philippine rural banks and economic development
TRANSCRIPT
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Philippine Rural Banks and Economic Development1
Céline CROUZILLE a, Jessica LOS BANOS b, Emmanuelle NYS a, Alain SAUVIAT a
a LAPE, Université de Limoges, 5 rue Félix Eboué, BP 3127, 87 031 Limoges Cedex, France b CBA, University of the Philippines, Diliman, Quezon City 1101, Philippines
Abstract:
This paper examines the link between financial and economic development at the regional level in the Philippines and focuses on the role played by rural banks in regional economic activity. We apply cointegration panel data analysis on regional banking and economic data for the period 1993 to 2005. We ranked the sixteen regions in three different groups based on their average economic development. The wealthier the region, the more important the financial depth is. However the Asian crisis prevents us to find a positive long term relationship from the cointegration analysis between finance and growth when studying all regions together. When investigating the less developed regions, our results show some support for a long-run relationship between financial development and economic growth. In that case, the findings also bear out a threshold effect of rural bank presence on economic activity encouraging government policy to strengthen rural finance.
JEL Classification: G21, O16
1This paper was prepared for the ASIA-LINK human resource development project: Euro-Philippines Network on Banking and Finance,
Safety and Soundness of the Financial System, coordinated by the University of Limoges (www.upd.edu.ph/~cba/asialink). ASIA-LINK is a programme of the European Commission that seeks to promote regional and multilateral networking among higher education institutions in Europe and developing economies in Asia.
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Section 1. Introduction
The link between financial development and economic growth has been the subject of extensive
research in recent years, often anchored on the seminal works of McKinnon (1973) and Shaw (1973).
McKinnon and Shaw posit that removing restrictions on interest rates increases interest levels and
amplifies the volume of money for lending, eventually leading to greater capital formation and
productivity, and consequently economic growth. Their influential work on financial liberalization laid
the groundwork for the renewed interest in the role of financial intermediation in the economic growth
process, which this present study investigates in the case of the Philippine regions.
Existing literature on the function of financial markets in economic growth include, among others King
and Levine (1993a, 1993b), Bencivenga and Smith (1991), and Beck et al. (2000). A number of
research themes are prominent in the growth literature (see Levine 1997, for a comprehensive review).
Some studies concentrate on exploring the channels through which financial development stimulates
economic growth (see Calderón and Liu 2003) such as capital formation and productivity. Others focus
on the impact of savings and lending on growth (Demetriades and Luintel 1997 and Bandiera et al.
2000). A number examine the resulting resource allocation efficiency (Bencivenga and Smith 1991) of
intermediaries ensuing from financial liberalization. This allocative efficiency is argued to aid in risk
management by savers and investors (Angbazo 1997), to lead to better identification of long-term
investments that are more productive than short-term ventures (Bencivenga and Smith 1991), to
improve investment decisions (Greenwood and Jovanovic 1990), and to facilitate information
collection and evaluation of investment projects (King and Levine 1993b and Boyd and Prescott 1986).
The more contentious area of research investigates the causality of the relationship between growth and
financial intermediation (King and Levine 1994, Demetriades and Hussein 1996, and Wachtel and
Rousseau 1995). The positive link between financial development and output growth is argued to be
complicated by the direction of the relationship (Christopoulos and Tsionas 2004). Some researchers
assert that it is financial development that follows growth (See King and Levine, 1993a,b, and
Demetriades and Hussein 1996) while others find otherwise, that it is growth that fuels financial
development (Christopoulos and Tsionas 2004) since improvements in productivity and economic
output would require increased investment and funding. Other studies claim that this causality is
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actually bi-directional (Demetriades and Hussein 1996) while a few do not find any link between
financial development and economic growth at all (Lucas, 1988, Chandavarkar, 1992).
