Efficiency and Expense Preference in the Philippines' Cooperative Rural Banks

25 Philippine Institute for Development Studies
1977
Surian sa mga Pag-aaral Pangkaunlaran ng Pilipinas
2002
Efficiency and Expense Preference
in the Philippines'
Cooperative Rural Banks
Mario B. Lamberte and Martin Desrocher
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Efficiency and Expense Preference in the Philippines’
Cooperative Rural Banks
Mario Lamberte? and Martin Desrocher+


October 2002

Abstract

This paper attempted to test whether efficient cooperative rural banks (CRBs) have a better
control of their agency costs. We used two different concepts of efficiency, namely, cost
efficiency and alternative profit efficiency, and found somewhat different results from both
approaches. Using Stochastic Frontier Approach and Distribution Free Approach, we tested
two different propositions. The first proposition is that an adequate corporate governance
scheme should improve efficiency of CRBs. We failed to find very conclusive evidence that
corporate governance theories apply to the Philippines’ CRBs. However, the results
confirmed both managers’ compensation theory and large stakeholders theory. The second
proposition is that agency costs should reduce efficiency of CRBs, and we found a much
clearer relationship on that issue. As expected, most efficient CRBs are characterized by a
better control of agency costs. These results are in accordance with previous studies on
shirking behavior among mutual financial intermediaries. We also found that rural CRBs are
most profit efficient, despite their somewhat normal cost-efficiency, a manifestation that they
are able to charge higher fees for the quality of services they offer. Large CRBs are not able
to pass their higher costs to customers through higher fees. We found that small CRBs
might have a better interest rate policy, that is, they offer lower rates on both loans and
deposits.

Keywords: Agency Costs, Corporate Governance, Efficiency.


1 Introduction

The population of the Philippines currently stands at 80 million. With a population
growth rate of 2.36 percent, which is well above the world population growth rate of 1.3
percent, at least 1.9 million people will be added each year to the country’s population. Since
the economy in the last 15 years has grown only modestly, the number of poor people
inevitably keeps on rising. Thus, despite the fact that the number of families falling below the
poverty line had declined from 44.2 percent in 1985 to 34.2 percent in 2000, the absolute
number of poor people had risen from 26.2 million to 31.3 million during the same period. A
great majority of the income earners of poor households are self-employed.

The 1998 Annual Poverty Indicators Survey (APIS) shows that 70 percent of the
poorest 40 percent of the respondents relied on entrepreneurial activities as main source of
income. However, only 25 percent out of the 8.5 million households with businesses surveyed
had obtained credit to finance their business. These households, while self-employed and
without access to credit, often experience fluctuations in income and sometimes need extra
funds to cope with emergencies, such as sickness and natural calamities. When any of these
happens, these households are often forced to use the working capital for their small
business and/or sell whatever fixed assets they have. Selling a cow or carabao and

? President, Philippine Institute for Development Studies, 106 Amorsolo Street, Legaspi Village, Makati
City, Metro Manila, Philippines
+ Centre de Recherche en Économie et Finance Appliquée, Faculté des Sciences de l’Administration,
Université Laval, Québec, CANADA, G1K 7P4.
This is part of the Community-Oriented Financial Intermediaries (COFI) project jointly conducted by the
Philippine Institute for Development Studies and University of Laval. The authors are grateful to Jocelyn
Alma R. Badiola and Herbert Rizal Docena for excellent research assistance. The authors also wish to
thank Ms. Ma. Chelo Manlagñit for carefully reviewing the draft .


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farm/business equipment is not an uncommon occurrence among poor households. These
poor households, therefore, need the services that would address the financing requirements
of their livelihood and consumption needs including lumpy nonfood expenses for health,
education and housing improvements.

Development practitioners and policymakers view microfinance as one of the
solutions to the growing demand for financial services by poor households and to the reality
that most formal financial institutions do not serve the poor because of perceived high risks,
high costs involved in small transactions, perceived low profitability and inability of the poor to
provide the required physical collateral (ADB 2000). Most if not all of these financial
institutions have a business culture that is not geared towards servicing the poor and low-
income households. Through microfinance, financial services like savings, credit, and
insurance facilities, can be delivered to poor households who will, in effect, be able to
smoothen their consumption, manage their risks, build their assets gradually, develop their
microenterprises, enhance their income earning capacity and enjoy an improved quality of
life. Without permanent access to institutional microfinance, most poor households continue
to rely on meager funds from savings and informal sources, which further limit their income
and production capacities.

