival criterion; (b) that debt and
equity markets are distinct; (c) that holding equity in itself may engender utility (e.g. from
owner-management and the control it implies) that makes equity-holders willing to accept less
than the return relevant to the investment risk class; and (d) that the owner-manager has at least a
certain amount of equity at inception of the business, e0 > 0.
The data shows that some young MFIs have operated at a loss due to the presence of a
negative ratio for return to assets, return to equity, capital-asset ratio and debt-to-equity ratios.
However, the older firms did not report a negative return from 2003 to 2010. Thus, firms operate
so as to achieve accounting profits. The ratio of debt to equity is not one, thus, the firms operate
where the debt and equity markets are distinct. Both young and old firms are holding a return to
25
equity ratio of 13% to 15%, manifesting a desire to achieve yields from loans that will enable
them to pay dividends.
Table 3. Descriptive Statistics of the MFIs in the Study
Source: MIX Market Information Portal for the Philippines
(http://www.mixmarket.org/mfi/country/Philippines )
Old MFIsBorrowers
per Staff
Depositors
per Staff
Portfolio at
Risk (30
days)
Return on
Euity
Return on
Assets
Capital-
Asset Ratio
Debt-to-
Equity
Ratio
Mean 134.95 152.27 0.04 0.15 0.03 0.23 4.32
Median 134.00 154.00 0.03 0.12 0.02 0.21 3.75
Maximum 185.00 302.00 0.12 0.68 0.10 0.47 12.02
Minimum 68.00 6.00 0.00 0.00 0.00 0.08 1.11
Std. Dev. 26.19 45.07 0.03 0.12 0.03 0.11 2.58
Skewness (0.65) 0.37 1.01 2.13 0.81 0.60 0.86
Kurtosis 3.36 7.55 3.17 9.70 2.45 2.22 3.39
Jarque-Bera 2.92 32.75 6.36 99.76 4.61 3.26 4.93
Probability 0.23 0.00 0.04 0.00 0.10 0.20 0.09
Sum 5,128.00 5,634.00 1.57 5.62 1.23 8.93 164.34
Sum Sq. Dev. 25,385.89 73,127.30 0.04 0.57 0.02 0.46 246.04
Observations 38 37 37 38 38 38 38
Young MFIsBorrowers
per Staff
Depositors
per Staff
Portfolio at
Risk (30
days)
Return on
Euity
Return on
Assets
Capital-
Asset Ratio
Debt-to-
Equity
Ratio
Mean 122.15 167.54 0.09 0.13 0.03 0.24 4.20
Median 115.00 153.00 0.07 0.14 0.02 0.18 4.35
Maximum 357.00 550.00 0.53 2.66 0.23 0.92 40.45
Minimum 20.00 0.00 0.00 -5.53 -0.26 -0.21 -27.43
Std. Dev. 52.69 96.84 0.09 0.52 0.05 0.16 4.59
Skewness 0.86 0.59 2.31 -6.11 -0.12 1.27 -0.59
Kurtosis 4.23 3.68 9.85 72.01 8.47 5.31 28.35
Jarque-Bera 51.24 20.12 710.45 50,554.88 308.03 139.97 7,595.77
Probability 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Sum 33,590 44,062 21 33 7 67 1,188
Sum Sq. Dev. 760,702 2,456,931 2 66 1 7 5,939
Observations 275 263 250 247 247 284 283
26
4. Empirical Methodology
The need to explain output, capital, debt, equity and profits with the use of specific
financial indicators across a time series through 46 firms would mean the need to perform a
panel regression, using a fixed effects model with White cross-section covariance method in
order to correct for heteroskedasticity. The difference in the life cycle of old (MFIs whose data
includes a phase with initial growth, consolidation and second growth phase, or about data from
1996 to 2010, a total of 5 firms) and young firms (MFIs whose data includes only a growth to
consolidation phase, or data from 2002 to 2010, a total of 41 firms). These 46 firms have to most
complete data set in the MIX Portal. Due to the incompleteness of the time series, the regression
will only include a regression from 2003 to 2010. Separate regressions shall be done for old and
young firms, as the expected signs of the explanatory variables may differ.
The variables suggested by Reid (2003) were operationalized using the performance
standards of the National Credit Council. 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 (P.E.S.O). These
standards are matched with the corresponding accounting variables which can be found in the
MIX Portal. The variables used are seen in Tables 4 and 5.
Table 4. Operationalization of the Variables
Theoretical Model Indicators Pertinent to MFIs
Main Equations Dependent
Variable
Explanatory
Variables
Dependent
Variable
MFI Performance
Standards
= Equity
Growth
For Equity:
π = Profits
rB = Borrowing Cost
D = Dividends
-Equity Growth Operational Self-
Sufficiency
= Capital
Growth
I = Investment
δK = Cost of Capital
-Gross Loan
Portfolio Growth
Financial Self-
Sufficiency in
Managing Debt
K = Capital
Stock
For Capital:
E = Equity
B = Borrowings
(Debt)
-Gross Loan
Portfolio
Operational Self-
Sufficiency
27
Table 4. Operationalization of the Variables (Continued)
Theoretical Model Theoretical Model
Main Equations Main
Equations Main Equations Main Equations Main Equations
π = Profits
For Profit:
PQ = Revenues
κP = Production
Inputs
-Return on Sales -
Yield on Gross Loan
Portfolio
Financial Self-
Sufficiency in
Managing Assets
Not
Applicable
For Output:
S(Q) = Sales
P(Q) = Value of
Output
Total Sales Outreach
Sources: Reid (2003), Karlan (2005, 2009, 2010), Meagher et al (2006), Krauss and Walter (2008, 2009)
Table 5. MFI Performance Standards and Indicators Used for the Regression
MFI
Performance
Standards
Explanation of the Performance Standards and
Chosen P.E.S.O. Indicators
P.E.S.O. and MIX
Indicator
Portfolio
Quality
Standard: Portfolio quality measures the amount of
risk in the current outstanding portfolio. It provides
information on the percentage of non-performing
assets, which in turn decrease the revenue and
liquidity position of the MFI
Indicator: Portfolio at risk (PAR) ratio-refers to the
balance of loans with at least one day missed
payments, as a percentage of the amount of the
portfolio outstanding including amounts past due as
well as refinanced and restructured loans.
P.E.S.O: Portfolio at
risk (30 days) should
not be greater than
5%
MIX: PAR (30 Days)
Operational
Self-Sufficiency
(Efficiency)
Standard: Efficiency indicators measure the cost of
providing microfinance services to generate
revenue. The indicators under this category show
whether the MFI is able to deliver micro finance
services at least cost to the institution. They
indicate the ability of the institution to generate
sufficient income to cover expenses related to the
microfinance operation.
Indicator: Operational Self-Sufficiency (OSS) is an
efficiency variable which indicates whether or not
enough revenues have been earned to cover the
organization’s costs.
P.E.S.O: Loan officer
productivity =
number of active
borrowers per number
of account officers.
For group lending: >
300 borrowers. For
individual lending: >
150 borrowers.
MIX: Depositors per
Staff, Borrowers per
Staff
28
Table 5. MFI Performance Standards and Indicators Used for the Regression (Continued)
MFI
Performance
Standards
Explanation of the Performance Standards and
Chosen P.E.S.O. Indicators
P.E.S.O. and MIX
Indicator
Financial Self-
Sufficiency
(Sustainability)
Standard: Sustainability measures the ability of the
institution to generate sufficient revenues to cover
the costs of its operations in the long run without
any subsidy.
Indicators: Financial self-sufficiency (FSS) ability
of the organization to earn enough revenue to
sufficiently cover in the long-run all operating costs
and at the same time maintain the value of its
capital and assets, without the need for subsidy.
P.E.S.O: FSS =
Operating Revenue /
(Financial Expense +
Loan Loss Provision
Expense + Adjusted
Expenses). Should be
greater than 100%;
MIX:
FSS for Managing
Assets- Financial
Revenues/Assets;
Capital-Asset
Ratio
FSS for Managing
Debt-Cost per
Borrower; Return
on Equity; Debt-
Equity Ratio
Outreach
Standard: Outreach indicators show the extent and
depth of reach of the MFI. The extent of outreach
is reflected by the growth in the number of active
clients (referring to those with outstanding
microfinance loans with the institutions) and
growth of the microfinance portfolio. The depth of
outreach is indicated by the ratio of the average
loan size to the GNP per capita to measure the scale
of MFI activities
P.E.S.O: Growth in
the number of active
clients; growth in the
microfinance loan
portfolio; depth of
outreach = total loans
outstanding / total
number of borrowers
MIX: Number of
active borrowers,
number of deposits,
Average Deposit
Balance per capita
Gross National
Income
Sources: Reid (2003), Karlan (2005, 2009, 2010), Meagher et al (2006), Krauss and Walter
(2008, 2009)
Applying the pecking order framework, whose results are equivalent to Reid (1991)
model, the expected behavior of the explanatory variables are indicated as follows.
29
Objective 1 involves a description of the growth trajectory, also termed as life cycle, of
microfinance firms in the Philippines. The dependent variables are total assets (output), gross
loan portfolio and total equity (capital), and total borrowings (debt). These are the growth
trajectories shown in Figures 1 and 2. The explanatory variables are: number of active
borrowers and deposits (for outreach), financial revenues over assets and the capital-asset ratio
(for financial self-sufficiency in managing assets), return on equity and debt-to-equity ratio (for
financial self-sufficiency in managing debt), cost per borrower (indicator for interest level for
debt) and number of borrowers or depositors per staff (for operational self-sufficiency).
Table 6. Explanation of Variables used for Objective 1
Objective 1. Explanatory Variables (Outcomes of Micro Firms forming part of MFI) with Expected Sign on Regression
Indicator
Financial Variable
Used (Dependent
Variable)
Number of
Active
Borrowers
(Outreach))
Deposits
(Indicator for
Savings Balance
and Outreach)
Financial
Revenues over
Assets
(Financial Self-
Sufficiency)
Capital-Asset
Ratio
(Financial Self-
Sufficiency)
Cost per Borrower
(Indicator for interest
level on debt)
Output Total Assets
Gross Loan
Portfolio (Loan Size)
Total Equity
Debt Total Borrowings
Output Total Assets
Gross Loan
Portfolio (Loan Size)
Total Equity
Debt Total Borrowings
For Firms Experiencing Consolidation to Second Growth Phase (i.e. Operating since 1996)
For Firms Experiencing Growth and Consolidation (i.e. Operating since 2003 only)
Positive. MFIs
would like to
expand
operations to
improve
outreach
Positive. MFIs start
to resort to equity
financing, thus, more
depositors are
needed
Negative. MFIs
continue to
increase their
asset base.
Negative. MFIs
contiue to
increase their
capital base.
Positive. MFI may resort
to external sources of
loans to increase its loan
portfolio.
Capital
Positive. Debt, at this
phase is resorted to by the
micro-firms. MFIs have to
monitor borrowers,
increasing cost. MFI may
incur loans
Capital
Positive. MFIs
would like to
expand
operations to
improve
outreach
Positive. Since debt
finance is used,
MFIs resorts to
deposits to finance
operations
Negative. The
MFIs are still in
the process of
increasing their
asset base.
Negative. MFIs
are still in the
process of
increasing their
capital base.
30
Table 6. Explanation of Variables used for Objective 1
Objective 2 involves determining the factors affecting the performance of microfinance
firms using standard performance indicators. The MFIs shall be grouped by age and common
trends of growth trajectories shall be done and explained in terms of outreach, financial and
operational self-sufficiency. These are the variables which are observable through the available
accounting data provided by the firms in the MIX portal. Panel regression shall be performed
again as in the method for Objective 2. The variables and their expected outcomes follow.
The dependent variables to be used are return on assets and yield on gross loan portfolio.
The explanatory variables are: number of active borrowers and average deposit balance per
capita gross national income (for outreach), financial revenues over assets and capital-asset ratio
(for financial self-sufficiency in managing assets), return on equity and debt-to-equity ratio (for
financial self-sufficiency in managing debt), cost per borrower (indicator for cost of debt),
Objective 1. Explanatory Variables (Continued)
Indicator
Financial
Variable Used
(Dependent
Variable)
Return on Equity
(Indicator for
interest level on
equity, financial
self-sufficiency)
Debt-Equity Ratio
(Indicator for debt
and equity levels,
financial self -
sufficiency)
Borrowers or
depositors per
staff
(Operational
self-sufficiency)
For Firms Experiencing Growth and Consolidation (i.e. Operating since 2003 only)
Output Total Assets
Negative. Equity
is not increasing and dividends are
not yet paid as the
MFI re-invests
yield
Positive or not
significant. At the consolidation phase,
the MFI does not
increase equity nor
loan portfolio.
Positive.
Increasing number
of borrowers upon the early growth
phase
Capital
Gross Loan
Portfolio (Loan
Size)
Total Equity
Debt Total Borrowings
For Firms Experiencing Consolidation to Second Growth Phase (i.e. Operating since 1996)
Output Total Assets
Positive. Equity is
increasing and
dividends are being paid.
Negative. At the second growth phase,
the MFI decreases
debt but equity has
increased due to previous re-
investment of yield
from loans.
Positive but not
significant. May
resort to increase membership
Capital
Gross Loan
Portfolio (Loan
Size)
Total Equity
Debt Total Borrowings
31
portfolio at risk for 30 days (for portfolio quality) and number of depositors per staff (for
operational self-sufficiency).
Table 7. Explanation of Variables used for Objective 2
Objective 2. Explanatory Variables (Outcomes of Micro Firms forming part of MFI) with Expected Sign on Regression
Indicator
Financial Variable
Used (Dependent
Variable)
Number of
Active
Borrowers
(Outreach)
Average Deposit
Balance per capita
Gross National
Income
(Outreach)
Financial
Revenues over
Assets
(Financial Self-
Sufficiency)
Capital-Asset
Ratio
(Financial Self-
Sufficiency)
Cost per Borrower
(Indicator for interest
level on debt)
Return on Assets
(Profitability of
MFI)
Perfo
rm
an
ce
Perfo
rm
an
ce
Negative.
Unmonitored
increase in the
number of
active
borrowers
would increase
cost
Positive. An
increase in deposits
increases the asst
base, and thus,
increases
profitability.
Positive. An
increase in
liquidity means
greater
possibilities for re-
investment
For Firms Experiencing Consolidation to Second Growth Phase (i.e. Operating since 1996)
Yield on Gross
Loan Portfolio
(Profitability and
Capacity to Re-
invest)
Negative.