Existing empirical studies mainly focus on the influence of the financial development on economic
growth across countries, and therefore they need to control for institutional, social and political
disparities. In this paper, we only study the case of the Philippines which enables us to assume that
macroeconomic conditions and political governance (monetary and exchange rate policy, banking
regulation, education and health policy, industrial policy…) are relatively homogeneous across the
regions of this country. Therefore we can focus on structural differences in the banking industry among
the regions in order to provide deeper insights into the finance and growth nexus. In the case of the
Philippines, this question is of particular interest. The formal banking system is composed of three
categories of banks: universal and commercial banks (UKB), thrift and private development banks
(TB) and regional rural and cooperatives banks (RB). Although the formal banking system is
dominated by commercial banks, rural banks in the Philippines were primarily established to promote
and expand the rural economy. They generally cater to small borrowers including farmers,
entrepreneurs, market vendors, business owners, wage earners, teachers and cooperatives. From the
1960s to the 1980s, rural banks served as conduits of subsidized loan funds from the government and
international donors and were plagued by high default rates, insolvent lending programs, and high
operating costs to name a few (Agabin and Daly 1996). In response, regulations were passed in the
1990s covering minimum capitalization requirements, limitations and restrictions to single borrowers
and shareholders, and increase in capital adequacy ratio for all banks at 10% of risk-weighted assets.
Interest rate restrictions were also removed and the liberalization of new bank openings and branching
was pursued.
Using regional banking and economic data for the period 1993 to 2005, we test whether the presence of
rural banks positively affects regional economic activity. Our estimation model is designed to address
the heterogeneity of economic development and banking coverage of the regions in the Philippines and
to enable us to efficiently utilize the limited regional data available presently. We apply the model on
three sub-samples of regions in the Philippines (all regions, intermediate developed regions and less
developed regions). Regions are classified as less-economically-developed, intermediate-developed,
and developed using macroeconomic data from the Philippine National Statistics Office, National
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Statistical Coordination Board and the Bangko Sentral ng Pilipinas. We build on the work of Apergis et
al. (2007) and Christopoulos and Tsionas (2004) for our cointegration panel data analysis which aims
to assess the special role of rural banks on regional economic activity in the Philippines. Our estimation
results find support for a long-run relationship between financial development and economic growth in
the Philippines. Our findings bear out a threshold effect of rural banking financial depth for the less
developed regions in the Philippines.
The paper is organized as follows. Section 2 follows the introduction and provides a brief survey on
regional economic development. Section 3 presents the role of rural banks and banking coverage in the
Philippine regions. Section 4 presents the econometric methodology and discusses the results and
Section 5 concludes the paper.
Section 2. Disparities of Regional Economic Development in the Philippines
The Philippines is divided into seventeen (17) geographic regions, with seven regions in Luzon,
including the National Capital Region (NCR), three in the Visayas and seven regions in Mindanao. For
this study however, we refer to only 16 regions, having integrated Region 4-A, Calabarzon and Region
4-B, Mimaropa (Region 4 was divided into two separate jurisdictions only in 2002).
Stages of economic development of the regions in the Philippines are distinct and divergent. The
heterogeneity of their economic performance is obviously due to differences in the impact of political,
economic, social, environmental and other factors affecting economic growth. The per capita real gross
regional domestic product (PC_RGRDP) rankings of the regions have remained relatively constant
over the period covered by this study. Table 1 presents the real per capita gross regional product of the
regions. NCR continues to be the most economically developed and the ARMM, the worst performer
among the regions in the country. Those regions whose economic performance has deteriorated have
period growth rates that are significantly lower than those whose positions have significantly improved
and those who have retained their PC_RGRDP rankings.
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Table 1 Per Capita Real Gross Regional Domestic Product
Source: National Statistical Coordination Board . *CARAGA figure corresponds to 1997.
In view of the heterogeneity of the stages of economic development and in line with our estimation
model, we classify the regions into three groups: less-economically developed, intermediate developed
and developed regions.
