Financial Inclusion in the Philippines:
A Regional Assessment	  Submitted by:
Tatum Blaise Tan
51-138216
The University of Tokyo	  As a requirement for the course
Case Study in International Political Economy
Summer 2014
	  Submitted to:Professor Nobuhiro HiwatariInstitute of Social Sciences
The University of Tokyo
	
  

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Abstract
The current state of financial inclusion in the Philippines appears relatively weak, lagging
behind some of its comparators. Assessing financial inclusiveness on a regional basis byconstructing an index of financial inclusion (IFI), the study finds that five regions areclassified as high IFI regions, three regions are categorized as medium IFI regions, whilenine out of the 17 regions in the country fall under the low IFI group. Low IFI regions areassociated with relatively lower income, urbanization levels, and functional literacy rates.
Moreover, the study finds that low IFI regions in the Philippines are likewise associatedwith dominance of entrepreneurial activity as main source of income and have higher
shares of employment in agriculture.
I. Introduction
Defining Financial Inclusion
Over the past decade, financial inclusion has become the maxim in the financial andeconomic arena. Highlighting its importance in achieving sustainable growth, financial
stability, and poverty alleviation. In fact, it has even transcended from simply being an
economic/financial agenda to an important social agenda. The idea of financial inclusion
has garnered support from various multilateral agencies like the Asian Development
Bank (ADB), International Monetary Fund (IMF) and World Bank (WB). Cognizant to
the importance of this financial development, this has likewise become a widely
recognized priority issue by policy makers in the Philippines. According to Hannig andJansen (2010),“Financial inclusion aims at drawing the “unbanked” population into the formal
financial system so that they have the opportunity to access financial services
ranging from savings, payments, and transfers to credit and insurance.”
As the current administration embarks on a quest for an inclusive growth, the importanceof financial inclusion becomes more apparent. Economic growth is no longer sufficient,rather a growth that trickles down all the way down to the bottom of the pyramid is now anecessity. Despite being among the countries in Southeast Asia to register marked
economic performance and displayed resilience amid the global economic crunch, thePhilippines continues to be plagued by a “jobless growth,” with the rate of unemployment
pinned at seven percent. To quote Mthuli Ncube, Chief Economist & Vice-President,
African Development Bank Group,“The concept of inclusive growth is multifaceted and has financial inclusion as
one of its main building blocks. For sustained and inclusive development tothrive, a great deal of innovation and thinking is needed to ensure thatappropriate financial services and instruments are put in place for the benefit of
the poor and other vulnerable groups.” (Ubinas, 2013)
Financial inclusion plays an critical role in overall development of the financial systemand economy. According to Khan (2012), the overall financial system benefits from
	
  

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greater inclusion by: “1) enhance saving and credit to the people at the bottom of the
pyramid; 2) providing banks a more stable deposit base contributed by retail customers;and 3) promoting financial stability and discourages informal sectors.” In addition,
financial inclusion generates a “multiplier effect on the economy through enhancedsaving and credit outreach as well as makes monetary policy more effective.” Khan
(2012)
Given the importance of financial inclusion, how can we assess or gauge acountry/state’s level of inclusiveness? While there are several indicators available toassess the state of various financial aspects, each of these indicators fails to capture theoverall extent of financial inclusion when evaluated on its own. This could potentiallyresult in misleading assessments on the country’s overall level of financial inclusion.
(Sarma, 2008) Thus, an index is crucial in evaluating a country/state’s level of financial
inclusiveness at a given time. Moreover, Conrad et al. and Beck et. al (2008, 2006) alsostress the importance of gauging banking outreach on a regional level, considering thatbank branches and ATMs could be concentrated in the urban areas and the distribution ofloans and deposits could be disproportionate across regions/states.
The rest of the paper is organized as follows: section II explains the motivation of thestudy; section III provides a comprehensive overview of the current state of financial
inclusion in the Philippines; section IV discusses the relevant literature; section V tackles
the research design of the paper; section VI presents the results and an analytical
discussion of the index; and section VII concludes the paper as well as presents policy
recommendations.
II. Motivation of the Study
The study intends to delve deeper in explaining the relatively weak state of financial
inclusion in the Philippines by providing a more empirical regional-wise analysis on theissue and identifying the socio-economic factors that could shed light on the financial
inclusion gap across the different regions. The study first constructs an index of financial
inclusion (IFI) in order to gauge the financial inclusiveness in each region as well as
categorically distinguish the regions that are falling behind. From there, the paper gathers
insights on which dimension particularly needs focus and examine key factors that areassociated with low IFI regions.
In addition, other future potential contributions of this research include the following:1) Establish an index that will serve as an input in financial policy formulation;2) Enable the concerned policy makers to monitor the progress and effectiveness ofpolicy initiatives on financial inclusion, both in magnitude and pace; and
3) Support further empirical studies relevant to financial inclusion and its impact on
growth, poverty and financial stability. Sarma (2008)
Table 1 presents the 17 regions that comprise the Philippines and the provinces undereach region.
	