Unmonitored
increase in the
number of
active
borrowers
would increase
cost
Negative. Unmonitored
increase in the number of
active borrowers would
increase cost
Return on Assets
(Profitability of
MFI)
Yield on Gross
Loan Portfolio
(Profitability and
Capacity to Re-
invest)
Positive. An
increase in
solvency lessens
the risk of the
MFI
Negative. Unmonitored
increase in the number of
active borrowers would
increase cost
Positive. An
increase in deposits
increases the asst
base, and thus,
increases
profitability.
Positive. An
increase in
liquidity means
greater
possibilities for re-
investment
Positive. An
increase in
solvency lessens
the risk of the
MFI
For Firms Experiencing Growth and Consolidation (i.e. Operating since 2003 only)
32
Table 7. Explanation of Variables used for Objective 2
5. Results
The regression results shall be presented for the four variables comprising the growth
trajectories, for objective one. In summary, the trend lines for old and young firms follow the
expected results from the life cycle model.
a. Regression Results for Gross Loan Portfolio
The expected positive coefficient for outreach (number of active borrowers, deposits) was
obtained. Old and young MFIs would increase their loan portfolios in order to increase the
participation of micro firms. The expected negative coefficient for financial self-sufficiency in
managing assets (financial revenues over assets and the capital-asset ratio) was also achieved.
The increase in borrowings depletes financial revenues fast. The debt-to-equity and return on
equity ratios are not significant to explain gross loan portfolio. The financial self-sufficiency for
managing debt indicators does not capture the gross loan portfolio trend. Cost per borrower is
Indicator
Financial Variable
Used (Dependent
Variable)
Return on Equity
(Indicator for
interest level on
equity)
Debt-Equity Ratio
(Indicator for debt
and equity levels)
Portfolio at Risk
30 Days
(Portfolio
Quality)
Depositors
per staff
(Operational
Self-
Sufficiency)
Negative.
Profitability is not
priority as efforts are
put into monitoring
the quality of
depositors and
borrowers
Positive.
Greater
productivity
per staff
improves
profitability.
Positive.
Greater
productivity
per staff
improves
profitability.
Objective 2. Explanatory Variables (Continued)
For Firms Experiencing Growth and Consolidation (i.e. Operating since 2003 only)
For Firms Experiencing Consolidation to Second Growth Phase (i.e. Operating since 1996)
Negative. Debt is
expected to decrease,
thus, the ratio is
expected to be
decreasing
Negative. The
number of unpaid
loans lessens the
MFIs capacity to
generate re-
investments
Positive. The MFI is
expected to pay
dividends as it starts
to earn from re-
investments.
Negative or not
significant. Debt is
expected to level off,
thus, the ratio is
expected to be stable or
be decreasing
Negative. The
number of unpaid
loans lessens the
MFIs capacity to
generate re-
investments
Per
form
an
ce
Return on Assets
(Profitability of MFI)
Yield on Gross Loan
Portfolio
(Profitability and
Capacity to Re-
invest)
Per
form
an
ce
Return on Assets
(Profitability of MFI)
Yield on Gross Loan
Portfolio
(Profitability and
Capacity to Re-
invest)
33
not significant, as well as the borrowers per staff, or the operational self-sufficiency indicator.
(See Table 8)
Table 8. Panel Regression Results for Gross Loan Portfolio
b. Regression Results for Total Assets
The expected positive coefficient for outreach (number of active borrowers, deposits) was
obtained. Old and young MFIs would increase their asset size in order to attract investors and
depositors. The expected negative coefficient for financial self-sufficiency in managing assets
(financial revenues over assets and the capital-asset ratio) was also achieved. The increase in
deposits is offset by the increase in borrowings by micro firms, or a smaller net deposit level
thereby decreasing financial revenues. The debt-to-equity and return on equity ratios are not
significant for young firms, as expected. However, return to equity is not significant but is
positive for older MFIs. The debt-to-equity ratio is negative and significant for older MFIs
Dependent Variable: GROSS LOAN PORTFOLIO (Old MFIs) Dependent Variable: GROSSLOANPORTFOLIO (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:17 Date: 10/11/11 Time: 11:23
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 37 Total panel (unbalanced) observations: 202
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C 10462795 4823645 2.17 0.04 C 3077337 2210601 1.39 0.17
Number of Active Borrowers 58.80079 9.901651 5.94 0.00 Number of Active Borrowers 41.89715 3.293482 12.72 0.00
DEPOSITS 0.772191 0.096904 7.97 0.00 DEPOSITS 1.030894 0.03972 25.95 0.00
Financial Rev / Assets -32066320 6809362 -4.71 0.00 Financial Rev / Assets -6345716 3672887 -1.73 0.09
Capital-Asset Ratio -22133533 12074576 -1.83 0.08 Capital-Asset Ratio -2691207 1184279 -2.27 0.02
Cost per Borrower 233137.8 39177.55 5.95 0.00 Cost per Borrower 94.71957 8580.288 0.01 0.99
Return on Equity 2302933 1658826 1.39 0.18 Return on Equity -36297.52 71634.23 -0.51 0.61
Debt-Equity Ratio -145000.5 617926.7 -0.23 0.82 Debt-Equity Ratio 5696.559 12043.88 0.47 0.64
Borrowers per Staff -19346.26 25421.48 -0.76 0.45 Borrowers per Staff -6514.936 6192.099 -1.05 0.29
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.985337 R-squared 0.981256
Adjusted R-squared 0.978006 Adjusted R-squared 0.975376
S.E. of regression 1702225 S.E. of regression 1345953
Log likelihood -575.3483 Log likelihood -3109.313
Durbin-Watson stat 2.160534 Durbin-Watson stat 1.612518
Mean dependent var 15796425 Mean dependent var 6485129
S.D. dependent var 11477869 S.D. dependent var 8577322
F-statistic 134.3986 F-statistic 166.871
Prob(F-statistic) 0 Prob(F-statistic) 0
34
manifesting that older MFIs are starting to decrease debt and yielding higher equity. The Total
Asset regression captures the effect of equity financing for older MFIs. This behavior is more
likely to be expected for the total asset trend than for gross loan portfolio. Both young and old
MFIs manifest financial prudence in managing debt. (Financial self-sufficiency in managing
debt).
Borrowers per staff, or the operational self-sufficiency indicator, is not significant for
young MFIs. However, the cost per borrower is significant for both young and old MFIs. Thus,
in order to achieve efficiency in the management of assets, MFIs have to monitor borrowers,
thereby incurring higher costs. (See Table 9)
Table 9. Panel Regression Results for Total Assets
Dependent Variable: TOTAL ASSETS (Old MFIs) Dependent Variable: TOTAL ASSETS (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:18 Date: 10/11/11 Time: 11:23
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 36 Total panel (unbalanced) observations: 198
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C 10367534 5857489 1.77 0.09 C 2411775 1972304 1.22 0.22
Number of Active Borrowers 59.38867 6.290354 9.44 0.00 Number of Active Borrowers 13.61854 2.647934 5.14 0.00
DEPOSITS 1.369716 0.109829 12.47 0.00 DEPOSITS 1.638989 0.036688 44.67 0.00
Financial Rev / Assets -40686811 9012921 -4.51 0.00 Financial Rev / Assets -7618897 4059215 -1.88 0.06
Capital-Asset Ratio -30724493 8517680 -3.61 0.00 Capital-Asset Ratio -2000218 1243613 -1.61 0.11
Cost per Borrower 409753.7 51741.67 7.92 0.00 Cost per Borrower 10531.03 6602.178 1.60 0.11
Return on Equity 3647988 5701475 0.64 0.53 Return on Equity -126154.9 71329.93 -1.77 0.08
Debt-Equity Ratio -985360.4 412152.3 -2.39 0.03 Debt-Equity Ratio 14259.05 13558.03 1.05 0.29
Borrowers per Staff 7516.276 17910.66 0.42 0.68 Borrowers per Staff 1057.103 1784.264 0.59 0.55
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.988982 R-squared 0.989971
Adjusted R-squared 0.983233 Adjusted R-squared 0.98674
S.E. of regression 2110534 S.E. of regression 1494649
Log likelihood -567.2656 Log likelihood -3067.848
Durbin-Watson stat 1.364 Durbin-Watson stat 1.400111
Mean dependent var 23537180 Mean dependent var 10020025
S.D. dependent var 16299118 S.D. dependent var 12980015
F-statistic 172.0357 F-statistic 306.4219
Prob(F-statistic) 0 Prob(F-statistic) 0
35
c. Regression Results for Total Borrowings
The expected positive coefficient for outreach (number of deposits) was obtained but is
negative for number of active borrowers for young MFIs. Due to the need to finance debt
through internally generated funds, the increase in the number of active borrowers depletes the
available loan portfolio for creditors. On the other hand, older MFIs can increase their outreach
and can handle externally sourced debt as they have already increased their capital and asset
base. The expected negative coefficient for financial self-sufficiency in managing assets
(financial revenues over assets and the capital-asset ratio) was also achieved for old and young
MFIs. This result shows the prudent behavior of MFIs as regards borrowings and maintaining
sufficient assets for operations. The debt-to-equity and return on equity ratios are not significant
for young firms, as expected. However, return to equity and the debt to equity ratio are positive
and significant for older MFIs. This result is expected due to the behavior of MFIs to decrease
its debt structure during a period of expansion. Older firms have to achieve greater financial
self-sufficiency in managing debt. Borrowers per staff and the cost per borrower, or the
operational self-sufficiency indicators, are not significant for young MFIs. However, these two
indicators are both positive and significant for old MFIs. Thus, in order to achieve efficiency in
the management of borrowings, MFIs have to monitor creditors, thereby incurring higher costs.
(See Table 10)
36
Table 10. Panel Regression Results for Total Borrowings
d. Regression Results for Total Equity
The expected positive coefficient for outreach (number of active borrowers, deposits) was
obtained. Old and young MFIs would increase their number of borrowers in order to achieve in
an increase in outreach. However, for old MFIs, the number of depositors is not significant as
the firm is able to generate funds through external sources and re-investment of yields. The
expected negative coefficient for financial self-sufficiency in managing assets (financial
revenues over assets) was also achieved, but this variable is insignificant for both old and young
firms. On the other hand, the capital to asset ratio is positive and significant for both old and
young firms. The result from the financial self-sufficiency in managing assets shows that both
old and young firms intend to increase equity through a combination of increasing the number of
depositors and by increasing yields from loans. The debt-to-equity and return on equity ratios
are not significant for young firms, as expected. But these indicators are not significant for older
MFIs as well. This denotes that other indicators for financial self-sufficiency in managing debt
Dependent Variable: TOTAL BORROWINGS (Old MFIs) Dependent Variable: TOTAL BORROWINGS (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:20 Date: 10/11/11 Time: 11:24
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 37 Total panel (unbalanced) observations: 202
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C 35108774 5957294 5.89 0.00 C 3008581 1473161 2.04 0.04
Number of Active Borrowers 38.65543 11.77703 3.28 0.00 Number of Active Borrowers -5.031263 1.660209 -3.03 0.00
DEPOSITS 0.213434 0.115887 1.84 0.08 DEPOSITS 0.374031 0.022798 16.41 0.00
Financial Rev / Assets -37238534 10933245 -3.41 0.00 Financial Rev / Assets -5507343 3033757 -1.82 0.07
Capital-Asset Ratio -48225255 8104807 -5.95 0.00 Capital-Asset Ratio -3844717 794109.9 -4.84 0.00
Cost per Borrower 146194.9 40857.37 3.58 0.00 Cost per Borrower 1723.778 6432.137 0.27 0.79
Return on Equity 6808558 3015603 2.26 0.03 Return on Equity -27025.1 58133.67 -0.46 0.64
Debt-Equity Ratio -997539.1 323513.7 -3.08 0.01 Debt-Equity Ratio 11480.59 10299.38 1.11 0.27
Borrowers per Staff -99507.95 26892.44 -3.70 0.00 Borrowers per Staff -3500.286 3220.62 -1.09 0.28
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.962878 R-squared 0.905186
Adjusted R-squared 0.944317 Adjusted R-squared 0.875441
S.E. of regression 1763276 S.E. of regression 1190702
Log likelihood -576.652 Log likelihood -3084.556
Durbin-Watson stat 1.843637 Durbin-Watson stat 1.26657
Mean dependent var 7532825 Mean dependent var 2037593
S.D. dependent var 7472403 S.D. dependent var 3373770
F-statistic 51.87675 F-statistic 30.43108
Prob(F-statistic) 0 Prob(F-statistic) 0
37
can explain decisions on total equity. MFI banks have other sources for equity unlike NGOs.
The operational self-sufficiency indicators are not significant for both old and young MFIs as the
increase in total equity cannot be explained by the productivity of the MFIs staff alone.
Table 11. Panel Regression Results for Total Equity
The behavior of firms by age group is expected to vary, for objective 2. For example, the
signs of the coefficients for return to equity and debt may differ for old and young firms. Both
return to equity is expected to be positive for old firms, and the debt-to-equity ratio is expected to
be negative and significant for old firms, due to equity financing as a mode to expand.
e. Return on Assets
The expected results for outreach differ. The number of borrowers is expected to have a
negative coefficient but this result is significant only for old MFIs. The average deposit balance
Dependent Variable: TOTAL EQUITY (Old MFIs) Dependent Variable: TOTAL EQUITY (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:21 Date: 10/11/11 Time: 11:24
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 36 Total panel (unbalanced) observations: 198
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C -7508700 3053563 -2.46 0.02 C -485353 667480.4 -0.73 0.47
Number of Active Borrowers 27.7827 2.607863 10.65 0.00 Number of Active Borrowers 14.69024 3.744255 3.92 0.00
DEPOSITS 0.03133 0.045457 0.69 0.50 DEPOSITS 0.200156 0.007179 27.88 0.00
Financial Rev / Assets 1789489 4619669 0.39 0.70 Financial Rev / Assets -316886.3 1890557 -0.17 0.87
Capital-Asset Ratio 12895588 4785569 2.69 0.01 Capital-Asset Ratio 2494952 446955.9 5.58 0.00
Cost per Borrower 97609.64 8678.089 11.25 0.00 Cost per Borrower 7261.923 879.772 8.25 0.00
Return on Equity -2203460 2049848 -1.07 0.29 Return on Equity -51219.31 68629.09 -0.75 0.46
Debt-Equity Ratio -91996.25 135968.6 -0.68 0.51 Debt-Equity Ratio -7141.529 11745.11 -0.61 0.54
Borrowers per Staff 8560.51 7482.793 1.14 0.26 Borrowers per Staff -875.3869 878.8235 -1.00 0.32
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.973955 R-squared 0.951272
Adjusted R-squared 0.960367 Adjusted R-squared 0.935575
S.E. of regression 752043.3 S.E. of regression 506819.1
Log likelihood -530.1171 Log likelihood -2853.712
Durbin-Watson stat 1.789321 Durbin-Watson stat 0.900189
Mean dependent var 5084692 Mean dependent var 1896838
S.D. dependent var 3777582 S.D. dependent var 1996758
F-statistic 71.67506 F-statistic 60.60027
Prob(F-statistic) 0 Prob(F-statistic) 0
38
is expected to be positive for both MFIs but is significant only for young firms. Other factors
seem to explain how outreach can improve profitability. The expected positive coefficient for
financial self-sufficiency in managing assets (financial revenues over assets and the capital-asset
ratio) was also achieved for young MFIs. Only the financial revenues over assets is significant
for old MFIs. From the data, improving the liquidity of the MFI seems to have greater
explanatory power to improving returns on assets. The debt-to-equity and return on equity ratios
are significant and positive for young firms, unexpectedly. Older firms have a positive and
significant coefficient for equity returns but negative and significant for debt-equity ratio, as
expected. This behavior shows that young firms try to achieve both an increase in debt and
equity while they go through the consolidation phase in order to maintain their targeted
profitability. (Financial self-sufficiency in managing debt) For operational self-sufficiency,
portfolio at risk is negative and very significant for both old and young firms. The other
indicators do not have significant results.