Sustainability of microfinance institutions is a crucial consideration for the poorest of
the poor, and a thorough review of the characteristics of healthy microfinance institutions
should be given a high priority by all regulatory agencies supervising these institutions. This
study, therefore, attempts to analyze the characteristics of the most efficient cooperative rural
banks (CRBs) of Philippines with the aim of providing better information to regulatory
agencies in regulating and supervising microfinance institutions (MFIs).

1.1 The Philippines’ microfinance system

The Philippine financial system consists of formal and informal financial
intermediaries. The informal sector is composed of heterogeneous players, such as
moneylenders and ROSCAS. The formal financial system can further be broken down into
banking institutions, which are authorized to provide credit and accept deposits from the
general public, and non-bank institutions, which are authorized to extend loans but are not
permitted to accept deposits from the general public.

The banking system is composed of the commercial banking system (universal and
ordinary commercial banks), the thrift banking system (savings and mortgage banks, private
development banks and stock saving and loans associations), the rural banking system
(ordinary or stock rural banks and cooperative rural banks), and government-owned banks. In
terms of assets, the banking system overwhelmingly dominates the financial system. Its total
assets as of December 2000 amounted to PhP3.3 trillion, or 82 percent of the total assets of
the financial system. In the Philippines, MFIs are categorized into the following: rural banks
including CRBs; credit-granting non-government organizations (NGOs); and credit
unions/cooperatives. While MFIs have steadily increased the volume of loans granted to their
clients through the years, their combined market share has remained below 5 percent (Agabin
1998) [1].

1.2 The Philippine rural banking system

Both stock and cooperative rural banks are active in the Philippines’ microfinance
sector. Rural banks are private banks that were established in the 1950s with government
assistance and subsidies to provide services to the agricultural sector. There were about 800
rural banks in 2000 scattered all over the country. Up until the 1980s, they constituted a
system of unit banks that is unique in the developing world, and many of them grew out of the
operations of moneylenders. The number of CRBs rose from 15 in 1975 to 50 in 2000.

A CRB has a dual personality, that of being a cooperative, on one hand, and a bank,
on the other. A CRB is, thus, governed by both banking and cooperative laws, particularly, the
New Rural Bank Act or RA 7353, the General Banking Law of 2000 or RA 8791, the
Cooperative Code or RA 6938 and the Cooperative Development Authority Act or RA 6939.

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CRBs are organized primarily to provide financial and credit services to cooperatives and may
perform any or all of the services offered by stock rural banks. Only duly established
cooperatives and federations of cooperatives which are registered or re-registered with the
Cooperative Development Authority under Republic Act 6938 may become
members/organizers of CRBs. A CRB services an average of 5,000 individual borrowers
(Guanlao 1999) [19].

Besides increasing geographical diversification, stock and cooperative rural banks
have, over time, been increasingly diversifying their loan portfolio across major economic
activities. In contrast to the 1980s, today CRBs’ loans are less concentrated in the agricultural
sector. There has also been a substantial change in the way rural banks finance their lending
operations over the years. In 1980, deposits comprised only 43 percent of their total liabilities.
A big chunk of their liabilities consisted of borrowings from the Central Bank and other special
credit programs of the government. The radical change in rediscounting and interest rate
policies in the mid-1980s has encouraged rural banks to mobilize deposits and to rely less on
the rediscounting window of the Central Bank for funds. Thus, by 1998, the share of deposits
in the total liabilities of rural banks rose to 74 percent. An increase of deposits financing is a
normal trend for growing microfinance institutions. In effect, as confidence of consumers
towards the institution grows, governmental and grant financing can be reduced, in favor of
greater amounts of customers’ deposits. This trend is very positive, though it also brings an
additional consideration: It decetralizes the sources of financing from a few major donors to
various small depositors. Greater diffusion in sources of funds enables managers to act more
freely, and thus gives room for expense preference. This creates a common phenomenon in
microfinance institutions: The cycle ”growth-diffusion of financing-failure”. In order to avoid
this cycle, regulators should always keep in mind the phenomenon and thus maintain a strong
control over expenses of large CRBs.