  

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Table 1: Regional Composition of the Philippines
RegionProvinces
National Capital Region (NCR)

Cordillera Administrative Region(CAR)
Region I – Ilocos Region
Region II – Cagayan Valley
Region III – Central Luzon
Region IV A – CALABARZON(Cavite, Laguna, Batangas, Rizal,
and Quezon )Region IV-B MIMAROPA(Mindoro, Marinduque, Romblon,and Palawan)
Region V – Bicol Region
Region VI – Western Visayas
Region VII – Central Visayas
Region VIII – Eastern Visayas
Region IX – Zamboanga Peninsula

Region X – Northern Mindanao
Region XI – Davao Region
Region XII –SOCCSKSARGEN(South Cotabato, Cotabato, Sultan
Kudarat, Sarangani and General
Santos City)
Region XIII – Caraga
Autonomous Region in Muslim
Mindanao (ARMM)

Cities of Caloocan, Las Piñas, Makati, Malabon,
Mandaluyong, Manila, Marikina, Muntinlupa, Navotas,
Parañaque, Pasay, Pasig, Quezon, San Juan, Taguig andValenzuela, and the Municipality of Pateros
Abra, Benguet, Ifugao, Kalinga Apayao, and Mountain
Province
Ilocos Norte, Ilocos Sur, La Union, and Pangasinan
Batanes, Cagayan, Isabela, Nueva Vizcaya, and Quirino
Aurora, Tarlac, Pampanga, Zambales, Bataan, Nueva
Ecija, and Bulacan
Cavite, Laguna, Batangas, Rizal, and Quezon

Occidental Mindoro, Oriental Mindoro, Marinduque,Romblon, and Palawan
Camarines Norte, Camarines Sur, Albay, Masbate,
Sorsogon, and Catanduanes
Negros Occidental, Iloilo, Antique, Guimaras, Aklan and
Capiz
Bohol, Cebu, Negros Oriental, and Siquijor
Leyte, Southern Leyte, Biliran, Eastern Samar, Northern
Samar, and Samar
Zamboanga Sibugay, Zamboanga del Norte, Zamboangadel Sur, Zamboanga City (Zamboanga Peninsula), andIsabela City (Basilan Province)
Bukidnon, Camiguin, Misamis Oriental, Misamis
Occidental, and Lanao del Norte
Davao City, Davao del Norte, Davao del Sur, Davao
Oriental, and Compostela Valley
North Cotabato, South Cotabato, Sultan Kudarat
Province, Saranggani, and the cities of Cotabato, General
Santos, Kidapawan, Koronadal, and Tacurong
Agusan del Norte, Agusan del Sur, Surigao del Norte,and Surigao del Sur
Basilan, Lanao del Sur, Sulu and Tawi-Tawi, andMaguindanao Province	  	
  Source:	
  Bangko	  Sentral	
  ng	
  Pilipinas	  (2010),	
  Regional	
  Economic	
  Developments	
  in	
  the	
  Philippines	  III. Financial Inclusion: How Does the Philippines Fair?
According to the World Bank (2012), 2.5 billion people remains “unbanked” and threequarters of the poor people worldwide do not have a bank account. Table 2 presents across-country summary of selected financial inclusion indicators sourced from the World
DataBank G20 Financial Inclusion Indicators. Based on the table, the Philippines lags
	