Table 12. Panel Regression Results for Return on Assets
Dependent Variable: Return on Assets (Old MFIs) Dependent Variable: Return on Assets (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:53 Date: 10/11/11 Time: 11:24
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 34 Total panel (unbalanced) observations: 190
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C -0.052793 0.040723 -1.30 0.21 C -0.103952 0.044717 -2.32 0.02
Number of Active Borrowers -1.22E-07 3.96E-08 -3.08 0.01 Number of Active Borrowers -1.68E-08 1.45E-07 -0.12 0.91
Ave Depos it Ba lance per Income 0.139922 0.149747 0.93 0.36 Ave Depos it Ba lance per Income 0.053914 0.017675 3.05 0.00
Financial Revenues / Assets 0.232811 0.091497 2.54 0.02 Financial Revenues / Assets 0.295972 0.09759 3.03 0.00
Capital-Asset Ratio -0.010397 0.036842 -0.28 0.78 Capital-Asset Ratio 0.074614 0.057329 1.30 0.20
Cost per Borrower -6.90E-05 0.000255 -0.27 0.79 Cost per Borrower -0.000137 0.000167 -0.82 0.41
Return on Equity 0.105648 0.039267 2.69 0.01 Return on Equity 0.02121 0.009667 2.19 0.03
Debt-Equity Ratio -0.002842 0.001094 -2.60 0.02 Debt-Equity Ratio 0.002253 0.001102 2.04 0.04
Portfolio at Risk 30 days -0.16922 0.084967 -1.99 0.06 Portfolio at Risk 30 days -0.141988 0.062163 -2.28 0.02
Number of Depositos per Staff 0.000158 0.000154 1.03 0.32 Number of Depositos per Staff 0.000168 4.24E-05 3.96 0.00
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.917545 R-squared 0.751683
Adjusted R-squared 0.863949 Adjusted R-squared 0.664773
S.E. of regression 0.009817 S.E. of regression 0.031235
Log likelihood 117.9798 Log likelihood 417.9943
Durbin-Watson stat 1.432074 Durbin-Watson stat 1.988528
Mean dependent var 0.033974 Mean dependent var 0.027831
S.D. dependent var 0.026616 S.D. dependent var 0.053947
F-statistic 17.11965 F-statistic 8.648903
Prob(F-statistic) 0 Prob(F-statistic) 0
39
f. Yield on Gross Loan Portfolio
The expected results for outreach differ. The number of borrowers is expected to have a
negative coefficient but this result is significant only for old MFIs. The average deposit balance
is expected to be positive for both MFIs but is significant only for young firms. Other factors
seem to explain how outreach can improve yield on gross loan portfolio. The expected positive
coefficient for financial self-sufficiency in managing assets (financial revenues over assets and
the capital-asset ratio) was achieved for young and old MFIs. From the data, improving the
liquidity of the MFI seems to have greater explanatory power to improving yield on loans. The
debt-to-equity and return on equity ratios are significant and negative for young firms, as
expected. Older firms have a positive and significant coefficient for equity returns but negative
and significant for debt-equity ratio, as expected. This behavior shows that young firms try to
achieve both an increase in debt and equity while they go through the consolidation phase in
order to maintain their targeted profitability but their current loan portfolio is not yet generating
yields. (Financial self-sufficiency in managing debt) For operational self-sufficiency, portfolio
at risk is negative and very significant for young firms but is positive for older firms. The other
indicators do not have significant results. The positive result for older firms may indicate that
they have a capacity to earn even when some micro firms have a high leverage.
40
Table 13. Panel Regression on Yield of Gross Loan Portfolio
Like the results on growth trajectories, the expected results for the profitability ratios are
consistent with the life cycle model suggested by Reid (2003)
A summary of the results by objective is presented in Table 14.
Dependent Variable: YIELD ON GROSS LOAN PORTFOLIO (Old MFIs) Dependent Variable: YIELD ON GROSS LOAN PORTFOLIO (Young MFIs)
Method: Panel Least Squares Method: Panel Least Squares
Date: 10/11/11 Time: 11:52 Date: 10/11/11 Time: 11:52
Sample: 2003 2010 Sample: 2003 2010
Cross-sections included: 5 Cross-sections included: 41
Total panel (unbalanced) observations: 34 Total panel (unbalanced) observations: 190
White cross-section standard errors & covariance (d.f. corrected) White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Stat Prob. Variable Coefficient Std. Error t-Stat Prob.
C 0.043574 0.163813 0.27 0.79 C 0.105501 0.034819 3.03 0.00
Number of Active Borrowers 1.30E-07 8.08E-08 1.61 0.12 Number of Active Borrowers -8.36E-07 1.12E-07 -7.45 0.00
Ave Depos it Ba lance per Income -0.698877 0.424757 -1.65 0.12 Ave Depos it Ba lance per Income 0.111685 0.045637 2.45 0.02
Financial Revenues / Assets 0.96622 0.337479 2.86 0.01 Financial Revenues / Assets 0.896962 0.096421 9.30 0.00
Capital-Asset Ratio 0.239738 0.14669 1.63 0.12 Capital-Asset Ratio 0.245668 0.042122 5.83 0.00
Cost per Borrower -0.001448 0.000897 -1.61 0.12 Cost per Borrower -0.000239 0.00017 -1.40 0.16
Return on Equity 0.168285 0.098219 1.71 0.10 Return on Equity -0.006075 0.001648 -3.69 0.00
Debt-Equity Ratio 0.01714 0.010058 1.70 0.10 Debt-Equity Ratio -0.001197 0.000522 -2.30 0.02
Portfolio at Risk 30 days 1.092069 0.227356 4.80 0.00 Portfolio at Risk 30 days -0.158685 0.076725 -2.07 0.04
Number of Depositors per Staff -0.00011 0.000448 -0.25 0.81 Number of Depositors per Staff 0.000117 6.44E-05 1.82 0.07
Effects Specification : Cross-section fixed (dummy variables) Effects Specification : Cross-section fixed (dummy variables)
R-squared 0.862924 R-squared 0.967273
Adjusted R-squared 0.773824 Adjusted R-squared 0.955818
S.E. of regression 0.036796 S.E. of regression 0.035556
Log likelihood 73.05709 Log likelihood 393.3754
Durbin-Watson stat 1.935398 Durbin-Watson stat 1.650009
Mean dependent var 0.5035 Mean dependent var 0.416047
S.D. dependent var 0.077371 S.D. dependent var 0.169158
F-statistic 9.68494 F-statistic 84.44465
Prob(F-statistic) 0.000006 Prob(F-statistic) 0
41
Table 14. Summary of Results
6. 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 a lot of 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.
Young and old MFIs manage their assets and debt through a close monitoring of the
financial self-sufficiency indicators: capital-asset ratio and financial revenues over assets.
Operational self-sufficiency is achieved through a close monitoring of the portfolio at risk 30
days variable.
ObjectiveOld MFIs (Data available from 1996
to 2010)
Young MFIs (Data available from 2003
to 2010)
The return to equity and debt-equity ratios
(financial self-sufficiency inmanaging
debt) have good explanatory power for
total assets, total borrowings, total equity
and gross loan portfolio for old MFIs.
As expected, the return to equity and debt-
equity ratios are not significant when
explaining gross loan portfolio, total assets,
total borrowings and total equity for young
MFIs.
The expected behavior of firms on debt financing (via internally generated funds) and
equity financing (after re-investments have been made during the growth phase) are all
able to explain the behavior of old and young MFIs as regards profitability and yield on
loans in order to pay dividends to shareholders.
Objective 2.
Performance
Indicators:
Profitability
and Yield on
Loans
Over-all, the life-cycle model has good explanatory power on the behavior of MFIs
across their growth phase. The outreach and financial self-sufficiency indicators for
managing debt and assets have satisfactory explanatory power for total assets but with
varying results for operational self-sufficiency.
Only the financial self-sufficiency indicators for managing assets have satisfactory
explanatory power for gross loan portfolio, total borrowing and total equity.
Objective 1.
Use of a Life
Cycle Model
to Explain
Growth and
Consolidation
The financial self-sufficiency indicators for managing debt and assets have very good
explanatory power for young and old MFIs.
The operational self-sufficiency indicator, portfolio at risk 30 days, is the only consistent
indicator for both types of firms.
42
As predicted by the model, young MFIs resort to debt financing, i.e. internally generated
funds. Yield on loans is low and equity is not growing as fast as assets. Profitability is not a
priority during the growth-consolidation phase.
Once an MFI goes beyond the consolidation phase and enters the second growth stage,
equity financing is resorted to because the old MFI has sufficiently re-invested yields on loans to
creditors in their previous growth phase. Debt decreases while equity increases. In this phase,
returns to equity is positively related to total assets, total equity and total borrowings, returns on
assets and yields on the gross loan portfolio.
For older MFIs, the debt-equity ratio is negatively related to total assets, total equity and
total borrowings, as well on the profitability indicators of asset returns and yields on loans.
Commercial investors can therefore predict the future performance of MFIs through a
close monitoring of their reported financial indicators. However, as of 2010, only 46 MFIs have
been regularly submitting their financial figures from a total 87 listed MFIs in the MIX Portal.
Once the other 41 firms, are able to complete a larger data set, then the analysis of performance
can extend not only by age, but also in terms of organizational structure, that is, rural bank or
NGO with lending mechanisms of either group, individual or a combination. Micro firms and
microfinance institutions definitely seem to follow the behavior of profit-oriented small
businesses.
43
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Reid, Gavin C. (1996) “Financial Structure and the Growing Small Firm: Theoretical
Underpinning and Current Evidence,” Small Business Economics, Vol. 8, No. 1, Special
Issue on Financing and Small Firm Dynamics (Feb., 1996), pp. 1-7
(http://www.jstor.org/stable/40228754. Accessed: 31/07/2011 at 00:19).
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=un1JmcFa4QIC&oi=fnd&pg=PA186&ots=Z
yPAWEo4cC&sig=B6EH8S72S58T8fh8flO8rfX_F1w#v=onepage&q&f=false. Accessed:
09/08/2011 at 12:06)
Reid, Gavin C. (2003) “Trajectories of Small Business Financial Structure,” Small Business
Economics, Vol. 20, No. 4 (Jun., 2003), pp. 273-285 (http://www.jstor.org/stable/40229267.
Accessed: 31/07/2011 at 00:20)
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, Vol. 18, No. 4 (Jun., 2002), pp. 257-267 (http://www.jstor.org/stable/40229208.
Accessed: 31/07/2011 at 00:17)
Vickers, D. (1987) Money Capital in the Theory of the Firm. Cambridge: Cambridge University
Press.
45
Appendix 1: Philippines Microfinance Performance Standards (P. E. S. O.)
