explaining growth and consolidation in rp microfinance institutions

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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

[email protected]

[email protected]

[email protected]

mailto:[email protected]



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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

http://www.themix.org/about/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

http://www.themix.org/about/microfinance

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.

http://www.mixmarket.org/

http://www.themix.org/about-mix/about-mix#ixzz1UUlw3jXI

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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

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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

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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.

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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


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.


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


Tota

l Equity




MFI: Banco Santiago de Libon


Figure 7.


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


Tota

l Equity




MFI: Center for Agricultural and Rural Development (CARDNGO)


Figure 9.


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


Tota

l Equity




MFI: CARD Bank


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

http://www.mixmarket.org/mfi/country/Philippines

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


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


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)


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



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


31

portfolio at risk for 30 days (for portfolio quality) and number of depositors per staff (for

operational self-sufficiency).


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


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


32


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)



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


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)



Sample: 2003 2010 Sample: 2003 2010





C 10367534 5857489 1.77 0.09 C 2411775 1972304 1.22 0.22




Capital-Asset Ratio -30724493 8517680 -3.61 0.00 Capital-Asset Ratio -2000218 1243613 -1.61 0.11




Borrowers per Staff 7516.276 17910.66 0.42 0.68 Borrowers per Staff 1057.103 1784.264 0.59 0.55











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


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)



Sample: 2003 2010 Sample: 2003 2010





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



Capital-Asset Ratio -48225255 8104807 -5.95 0.00 Capital-Asset Ratio -3844717 794109.9 -4.84 0.00




Borrowers per Staff -99507.95 26892.44 -3.70 0.00 Borrowers per Staff -3500.286 3220.62 -1.09 0.28











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)



Sample: 2003 2010 Sample: 2003 2010





C -7508700 3053563 -2.46 0.02 C -485353 667480.4 -0.73 0.47



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


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




S.E. of regression 752043.3 S.E. of regression 506819.1







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)



Sample: 2003 2010 Sample: 2003 2010





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


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





Log likelihood 117.9798 Log likelihood 417.9943


Mean dependent var 0.033974 Mean dependent var 0.027831

S.D. dependent var 0.026616 S.D. dependent var 0.053947



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)



Sample: 2003 2010 Sample: 2003 2010





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





Log likelihood 73.05709 Log likelihood 393.3754


Mean dependent var 0.5035 Mean dependent var 0.416047

S.D. dependent var 0.077371 S.D. dependent var 0.169158


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

References:

Charitonenko, Stephanie (2003) Commercialization of Microfinance: The Philippines, Asian

Development Bank: Manila.

Chittenden, Francis, Graham Hall, Patrick Hutchinson (1996) “Small Firm Growth, Access to

Capital Markets and Financial Structure: Review of Issues and an Empirical Investigation,”

Small Business Economics, Vol. 8, No. 1, Special Issue on Financing and Small Firm

Dynamics (Feb., 1996), pp. 59-67 (http://www.jstor.org/stable/40228760. Accessed:

01/08/2011 at 23:30)

De Aghion, Beatriz Armendáriz (1999) “On the design of a credit agreement with peer

monitoring,” Journal of Development Economics, Vol. 60, pp. 79–104.

(http://www.economics.harvard.edu/faculty/armendariz/files/design.pdf. Accessed:

07/08/2011 at 17:01)

Karlan, Dean S. and Jonathan Morduch (2009) “Access to Finance,” Chapter 2. Handbook of

Development Economics. Dani Rodrik and Mark Rosenzweig, (eds). Elsevier: North

Holland. Volume 5.

Karlan, Dean S. and Jonathan Zinman (2010) “Expanding Credit Access: Using Randomized

Supply Decisions to Estimate the Impacts.” Review of Financial Studies 23(1): 433-464

Karlan, Dean S. and Nathaniel Goldberg (2011) “Microfinance Evaluation Strategies: Notes on

Methodology and Findings,” Beatriz Armedáriz and Marc Labie, (eds) The Handbook of

Microfinance. World Scientific Publishing, Co: Singapore. pp. 17-59

Krauss, Nicolas and Ingo Walter (2009) “Can Microfinance Reduce Portfolio Volatility.”

Economic Development and Cultural Change, Vol. 58, No. 1, (October)

Meagher, Patrick, Pilar Campos, Robert Peck Christen, Kate Druschel, Joselito Gallardo, and

Sumantoro Martowijoyo (2006) “Microfinance Regulation in Seven Countries: A

Comparative Study," Report submitted to Sa-Dhan, New Delhi, IRIS Center, University of

Maryland. (http://www.microfinancegateway.org/gm/document-1.9.24382/26.pdf. Accessed:

07/08/2011 at 17:12)

Modigliani, Franco and Merton H. Miller (1963) “Corporate Income Taxes and the Cost of

Capital: A Correction,” The American Economic Review, Vol. 53, No. 3 (Jun., 1963), pp.

433-443. (http://www.jstor.org/stable/1809167. Accessed: 02/08/2011 at 11:14)

Modigliani, Franco and Merton H. Miller (1958) “ The Cost of Capital, Corporation Finance and

the Theory of Investment,” American Economic Review, Vol. 48, No. 3 (Jun., 1958), pp. 261-

297 ( http://www.jstor.org/stable/1809766. Accessed: 02/08/2011 at 11:14)

Myers, Stewart C. (1984) “The Capital Structure Puzzle,” Journal of Finance, Vol. 39, No. 3,

Papers and Proceedings, Forty-Second Annual Meeting, American Finance Association, San

http://www.jstor.org/stable/40228760

http://www.economics.harvard.edu/faculty/armendariz/files/design.pdf

http://www.microfinancegateway.org/gm/document-1.9.24382/26.pdf



44

Francisco, CA, December 28-30, 1983 (Jul., 1984), pp. 575-592.

(http://www.jstor.org/stable/2327916. Accessed: 02/08/2011 at 00:07)

Reid, Gavin C. (1991) “Staying in Business,” International Journal of Industrial Organization,

Vol. 9, pp. 545-556.

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.



http://www.google.com/books?hl=en&lr=&id=un1JmcFa4QIC&oi=fnd&pg=PA186&ots=ZyPAWEo4cC&sig=B6EH8S72S58T8fh8flO8rfX_F1w#v=onepage&q&f=false

http://www.google.com/books?hl=en&lr=&id=un1JmcFa4QIC&oi=fnd&pg=PA186&ots=ZyPAWEo4cC&sig=B6EH8S72S58T8fh8flO8rfX_F1w#v=onepage&q&f=false



45

Appendix 1: Philippines Microfinance Performance Standards (P. E. S. O.)

47

Appendix 2. Database

MFI

Ye

ar

Ave

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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

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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

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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

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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



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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

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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

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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

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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

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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

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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

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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

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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

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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



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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


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



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





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



Development (CARD) Bank

Loans, Voluntary Savings, Insurance,

Training and Consulting, Full-Scale

Financial Services

91-100 1-Jan-86


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


% of Operations

Comprised by

Micro Firms

Date

Established


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 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 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




% of Operations

Comprised by

Micro Firms

Date

Established


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


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


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


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


Donations, Guarantees,

Capacity Building Grants

45 Tulay Sa Pag-Unlad, Inc. (TSPI)Loans, Insurance, Training and

Consulting91-100 1-Jan-81


Capacity Building Grants

46 Valiant Rural Bank (Iloilo City) Inc.Loans, Voluntary Savings, Full-Scale



explaining growth and consolidation in rp microfinance institutions

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