Based on simple statistical analysis of the above data, we identify the less-economically developed
regions to be the following: Ilocos, Cagayan Valley and Bicol in Luzon, the Eastern Visayas region in
the Visayas and Zamboanga Peninsula, the Autonomous Region in Muslim Mindanao (ARMM) and
the CARAGA in Mindanao. These regions are basically agriculture intensive with lower levels of
industrialization. Their regional contribution to the Philippine GDP as of 2005 is below 2.9% whereas
their inhabitants account for 26.4% of the Philippine population. Except for Bicol which has one
economic zone and a single locator, there is no economic zone presence in the regions. The human
1993 1993 Rank 2005
2005 Rank
Period Growth
Developed regions
NCR 24793 1 35742 1 44%
CAR 11561 3 17919 2 55%
Northern Mindanao 9721 6 14829 3 53%
Intermediate developed regions
Davao 10169 5 13892 4 37%
Central Visayas 9464 7 13518 5 43%
South Luzon 12477 2 13447 6 8%
Western Visayas 9405 8 12825 7 36%
Socksargen 9021 9 11477 8 27%
Central Luzon 10688 4 11142 9 4%
Less developed regions
Zamboanga Peninsula 7620 10 10159 10 33%
Ilocos 5388 13 7727 11 43%
Cagayan Valley 5591 12 7649 12 37%
CARAGA* 6293 11 6690 13 6%
Eastern Visayas 5305 14 6678 14 26%
Bicol 5224 15 6632 15 27%
ARMM 3439 16 3433 16 0%
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development indices, poverty incidence of families, and education spending for this group are among
the worst in the country.
The developed regions, NCR, CAR and Northern Mindanao are those with a strong service sector
coupled with a vibrant industrial sector. There is also a robust presence of economic zones and a large
number of business establishments in these regions. Their per capita gross regional domestic products
are among the highest in the country. CAR is classified as developed in view of the presence of the
province of Benguet in the region, which is highly developed and which greatly improves the ranking
of the region despite the significantly poorer economic performance of the other provinces in the
region. Central Visayas (with Cebu province) and Davao (with Davao del Sur province) regions,
despite being more highly urbanized than Northern Mindanao and the CAR, were not classified in this
group in view of the lower ranking of their per capita GDRP levels as of 2005.
The intermediate economically-developed regions are comprised of those regions that were not
classified as developed and include Central Luzon and Southern Luzon regions in Luzon, the Western
Visayas and Central Visayas regions in the Visayas, and Davao and Socsargen in Mindanao.
Section 3. Banking system and role of rural banks in the Philippines
The formal financial sector2 is dominated by banks, which as of 2005, are comprised of the universal
and commercial banks (UKBs) which number 40 and with 56% of the total bank offices in the
Philippines, 83 thrift and private development banks (TBs) with 17% of bank office share and 861
regional rural and cooperative banks (RCBs) with the remaining 27% of the total banking offices
operating in the country (BSP 2007). To date, the RCBs remain the major source of agricultural credit
with the bulk of their average net loan portfolio allocated to the agriculture, forestry and fishery (AFF)
sectors.3 The net loan portfolio (NLP) share of the RCBs for the AFF, industry and service sectors over
the period 2000-2005 are provided in Table A1.
2 In this paper, we do not aim to study the semi-formal and informal financial sectors. For a presentation of the financial system in the Philippines, see Dauner Gardiol, Helms and Deshpande (2005). For a detailed study of rural finance, see Llanto (2005). 3 This figure was derived from data on rural bank net loan portfolio for the period 2000-2005 provided by the BSP.
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As with regional economic development, banking structure and coverage of the regions is also
heterogeneous, with UKBs accounting for a significant share of the financial intermediation taking
place in the regions. Those regions that are classified as less-economically developed are served by less
than 1/5 of the total banking offices in the country. Of these, almost one-half of the offices are rural
banks. The intermediate developed regions in turn share almost 1/2 of the total bank offices, with the
remaining 1/3 located in the developed regions, and with UKBs accounting for 3/4 of the total regional
offices. With large number of offices, bank density of the RCBs in the less developed regions, while
peaking during the financial crisis period have not returned to 1993 levels except for the CARAGA
region. All banks located in the less developed regions operate with only 2.9% of the total banking
assets, among which 14.14% are held by RCBs. The majority of total banking assets, around 87%, are
located in the most developed regions and the remaining 10% in the intermediate developed regions.