46
47
Appendix 2. Database
MFI
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1st Valley Bank 1 2003 13.00% 158 15.98% 53 5.26 305 4684848 4 21.87%
Rural Bank1 2004 16.00% 150 13.91% 62 6.19 271 7074579 4 21.25%
2005 29.00% 176 14.34% 70 5.97 175 10914232 4 25.40%
2006 38.00% 185 12.00% 76 7.33 179 17186558 4 22.78%
2007 48.00% 207 11.95% 72 7.37 193 30154412 4 19.62%
2008 18.00% 199 13.40% 81 6.46 331 32864744 4 22.17%
2009 26.00% 127 11.64% 83 7.59 232 43451642 4 19.87%
2010 133 13.14% 103 6.61 252 58204992 4 16.80%
ABS-CBN 2 2003
NGO1 2004 142 61.29% 0.63 0 4
2005 3.00% 130 57.38% 37 0.74 130 1214878 4 43.66%
2006 3.00% 128 57.51% 50 0.74 128 1718940 4 48.14%
2007 5.00% 81 42.56% 75 1.35 81 3120455 4 43.79%
2008 3.00% 86 51.77% 110 0.93 86 2385149 4 51.14%
2009 2.00% 63.11% 0.58 1387481 4 47.53%
2010
ASA 3 2003
NGO2 2004 1.00% 41 90.47% 0.11 62 16239 4
2005 2.00% 107 46.34% 37 1.16 116 302592 4 44.36%
2006 3.00% 144 28.27% 33 2.54 159 1198165 4 53.64%
2007 3.00% 179 21.12% 31 3.73 192 3643515 4 49.00%
2008 2.00% 177 23.62% 30 3.23 186 4131319 4 48.15%
2009 2.00% 209 19.30% 28 4.18 210 7098945 4 54.84%
2010 234 17.01% 28 4.88 234 12055330 4 53.55%
ASHI 4 2003
NGO3 2004 2.00% 115 52.08% 0.92 115 281598 4
2005 2.00% 93 41.31% 49 1.42 108 387958 4 32.36%
2006 3.00% 98 47.07% 56 1.12 116 497858 4 31.83%
2007 3.00% 95 35.61% 68 1.81 111 700615 4 28.99%
2008 3.00% 97 33.91% 77 1.95 112 1025481 4 27.53%
2009 4.00% 118 28.88% 65 2.46 117 1262711 4 24.80%
2010 135 26.94% 58 2.71 135 1361266 4 19.51%
ASKI 5 2003 96 20.43% 24 3.89 0 4 38.54%
NGO4 2004 102 27.49% 28 2.64 0 4 41.63%
2005 114 19.12% 36 4.23 0 4 39.19%
2006 3.00% 140 17.29% 38 4.78 115 1680182 4 35.63%
2007 3.00% 96 16.53% 51 5.05 124 2190185 4 34.13%
2008 3.00% 97 11.93% 63 7.38 136 2761281 4 35.35%
2009 9.00% 93 14.11% 77 6.09 41 2974994 4 32.56%
2010 3.00% 87 13.98% 84 6.16 101 3402799 4 30.12%
48
MFI
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Bangko S de Libon 6 2003 15.21% 5.57 4
Rural Bank2 2004 63 15.40% 5.49 150 4 34.09%
2005 4.00% 93 16.81% 40 4.95 174 1133794 4 38.96%
2006 5.00% 79 17.72% 49 4.64 164 1426355 4 36.01%
2007 6.00% 74 17.84% 53 4.61 164 2069675 4 28.31%
2008 5.00% 91 18.41% 51 4.43 175 2016260 4 29.33%
2009 8.00% 18.64% 44 4.37 2552543 4 30.89%
2010 7.00% 96 22.34% 51 3.48 162 2712712 4 29.30%
Bangko Kabayan 7 2003 20 14.11% 6.09 375 4
Rural Bank3 2004 35.00% 46 16.20% 253 5.17 267 15773286 4 12.87%
2005 31.00% 51 15.49% 172 5.46 256 18211977 4 13.47%
2006 39.00% 45 16.84% 166 4.94 194 21119973 4 12.86%
2007 37.00% 53 18.15% 250 4.51 233 29127757 4 13.04%
2008 30.00% 34 18.83% 258 4.31 199 27066649 4 13.12%
2009 36.00% 44 18.94% 267 4.28 194 29700683 4 11.87%
2010 20.04% 261 3.99 32464867 4 11.06%
Bangko Mabuhay 8 2003 48 10.64% 8.39 244 4
Rural Bank4 2004 57 10.73% 238 8.32 223 4 13.50%
2005 48.00% 49 11.67% 223 7.57 223 9275249 4 11.32%
2006 55.00% 51 11.31% 271 7.84 195 11069807 4 11.78%
2007 49.00% 52 12.21% 298 7.19 235 14761724 4 12.12%
2008 32.00% 54 12.79% 327 6.82 244 13816760 4 12.47%
2009 37.00% 57 12.91% 286 6.74 272 16612795 4 12.25%
2010
BCB 9 2003 141 16.13% 5.2 359 4
Rural Bank5 2004 147 16.80% 49 4.95 334 4 28.10%
2005 6.00% 159 15.87% 49 5.3 299 1380020 4 29.37%
2006 6.00% 130 19.63% 59 4.09 298 1758857 4 27.11%
2007 9.00% 152 19.24% 72 4.2 222 2239737 4 23.72%
2008 6.00% 46 14.99% 114 5.67 218 2297166 4 25.13%
2009 15.30% 5.54 2457187 4 23.61%
2010
Cantilan Bank 10 2003 84 14.62% 5.84 250 4 34.95%
Rural Bank6 2004 6.00% 115 12.00% 89 7.33 313 3630789 4 34.10%
2005 7.00% 114 14.19% 76 6.05 288 4133871 4 32.72%
2006 8.00% 119 15.77% 75 5.34 285 5872998 4 30.31%
2007 10.00% 107 16.56% 99 5.04 255 8768774 4 29.91%
2008 8.00% 101 17.44% 114 4.74 264 8487364 4 30.40%
2009 11.00% 67 15.58% 109 5.42 174 10703331 4 26.29%
2010 82 237 13869242 4
49
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Card Bank 11 2003 13.00% 155 16.02% 65 5.24 165 4472154 4 35.98%
Rural Bank7 2004 12.00% 128 23.10% 55 3.33 131 3589562 4 34.60%
2005 16.00% 117 19.64% 57 4.09 117 4956479 4 33.56%
2006 9.00% 136 23.96% 62 3.17 173 6169383 4 35.57%
2007 7.00% 164 18.67% 51 4.36 164 12201804 4 36.39%
2008 4.00% 167 15.57% 45 5.42 206 16817850 4 41.67%
2009 4.00% 152 12.43% 47 7.04 254 27645798 4 39.21%
2010 163 12.75% 54 6.84 302 35083134 4 34.77%
Card NGO 12 2003 2.00% 159 32.61% 2.07 171 1717279 4 27.96%
NGO5 2004 2.00% 146 45.62% 28 1.19 160 2238633 4 25.75%
2005 3.00% 142 44.29% 30 1.26 157 3523477 4 34.57%
2006 2.00% 146 34.94% 32 1.86 173 5797649 4 36.32%
2007 2.00% 185 25.84% 35 2.87 185 11928227 4 37.22%
2008 2.00% 157 23.38% 42 3.28 169 14741166 4 42.15%
2009 2.00% 177 24.79% 37 3.03 177 18980596 4 39.11%
2010 2.00% 23260224 4
CBMO 13 2003 110 20.94% 3.78 385 4
Rural Bank8 2004 88 22.66% 56 3.41 298 4 20.96%
2005 15.00% 64 22.79% 76 3.39 108 2677050 4 22.49%
2006 6.00% 151 24.91% 63 3.01 315 3322147 4 21.48%
2007 10.00% 157 25.20% 51 2.97 217 4607497 4 20.75%
2008 7.00% 141 20.62% 56 3.85 267 5376701 4 19.72%
2009 8.00% 137 20.84% 58 3.8 262 6291840 4 19.32%
2010
CEVI 14 2003
NGO6 2004 1.00% 186 53.80% 0.86 186 190857 4
2005 1.00% 157 60.25% 25 0.66 157 202813 4 32.17%
2006 1.00% 127 50.42% 32 0.98 136 237922 4 26.51%
2007 1.00% 141 47.60% 37 1.1 144 454410 4 30.85%
2008 114 34.40% 51 1.91 656456 4 33.24%
2009 1.00% 131 33.27% 45 2.01 139 716678 4 32.61%
2010 2.00% 135 21.40% 51 3.67 140 1170985 4 32.73%
CMEDFI 15 2003 3.00% 115 -20.96% 30 -5.77 115 128572 4 70.75%
NGO7 2004 3.00% 128 -12.86% 29 -8.78 106 169448 4 61.46%
2005 3.00% 110 2.41% 28 40.45 110 240306 4 65.93%
2006 4.00% 70 8.27% 43 11.09 70 320640 4 60.23%
2007 4.00% 71 13.40% 65 6.46 80 525732 4 61.89%
2008 3.00% 73 20.68% 79 3.83 88 457547 4 56.84%
2009 3.00% 73 28.08% 79 2.56 86 525541 4 48.48%
2010 2.00% 102 31.96% 69 2.13 128 699987 4 54.63%
50
MFI
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DSPI 16 2003 237 39.55% 1.53 0 3
Grameen NGO8 2004 208 38.67% 21 1.59 0 3 40.59%
2005 219 31.39% 18 2.19 0 3 33.85%
2006 230 25.74% 20 2.89 0 3 39.33%
2007 1.00% 183 20.30% 20 3.93 183 562155 3 34.59%
2008 1.00% 222 10.98% 19 8.1 222 690325 3 27.75%
2009 3.00% 100 10.56% 22 8.47 100 823995 3 30.60%
2010 284 13.44% 22 6.44 119 1099176 3 31.69%
ECLOF-RP 17 2003 357 75.88% 0.32 0 4
NGO9 2004 171 91.87% 25 0.09 0 0 4 20.04%
2005 1.00% 121 70.64% 46 0.42 121 37678 4 19.92%
2006 1.00% 117 67.53% 59 0.48 117 100075 4 26.61%
2007 3.00% 106 61.87% 65 0.62 91 201690 4 23.95%
2008 3.00% 81 58.02% 94 0.72 81 258390 4 28.42%
2009 4.00% 77 60.62% 150 0.65 77 363123 4 26.76%
2010
FAIR Bank 18 2003 98 15.97% 5.26 251 4
Rural Bank9 2004 123 14.53% 27 5.88 280 4 37.20%
2005 112 13.24% 25 6.55 328 4 36.86%
2006 120 11.49% 28 7.7 359 4 37.28%
2007 4.00% 109 12.47% 57 7.02 225 4814042 4 36.81%
2008 6.00% 84 13.21% 83 6.57 103 4537068 4 36.54%
2009 6.00% 61 16.39% 97 5.1 90 3526756 4 28.86%
2010
FICO 19 2003 78 19.91% 4.02 127 4
Rural Bank (Coop)10 2004 17.00% 69 22.34% 102 3.48 141 3240315 4 26.43%
2005 21.00% 61 21.87% 130 3.57 127 4515235 4 24.88%
2006 36.00% 71 20.04% 153 3.99 98 6883351 4 23.17%
2007 40.00% 78 18.46% 166 4.42 105 11334229 4 22.41%
2008 36.00% 93 15.42% 138 5.48 109 15905985 4 20.22%
2009 46.00% 86 13.93% 126 6.18 92 20545827 4 19.39%
2010 86 95 28343214 4
First Macro Bank 20 2003 70 9.99% 9.01 550 3
Rural Bank11 2004 49 10.38% 141 8.64 412 3 15.99%
2005 53 9.56% 143 9.46 360 3 15.57%
2006 14.00% 46 10.42% 152 8.6 329 8573078 3 14.53%
2007 16.00% 9.60% 9.42 11445521 3 14.24%
2008 13.00% 50 9.59% 9.43 305 10392415 3 15.58%
2009 15.00% 49 10.44% 207 8.58 280 10785361 3 15.54%
2010 17.00% 50 240 12693514 3
51
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Green Bank 21 2003 136 14.46% 5.91 340 4
Rural Bank12 2004 137 12.97% 71 6.71 365 4 27.83%
2005 7.00% 97 12.42% 66 7.05 286 18248077 4 24.88%
2006 10.00% 116 15.25% 69 5.56 259 21654758 4 23.30%
2007 10.00% 106 14.97% 98 5.68 279 30416578 4 22.54%
2008 6.00% 98 13.56% 127 6.37 325 27417704 4 25.17%
2009 8.00% 1,040 10.72% 130 8.33 3,169 29274087 4 23.53%
2010 70 230 31740116 4
HSPFI 22 2003
NGO10 2004 261 28.82% 2.47 0 3
2005 278 30.20% 11 2.31 0 3 19.39%
2006 223 29.63% 2.38 0 3
2007 1.00% 219 31.87% 17 2.14 222 286159 3 21.40%
2008 1.00% 201 29.80% 26 2.36 201 313287 3 26.17%
2009 3
2010 205 19.15% 4.22 205 404840 3
Kasagana-Ka 23 2003
NGO11 2004 2.00% 230 20.52% 3.87 230 137627 4
2005 1.00% 171 22.48% 20 3.45 269 192558 4 57.73%
2006 1.00% 126 26.96% 29 2.71 254 273806 4 57.22%
2007 3.00% 142 20.43% 48 3.89 142 444621 4 55.13%
2008 2.00% 132 22.43% 49 3.46 132 547047 4 61.10%
2009 2.00% 135 30.46% 48 2.28 135 662802 4 59.59%
2010 126 34.13% 51 1.93 840255 4 56.53%
Kazama Grameen 24 2003
Grameen NGO12 2004 140 35.21% 1.84 4
2005 3.00% 152 45.21% 28 1.21 152 676191 4 40.68%
2006 3.00% 134 45.14% 36 1.22 134 959057 4 39.89%
2007 3.00% 130 42.01% 46 1.38 130 1202559 4 34.11%
2008 3.00% 115 34.97% 51 1.86 115 1228545 4 37.05%
2009 3.00% 144 30.21% 50 2.31 131 1130594 4 34.17%
2010 5.00% 206 37.96% 52 1.63 115 1542862 4 42.41%
KBank 25 2003
Bank13 2004
2005 60.01% 0.67 3
2006 70.00% 114 45.84% 53 1.18 15 2024852 3 34.70%
2007 14.00% 109 42.63% 67 1.35 55 2867897 3 32.00%
2008 4.00% 73 37.26% 73 1.68 99 2790766 3 34.75%
2009 67 31.56% 106 2.17 5788582 3 40.09%
2010
52
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KCCDFI 26 2003
NGO13 2004
2005 111 44.08% 1.27 4
2006 3.00% 95 11.69% 40 7.55 95 422726 4 57.12%
2007 2.00% 131 9.60% 39 9.41 134 711827 4 49.37%
2008 2.00% 130 18.68% 48 4.35 130 805018 4 67.54%
2009 2.00% 110 17.13% 56 4.84 112 764736 4 52.33%
2010 2.00% 122 15.66% 63 5.39 129 1061257 4 44.34%
KMBI 27 2003 2.00% 164 41.35% 34 1.42 164 644675 4 37.38%
NGO14 2004 2.00% 169 27.58% 30 2.63 169 1738138 4 49.00%
2005 3.00% 174 42.52% 28 1.35 189 2826205 4 60.64%
2006 3.00% 151 53.72% 31 0.86 163 3305318 4 49.37%
2007 3.00% 166 49.93% 40 1 178 5139118 4 46.46%
2008 2.00% 156 54.11% 46 0.85 170 4809895 4 49.56%
2009 2.00% 171 49.96% 43 1 181 6321167 4 52.68%
2010 183 45.18% 44 1.21 14 8858497 4 62.08%
Life Bank Found 28 2003 108 -3.78% -27.43 132 3
NGO15 2004 198 15.85% 22 5.31 207 3 56.75%
2005 160 26.59% 23 2.76 169 3 62.27%
2006 2.00% 136 24.66% 36 3.06 147 1992409 3 70.71%
2007 2.00% 167 25.48% 32 2.92 174 4540080 3 55.37%
2008 2.00% 181 33.59% 30 1.98 192 7810739 3 60.26%
2009 2.00% 182 188 8655485 3
2010
Mallig Plains RB 29 2003
Rural Bank14 2004 143 14.48% 5.9 196 4
2005 9.00% 131 14.25% 41 6.02 174 3397185 4 21.66%