1.3 Regulatory issues

One of the lessons of the East Asian financial crisis is that banks must be well
regulated and adequately supervised. However, new prudential regulations, if applied
uniformly to all types of financial institutions, could further force financial intermediaries to
ration out small borrowers. Thus, the newly passed General Banking Law tries to achieve a
balance between the objectives of tightening up prudential regulations and ensuring the flow
of financial services to microenterprises and poor households. This Law includes three
provisions concerning microfinance to encourage banks to lend to microfinance borrowers not
on the basis of a collateral they can present, which many of them do not have any, but rather
on the basis of their cash flows.

The existence of adequate banking offices in all areas in the country can improve
access of poor households to banking services. Beginning in 1989, the Central Bank relaxed
the regulation on bank entry and branching. This led to the proliferation of banks and
branches in the country. Many of these banks became in distress in the aftermath of the East
Asian financial crisis and the ”El Niño” weather phenomenon that struck in 1998. Thus, the
Bangko Sentral ng Pilipinas (BSP) the country’s central, bank has declared a moratorium on
the opening of new banks and has encouraged merger/consolidation to strengthen their
financial position. However, to ensure that microfinance services will not diminish especially in
rural areas, the BSP recently approved a partial lifting of the general moratorium on the
licensing of new thrift and rural banks to allow entry of microfinance-oriented banks. A rural
bank to be established as a microfinance bank is required to have a minimum paid-in capital
of PhP5 million (about US$100,000) while the existing capitalization requirement for thrift
banks apply. The regulatory framework of rural banks in the Philippines is substantially
different from existing systems found in African and Latin American countries. More
specifically, a prospective cooperative bank shall file its application for licensing as a bank
with the BSP and upon approval, shall be registered with the Cooperative Development
Authority. However, only the BSP is responsible for regulating and supervising all CRBs.
Since CRBs operate under the New Rural Bank Act, the BSP treats them like ordinary or
stock rural banks. Only one (1) cooperative bank shall be established per province. Market is
thus segmented by Law to avoid excessive competition and inefficient use of branch

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branches among CRBs. However, CRBs compete with thrift banks, rural banks and branches
of rural banks operating in their respective provinces.


1.4 Conceptual framework

Experience shows that the CRBs, whose ownership is generally more diffuse than rural
banks, had a weaker performance through time than the latter. Reliance on government funds
might be associated with this weaker performance. Also interestingly, cooperatives that did
not focus on agricultural, electric and transport activities between 1973 and 1986, while the
government was pursuing a development policy for these sectors, had a self-reliant and
progressive development that contributed to their business viability and success. We propose
to study the characteristics of most efficient CRBs to see whether their corporate governance
has improved their sustainability through better control of agency costs. We formulate two
propositions:

Proposition 1:
Efficient CRBs have a better corporate governance scheme.
Proposition 2:
Efficient CRBs have a better control of agency costs.

Each proposition will be decomposed further into several aspects, which will be tested in
section 6. We will first discuss in detail the concept of efficiency. Efficient financial
intermediaries produce a quantity of output at a lower cost than any other intermediary
producing the same level of output. Efficiency measurement refers to a comparison of costs
of a particular CRB and the most efficient CRB producing the same level of output. For each
level of output, we can find the most efficient CRB, and the combination of all these most
efficient CRBs produces the efficient cost frontier. Then we estimate the deviation of specific
CRBs from their most efficient counterparts, and try to explain these differences. We propose
to test the relationship of efficiency with various correlates of corporate governance and
agency costs, among others. A similar approach is made for profit efficiency.


2 Review of literature

The review of literature presented below is divided into two sections. The first deals
with efficiency of financial intermediaries, and the second, ownership and corporate
governance. Since an extensive review of literature on agency costs theory and empirical
studies is presented in several studies mentioned in the reference section and other studies
of the COFI project, this paper will not cover this literature.

2.1 Efficiency of financial intermediaries

Existing studies estimated the efficiency of financial intermediaries using either
parametric or non-parametric techniques. The former assumes a random component in the
measurement of efficiency, while the latter assumes that the random component is absent
and differences in total costs (or profits) are completely explained by differences in efficiency.
According to Berger and Humphrey (1997) [6], most U.S. studies used parametric techniques
(110 studies), rather than non-parametric techniques (78 studies). We would like to note that
parametric and non-parametric techniques are further divided into various methodologies. We
will not judge the relative strengths of these methods here. We rather refer the readers to
Berger and Mester (1997) [7] for a thorough review of literature. These authors advocate for
the use of parametric methods rather than non-parametric methods to take into account not
only technical efficiency but also price-effects (allocative efficiency).