In terms of deposit taking, the same trend can be observed across the three groups with a majority of
deposits being placed in UKBs by depositors in the most developed regions of the country. Savings in
the less developed regions account for less than 1/10 of the total deposits in the regions, implying that
UKBs and TBs are still preferred over RCBs as depositories. Individuals and firms in the more
developed regions are also found to be saving significantly more.
As regards borrowing, UKBs are the preferred banks to obtain financing from. However UKBs have
not yet recovered the level of net loan portfolio they have reached before the Asian crisis. On the other
hand, despite the initial decrease of loans they granted just after 1997, RCBs have been able to increase
in most regions their loan market share, and to exceed the 1997 peak.
Individuals and firms in the less developed regions engaged in significantly fewer economic activities
borrowing less than 3% of the total national loan portfolio as of 2005 consistent with their economic
weight. However, creditors from the less-developed regions increasingly borrow from RCBs with
32.5% of their financing requirements obtained from the RCBs in 2005 even if a significant portion of
the market share for loans still goes to UKBs.
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As a consequence the intermediation rate, derived by dividing net loan portfolio by deposit liabilities
has decreased after the 1997 financial crisis in the less developed and the intermediate developed
regions. Moreover financial depth of the regions, measured by the ratio of the net loan portfolio over
gross regional domestic product, has significantly declined in all regions.
Section 4. Empirical framework and estimation results
4.1 Rank-order correlation tests
To begin our investigation on the relationship between economic growth and financial intermediation
in the Philippines regions and on the possible role of rural banks, we initially test for correlation
between selected banking and economic development indicators. The current financial system in the
Philippines is considered to be bank-based because of the dominance of banks in the country as
evidenced by the limited presence of equity markets in the regions, and the fact that only the largest
corporations are listed in the country’s stock exchanges. Hence funding for the majority of businesses
in the country is expected to be sourced primarily from banks and not through financial markets. The
use of bank-based financial proxies is thus appropriate.
Our study over the period 1993 – 20054 relies heavily on data obtained from Bangko Sentral ng
Pilipinas, the National Statistics Office and the National Statistical Coordination Board. Two measures
of the regional economic structure are used: the per capita real gross regional domestic product
(PC_RGRDP) and the per capita real gross added value in the agricultural and fishery sector
(PC_RAgri). To measure financial depth and local intermediation, four different measures are used: the
share of total net loans over nominal gross regional domestic product (Credit), the share of total
deposits over gross regional domestic product (Deposit), the number of banking offices per capita
(Banking office density) and the total net loans over total deposits (Intermediation). To detect an
additional effect of rural banks, two measures for rural banks presence are computed: the share of net
loans granted by rural banks per region over total net loans granted per region (RB Credit share), and
the share of total resources of rural banks per region over total resources for all banks per region (RB
4 Except for the CARAGA region which was created in 1995.
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resources share). We perform these tests for four samples (all regions, economically developed regions,
intermediate developed regions, and less economically developed regions).
Table 2 presents the results of our correlation analysis for the different groups of series for the four
samples of regions in the Philippines using Spearman rank-order tests. The null hypothesis is the
absence of rank-order correlation between two series for a sample of regions.
Table 2. Correlation Analysis: Spearman rank-order with PC_RGRDP as referent variable All regions Developed
regions Intermediate
developed regions Less developed regions
- PC_RAgri 0.083 -0.648*** 0.721 0.705***
Financial depth
- Credit 0.233*** 0.841*** -0.824 0.043
- Deposit 0.244*** 0.884*** 0.247** 0.246**
- Banking office density 0.652*** 0.948*** 0.314*** 0.358***
Local intermediation
- Intermediation 0.181*** 0.485*** -0.397*** 0.110
Rural banks market share
- RB Credit share -0.261*** -0.489*** 0.297*** 0.284***
- RB Resources share -0.380*** -0.653*** 0.080 0.313***
Boldface values denote a presence of a rank-order correlation. (***), (**) and (*) signify rejection of the null hypothesis of absence of rank-order correlation at the 1%, 5% and 10% levels respectively.