2006 8.00% 134 14.59% 44 5.85 162 3403408 4 20.26%
2007 10.00% 132 15.96% 49 5.26 155 4852589 4 20.70%
2008 9.00% 124 16.70% 56 4.99 133 4804217 4 21.45%
2009 12.00% 111 18.07% 60 4.53 121 5131819 4 22.62%
2010 12.00% 98 19.06% 71 4.25 109 6121712 4 20.40%
MILAMDEC 30 2003
NGO16 2004 2.00% 111 39.09% 27 1.56 120 286387 4
2005 2.00% 116 26.89% 26 2.72 125 318166 4 36.97%
2006 2.00% 177 24.93% 23 3.01 166 428540 4 35.57%
2007 16.64% 5.01 689227 4
2008 1.00% 116 16.10% 5.21 158 710903 4 32.87%
2009 1.00% 869817 4
2010
53
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RBVictorias 31 2003 26.00% 41 19.91% 4.02 105 1399452 4 30.45%
Rural Bank15 2004 24.00% 52 23.88% 127 3.19 107 1465809 4 28.32%
2005 22.00% 62 20.21% 107 3.95 107 1761957 4 23.60%
2006 23.00% 65 22.09% 104 3.53 90 1820488 4 24.96%
2007 33.00% 68 21.67% 109 3.62 67 2135266 4 25.01%
2008 14.00% 66 18.90% 131 4.29 108 1681693 4 27.08%
2009 20.00% 42 22.47% 161 3.45 97 2031470 4 25.35%
2010
NWTF 32 2003 3.00% 115 22.75% 30 3.4 115 1564346 4 32.68%
Grameen NGO17 2004 2.00% 126 19.08% 30 4.24 126 1381568 4 30.39%
2005 2.00% 131 15.05% 33 5.64 131 1845325 4 31.32%
2006 2.00% 123 15.71% 37 5.37 127 2069482 4 30.60%
2007 2.00% 144 15.06% 37 5.64 151 3082715 4 26.91%
2008 2.00% 147 16.25% 40 5.15 147 2735030 4 30.96%
2009 2.00% 155 27.63% 38 2.62 166 2974798 4 28.67%
2010 153 28.09% 41 2.56 6203178 4 26.13%
OK Bank 33 2003 3.00% 163 43.52% 43 1.3 140 849851 4 41.03%
Bank16 2004 3.00% 144 45.12% 35 1.22 114 713683 4 34.28%
2005 6.00% 132 42.31% 42 1.36 98 1517841 4 25.02%
2006 6.00% 162 43.57% 43 1.3 94 1167939 4 21.09%
2007 9.00% 93 38.73% 55 1.58 92 2229188 4 22.80%
2008 7.00% 75 37.86% 86 1.64 77 1843692 4 23.74%
2009 67 31.56% 137 5788582 4 46.86%
2010 60 26.75% 115 2.74 35 5692179 4 33.98%
PALFSI 34 2003
NGO18 2004 2.00% 154 23.29% 3.29 154 318714 4
2005 3.00% 147 24.88% 34 3.02 147 413081 4 32.02%
2006 2.00% 137 16.68% 36 4.99 137 546107 4 26.39%
2007 2.00% 158 -5.45% 36 -19.33 158 721052 4 30.54%
2008 2.00% 147 -7.70% 36 -14 147 916014 4 24.63%
2009 0.00% 236 -5.40% 30 -19.53 236 0 4 25.68%
PBC 35 2003 18.39% 4.44 4
Rural Bank17 2004 175 18.26% 20 4.48 91 4 25.83%
2005 149 18.33% 26 4.46 105 4 27.41%
2006 14.85% 5.74 4 24.29%
2007 13.47% 6.42 4672512 4 23.31%
2008 8.00% 12.34% 7.1 5199932 4 25.71%
2009 5.00% 150 13.10% 45 6.63 194 6525626 4 24.55%
2010 5.00% 111 14.96% 47 5.68 194 8674082 4 24.06%
54
MFI
Ye
ar
Ave
rage
de
po
sit
bal
ance
pe
r d
ep
osi
tor
/ G
NI p
er
cap
ita
Bo
rro
we
rs p
er
staf
f
me
mb
er
Cap
ital
/ass
et
rati
o
Co
st p
er
bo
rro
we
r
De
bt
to e
qu
ity
rati
o
De
po
sito
rs p
er
staf
f
me
mb
er
De
po
sits
Dia
mo
nd
s
Fin
anci
al r
eve
nu
e/
asse
ts
RBDigos 36 2003 109 15.08% 5.63 429 4
Rural Bank18 2004 88 12.80% 96 6.81 313 4 22.42%
2005 15.00% 93 12.29% 103 7.14 287 3266088 4 21.07%
2006 14.00% 101 11.61% 127 7.61 343 4584791 4 21.61%
2007 24.00% 100 12.65% 136 6.9 233 6335500 4 19.40%
2008 13.00% 125 12.62% 149 6.92 364 6130449 4 20.58%
2009 16.00% 86 14.75% 159 5.78 260 6637891 4 20.10%
2010
RBMabitac 37 2003
Rural Bank19 2004 48 11.14% 7.98 4
2005 17.00% 69 12.36% 102 7.09 159 2746515 4 18.68%
2006 22.00% 66 12.53% 95 6.98 130 3427073 4 18.13%
2007 27.00% 65 11.51% 106 7.69 128 4879072 4 15.74%
2008 15.00% 91 12.07% 92 7.29 127 4938977 4 18.82%
2009 17.00% 113 13.18% 72 6.59 166 5880386 4 20.31%
2010 15.00% 6292141 4
RBMontevista 38 2003 262 11.59% 7.63 0 3
Rural Bank20 2004 237 11.34% 54 7.82 1 3 20.96%
2005 217 8.45% 58 10.83 1 3 23.47%
2006 260 8.40% 71 10.91 1 3 23.67%
2007 4844.00% 8.73% 10.45 5037459 3 22.03%
2008 4.00% 139 9.76% 9.24 336 4753476 3 24.67%
2009 5.00% 107 12.47% 54 7.02 313 5170205 3 23.09%
2010
RBOroquieta 39 2003
Rural Bank21 2004 54 15.82% 5.32 4
2005 34.00% 94 10.95% 107 8.13 216 2869674 4 15.22%
2006 18.00% 97 10.33% 111 8.68 491 4387067 4 15.36%
2007 39.00% 113 10.24% 136 8.76 301 6262897 4 12.63%
2008 28.00% 125 10.82% 133 8.24 279 5491888 4 13.75%
2009 38.00% 112 12.53% 109 6.98 202 5595478 4 14.00%
2010
RBSolano 40 2003 227 16.54% 5.05 261 4
Rural Bank22 2004 206 20.33% 45 3.92 244 4 21.23%
2005 33.00% 195 23.97% 51 3.17 255 2981197 4 18.43%
2006 45.00% 183 28.32% 63 2.53 191 3285766 4 17.70%
2007 47.00% 147 27.68% 79 2.61 165 4248929 4 12.88%
2008 37.00% 122 31.50% 89 2.17 112 3331289 4 15.55%
2009 139 33.15% 79 2.02 3440525 4 13.73%
2010
55
Source: MIX Market Information Portal for the Philippines
(http://www.mixmarket.org/mfi/country/Philippines )
MFI
Ye
ar
Ave
rage
de
po
sit
bal
ance
pe
r d
ep
osi
tor
/ G
NI p
er
cap
ita
Bo
rro
we
rs p
er
staf
f
me
mb
er
Cap
ital
/ass
et
rati
o
Co
st p
er
bo
rro
we
r
De
bt
to e
qu
ity
rati
o
De
po
sito
rs p
er
staf
f
me
mb
er
De
po
sits
Dia
mo
nd
s
Fin
anci
al r
eve
nu
e/
asse
ts
RBTalisayan 41 2003 92 13.72% 6.29 4
Rural Bank23 2004 6.00% 88 14.60% 47 5.85 184 1557460 4 28.47%
2005 7.00% 98 13.29% 51 6.52 186 2048947 4 28.72%
2006 8.00% 109 13.71% 52 6.29 187 2494935 4 28.12%
2007 10.00% 112 13.78% 60 6.26 178 3687821 4 28.40%
2008 9.00% 126 14.57% 66 5.86 153 3898635 4 30.89%
2009 12.00% 140 17.64% 55 4.67 144 4022623 4 26.12%
2010
RSPI 42 2003
NGO19 2004 98 16.54% 5.05 0 3
2005 141 18.32% 35 4.46 0 3 51.92%
2006 179 18.23% 32 4.49 0 303512 3 53.25%
2007 2.00% 146 21.51% 36 3.65 146 478685 3 48.04%
2008 2.00% 161 25.96% 40 2.85 161 696489 3 51.30%
2009 2.00% 130 34.81% 43 1.87 151 828143 3 54.72%
2010 140 155 1194839 3
Serviamus 43 2003
NGO20 2004 154 30.78% 2.25 4
2005 108 42.63% 35 1.35 22 4 40.20%
2006 4.00% 114 53.17% 36 0.88 115 455268 4 30.55%
2007 3.00% 136 50.56% 45 0.98 138 427429 4 30.29%
2008 3.00% 124 48.56% 40 1.06 124 471223 4 28.70%
2009 3.00% 149 48.93% 34 1.04 149 512122 4 27.11%
2010 3.00% 153 47.30% 33 1.11 153 665160 4 29.65%
TSKI 44 2003 3.00% 126 11.18% 23 7.94 126 2713559 3 33.10%
NGO21 2004 3.00% 132 7.68% 23 12.02 145 4748289 3 29.95%
2005 2.00% 144 11.26% 24 7.88 163 5230200 3 35.19%
2006 3.00% 113 11.30% 38 7.85 133 7882478 3 35.91%
2007 3.00% 92 14.36% 47 5.96 114 10437713 3 33.30%
2008 2.00% 80 9.69% 54 9.32 108 7614672 3 33.62%
2009 2.00% 78 16.23% 50 5.16 125 9556128 3 33.13%
2010 68 13.02% 61 6.68 6 11580409 3 34.66%
TSPI 45 2003 3.00% 125 39.97% 33 1.5 154 3140876 4 39.57%
NGO22 2004 3.00% 154 37.36% 34 1.68 183 4165182 4 41.72%
2005 4.00% 122 35.40% 42 1.83 154 6336226 4 44.12%
2006 4.00% 132 37.50% 49 1.67 155 8411381 4 39.38%
2007 4.00% 112 47.46% 65 1.11 139 9919521 4 46.09%
2008 3.00% 132 37.37% 55 1.68 132 10573869 4 40.95%
2009 3.00% 130 29.53% 49 2.39 130 13800938 4 41.75%
2010 114 34.73% 59 1.88 114 17502088 4 45.46%
ValiantRB 46 2003 11.32% 7.83 4
Rural Bank24 2004 88 9.50% 9.53 4 8.57%
2005 120.00% 55 8.44% 76 10.85 71 7344224 4 8.37%
2006 118.00% 50 8.15% 110 11.27 78 13132801 4 8.77%
2007 146.00% 52 8.15% 123 11.28 66 20571628 4 8.51%
2008 83.00% 70 10.88% 109 8.19 76 21210769 4 10.61%
2009 93.00% 100 10.81% 70 8.25 101 27393119 4 10.58%
2010
56
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
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Tota
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win
gs
Tota
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ross
po
rtfo
lio
(no
min
al)
1st Valley Bank 1 2003 7188694 17973 13.44% 2.42% 14.03% 8971220 2141103 1433285 26.52%
Rural Bank1 2004 10906346 21350 5.03% 2.96% 20.08% 13077794 3777788 1818749 25.08%
2005 15729638 30239 4.51% 4.08% 28.83% 18906315 4544748 2711903 27.55%
2006 22440332 34225 5.07% 2.64% 20.48% 30051929 8066998 3605835 28.50%
2007 36018303 42064 4.61% 2.58% 21.53% 51752800 13139345 6184382 26.79%
2008 45548689 57609 0.26% 2.74% 21.58% 55913198 12732115 7492439 28.63%
2009 55827098 52939 10.48% 2.66% 21.55% 81916455 22751385 9532162 26.20%
2010 66351843 63676 11.39% 2.37% 19.00% 98938243 24140133 13000583 24.56%
ABS-CBN 2 2003
NGO1 2004 2445826 38422 3544137 130671 2172274
2005 2560717 37434 12.54% 1.78% 3.01% 4346227 419397 2493747 66.20%
2006 4092116 39756 9.86% 8.19% 14.25% 6177043 632782 3552675 66.92%
2007 7666327 35767 3.47% 5.34% 11.14% 10866471 2553964 4624297 59.92%
2008 5196341 39991 18.14% -10.93% -23.74% 6547273 518990 3389777 62.18%
2009 3001218 -5.06% -8.97% 4561358 0 2878790 55.90%
2010
ASA 3 2003
NGO2 2004 71049 980 0.00% 175580 158847
2005 519967 9954 0.81% -9.05% -16.50% 734184 87398 340207 67.34%
2006 2068824 28848 0.70% 0.03% 0.08% 2266451 311988 640687 61.70%
2007 6703310 65505 1.09% 10.80% 47.41% 7546177 1605078 1593869 53.08%
2008 7943399 97409 0.14% 4.72% 21.11% 7249033 455268 1712181 47.26%
2009 13861339 179626 0.01% 6.83% 32.74% 12675654 1601813 2446104 48.71%
2010 24452395 299433 0.03% 6.17% 34.59% 21781649 2870196 3705639 46.13%
ASHI 4 2003
NGO3 2004 937985 12065 3.72% 1512620 418733 787793
2005 1490841 11466 2.41% -4.50% -9.89% 2343606 898136 968109 49.91%
2006 1837340 12194 2.31% 2.66% 5.99% 2776020 870870 1306693 48.68%
2007 3024357 13438 1.59% 0.98% 2.46% 5104971 2343949 1817762 45.19%
2008 3265812 14932 2.40% 1.17% 3.35% 5270753 2190375 1787104 43.85%
2009 3816893 19129 2.26% 0.53% 1.70% 6740393 3249349 1946547 40.84%
2010 5213312 22196 1.90% -1.45% -5.22% 8826259 4769212 2377569 33.97%
ASKI 5 2003 1275487 22573 13.81% 6.21% 32.35% 2313627 472763
NGO4 2004 1427716 25352 4.25% 1.74% 7.20% 2567102 705719
2005 2502414 35453 3.43% 1.60% 7.26% 4734332 2170238 905008
2006 4232591 47077 5.81% 0.92% 5.11% 8219546 4507427 1421015 60.46%
2007 6108855 38942 6.12% 0.29% 1.70% 9699603 4727863 1603418 54.75%
2008 8622392 41303 6.79% 2.09% 15.07% 13684113 7625638 1632647 53.34%
2009 9589716 41451 9.69% 1.65% 12.62% 15623705 9278068 2204327 50.18%
2010 13611877 48094 6.02% -0.05% -0.33% 21038757 12731255 2940195 38.30%
57
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
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rro
win
gs
Tota
l eq
uit
y
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ld o
n g
ross
po
rtfo
lio
(no
min
al)
Bangko S de Libon 6 2003 562292 908176 138175
Rural Bank2 2004 852201 5376 5.09% 33.21% 1266744 83143 195111
2005 1218386 10669 11.82% 5.18% 32.02% 1595175 72056 268196 52.21%
2006 1356155 9333 23.30% 2.45% 14.15% 2112935 225568 374434 50.36%
2007 1817894 9455 28.69% 3.42% 19.25% 2966625 239683 529214 42.67%
2008 1886339 10709 8.29% 2.92% 16.13% 2931388 243752 539637 44.20%
2009 2864502 11066 16.54% 4.41% 23.78% 3722501 294235 693838 41.21%
2010 3460062 10884 16.28% 5.48% 26.56% 4348783 474638 971446 36.30%
Bangko Kabayan 7 2003 6136011 2493 18745449 177824 2644277
Rural Bank3 2004 6670379 6655 5.35% 2.98% 19.63% 19231937 54030 3115269 26.29%