McNulty and Verbrugge (1988) studied stock and mutual S&L, and found no clear
difference in cost efficiency between both types. So, we can expect that the methodologies
applied to profit maximizing institutions would also be valid for non- profit institutions.

Several studies on efficiency measurement consider the relationship of efficiency with
various correlates. Among the most frequently used correlates of inefficiency of financial

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intermediaries, we find a negative relationship with size (Hardwick (1990) [21], Drake and
Weyman-Jones (1992)[11], Cebenoyan at al. (1993)[8], Mester (1993)[36], and Eisenbis,
Ferrier, and Kwan (1999) [12]), a negative relationship with capital ([36], [12] ), and a positive
relationship with portfolio risk (Eisenbis, Ferrier, and Kwan (1999) [12]). Considering financial
cooperatives more specifically, Worthington (1998) [41] also found a negative relationship
with size, a negative relationship with capital, and a negative relationship with the number of
branches.

2.2 Ownership and corporate governance

Corporate governance can be defined as the combination of all measures that ensure
managers to act in the best interest of investors, e.g. to ensure that they receive an adequate
return on their investment. It has been shown that an adequate corporate governance
scheme can reduce agency costs within corporations (See Shleifer and Vishny (1997) [38]).
These authors presented two ways of increasing efficiency of corporate governance. One is
to adopt appropriate legal protection of both small and large investors, and the other, to
increase concentration of ownership.

It has been demonstrated that concentration of ownership induces managers to be
more efficient (see Holderness and Sheehan (1988)[23]). This is because major stakeholders
have stronger negotiating power when they face managers, as well as better incentives to
keep track of decisions of the latter. This view is generally known as the Large Shareholders
Theory and it constitutes the first part of the tests we will perform later. Some results were
found for a cooperative form of ownership though (See Hansmann (1988) [20], Hart and
Moore (1994) [22], and Schleifer and Vishny (1997) [38]). These authors suggested that when
large non-shareholder constituencies, such as managers, employees or any other
stakeholders, are left with little rent to capture, a greater concentration of ownership might not
be optimal for value maximization of the firm.

Jensen’s Free Cash Flow Theory stipulates that an appropriate policy to control
agency costs is to limit free cash flows available to constrain the expense preference behavior
of managers, and this could be done by having an adequate level of debt, and a strong
control from the institution’s owners. Increased concentration of ownership and greater
financial leverage limit managers’ incentives to spend on perks and other wasteful activities.
Existing literature on this subject concentrated on performance of LBO versus non-LBO firms.
Leveraged firms appeared to be more efficient than their non-LBO counterparts (see Jensen
(1989) [30], Kaplan (1989) [32], Smith (1990) [39] and other authors in the same number).
The recent tendency of CRBs to mobilize more deposits to finance their lending operations
seems to fit well with the propositions of recent theory on corporate governance. We thus
propose to test whether most efficient CRBs actually had a greater proportion of deposit
financing.

Another important aspect of corporate management is included under the Managers’
Compensation Theory, which suggests that a higher compensation for managers may give
them sufficient incentives to improve efficiency. A number of studies have argued that
performance-based compensation is preferable to fixed compensation in order to give
adequate incentives to managers to maximize the value of the firm (see Holmstrom (1979)
[24], Grossman and Hart (1983) [18], Lambert and Larcker (1987) [34], Jensen and Murphy
(1990) [31], and Mehran (1995) ). Houston and James (1995) [25] have demonstrated that
existing compensation scheme of bank’s managers is not sufficiently performance-based to
encourage them to take sufficient risks to maximize the value of the firm.


3 Methodology

The main objective in estimating a production function is to explain the quantity of
output produced given certain levels of inputs and other relevant factors that might explain the
quantity of output produced. Both the production and the intermediation approaches have
been used to model the production of financial institutions. The former considers the

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institution as a producer of two goods, namely, loans and deposits. The outputs are estimated
in number of accounts, while operational costs are represented on the left hand side. The
latter, on the other hand, considers the amount of loans and investments as the outputs, while
the amount of deposits, capital and wages are considered as the inputs. Interests are added
to operating costs on the left hand side to reflect the addition of deposits as an input.