  

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behind its Southeast Asian comparators, espacially Malaysia and Thailand in bothbanking penetration and availability of financial services. In terms of number of deposit
accounts per 100 adults and ATMs per 100,000 adults, the Philippines barely comprises athird of the said countries’ figures.
Table 2: Selected Financial Inclusion Indicators Across South East Asia
Cambodia Indonesia Philippines Malaysia Thailand
Banking Penetration
Account at a formal financial
institution (% age 15+)
3.7
19.6
26.6
66.2
72.7
Number of deposit accounts withcommercial banks per 1,000 adults 131.5
623.7
492.8
1642.21414.3Availability of Banking Services
Commercial bank branches (per100,000 adults)
4.3
8.5
8.1
10.5
11.3
Automated teller machines(ATMs) (per 100,000 adults)
6.1
16.5
17.7
56.4
77.9
Source: World DataBank G20 Financial Inclusion Indicators

Sarma (2008) evaluated several countries’ financial inclusiveness for the year 2004 andfinds that the Philippines are among the countries that fall under the low financial
inclusion category. In particular, the Philippines ranks 34th out of 55 countries in thethree-dimensional index and 53rd out of 100 countries in the two-dimensional index.
Table 3 presents the constructed index and the ranking of selected countries based on the
research, only Malaysia and Thailand reached the medium financial inclusion category
while the rest are in the low financial inclusion group.
Table 3: Index of Financial Inclusion of Selected Southeast Asian Countries, 2004
Country
IFI
Rank
IFI
Rank
3-Dimensional
2-Dimensional
Malaysia
0.406
10
0.367
20
Thailand
0.360
14
0.303
30
Philippines
0.146
34
0.163
53
Indonesia
0.141
60
Cambodia
0.015
100
Notes: 1) IFI values between 0.5 to 1 is classified as high financial inclusion, while between 0.3
to 0.5 is classified under medium financial inclusion, and values under 0.3 are considered as low
financial inclusion; and 2) The two-dimensional index excludes the banking penetration
dimension, measured as the number of bank accounts as a proportion to the total population.
Source: Sarma, M. (June 2008). Index of Financial Inclusion.

In Sarma’s (2012) succeeding research on the same area, the Philippines continues to bein the low financial inclusion category, albeit a recording a higher ratio at 0.258 in 2010.
Table 4 shows the index of financial inclusion for selected Southeast Asian countries forthe periods 2004-2010. The Philippines advanced at a much slower pace compared to its
other neighbors over the past six years, particularly Malaysia and Thailand. These
	
  

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countries have transcended to the high financial inclusion group, with Malaysia
displaying a relatively swift progress.
Table 4: Index of Financial Inclusion of Selected Southeast Asian Countries
2004 - 2010
Country
2004
2005
2006
2007
2008
2009
2010
Malaysia
0.636
0.692
0.709
0.710
0.720
0.710
0.791
Thailand
0.643
0.655
0.655
0.673
Philippines
0.216
0.221
0.218
0.228
0.231
0.258
Cambodia
0.037
0.054
0.061
0.074
0.087
Note: IFI values between 0.6 to 1 is classified as high financial inclusion, while between 0.3 to
0.6 is classified under medium financial inclusion, and values under 0.3 are considered as low
financial inclusion
Source: Sarma, M. (July 2021). Index of Financial Inclusion- A Measure of Sector Inclusiveness.