2005 9785806 9288 7.51% 2.57% 16.26% 23558085 472460 3649269 26.69%
2006 11320851 9234 6.57% 3.12% 19.27% 27290919 365314 4595589 22.57%
2007 14641207 11149 6.80% 2.74% 15.55% 37805049 1211240 6862875 27.52%
2008 17075123 8135 0.06% 2.87% 15.56% 35265016 1053075 6641200 24.84%
2009 19049943 11029 6.35% 3.00% 15.87% 40103804 2152661 7596530 20.04%
2010 23046811 11145 3.22% 2.31% 11.85% 44557600 2737395 8929109 18.83%
Bangko Mabuhay 8 2003 2523744 2365 8287877 882200
Rural Bank4 2004 2866330 3491 1.97% 18.45% 9546872 0 1024533
2005 3068685 3363 9.51% 1.76% 15.63% 11246051 0 1312450 27.46%
2006 4058789 3912 9.83% 2.19% 19.04% 12845357 105627 1453283 29.84%
2007 6263640 4193 13.07% 2.25% 19.04% 17092172 23547 2087210 27.00%
2008 6148969 5042 0.64% 1.99% 15.91% 16534207 378314 2115123 27.06%
2009 7734643 5526 4.98% 1.89% 14.73% 19570667 199134 2527076 25.62%
2010
BCB 9 2003 1969336 7625 2775627 447793
Rural Bank5 2004 2289177 10467 5.06% 30.67% 3262360 1286308 547968
2005 2875952 10462 15.97% 4.19% 25.74% 4134564 1855077 656247 25.10%
2006 3081343 9513 15.17% 4.32% 24.17% 4741776 1826363 930837 32.82%
2007 4387351 10935 12.83% 2.72% 14.03% 6226243 2463997 1197860 29.99%
2008 5074220 4065 0.88% 2.96% 17.43% 6862722 3217773 1028671 34.35%
2009 5509136 2.17% 14.30% 8123682 3738157 1242596 26.39%
2010
Cantilan Bank 10 2003 4094752 13733 6.20% 3.06% 21.62% 5123221 776457 749192
Rural Bank6 2004 5160630 17627 13.60% 1.42% 10.76% 5887265 1260199 706616 38.49%
2005 5848665 19813 14.29% 1.36% 10.35% 6906572 1339612 979770 36.16%
2006 6690246 21234 6.73% 1.56% 10.34% 8350308 564113 1316514 34.43%
2007 10108187 22344 7.21% 2.47% 15.19% 11914664 718937 1972519 32.98%
2008 9992766 21159 0.62% 2.37% 13.93% 12845756 1521399 2239665 34.35%
2009 13663362 22161 8.11% 2.90% 17.72% 17761806 3205351 2766928 23.49%
2010 17091535 20541 1.75%
58
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
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ld o
n g
ross
po
rtfo
lio
(no
min
al)
Card Bank 11 2003 6132985 31090 10.95% 1.48% 10.33% 7641333 1655389 1224335 43.70%
Rural Bank7 2004 4876471 26034 8.94% 1.88% 9.87% 5807535 482899 1341635 39.70%
2005 5096452 24955 12.10% 1.30% 6.15% 7451122 946288 1463404 41.60%
2006 6930075 40634 6.78% 4.88% 22.14% 9326361 752433 2235054 43.03%
2007 13230863 117195 3.20% 1.90% 9.27% 17429807 1577552 3254200 46.91%
2008 20138533 205097 1.87% 2.11% 12.57% 26159361 3991056 4073818 53.04%
2009 30996976 228460 1.50% 4.04% 29.66% 43910981 8731106 5459619 52.06%
2010 44713331 267282 1.84% 3.57% 28.30% 63354055 17217360 8079129 47.50%
Card NGO 12 2003 6087612 74182 5.47% 1.28% 3.64% 11557463 4950049 3768934 50.02%
NGO5 2004 6826182 73065 5.25% 3.65% 9.43% 10217842 2544473 4661570 40.54%
2005 8596627 98194 4.24% 8.11% 18.06% 12757173 3019683 5649853 44.31%
2006 16105744 159673 1.99% 9.56% 24.90% 21593374 7464986 7544020 45.53%
2007 33840694 320299 0.49% 6.98% 24.16% 42355776 17915641 10944678 44.95%
2008 36624936 364483 1.07% 6.63% 27.06% 49680700 20561270 11617079 53.90%
2009 46207824 497441 1.02% 6.40% 26.46% 63781690 25221482 15813905 52.59%
2010 66808378 684428
CBMO 13 2003 3287681 9817 5259815 1101410
Rural Bank8 2004 3673960 11026 5.21% 23.88% 5668281 1999765 1284177
2005 4708080 8224 10.89% 5.79% 25.49% 6939941 2237640 1581904 30.19%
2006 6057765 19946 9.02% 5.71% 23.84% 8622325 2820474 2147876 28.42%
2007 7691180 20846 10.72% 6.38% 25.46% 10703102 2827762 2696978 27.23%
2008 8952335 22783 0.61% 6.17% 27.11% 12302992 3920492 2536484 25.53%
2009 10501265 24029 9.71% 5.31% 25.60% 13589082 3037939 2832052 23.72%
2010
CEVI 14 2003
NGO6 2004 1012090 15245 5.08% 1607608 550379 864841
2005 1181779 16989 13.51% 3.97% 6.94% 1840395 523294 1108790 49.66%
2006 1142264 16376 15.78% -3.38% -6.16% 2231263 761208 1125003 44.88%
2007 1695503 18661 4.55% 1.06% 2.17% 2757829 826007 1312791 49.60%
2008 1948487 20899 3.90% -4.20% -10.40% 3352377 1479585 1153362 51.33%
2009 2500816 25321 4.65% 0.52% 1.55% 3702034 1570139 1231842 49.31%
2010 4236183 32779 3.52% -4.14% -15.81% 5540999 2715286 1185917 43.93%
CMEDFI 15 2003 248038 3781 13.52% 7.59% -29.57% 244994 150035 -51343 69.95%
NGO7 2004 322060 5628 9.25% 3.40% -20.80% 321525 157857 -41334 61.07%
2005 448549 6378 7.87% 13.58% -347.84% 455548 143293 10991 66.49%
2006 638247 6088 9.47% 7.74% 128.86% 722124 246875 59709 65.26%
2007 1069700 6931 6.91% 8.54% 74.29% 1219021 336880 163351 70.34%
2008 1139002 7219 7.01% 10.54% 61.53% 1283543 320796 265486 64.04%
2009 1060808 7895 7.35% 5.33% 21.62% 1499295 0 421053 61.32%
2010 1825189 11715 5.94% 11.80% 38.90% 2073352 378733 662626 60.94%
59
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
DSPI 16 2003 739305 13487 2.70% 1074078 424754
Grameen NGO8 2004 965993 18544 4.13% 7.83% 20.04% 1332571 515300
2005 1758246 31686 4.56% 6.45% 19.02% 2550760 800615
2006 3114445 56626 7.58% 3.11% 11.10% 3833113 2113784 986496
2007 3229053 53799 40.10% -3.05% -13.29% 4145747 2593803 841396 42.49%
2008 2910026 59057 53.04% -9.46% -59.13% 3557284 2366975 390719 34.81%
2009 2761618 18708 48.79% 1.46% 13.56% 3574200 2292580 377350 32.38%
2010 3481469 55691 3.78% 31.15% 4275402 2424166 574705 37.11%
ECLOF-RP 17 2003 608282 7860 23.50% 959350 44851 727958
NGO9 2004 578690 4611 16.45% -8.98% -10.73% 930984 44508 855249 29.02%
2005 1188999 5560 19.04% -4.33% -5.56% 1802557 423502 1273301 29.51%
2006 1548832 5865 25.72% -0.48% -0.70% 2127279 512820 1436509 35.93%
2007 2015365 5418 15.98% -6.48% -10.08% 2833649 789802 1753223 32.39%
2008 1863056 4451 5.91% 1.46% 2.43% 2686692 716235 1558910 30.46%
2009 2158201 4996 10.19% 1.61% 2.71% 3363937 796213 2039240 27.94%
2010
FAIR Bank 18 2003 965457 4232 5.13% 1135271 181295
Rural Bank9 2004 1676166 9972 4.05% 14.98% 99.53% 2027661 294626
2005 2759177 16129 4.65% 16.11% 117.64% 3703748 490273
2006 5613176 28653 2.30% 14.29% 118.16% 7052038 3216528 810432
2007 10034892 35149 1.42% 6.84% 56.46% 12878695 5813965 1605684 46.01%
2008 10894532 33093 4.58% 4.12% 32.06% 14573572 7438148 1924489 45.68%
2009 9184053 21775 17.79% 0.63% 4.28% 13327934 6397542 2185064 35.95%
2010
FICO 19 2003 3201421 8379 5526504 1338673 1100171
Rural Bank (Coop)10 2004 3884906 7825 7.17% 4.78% 22.55% 6333311 1565551 1415013 37.89%
2005 5218259 8218 5.00% 5.17% 23.44% 8743305 2139986 1911756 34.78%
2006 8427408 10195 5.77% 4.78% 23.03% 12947684 3123167 2594664 31.23%
2007 12361490 13253 6.96% 5.20% 27.22% 19477702 3886190 3596175 29.62%
2008 13076832 19804 0.10% 4.32% 25.75% 23814054 3463973 3673095 30.18%
2009 23552982 24085 3.79% 3.66% 25.19% 34574902 7968684 4816548 26.09%
2010 26967335 26059 6.49%
First Macro Bank 20 2003 5752577 6262 0.00% 8021154 801104
Rural Bank11 2004 5988199 5402 1.51% 14.79% 8471388 879165
2005 6651566 6879 0.00% 1.91% 19.16% 9568247 1146507 914814
2006 7032483 6120 16.01% 1.03% 10.29% 10807943 630751 1125870 20.77%
2007 9844506 1.00% 10.03% 14636503 892215 1404511 20.57%
2008 9439166 7086 0.11% 0.65% 6.79% 13385333 1024498 1283068 21.65%
2009 9882480 7268 11.77% 1.40% 13.94% 14038190 1008462 1465081 20.32%
2010 11586584 7617 5.41%
60
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
Green Bank 21 2003 15725666 54700 21278707 3077580
Rural Bank12 2004 18319399 60095 1.93% 14.14% 24989470 4780066 3240692
2005 18582884 67193 14.12% 1.20% 9.48% 29032683 4241535 3605171 34.12%
2006 19550081 69386 9.38% 1.89% 13.54% 32778089 4035734 4999213 35.10%
2007 31430895 72742 9.45% 1.12% 7.44% 51478235 10892925 7703737 36.83%
2008 33905154 76161 1.61% 0.96% 6.76% 53440111 16925127 7246764 39.75%
2009 37150928 66561 9.39% -0.01% -0.08% 61864115 20804459 6633236 35.66%
2010 35918780 53448
HSPFI 22 2003
NGO10 2004 420291 13333 781993 225345
2005 423817 13907 -4.92% -16.66% 786235 237465
2006 747974 11798 1247916 609816 369705
2007 1001099 12914 2.89% 1.10% 3.55% 1602846 770479 510862 30.99%
2008 1178826 14271 2.83% 0.66% 2.14% 2031318 1018334 605345 41.08%
2009
2010 1710812 18002 2.91% 2857468 1414504 547187
Kasagana-Ka 23 2003
NGO11 2004 325463 6209 4.50% 357200 136135 73291
2005 464585 8553 17.42% 10.45% 48.10% 581757 240962 130768 68.44%
2006 589159 8544 7.04% 8.43% 33.73% 742321 248488 200132 71.16%
2007 1060391 11099 4.24% 2.05% 8.97% 1275508 472049 260590 65.60%
2008 1283848 15083 2.32% 9.93% 46.09% 1607042 556615 360456 72.25%
2009 1489166 15537 3.67% 9.21% 34.63% 1724242 456659 525270 64.86%
2010 2013976 17800 1.51% 10.02% 30.77% 2334699 540739 796826 65.16%
Kazama Grameen 24 2003
Grameen NGO12 2004 1308139 15709 1702077 145070 599372
2005 1611093 19733 4.65% 12.96% 31.88% 2038278 184739 921485 48.60%
2006 2291191 21417 5.63% 8.10% 17.94% 2920889 265748 1318579 47.06%
2007 2769615 21761 6.38% 3.41% 7.87% 3791089 270139 1592494 40.84%
2008 2524365 21757 6.58% 0.12% 0.32% 3367467 253818 1177566 47.40%
2009 2754467 26082 0.72% 0.07% 0.21% 4047624 476711 1222946 45.67%
2010 3392526 27811 3.47% 0.67% 1.98% 3602366 392682 1367322 51.50%
KBank 25 2003
Bank13 2004
2005 1429625 21926 1.17% 2318242 0 1391201
2006 1692649 16619 3.38% 2.84% 5.59% 4437022 0 2033982 74.51%
2007 3643851 24580 6.79% 2.16% 4.92% 6331869 96899 2699054 62.42%
2008 4420061 25967 10.10% 1.85% 4.64% 6506389 620939 2424315 53.15%
2009 5410927 33815 15.17% -5.29% -15.68% 10666320 662787 3366182 64.82%
2010
61
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
KCCDFI 26 2003
NGO13 2004
2005 942979 10964 1044006 196231 460210
2006 1101598 12269 26.56% 0.39% 1.41% 1093581 449627 127869 56.29%
2007 2075667 21744 5.12% 4.09% 39.92% 2415257 1072540 231919 51.14%
2008 2413618 23493 6.26% 11.33% 79.87% 2463744 850451 460224 72.38%
2009 2286411 18750 1.49% 2.33% 13.11% 3230925 1289897 553573 63.02%
2010 3380169 24299 3.91% 3.24% 19.95% 4606130 2011393 721174 60.65%
KMBI 27 2003 1612326 27266 0.89% 4.98% 9.79% 2838899 592034 1173905 70.29%
NGO14 2004 4130129 80078 0.20% 5.87% 18.11% 5294020 1817115 1460295 68.84%
2005 5124529 82076 1.13% 20.60% 56.61% 7619369 1243544 3239764 81.91%
2006 5675951 83167 2.17% 15.22% 31.29% 9228118 460910 4957337 75.52%
2007 9107964 117721 1.77% 10.81% 21.05% 15043015 1461258 7511710 75.18%
2008 8138079 123913 2.52% 3.82% 7.36% 13109684 210615 7093059 73.46%
2009 11269717 186170 5.86% 2.72% 5.25% 15386828 0 7686618 70.74%
2010 14724279 235482 7.42% 6.13% 12.96% 19467095 802384 8795670 82.78%
Life Bank Found 28 2003 208937 4208 0.00% 232628 -8801
NGO15 2004 885257 15252 0.02% 14.79% 121.87% 996071 157926
2005 1705822 25852 0.52% 22.93% 99.94% 1938919 624343 515509