We use the intermediation approach and introduce the possibility of non-linear
demand for inputs (the ?ij terms), as well as a random component, which can be decomposed
into an efficiency component Ln(uc) and a random component Ln(?c). The random component
simply means that the total costs can be explained not only by input prices but also by an
inefficiency factor specific to each institution, and a random component including all other
factors that might affect total costs. To decompose these effects, we use two well-recognized
methodologies, namely, the stochastic frontier approach (SFA) and the distribution-free
approach (DFA). Each of these approaches has its own strengths and weaknesses, which we
will elaborate below. The main reason for using these two approaches is to strengthen the
conclusions that we can derive from the results of our analysis. We do not introduce the usual
restrictions of cost minimization (profit maximization) as we are assuming that CRBs do not
minimize costs (maximize profits).

A critical assumption associated in the SFA models is that the error term can be
decomposed into a random component (Ln(?c)), following a normal distribution, and an
efficiency component (Ln(uc)), following a half-normal distribution. Many alternative models
have been proposed to avoid this critical assumption as the hypothesis of half-normal
distribution of the efficiency component received some criticisms recently (Greene (1990)[17]
and Berger (1993)[4]). We concentrate on three alternatives. The first consists of eliminating
the random component of the error term with the use of a non-parametric model, such as the
Data Envelopment Analysis (DEA) or the Free Disposal Hull (FDH). As mentioned earlier,
DEA avoids the decomposition of errors between efficiency and randomness by assuming
that the random component is simply not present and that the differences in the total costs
are completely explained by differences in efficiency (Aly et al.(1990) [3]; Ferrier and Lovell
(1990)[15]; Eliasiani and Mehdian (1990)[13]; Ferrier et al. (1991)[14]; Fixler and Zieschang
(1991)[16]; Aly et al.(1990) [3]).

The second alternative model, such as the TFA (Lozano (1997)[35]), sets the limit
between random error and efficiency. This methodology assumes that the deviations from
predicted costs within the lowest quartile are attributable to random error, while deviations in
the remaining quartiles are attributable to efficiency. As discussed by Berger (1993)[4], TFA
only substitutes the assumption about the distribution of the error term for an equally arbitrary
assumption about where efficiency stops and the random error begins.

The results significantly differ from one methodology to another. This has prompted
some critics to further elaborate both approaches. Berger and Humphrey attribute the
inconsistent rankings to the major ”sins” of these two approaches i.e., too little account of
random error by the non-parametric studies and too much structure imposed on the frontier
by the parametric approaches.[5]

The third alternative model is to consider a random error component, but eliminate all
the distributional constraints by using a panel data set. The virtues of Distribution Free
Approach (DFA) estimates, obtained with a panel data set, were described originally in
Schmidt and Sickles (1984)[37] and later in Berger (1993) [4], and Berger and Mester (1997)
[7]. Robert DeYoung ([10]) also developed a methodology to evaluate the most adequate
number of years to consider, with data covering 618 U.S. commercial banks over eleven (11)
years. He found that a six-year period is the best compromise between too few (which
introduces a large dispersion of residuals) and too much time periods (which is delicate if
some trends are included in the data). In our case, the method consists of estimating the
average inefficiency of each CRB over the five-year period extending from December 1995 to
December 1999. This constitutes the non-random component (Ln(uc)) attributable to
inefficiency. This measure is compared to the most efficient CRB over the same period,
avoiding short-term variations. The assumption here is that each CRB has a specific
inefficiency that is observed over this five-year period, but is also subject to some random

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error due to external factors such as macroeconomic problems, or unusual weather
conditions, such as ”El Niño” weather phenomenon. We do not impose the homotheticity
assumption by adding the quantities of outputs on the right hand side (RHS), as well as all the
cross-product between prices of inputs and quantities of outputs. In effect, when introducing
outputs as independent variables, we relax the simple relationship between inputs and costs.
The addition of outputs on the RHS is common within the intermediation approach and
frequently used to study economies of scale in financial institutions.