According to Socioeconomic Planning Secretary Artemio Balisacan, “only 41 out of
100,000 Filipinos own a deposit account.” Moreover, the number of deposit accounts per
100,000 population is likewise still very far from the target of 750 by 2016, registering
only at 41.3 in the first half of 2012. (NEDA, 2013 and De Leon, 18 February 2013)
These statements are parallel to the results of the Consumer Finance Survey (CFS)
conducted by the Bangko Sentral ng Pilipinas (BSP). 1 Based on the survey eight out often households are unbanked, and among those with deposit accounts, approximately 71
percent only have one bank account. The percentage of banked households in NCR,
about 28 percent, is significantly higher than that in AONCR, roughly 16 percent only. In
terms of credit availment, the survey results revealed that most of the respondents
acquired their assets/properties through cash payment instead of borrowing, and on
average, less than seven percent availed of loans to acquire properties/assets. (CFS, 2009)III. Review of Related Literature
The Index
Sarma’s (2008) cross-country research on financial inclusion introduces the concept of
constructing an index of financial inclusion (IFI) in order to measure a country’s current
state of financial inclusion. The study provides three-dimensional IFIs for 55 countries
and two-dimensional IFIs for 100 countries for the year 2004. The two-dimensional IFI
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1

Reference year: 2009 as the reference year. Coverage: National Capital Region (NCR), Region
1- Ilocos Region, Region 7- Central Visayas and Region 11- Davao Region (the last three regions
are classified as Areas Outside NCR (AONCR)). The survey looks at the financial conditions ofhouseholds, including their sources of financing and level of indebtedness.
2	
  Data	  sources:	
  2012	
  Family	
  Income	
  and	  Expenditure	
  Survey,	
  NSO	
  and	
  NSCB.	
  3	
  “Poverty	
   incidence	
   proportion	
   of	
   families/individuals	
   whose	
   income	
   is	
   below	
   the	   poverty	  threshold	
  or	
  whose	  income	
  is	
  not	  sufficient	
  to	
  buy	  the	
  basic	
  food	  and	
  non-­‐food	
  requirements,	
  ,	
  while	  the	
  subsistence	  incidence	
  is	
  proportion	
  of	
  families/individuals	
  whose	  income	
  is	
  below	  the	  food	
  threshold	
  or	
  whose	  income	
  is	
  not	  sufficient	
  to	
  buy	
  food	  items	
  that	  will	  meet	  the	
  basic	
  food	  requirements.”	
   (NSCB	
   Resolution	
   No.	
   2,	   series	
   of	
   2007)	
   The	   annual	
   per	
   capita	
   poverty	  threshold	
  Php	
   18,935,	
   while	
   the	   annual	
   per	
   capital	
   subsistence	
   threshold	
   is	
   Php13,232	
   in	
  
	
  
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excludes the dimension pertaining to banking penetration. The dimensions are as follows
with their corresponding purpose and indicators employed:
Dimension
Banking
penetration
Availability of
Banking Services
Usage

Captures/Measures
Size of “banked”
population
Accessibility of banking
services to its users
Utilization of banking
services

Indicator Employed
Number of bank accounts as a proportion of
the total population
Number of bank branches per 1000 population
Volume of credit and deposit as a proportion of
the country’s gross domestic product (GDP)The paper also provides a discussion on the significance of the said dimensions inmeasuring financial inclusion:
1. Banking penetration (dimension 1): An inclusive financial system should have as
many users as possible, that is, an inclusive financial system should penetrate
widely amongst its users… The proportion of people having a bank account is ameasure of the banking penetration of the system;
2. Availability of banking services (dimension 2): In an inclusive financial system,banking services should be easily available to the users. Indicators of availability
are banking outlets (offices, branches, ATMs and so on); and3. Usage (dimension 3): This dimension is motivated by the notion of ‘underbanked’or ‘marginally banked’ people... These underbanked people, despite having
access to the formal financial services, are unable to use the financial services,
due to various reasons... These factors reflect negatively on the inclusiveness of afinancial system. Thus, merely having a bank account is not enough for an
inclusive financial system; it is also imperative that the banking services areadequately utilized.
Sarma (2008) uses an approach similar to the United Nations Development Programme’s(UNDP) method in measuring development indices. Each dimension is computed as! !  !
follows: 𝑑! =    !! !! , where: Ai = actual value of the dimension, Mi = maximum value of!!  