Three main results are obtained from the rank order tests. First, for the four different samples, a
positive and significant correlation between economic growth and financial depth at the regional level
is obtained when financial depth is measured by banking office density and deposit. This result is
consistent with the existing empirical literature on the finance growth nexus. The correlation obtained
is stronger for the economically developed regions than for the intermediate and less economically
developed regions. When credit is used as indicators of financial depth, the correlation is also
significant for the economically developed regions but not for the intermediate and less economically
developed regions. Results are the same as the latter for correlation between intermediation and
PC_RGDRP.
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Second, when considering the relationship between regional economic growth and the agricultural
share a significant negative correlation is obtained between PC_RGDRP and PC_RAgri for the
economically developed regions whereas the correlation is positive and significant for the less
economically developed regions. In other words, as expected, the agricultural sector is of main
importance to favor the growth of poor regions whereas the growth of the wealthy regions comes from
the industrial and service sectors.
Third, the most interesting result with regard to our issue is related to the role of rural banks on regional
economic activity. A negative and significant correlation is obtained between growth and rural banks
presence (whatever the indicators were used) on the sample “all regions” as well as for the
economically developed regions. And for this last group, the effect is stronger. On the contrary, a
positive and significant correlation is obtained between the variables PC_RGDRP and the market share
of rural banks. For the less economically developed regions, we find that the higher the market share of
rural banks, the higher is the economic growth.
4.2 Panel data tests
The lack of agreement on the role, in terms of existence, the level or the direction of financial
development in the process of economic growth is argued to arise primarily from the estimation
techniques that should be properly applied to the available data set (Apergis et al. 2007). According to
Apergis et al (2007), a problem with cross-sectional estimation is a possible omission to discuss the
integration and cointegration properties of the data, leading to a failure to examine the direction of
causality between financial development and economic growth. In estimating panel data, Apergis et al.
(2007) point out that using instrumental variables and GMM dynamic panel estimators alone to account
for potential biases induced by simultaneity of regressors, omitted variables and unobserved country-
specific effects on the finance-growth nexus may be insufficient and that the integration properties of
the data should still be considered. In order to explore the long-run equilibrium relationship between
finance and growth and to detect an additional positive effect of rural banks, we first conduct panel unit
root tests. We used Im, Pesaran and Shin t-test5. Results are presented in Table 3.
5 The IPS test is based on individual ADF regressions and assumes separate unit roots between the cross-sections units.
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Table 3. Im, Pesaran and Shin (IPS) panel unit root tests
Variable in level Variable in first difference IPS IPS
PC_RGDRP 2.77 -3.86***
Financial depth
- Credit 0.25 -2.75***
- Deposit -1.55**
- Banking office density -2.83 -1.52**
Local intermediation
- Intermediation 0.91 -2.67***
Rural banks market share
- RB Credit share 6.37 -2.04***
- RB Resources share 1.93 -2.69*** (***), (**) and (*) signify rejection of the null hypothesis of absence of unit root at the 1%, 5% and 10% levels respectively.
Panel unit root tests support the hypothesis of a unit root for most variables in level. For Deposit, we
reject the null hypothesis at the 5% level with the IPS test and at the 10% level with ADF test. In first
difference, unit root tests show that all variables are stationary.
As a second step, we conduct panel cointegration tests. To test for the presence of a long run
relationship between financial and economic development, we used the methodology proposed by
Pedroni ((1999);(2004)). This procedure is based on Engle-Granger (1987) two-step cointegration tests.