3.1 Model 1: Cost efficiency

The cost function we estimate is represented by Equation 1:



5
2 5 2
Ln(C) = ? + ? ?i Ln (pi) + ? ?k Ln(yk) + ½ ? ? ?ij Ln(pi) Ln (pj)
i=1
k=1

i=1 j=1

2 5 5 2
+ ½ ? ? ?km Ln(yk) Ln (ym) + ? ? ?ik Ln(pi)Ln(yk) + Ln(uc) + Ln(?c)
(1)
k=1 m=1


i=1 k=1

The model is estimated using Seemingly Unrelated Regressions (SUR) for the DFA
and maximum likelihood estimation procedure for the SFA developed by Coelli (1994)1.

We now present in more detail the variables we used to estimate the cost function.
Table I shows the definitions and characteristics of each variable included in our first model
(Equation 1). The dependent variable is the total costs of each CRB, in million pesos, deflated
by the national Consumer Price Index using 1995 as the base year. The outputs we consider
are loans and securities, while the prices we include are real wages, real cost of materials,
interest rate on deposits, interest rate on financial obligations and cost of other inputs.2 After
estimating the cost efficiency for each CRB, we proceed to compare with the most efficient
CRB. For the DFA, we estimate the following ratio for each CRB:

Cost Efficiency = Cmin = u min

c
(2)


C
uc

where Cmin is the minimal cost, associated with the most efficient CRB, and C is the cost of
a specific CRB. Equation 2 gives the proportion of costs that is efficiently used by the CRB.
For example, if Cmin is representing 70 percent of C, 70 percent of costs of this CRB is used
efficiently, and 30 percent is wasted inefficiently. The SFA, on the other hand, produces
estimates of the inefficiently (rather than efficiently) of each CRB. For example, a figure of 10
percent means that the CRB concerned incurs 10 percent more than the cost of the most
efficient CRB for the same quantity of outputs produced. They are equivalent, however, in the
sense that the most cost-inefficient CRB in the SFA is taken to mean the least cost efficient in
the DFA.

We define four size groups based on the real value of assets. The first group includes

1 The model was estimated using Coelli’s (1994) program [9].
2 Hugues, Mester, and Moon (2000)[29], with a data set consisting of 441 bank holding companies
demonstrated that inclusion of capital structure (see also [28], [36],[26], [27] and [7]) and risk-taking into
efficiency measurement improves the estimated coefficients, the two variables also included in Hugues
and Mester (1998). We included the ratio of liabilities over capital as an independent variable to take into
consideration the impact of leverage on costs, as a small CRB does not have access to the same
amount of deposits due to a lesser capital. Bigger CRBs have more possibilities to generate profit
because they already accumulated some reserves over time that give them more flexibility in their
assets-management. We also included ex post credit risk as another independent variable, to consider
the fact that greater risk-taking might increase profits, but also endangers the sustainability of the
institution. Notwithstanding, none of these two variables has significant signs so we do not include them
into our final regressions.

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CRBs whose assets are below PhP20 million, the second group between PhP20 million and
PhP30 million, the third group between PhP30 million and PhP60 million, and the fourth group
above PhP60 million.

3.2 Model 2: Profit efficiency

As discussed earlier, CRBs could have provided better services to their members.
However, doing so only increases the costs of the CRB. In effect, we would be penalizing
institutions for adopting a strategy of providing better services to their clients, despite the fact
that some clients might be willing to pay the additional costs to benefit from improved
services. To avoid penalizing CRBs that are providing better services to their clients, we also
estimate an alternative profit function besides the cost function. This function enables CRBs
to have greater costs but still be competitive through improved services, as reflected by
higher profits. This proposition is inspired by Berger and Mester (1997) [7]. The following
profit function has the same specification as the cost function defined previously:



5 2 5 2

Ln(?) = ? + ? ?i Ln (pi) + ? ?k Ln(yk) + ½ ? ? ?ij Ln(pi) Ln (pj)
i=1
k=1
i=1 j=1
2 5 5 2
+ ½ ? ? ?km Ln(yk) Ln (ym) + ? ? ?ik Ln(pi)Ln(yk) + Ln(uc) + Ln(?c)
(3)
k=1 m=1


i=1 k=1


The model is estimated by Ordinary Least Squares (OLS) regression for the DFA and
maximum likelihood estimation procedure for the SFA. In order to compare efficiency of
CRBs, we need to establish a common measure of what we will call a ”target model”, an
exercise similar to the one we have done with the ”Cost efficiency function”. This target is
defined as the maximum profit that was realized for a specific level of assets. Each CRB is
then compared to this level of profit.