!

dimension i, and mi = minimum value of dimension i. This ensures that the value of diwill fall between 0 and 1. To construct the IFI for a particular country, Sarma (2008)
measured the normalized inverse Euclidean distance of the point representing the country
in the n-dimensional Cartesian plane from the ideal point, which is the point where all
dimensions equal to one. The formula is as follows:
𝐼𝐹𝐼! = 1 −   1 − 𝑑! ! +    1 − 𝑑! ! + ⋯ +    1 − 𝑑! !
𝑛where each di captures a particular dimension (i.e. penetration, availability or usage) andn is the number of dimensions.
	
  

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The study finds that among the 55 countries (under the three-dimensional IFI) only fivecountries belong to the high IFI classification (IFI values of 0.5 and up) led by Spain at
0.737, while nine countries fall under the medium IFI category (IFI values between 0.3 to0.5), and the rest of the countries are under the low IFI classification (IFI values below0.3), with Madagascar registering the lowest IFI value at 0.155. Furthermore, the study
concludes that most countries in the high IFI group belonged to high-income OECD
countries, while the frontrunners in the Southeast region are Malaysia, Singapore andThailand. The Philippines, belonging to the low IFI group and ranks 34th out of the 55,
recorded an IFI value of 0.146.
Chattopadhyay (2011) applies the methodology developed by Sarma (2008) to generate astate-wise index for India in order to assess each state’s financial inclusiveness. The study
includes the years 2006-2009 and zooms in on West Bengal by conducting a survey inthe said state to reinforce the paper’s initial findings. The research’s methodology varies
slightly in the indicators used to measure the various dimension (summarized below), inlight of the recent literary developments in the field and data availability.
Dimension
Banking penetration

Availability of Banking
Services
Usage

Chattopadhyay (2011)
Sarma (2008)
Indicator
Number of bank accounts (deposit
Number of bank accounts asand credit) as a proportion of the total a proportion of the total
population
population
Number of bank branches per 1000
Number of bank branches
adult population;
per 1000 population
Number of branches per square km
Volume of outstanding deposit andVolume of credit and deposit
credit as proportion of state domestic
as a proportion of the
product
country’s GDP

The research finds that only two states, Maharashtra and Karnataka, belong to the high
IFI group, while five states fall under the medium IFI group. The remaining 16 states areclassified as low IFI. The study notes that all the southern states belonged to either thehigh or medium IFI groups, while all the Eastern, Northeastern, and Central states fallunder the low IFI category. In terms of monitoring policy initiatives, the paper concludes
that there has been no considerable success in financial inclusion in India since theinitiatives started in 2005-06.
Sarma (2012) continued the research on constructing a financial inclusion index byemploying a more sophisticated methodology to measure the financial inclusiveness of
94 countries over the period 2004 to 2010. This approach differs from the one employedin Sarma’s earlier study in the following aspects: 1) introduction of weights to thedimensions, depending on its perceived importance in gauging financial inclusiveness;2) redefinition of the maximum value in the calculation of dimensions to correct foroutliers and the varying values of the upper bound in the time series; 3) revision of the
IFI formula; and 4) revision of the ranges for the classification of IFI values. The tablebelow summarizes the dimensions, the weights assigned to each dimension, the upper
limits, and the indicators employed to measure the dimension:
	
  

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Dimension
Banking
penetration
Availabilityof banking
services

Weight(wi)
1

Indicator

Upper Limit*

Number of deposit bank accounts 2500 – average of at least two
per 1000 adult population
deposit accounts per adult
0.5
Number of bank outlets per 100060 – about 1667 clients per
populationbank branch
Number of ATMs per 1000
120 – 1 ATM per 833 adults
population
Usage
0.5
Volume of credit to the private
300 – credit plus deposit tosector and deposit mobilized from GDP ratio of 3
the private sector as proportion of
the country’s GDP* Represents the 90th percentile of the distribution of the dataset except for the ATM dimension,
which represents approximately the 92th percentile.
! !  !!