Pedroni proposed eleven statistics that allow for heterogeneous intercepts and trend coefficients across
cross sections. Two alternatives hypothesis are tested: homogeneous alternative (within dimension
tests) and heterogeneous alternative (group statistics tests). Cointegration tests are performed using
alternatively as explanatory variables (i) one of the two I(1) measures for financial depth (Credit or
Banking office density)6 or the local intermediation variable and (ii) one of the two I(1) measures for
rural banks market share (RB Credit share or RB Resources share).
6 The deposit variable is I(0).
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We then test the null hypothesis of no cointegration relation for these different groups of variables over
four samples. These samples have been chosen in light of the Spearman rank-order test. We focus our
analysis mainly on the intermediate and less developed regions in order to identify the role played by
rural banks.
Table 4 reports the results of the panel cointegration tests. On the sample “all regions”, we find some
evidence of cointegration relationship between financial and economic development. For three pairs of
explanatory variables, seven of the eleven Pedroni statistics are significant, and for the three other pairs
of explanatory variables, six statistics are significant. We therefore reject the null hypothesis of the
absence of a long term relationship between finance and growth.
When we restrict the sample to the less and intermediate economically developed regions, we obtain
stronger evidence of a cointegration relationship for all pairs of explanatory variables (we can even
reject the null hypothesis in nine cases for one pair (Credit; RB Credit share).
Finally, we perform the panel cointegration tests over the less economically-developed regions sample.
In most cases, the null hypothesis is rejected by four of the eleven Pedroni statistics. However stronger
results are obtained when we add a restriction on the level of rural banks market share (based on the
amount of loans granted).
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Table 4. Pedroni panel cointegration tests: Number of significant statistics tests (over 11 statistics) Dependent variable: PC_RGDRP
Rural bank market share
Financial depth/Local intermediation RB Credit share RB Resources share
All regions
(N1 = 204 ; N2 = 16) Credit 7
2 (***); 3 (**); 2(*) 6
1 (***); 4 (**); 1(*) Banking office density 6
5 (***) ; 1 (**) 6
1 (***) ; 2 (**) ; 3(*) Intermediation 7
3 (***) ; 4 (**) 7
3 (***) ; 2 (**) ; 2(*)
Intermediate developed regions
(N1 = 78 ; N2 = 6)
Credit 7 5 (***); 1 (**) 1 (*)
1 1 (*)
Banking office density 6 6 (***)
4 3 (**); 1 (*)
Intermediation 5 3 (***); 1 (**); 1 (*)
3 1 (**); 2 (*)
Less developed regions
(N1 = 87 ; N2 = 7)
Credit 4 4 (**)
4 3 (***) ; 1 (**)
Banking office density 4 3 (***) ; 1 (*)
3 1 (***); 1 (**); 1 (*)
Intermediation 4 3 (***) ; 1 (*)
4 3 (***); 1 (**)
Less developed regions for which RB Credit share > 25%
(N1 = 48 ; N2 = 4)
Credit 9 6 (***) ; 1 (**) ; 2 (*)
7 4 (***) ; 1 (**) ; 2(*)
Banking office density 7 3 (***) ; 3 (**) ; 1 (*)
4 2 (**); 2 (*)
Intermediation 7 6 (***) ; 1 (**)
7 4 (***); 1 (**); 2(*)
The total number of significant calculated statistics is in bold. (***), (**) and (*) signify rejection of the null hypothesis of absence of a long run relationship at the 1%, 5% and 10% levels respectively. N1 and N2 are respectively the number of observations and the number of cross-section units.
Table 5 displays the vector error correction estimates of the cointegration relationships, however only
the long run estimations, and not for all pairs of variables, are shown, consistent with our research issue.
We present the results only for the variable “RB credit share” because signs the coefficients are the
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same as for “RB resources share” and their Pedroni statistics are better. Moreover with regard to the
“RB credit share” variable, we do not display results with the “local intermediation” variable because
the quality of the estimations is weak and does not allow a meaningful economic interpretation.