2006 4718656 61524 0.16% 20.88% 82.94% 5367923 1693730 1323694 77.17%
2007 11524652 130667 0.10% 17.27% 68.39% 13189185 4479799 3361209 59.49%
2008 19316234 207545 0.38% 22.37% 73.99% 18696382 4543795 6280812 61.04%
2009 19829145 236917
2010
Mallig Plains RB 29 2003
Rural Bank14 2004 4997037 21806 6131785 1766724 888163
2005 5752987 23634 12.75% -0.14% -0.97% 7043254 2229777 1003369 25.94%
2006 6540970 25112 12.34% -0.24% -1.67% 8024071 3000821 1170939 24.63%
2007 7986435 25016 11.30% 0.47% 3.04% 10356022 3295433 1653335 25.60%
2008 7320455 25079 1.80% 1.33% 8.18% 9190644 2617886 1535139 19.37%
2009 6917881 23600 11.27% 1.70% 9.78% 9580865 2455525 1731618 28.70%
2010 7990893 22665 10.51% 0.68% 3.66% 11488553 2926187 2189748 20.84%
MILAMDEC 30 2003
NGO16 2004 449461 9525 17.24% 695300 87996 271758
2005 573123 13024 10.53% -1.31% -4.15% 1090170 419179 293095 64.55%
2006 727447 16652 7.06% 0.70% 2.70% 1310106 504003 326623 65.64%
2007 1804375 2540942 1275774 422775
2008 1993963 20462 2.95% 1.47% 9.01% 2938979 1555444 473256 46.74%
2009 2835236 29836
2010
62
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
RBVictorias 31 2003 1436493 1946 11.71% 6.54% 35.39% 2018454 151067 401947 35.36%
Rural Bank15 2004 1180958 2610 14.36% 4.18% 19.04% 2101634 67738 501767 42.14%
2005 1146156 3796 18.85% 1.98% 9.06% 2439224 72762 492857 42.78%
2006 1472259 4160 17.26% 1.89% 8.93% 2727546 218227 602636 46.16%
2007 1905064 4119 7.84% 1.94% 8.88% 3042761 120611 659282 40.05%
2008 1993652 3845 2.08% 2.57% 12.67% 2924964 471501 552919 39.61%
2009 1987736 2529 17.29% 1.99% 9.58% 3242052 346604 728431 38.87%
2010
NWTF 32 2003 3617209 48152 11.81% 1.17% 4.94% 6592372 2924152 1499469 50.92%
Grameen NGO17 2004 5173535 54863 8.11% 0.82% 3.97% 8202791 4120563 1564696 48.35%
2005 6480759 67982 8.35% 0.83% 4.95% 10490478 5696210 1579198 48.27%
2006 7865485 66530 4.62% 0.47% 3.07% 11481726 5402974 1803312 46.41%
2007 10682187 76203 3.32% 0.39% 2.56% 15090696 6083601 2272188 38.20%
2008 9369720 84958 3.24% 2.51% 16.07% 14283245 4057128 2320762 44.01%
2009 8957004 78025 3.86% 2.08% 9.46% 14578221 2735532 4027798 37.09%
2010 11069786 85808 1.94% 6.97% 16625587 1604963 4669690 40.66%
OK Bank 33 2003 2011270 27191 9.69% 1.01% 2.07% 3547575 1057017 1543734 72.92%
Bank16 2004 1267295 27740 7.33% 0.22% 0.49% 3411703 1063302 1539528 64.74%
2005 2840159 29516 1.46% -4.54% -10.45% 5112884 1188382 2163069 45.33%
2006 3216038 26585 44.25% -15.95% -37.19% 4358439 964345 1898992 30.86%
2007 1964514 15566 17.37% -4.45% -10.87% 5270681 728856 2041089 37.25%
2008 1464420 12628 36.43% -5.81% -15.15% 4020168 252738 1521988 55.38%
2009 5410927 33815 -6.18% -18.58% 10666320 662787 3366182 92.68%
2010 5816718 30793 9.37% -3.34% -11.49% 11084502 1578994 2965179 64.00%
PALFSI 34 2003
NGO18 2004 1342560 10959 0.00% 1693484 698087 394419
2005 1350671 11750 3.75% 4.70% 19.46% 2110874 795411 525142 44.15%
2006 2762526 18210 4.08% 0.49% 2.47% 3508130 1774917 585323 35.16%
2007 3992790 22113 44.53% -25.85% -553.36% 4162358 2608231 -227044 34.07%
2008 3475960 20887 46.01% -16.95% 265.66% 2932995 2014243 -225701 23.04%
2009 2710940 21706 44.25% 2.21% -33.86% 3097514 3264692 -167178 24.67%
PBC 35 2003 2429124 27011 3308506 608354
Rural Bank17 2004 2832197 28288 0.68% 0.82% 4.47% 3819070 697350
2005 4502722 25541 0.42% 1.11% 6.05% 5529990 1013501
2006 6741964 0.39% 6.85% 42.34% 9006976 1337210
2007 11353327 0.00% 2.17% 15.49% 13719242 6756601 1848491 28.98%
2008 13224748 46931 4.77% 1.97% 15.29% 14817502 7316095 1828824 29.60%
2009 15474947 61776 2.51% 1.80% 14.10% 18187260 8271943 2383046 28.14%
2010 20799370 49671 8.84% 2.72% 19.19% 23848059 10830560 3568261 27.50%
63
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
RBDigos 36 2003 1879058 4918 3109567 468919
Rural Bank18 2004 2426766 5835 2.77% 20.01% 3881916 338680 497060
2005 3238817 5464 6.67% 1.69% 13.51% 4972871 706782 611065 26.17%
2006 4086534 6840 7.21% 2.46% 20.69% 6495968 763925 754430 25.69%
2007 6453846 7099 5.56% 3.27% 26.75% 9600440 1492212 1214649 23.17%
2008 7372276 8873 0.02% 2.09% 16.51% 9953537 1909984 1256202 23.46%
2009 7111276 7921 8.45% 2.09% 15.24% 10233406 1472716 1508933 22.17%
2010
RBMabitac 37 2003
Rural Bank19 2004 2085936 3699 33.61% 3418294 476181 380830
2005 2594952 5764 11.63% 2.02% 17.14% 3874032 555499 478759 24.22%
2006 2694881 5757 10.95% 0.79% 6.37% 4834485 704783 605743 28.07%
2007 4508464 5723 6.59% 0.93% 7.79% 7264188 1371027 836147 24.89%
2008 4674894 12500 1.21% 1.75% 14.85% 7440735 1354074 897853 29.66%
2009 4516735 14035 11.19% 1.22% 9.68% 8446430 1223087 1113000 34.02%
2010 6030100 12038
RBMontevista 38 2003 1780476 6545 0.31% 2773408 321514
Rural Bank20 2004 2294331 10665 0.39% -0.32% -2.79% 3226832 365985
2005 2730155 10209 0.28% 0.32% 3.30% 3891049 328949
2006 3640395 12461 0.79% 0.37% 4.41% 5082654 426770
2007 5808649 0.00% 0.34% 3.93% 7783187 1891482 679778 28.10%
2008 5329571 26699 0.80% 0.33% 3.54% 7260199 1657113 708798 30.46%
2009 4694478 20496 0.92% 0.84% 7.56% 7768185 1513054 968873 29.83%
2010
RBOroquieta 39 2003
Rural Bank21 2004 2093838 1573 2743080 473411 433822
2005 3103644 2912 12.54% -0.33% -2.59% 4099493 561108 449009 19.47%
2006 4466365 3602 8.99% 1.17% 11.06% 5818467 624890 601247 18.79%
2007 5764922 3733 8.73% 1.20% 11.62% 8285009 775783 848708 17.11%
2008 5785325 4637 0.08% 1.47% 14.01% 7607834 984001 823165 18.51%
2009 5300642 4708 5.72% 1.45% 12.40% 8080583 1182234 1012878 19.50%
2010
RBSolano 40 2003 2953543 5684 3595792 594603
Rural Bank22 2004 2840177 5567 5.47% 29.50% 4073376 114551 828163
2005 2951550 5448 20.33% 5.03% 22.61% 4433748 131000 1062889 21.77%
2006 2451308 5136 20.54% 5.29% 20.12% 4992344 43992 1413669 23.09%
2007 2554905 4982 19.34% 2.31% 8.27% 6378195 0 1765522 21.41%
2008 2445832 5113 0.91% 2.92% 9.94% 5245891 0 1652548 21.84%
2009 2859520 5146 13.80% 0.58% 1.79% 5530873 0 1833716 20.45%
2010
64
Source: MIX Market Information Portal for the Philippines
(http://www.mixmarket.org/mfi/country/Philippines )
MFI
Ye
ar
Gro
ss lo
an p
ort
foli
o
Nu
mb
er
of
acti
ve
bo
rro
we
rs
Po
rtfo
lio
at
risk
>
; 30
day
s
Re
turn
on
ass
ets
Re
turn
on
eq
uit
y
Tota
l ass
ets
Tota
l bo
rro
win
gs
Tota
l eq
uit
y
Yie
ld o
n g
ross
po
rtfo
lio
(no
min
al)
RBTalisayan 41 2003 1810734 9994 2615735 828351 358947
Rural Bank23 2004 1984017 10508 15.62% 1.84% 13.00% 2801764 737527 409061 37.71%
2005 2466445 11970 17.11% 0.67% 4.85% 3379614 739026 449124 37.43%
2006 2975917 13843 15.46% 0.88% 6.51% 4007927 800076 549641 36.22%
2007 3939233 15029 16.96% 1.29% 9.38% 5454171 759348 751705 35.37%
2008 4061902 17992 0.44% 2.15% 15.14% 5697976 651728 830071 42.23%
2009 3917011 18599 11.87% 2.00% 12.38% 6218975 507510 1096794 37.91%
2010
RSPI 42 2003
NGO19 2004 436758 6469 7.91% 651821 107807
2005 705902 10822 7.91% 5.12% 29.08% 999237 183082
2006 1021244 14819 8.84% 48.40% 1538642 633633 280425 73.04%
2007 1594953 18293 8.74% 9.23% 45.59% 2441095 915881 525109 70.22%
2008 1873405 21534 8.81% 7.86% 33.11% 2453326 960548 636772 68.81%
2009 1972227 21564 2.22% 13.27% 43.16% 2901242 842169 1009924 72.25%
2010 2818120 33771 2.12%
Serviamus 43 2003
NGO20 2004 945252 8768 1168671 359729
2005 747599 7421 11.27% 30.47% 1284301 234218 547484
2006 704669 7389 14.91% 6.80% 14.20% 1289202 31282 685518 54.13%
2007 916455 8868 2.00% 1.89% 3.66% 1695004 13456 857043 52.89%
2008 826492 9439 19.10% 3.39% 6.84% 1656272 0 804316 51.39%
2009 937758 9993 14.49% 5.57% 11.43% 1795065 0 878371 48.76%
2010 1178239 10563 12.34% 9.99% 20.78% 2086220 0 986738 50.59%
TSKI 44 2003 4147882 81005 3.46% 0.07% 0.47% 8482522 2894536 948397 48.58%
NGO21 2004 7357389 122832 1.65% 1.50% 16.74% 14982755 5267363 1150598 59.61%
2005 11376005 162867 3.59% 6.61% 67.92% 20066176 9684344 2258590 64.74%
2006 14706394 173002 6.26% 3.68% 32.61% 29305946 12218368 3311450 67.82%
2007 20935683 168661 6.13% 1.83% 14.00% 38531536 18032472 5534372 61.30%
2008 25990020 172857 3.67% 1.50% 12.56% 40397881 20046923 3915744 50.92%
2009 21008414 161299 5.66% 2.17% 16.79% 39316900 20345301 6380355 49.67%
2010 23594156 194660 6.00% 1.02% 7.00% 44997868 23304860 5860873 61.37%
TSPI 45 2003 6194932 77868 1.58% 7.75% 19.36% 9047400 785961 3615876 55.90%
NGO22 2004 9023724 109629 1.04% 7.12% 18.51% 12176616 1411639 4548750 57.76%
2005 10654793 113137 2.13% 5.66% 15.61% 16086647 933416 5694090 62.71%
2006 14227076 125980 1.35% 3.80% 10.40% 20758447 1343317 7783530 56.98%
2007 18457373 136705 1.28% 4.92% 11.43% 25732423 1968286 12213839 58.71%
2008 23061900 199087 1.96% 0.98% 2.32% 27172582 5109112 10153046 51.11%
2009 30198615 264089 1.51% 0.73% 2.22% 38096138 10661890 11249674 50.98%
2010 34338508 282920 5.57% 5.74% 17.64% 51740687 9155338 17970773 52.99%
ValiantRB 46 2003 2555307 4407948 499172
Rural Bank24 2004 3022946 3262 1.22% 11.82% 5739556 109687 545081
2005 3980173 3814 9.50% 1.17% 13.19% 8476438 177469 715410 13.00%
2006 5733256 5222 7.75% 1.60% 19.41% 15340295 679820 1250333 16.77%
2007 8814794 7000 9.66% 0.88% 10.74% 24151463 1165993 1967434 16.59%
2008 12157293 12475 1.56% 0.77% 8.11% 24445260 60416 2660251 18.40%
2009 16022892 16959 16.38% 0.25% 2.29% 32666831 1166745 3529922 17.62%
2010
65
Appendix 3. Description of Firms
MFI NameCurrent Legal
StatusAddress Main Funding Sources
1 1st Valley Bank Rural Bank Lanao del Norte
Loans, Voluntary
Savings, Insurance, Fund
Transfer Services
2 ABS-CBN Bayan Foundation, Inc. NGO EDSA, Quezon City
3 ASA NGOOrtigas Center, Pasig
CityLoans and Savings
4 Ahon Sa Hirap, Inc. (ASHI) NGO Cubao, Quezon City
5 Alalay Sa Kaunlaran, Inc. (ASKI) NGO Cabanatuan, Nueva Ecija Grants and Loans
6 Bangko Santiago de Libon Rural Bank Libon, AlbayLoans, Savings,
Shareholder Capital
7 Bangko Kabayan Rural Bank Ibaan, BatangasLoans, Savings,
Shareholder Capital
8 Bangko Mabuhay Rural Bank Tanza, CaviteSavings, Shareholder
Capital
9 Bukidnon Cooperative Bank (BCB) Rural BankMalaybalay City,
Bukidnon
Loans, Savings,
Shareholder Capital
10 Cantilan Bank, Inc. Rural BankCantilan, Sugao del Sur,
Surigao del Sur
Loans, Savings,
Shareholder Capital
11Center for Agricultural and Rural
Development (CARD) BankRural Bank San Pablo City, Laguna
Loans, Savings,
Shareholder Capital
12Center for Agricultural and Rural
Development, Inc (CARD NGO)NGO San Pablo City, Laguna Grants, Loans, Savings
13Cooperative Bank of Misamis
Oriental, Inc. (CBMO)Rural Bank
Cagayan de Oro City,
Misamis Oriental
Loans, Savings,
Shareholder Capital
14Community Ecoonomic Ventures, Inc.