The study calculates each dimension as follows: 𝑑! =    𝑤! !!

!!   !!

, where wi represents theweight assigned to that particular dimension. Albeit still based on UNDP’s approach, theindex differs in a sense that it is “based on a notion of distance from a worst and an ideal
situations.” It takes into account the distance between a particular point, X, from the ideal
point, W, as well as from the worst point, O, as illustrated graphically in Chart 2. The
calculation of the index uses a simple average of the Euclidean distance between X and O
and the inverse Euclidean distance between X and W. The equation below presents the
new IFI calculation for each country, where X1 represents the distance between point X
from the worst point O, and X2 represents the distance between point X and the ideal
point W. (Sarma, 2012)
𝐼𝐹𝐼! =

1
2

𝑑! ! +    𝑑! ! + ⋯ +    𝑑! !
𝑛

+ 1 −   1 − 𝑑! ! +    1 − 𝑑! ! + ⋯ +    1 − 𝑑! !

X1	
  

𝑛

X2	
  

The countries are classified according to their respective IFI values. Those with IFI
values between 0.6 and 1 are classified as high IFI countries, those with values between
0.3 and 0.6 are considered as medium IFI countries, and those with less than 0.3 areplaced under the low IFI group. Results show that most countries with high IFI values
belong to high-income countries, while those in the low IFI group are predominantly
from low and lower middle income countries.
Factors Affecting the State of Financial Inclusion
According to the World Bank (2012), the most prevalent reasons for not having a bankaccount are insufficient income, transactions costs, distance of financial institutions andbureaucratic barriers. Sarma (2012) offers a parallel finding that in general “financial
inclusion and income levels tend to move in the same direction.” The World Bank’s
	
  

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(2014) report on financial development also points out that barriers to financial access are
more pronounced for the poor, women, youth and rural-based segments of the society.
On a more micro-level, Chattopadhyay (2011) further assesses the degree of financial
inclusiveness in the state by looking at socio-economic factors as well as the inhabitants’opinion on banking services in the three districts of West Bengal, a low IFI state.
Chattopadhyay (2011) finds that a significant portion of the population is dependent on
cultivation and that in the district of Birbhum, the loan extensions are particularly very
low because the families living there “have no meaningful income and lack[s] direction
in business plans.” The research adds that those without bank accounts felt that they do
not have enough income to open a bank account, albeit the perceived adequate banking
facilities in the area.
In the case of the Philippines, the BSP and Alliance for Financial Inclusion (AFI)
Financial Inclusion Data Working Group (2014) highlights the country’s geographicaland physical constraints, given its archipelagic nature, in facilitating financial access. The
case study likewise adds that the “distribution of banking services is skewed highly tourbanized and populous regions,” citing that Metro Manila itself accounts for“43 percent of the total number of deposits and 68 percent of the amount of bankdeposits.” Meanwhile, demand side factors identified by the CFS (2009) include
insufficient money (92.8 percent), no need for bank/cash account (1.7 percent), unable tomanage an account (1.5 percent), minimum balance is too high (1.2 percent), and dislike
in dealing with bank/institutions (1.0 percent), among others. The survey concludes thatcredit access in some areas of the country is “relatively limited.”
IV. Methodology
This study constructs a regional IFI for thePhilippines in order to gauge the degree offinancial inclusiveness of each region forthe year 2012. The research methodology
adopts that of Sarma’s (2012) researchwith slight differences on the indicators
employed due to data availability and theminimum values used in computing thevalues for the dimensions in order tocorrect for outliers. The data are sourced
from the Bangko Sentral ng Pilipinas(BSP), National Statistics Office (NSO),National Coordination Board (NSCB), andthe report on the State of Financial
Inclusion in the Philippines (BSP, 2012).
The dimensions as well as the indicators
used in the calculation are as follows:	
  Chart	  2:	  The	
  Map	  of	
  the	
  Philippines	  	
  Source:www.top-­‐destination-­‐choice-­‐the-­‐philippines.com	
  