The sample “all regions”, where the weight of the richest regions is obviously predominant, shows a
counter-intuitive result that is a negative impact of financial depth on economic growth. This result
might be explained by a strong decrease in the level of loans granted by commercial banks following
the Asian crisis, whereas the economic activity recovered more rapidly. Indeed the role of commercial
banks is of main importance for the country because of their strong presence especially in the wealthy
regions. And therefore our results cannot establish any role for rural banks at the national level. When
we study the sample of the intermediate economically developed regions, we find the same results as
for the nationwide economy that we can interpret identically.
On the contrary, when we analyze the seven less developed regions, our results show a positive impact
of financial depth on economic growth. This effect is more substantial when financial depth is
measured as the ratio of credit to gross regional domestic product than by banking office density. To
favor the economic activity in these regions, it seems more important to facilitate the access to the
credit market rather than to develop the number of offices. Moreover for this sample, we observe that
rural bank presence impact positively on economic development as we could expect from the Spearman
rank-order tests. These less developed regions, where the GRDP is mainly driven by the agricultural
and fishery sector, fully benefit from the specificity of the rural finance well-fit to the primary sector.
Finally, these results for the less developed regions are strengthened when we restrict this sample to the
four regions (Ilocos, Cagayan Valley, Bicol, and CARAGA) for which the credit market share of the
rural banks represents at least 25% of loans granted at the end of the studied period. Our results suggest
that a threshold might exist, under which it is easiest to underline the link between financial and
economic development. Above this threshold regional economic activity is mainly driven by real
factors such as innovation, education, entrepreneurship, among others.
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Table 5. Cointegration relationship: long run estimation between financial and economic development
Dependent variable: PC_RGDRP
Financial depth
Credit Banking office density
All regions
(N1 = 204 (Included observations = 156) ; N2 = 16) Financial depth -1.14
(3.60) -2.35
(-1.94) Rural bank credit market share -0.85
(-0.87) -0.27
(-0.42)
Intermediate developed regions
(N1 = 78 (Included observations = 60) ; N2 = 6) Financial depth -2.48
(-3.30) -2.94
(-2.15) Rural bank credit market share -0.47
(-0.72) 0.75
(1.67)
Less developed regions
(N1 = 87 (Included observations = 66) ; N2 = 7) Financial depth 7.633
(0.954) 0.99
(0.28) Rural bank credit market share 5.68
(1.24) 1.28
(1.52)
Less developed regions for which RB Credit share > 25%
(N1 = 48 (Included observations = 36) ; N2= 4) Financial depth 2.00
(5.63) 1.18
(4.00) Rural bank credit market share 0.54
(3.34) 0.11
(1.50) N1 and N2 are respectively the number of observations and the number of cross-section units.
Section 5. Conclusion
This paper aims to identify the role played by rural banks, as part of the Philippine financial nexus, on
the economic development using macroeconomic data. The period studied on the one hand is relatively
short because of data availability, and on the other hand, includes the Asian crisis which makes more
difficult the identification of a long term relationship between financial and economic development.
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As expected, our results cannot show an impact of rural banks at the national level because rural
finance represents only a small proportion of the banking activity. However, in the case of the less
developed regions, and when rural bank presence is relatively significant, an increase in the credit
market share of rural banks strengthens the economic development of the region.
Our research may encourage continuing government efforts aimed at developing the Philippine rural
banking sector and in increasing the volume of investments in the regions. Policy implications may
include the need to enhance confidence in the Philippine rural banking system, to encourage savings in
regional rural banks, and to ensure efficient transfer of resources from savers to investors.