Profit efficiency = ? = uc

(4)
max
? max uc

Equation 4 gives the proportion of the actual profit of a specific CRB to its maximum
potential profit. Both the SFA and DFA use the same procedure for estimating profit efficiency
and hence, the results should be interpreted in the same manner.


4 Data

We used annual data of 50 CRBs operating in both rural and urban regions of the
Philippines for the period 1995-1999.3 The variables used to estimate efficiency are defined in
Table I together with their descriptive statistics. The correlates of efficiency are also
presented in the same table. The dependent variables differ for each model; that is, real cost
of inputs for Model I and real profit for Model II. The independent variables are the cost of
inputs and the quantity of outputs in real terms.



3 We eliminated observations with incomplete data, leaving 216 observations for the stochastic frontier
approach, and 209 for the distribution-free approach (we eliminated observations of less than three
years of data). The information is to conform with the requirements established within COFI (stands for
Community Oriented Financial Intermediaries) project. The complete list of variables is available upon
request.

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5 Results

5.1 Efficiency regressions

The results of estimating Equation 1 and Equation 3 are presented in Table II. We
present the results of both the stochastic frontier approach (SFA) and the distribution-free
approach (DFA). The signs of the coefficients of the cost function are generally the same for
both the SFA and DFA. In the case of the profit function, however, quite a number of
coefficients have different signs for the two approaches.

The efficiency measures by size of CRBs are shown in Table III. These are simple
averages of individual-efficiencies within each size group. Before proceeding with the
discussion of the results, care should be exercised in interpreting the results in Panel A. As
already mentioned, the SFA produces cost-inefficiency measures whereas the DFA
generates cost- efficiency measures.

As shown in Panel A, the average cost- efficiency varies very little among the asset
size groups regardless of the approach being used. Interestingly, they show similar pattern.
For SFA, the average cost-inefficiency increases as asset size increases, then declines as
asset size increases further (inverted U-curve). For DFA, it declines first, then increases as
asset size increases (U-curve). Both results are consistent with each other. However, there
are differences in the results produced by the two approaches. First, the SFA shows that
CRBs are on average 10.25 percent cost-inefficient while the DFA, 85.25 percent cost-
efficient. Second, the SFA results suggest that CRBs with asset size of less than PhP20
million are the least cost-inefficient CRBs, whereas the DFA shows that CRBs with assets of
more than PhP60 million are the most cost-efficient. Third, the most cost-inefficient CRBs in
the SFA are those with assets of PhP30-PhP60 million, whereas the least cost-efficient CRBs
in the DFA are those having assets of PhP20-PhP30 million. Thus, the results derived from
the two approaches would not allow us to make a conclusion regarding which asset-size
group is the most cost-efficient.

Panel B shows the average profit efficiency by the same asset-size groups. The SFA
produces higher average profit efficiency than the DFA. This is consistent with the results
obtained for cost efficiency measures discussed above. Here, the results obtained from the
two approaches are completely different from each other. SFA exhibits an inverted U-curve,
that is, the average profit efficiency increases first, then declines as asset size increases,
whereas the DFA shows a U-curve, that is, the average profit efficiency declines first, then
increases as asset size increases. Also, the least profit efficiency asset-size group in the SFA
is the most profit efficient in the DFA. Thus, we cannot reach a clear conclusion regarding
which asset-size group is the most profit efficient.

Interestingly, according to the SFA, small and large CRBs are more cost-efficient, and
yet they have the worst profit efficiency. These institutions, as argued by Berger and Mester,
could be characterized by a greater market power. In the case of very large CRBs we can
understand that their size can allow them to exercise some market power in the highly
concentrated CRB market. But the same cannot be said to very small CRBs. We have to
admit that some factors other than size could have affected the results. We suspect that
quality of relationship with members might have something to do with this greater market
power. This should be investigated further, but we would like to present some possibilities that
may be interesting to consider in the future. We estimated the median quality and diversity of
products, and found that small CRBs are not offering better quality of service or diversity of
products, as can be observed below:








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