(CEVI)NGO Tagbilaran City, Bohol Grants, Loans
15Cebu Micro-Enterprise Development
Foundation Inc. (CMEDFI)NGO Cebu City, Cebu
Grants, Loans, Savings,
Shareholder Capital
16 Daan Sa Pagunlad, Inc. (DSPI) Grameen NGO Balanga City, Bataan Grants, Loans
66
MFI NameCurrent Legal
StatusAddress Main Funding Sources
17Ecumenical Church Loan Fund
(ECLOF Philippines Foundation, Inc.)NGO EDSA, Quezon City Loans
18First Agro-Industrial Rural Bank
(FAIR Bank)Rural Bank Gairan, Cebu
Loans, Savings,
Shareholder Capital
19First Isabela Cooperative Bank
(FICO)
Cooperative
Rural BankCauayan City, Isabela
Loans, Savings,
Shareholder Capital
20First Macro Bank (Rural Bank of
Pateros)Rural Bank Pateros, Manila
Loans, Savings,
Shareholder Capital
21Rural Green Bank of Caraga, Inc.
(Green Bank)Rural Bank
Butuan City, Agusan del
Norte
Loans, Savings,
Shareholder Capital
22Hagdanan Sa Pag-uswag Foundation
Inc. (HSPFI)NGO
Cagayan de Oro City,
Misamis OrientalGrants, Loans
23Kasagana-Ka Development
Foundation, Inc.NGO
Commonwealth Avenue,
Quezon CityGrants, Loans
24 Kazama Grameen, Inc. Grameen NGO Subic, Zambales Grants, Loans
25 Kausawagan Bank (KBank) Bank Jaro, Iloilo CitySavings, Shareholder
Capital
26Kasanyangan-Mindanao Foundation,
Inc. (KCCDFI)NGO
Veterans Avenue,
Zamboanga CityGrants, Loans
27Kabalikat para sa Maunlad na Buhay,
Inc. (KMBI)NGO
Karuhatan, Valenzuela
City
Grants, Loans,
Shareholder Capital
28 Life Bank Foundation, Inc. NGO Barbara, Iloilo City Savings
29Mallig Plains Rural Bank (Isabela),
Inc.Rural Bank Mallig, Isabela
30 MILAMDEC Foundation Inc. NGOCarmen, Cagayan de Oro
City
31 New Rural Bank of Victorias Rural BankBacolod City, Negros
Occidental
Loans, Savings,
Shareholder Capital
32Negros Women for Tomorrow
Foundation, Inc. (NWTF)Grameen Bank
Verbena Street, Bacolod
CityGrants, Loans, Savings
67
Source: MIX Market Information Portal for the Philippines
(http://www.mixmarket.org/mfi/country/Philippines )
MFI NameCurrent Legal
StatusAddress Main Funding Sources
33Opportunity Kauswagan Bank (OK
Bank)Bank
Circumferential Road,
Antipolo City
Loans, Savings,
Shareholder Capital
34
People's Alternative Livelihood
Foundation of Sorsogon, Inc.
(PALFSI)
NGOBibincahan, Sorsogon
CityGrants, Loans
35People's Bank of Caraga (Rural
Bank of Talacogon - PBC)Rural Bank
San Francisco, Agusan
del Sur
Loans, Savings,
Shareholder Capital
36 Rural Bank of Digos Inc. (RB Digos) Rural BankDigos City, Davao del
Sur
Loans, Savings,
Shareholder Capital
37Rural Bank of Mabitac Inc. (RB
Mabitac)Rural Bank Mabitac, Laguna
Loans, Savings,
Shareholder Capital
38 Rural Bank of Montevista Rural BankCompostela Valley,
Davao del Norte
39 Rural Bank of Oroquieta Rural BankOroquieta City, Misamis
Occidental
Loans, Savings,
Shareholder Capital
40 Rural Bank of Solano Rural Bank Solano, Nueva VizcayaSavings, Shareholder
Capital
41Rural Bank of Talisayan - Misamis
Oriental Inc.Rural Bank
Poblacion Talisayan,
Misamis Oriental
42 Rangtay Sa Pagrangay Inc. (RSPI) NGOMagsaysay Avenue,
Baguio CityLoans
43 Serviamus Foundation NGOIligan City, Lanao del
NorteLoans, Savings
44 Taytay Sa Kauswagan Inc. (TSKI) NGO Iloilo City Grants, Loans, Savings
45 Tulay Sa Pag-Unlad, Inc. (TSPI) NGOGuadalupe Nuevo,
Makati CityGrants, Loans
46 Valiant Rural Bank (Iloilo City) Inc. Rural Bank Iloilo CityLoans, Savings,
Shareholder Capital
68
MFI Name Products and Services
% of Operations
Comprised by
Micro Firms
Date
Established
Looking for (Investment
Types)
1 1st Valley BankLoans, Voluntary Savings, Insurance,
Fund Transfer Services 0-10 1-Jan-56
Loans in Local Currency,
Capacity-Building Grants,
Equity Investments
2 ABS-CBN Bayan Foundation, Inc. Loans 91-100 1-Jan-97
3 ASA Loans, Voluntary Savings, Insurance 91-100 9-Jul-04
4 Ahon Sa Hirap, Inc. (ASHI) Loans, Insurance 91-100 24-Jul-91
5 Alalay Sa Kaunlaran, Inc. (ASKI) Loans, Training and Consulting 91-100 1-Jan-87
6 Bangko Santiago de Libon Loans, Full-Scale Financial Services 21-30 1-Jan-73
7 Bangko KabayanLoans, Voluntary Savings, Full-Scale
Financial Services 11-20 15-Aug-57
8 Bangko MabuhayLoans, Voluntary Savings, Full-Scale
Financial Services0-10 1-Jan-72
9 Bukidnon Cooperative Bank (BCB)Loans, Voluntary Savings, Full-Scale
Financial Services 11-20 1-Jan-77 Loans in Local Currency
10 Cantilan Bank, Inc.Loans, Voluntary Savings, Full-Scale
Financial Services 11-20 1-Jan-80 Loans in Local Currency
11Center for Agricultural and Rural
Development (CARD) Bank
Loans, Voluntary Savings, Insurance,
Training and Consulting, Full-Scale
Financial Services
91-100 1-Jan-86
12Center for Agricultural and Rural
Development, Inc (CARD NGO)Loans, Training and Consulting 91-100 1-Jan-86
13Cooperative Bank of Misamis
Oriental, Inc. (CBMO)Loans, Full-Scale Financial Services 11-20 1-Jan-79 Loans in Local Currency
14Community Ecoonomic Ventures, Inc.
(CEVI)
Loans, Insurance, Training and
Consulting, Full-Scale Financial
Services
91-100 17-Aug-00
15Cebu Micro-Enterprise Development
Foundation Inc. (CMEDFI)Loans, Voluntary Savings, Insurance 91-100 1-Jan-98 Capacity-Building Grants
16 Daan Sa Pagunlad, Inc. (DSPI) Loans, Voluntary Savings 91-100 1-Jan-94
69
MFI Name Products and Services
% of Operations
Comprised by
Micro Firms
Date
Established
Looking for (Investment
Types)
17Ecumenical Church Loan Fund
(ECLOF Philippines Foundation, Inc.)91-100 1-Jan-01
18First Agro-Industrial Rural Bank
(FAIR Bank)
Loans, Voluntary Savings, Fund
Transfer Services41-50 16-Jan-99
Loans in Local Currency
Capacity-Building Grants
19First Isabela Cooperative Bank
(FICO)
Loans, Voluntary Savings, Full-Scale
Financial Services 0-10 1-Jan-80
20First Macro Bank (Rural Bank of
Pateros)
Loans, Voluntary Savings, Insurance,
Full-Scale Financial Services21-30 19-Aug-60
21Rural Green Bank of Caraga, Inc.
(Green Bank)Loans, Full-Scale Financial Services 91-100 1-Jan-75
22Hagdanan Sa Pag-uswag Foundation
Inc. (HSPFI)Loans, Training and Consulting 91-100 1-Jan-87
23Kasagana-Ka Development
Foundation, Inc.Loans, Training and Consulting 91-100 1-Jan-03
Donations, Capacity
Building Grants
24 Kazama Grameen, Inc. Loans, Voluntary Savings 91-100 7-May-01
Loans in Local Currency,
Loans in USD, Capacity-
Building Grants, Donations,
Loans in EUR
25 Kausawagan Bank (KBank)Loans, Voluntary Savings, Full-Scale
Financial Services91-100 1-Jan-05
26Kasanyangan-Mindanao Foundation,
Inc. (KCCDFI)Loans, Insurance 91-100 1-Jan-02
27Kabalikat para sa Maunlad na Buhay,
Inc. (KMBI)Loans 91-100 1-Jan-86
28 Life Bank Foundation, Inc. Loans, Voluntary Savings, Insurance 91-100 1-Jan-03
29Mallig Plains Rural Bank (Isabela),
Inc.
Loans, Voluntary Savings, Training
and Consulting, Full-Scale Financial
Services
21-30 1-Jan-69
30 MILAMDEC Foundation Inc.Loans, Voluntary Savings, Training
and Consulting91-100 1-Mar-92
Loans in Local Currency,
Loans in USD, Capacity-
Building Grants, Donations,
Loans in EUR
31 New Rural Bank of Victorias
Loans, Voluntary Savings, Fund
Transfer Services, Full-Scale
Financial Services
21-30 1-Jan-61
32Negros Women for Tomorrow
Foundation, Inc. (NWTF)Loans, Lending and Deposit Vehicles 91-100 1-Jan-84 Guarantees
70
Source: MIX Market Information Portal for the Philippines
(http://www.mixmarket.org/mfi/country/Philippines )
MFI Name Products and Services
% of Operations
Comprised by
Micro Firms
Date
Established
Looking for (Investment
Types)
33Opportunity Kauswagan Bank (OK
Bank)Loans, Voluntary Savings 91-100 1-Jan-01
34
People's Alternative Livelihood
Foundation of Sorsogon, Inc.
(PALFSI)
Loans, Insurance 91-100 1-Jan-97 Loans in Local Currency
35People's Bank of Caraga (Rural
Bank of Talacogon - PBC)
Loans, Voluntary Savings, Training
and Consulting, Full-Scale Financial
Services, Business Devlopment
Services, Health, Education
41-50 24-Oct-72 Capacity-Building Grants
36 Rural Bank of Digos Inc. (RB Digos)Loans, Voluntary Savings, Full-Scale
Financial Services 0-10 17-Dec-55 Loans in Local Currency
37Rural Bank of Mabitac Inc. (RB
Mabitac)
Loans, Voluntary Savings, Full-Scale
Financial Services 11-20 1-Jan-74 Loans in Local Currency
38 Rural Bank of MontevistaFull-Scale Financial Services,
Education 0-10 31-Mar-77 Capacity-Building Grants
39 Rural Bank of OroquietaLoans, Voluntary Savings, Full-Scale
Financial Services 0-10 1-Dec-66
40 Rural Bank of SolanoLoans, Voluntary Savings, Full-Scale
Financial Services 0-10 1-Jan-70 Loans in Local Currency
41Rural Bank of Talisayan - Misamis
Oriental Inc.Loans, Voluntary Savings 91-100 1-Jan-86
42 Rangtay Sa Pagrangay Inc. (RSPI)Loans, Insurance, Busines
Development Services91-100 1-Jan-87
Loans in Local Currency,
Capacity-Building Grants
43 Serviamus Foundation Loans 91-100 1-Jan-97
44 Taytay Sa Kauswagan Inc. (TSKI)
Loans, Insurance, Training and
Consulting, Business Development
Services
91-100 1-Jan-86
Loans in Local Currency,
Donations, Guarantees,
Capacity Building Grants
45 Tulay Sa Pag-Unlad, Inc. (TSPI)Loans, Insurance, Training and
Consulting91-100 1-Jan-81
Loans in Local Currency,
Capacity Building Grants
46 Valiant Rural Bank (Iloilo City) Inc.Loans, Voluntary Savings, Full-Scale
Financial Services 11-20 1-Jan-97 Loans in Local Currency