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21
Table A1. Comparative Net Loan Portfolio of RCBs per Economic Sector (2005, 2000-2005)
Agriculture, Fisheries and
Forestry (AFF)
Industry (IND) Services (SERV)
Region 2005 Average 2005 Average 2005 Average
NCR 14.15% 17.07% 3.83% 10.44% 82.02% 72.49%
CAR 41.96% 17.84% 9.13% 25.20% 48.91% 56.95%
Northern Mindanao 39.07% 29.91% 0.81% 16.13% 60.12% 53.96%
Davao 45.92% 44.15% 1.48% 2.96% 52.59% 52.89%
Central Visayas 34.29% 22.12% 10.75% 29.86% 54.95% 48.02%
South Luzon 43.89% 45.29% 9.54% 9.22% 46.57% 45.49%
Western Visayas 59.00% 49.21% 2.82% 9.50% 38.18% 41.29%
Socksargen 47.19% 31.89% 2.43% 12.73% 50.38% 55.38%
Central Luzon 44.60% 45.80% 8.27% 8.35% 47.12% 45.85%
Zamboanga
Peninsula 36.97% 32.11% 1.11% 9.47% 61.93% 58.42%
Ilocos 48.49% 26.13% 7.70% 23.98% 43.81% 49.89%
Cagayan Valley 54.14% 50.65% 1.56% 15.96% 44.30% 33.39%
CARAGA* 27.74% 30.23% 2.41% 3.18% 69.85% 66.59%
Eastern Visayas 66.38% 50.28% 1.26% 16.65% 32.36% 33.07%
Bicol 50.27% 51.64% 18.23% 15.34% 31.50% 33.02%
ARMM 39.08% 13.19% 0.10% 14.73% 60.82% 72.08%
Average 43.32% 34.84% 5.09% 13.98% 51.59% 51.17%
22
Figure A1. Regional Market Share in Offices of Banks (1993-2005)
.0
.1
.2
.3
.4
.5
.6
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Ilocos
.0
.1
.2
.3
.4
.5
.6
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Northern Mindanao
.0
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Davao
.0
.1
.2
.3
.4
.5
.6
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Socsargen
.0
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
CAR
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
ARMM
.0
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
CARAGA
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
NCR
.0
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Cagayan Valley
.15
.20
.25
.30
.35
.40
.45
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Central Luzon
.20
.24
.28
.32
.36
.40
.44
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
South Luzon
.0
.1
.2
.3
.4
.5
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Bicol
.0
.1
.2
.3
.4
.5
.6
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Western Visayas
.1
.2
.3
.4
.5
.6
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Central Visayas
.0
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Eastern Visayas
.0
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
PBO_UCB PBO_TB PBO_RCB
Zamboanga
23
Figure A2. Regional Market Share in Total Assets of Banks (1993-2005)
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Ilocos
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Northern Mindanao
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Davao
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Socsargen
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CAR
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
ARMM
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CARAGA
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
NCR
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Cagayan Valley
.0
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Luzon
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
South Luzon
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Bicol
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Western Visayas
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Eastern Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Zamboanga
24
Figure A3. Regional Market Share in Deposit Liabilities of Banks (1993-2005)
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Ilocos
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Northern Mindanao
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Davao
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Socsargen
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CAR
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
ARMM
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CARAGA
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
NCR
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Cagayan Valley
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Luzon
.0
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
South Luzon
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Bicol
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Western Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Eastern Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Zamboanga
25
Figure A4. Regional Market Share in Net Loan Portfolio of Banks (1993-2005)
.0
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Ilocos
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Northern Mindanao
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Davao
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Socsargen
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CAR
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
ARMM
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
CARAGA
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
NCR
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Cagayan Valley
.1
.2
.3
.4
.5
.6
.7
.8
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Luzon
.1
.2
.3
.4
.5
.6
.7
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
South Luzon
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Bicol
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Western Visayas
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Central Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Eastern Visayas
0.0
0.2
0.4
0.6
0.8
1.0
93 94 95 96 97 98 99 00 01 02 03 04 05
UCB TB RCB
Zamboanga
26
Figure A5. Average Intermedition Rates for the Period 1993-2005
0%
100%
200%
300%
400%
500%
600%
Iloco
s
Cagay
an V
alley
Centra
l Luz
on
SouthL
uzon
Bicol
Western
Visaya
s
Centra
l Visa
yas
Easter
n Visa
yas
Zambo
anga
Pen
insula
Northe
rn M
indan
ao
Davao
Socks
argen
CARARMM
CARAGA* NCR
UKB TB RCB