	
  

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Dimension
Bank penetration
Availability of banking
services

Notation
Pi
ABSi

Usage

Ui

Indicator *Number of bank accounts per 1000 adult population
Number of banking offices per 1000 adult population
Number of ATMs per 1000 adult population
Number of alternative financial service providers per1000 adult population
Outstanding loans and deposits as proportion of theregion’s GDP

Notes:
1) The data on adult (individuals above 15 years old) population is based on the 2010 census of
population;2) The data on the number banking offices include head offices, regular branches, microfinanceoriented branches, extension offices, other banking offices (OBOs), and micro-banking office(MBOs); and
3) The data on the number of alternative financial service providers include Non-stock savingsand loan associations (NSSLAs), Credit Cooperatives, Non-bank financial institutions (NBFIs),
pawnshops, remittance agents, e-money agents, and money changers/foreign exchange (FX)
dealers.

Following Sarma’s (2012) formula in deriving the IFI values, the IFI equation for eachregion is as follows:
𝐼𝐹𝐼! =

1
2

𝑃! ! + 𝐴𝐵𝑆! ! +    𝑈! !
3

+ 1 −   𝑃! ! + 1 − 𝐴𝐵𝑆! ! +    1 − 𝑈! !
3

where i represents a given region and Pi, Ai and Ui represents the availability and usage
dimensions for that particular region.
Similarly, the estimates for the dimension (Di), Pi, ABSi and Ui, is calculated as! !  !
𝐷! =    !! !! , where Ai represents the actual value of the indicator (number of bank!!  

!

accounts, ATM, banking offices, alternative financial service providers, outstanding
deposits and loans as a proportion of the regional GDP), and Mi and mi, represents the
maximum and minimum values of the dataset, respectively. The upper bound likewiseuses the 90th percentile as the maximum value, while the minimum value uses the 10th
percentile as the lower bound to adjust for the outliers. Hence for a given dimension, any
value above the upper limit is given the highest value—equivalent to the 90th percentile—
and any value lower than the lower bound is given the minimum value—equivalent to the10th percentile. Since the availability dimension has there indicators (di), a simple averageof the three indicators was used, following the method by Gupte et al. (2012) inestimating dimensions that have multiple indicators.
As for categorizing the IFI values, the study follows Sarma’s (2012) classification,
wherein IFI values between 0.6 and 1 are classified as high IFI regions, those with values
between 0.3 and 0.6 are considered as medium IFI regions, and those with less than 0.3
are placed under the low IFI regions. Albeit, it is important to note that the top IFI does
	
  

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not imply perfect financial inclusion, rather it is deemed as the “best” state of financial
inclusion relative to the country under study, and the same goes for the bottom IFI.
V. Presentation and Analysis of Results
Table 4 presents the IFI values for the different regions for the year 2012. The IFI values
highly vary across the 17 regions, with the National Capital Region (NCR) on top and theAutonomous Region of Muslim Mindanao (ARMM) at the bottom. Among the 17
regions, five regions were able to attain high IFI values, to wit: National Capital Region(NCR), Region III-Central Luzon, Region IVA-CALABARZON, Region VII-Central
Visayas and Region XI- Davao Region. The medium IFI regions include Cordillera
Administrative Region (CAR), Region VI, and Region X. Meanwhile, nine out of the 17
regions fall under the low IFI group, namely: are Region I, Region II, Region IVB,
Region VIII, Region IX, Region XII, Region XIII, and the ARMM. The average index
value for “bank penetration” is 0.347, while the average for “availability of banking
services” is 0.445, and the average index value for “usage” of banking services is 0.395.
As for the overall IFI value, the average stands at 0.398, leaning more towards the lowerend of the medium IFI category.
Zooming in on the nine low IFI regions (kindly refer to Table 5), the average IFI value
registers at 0.142, falling under the middle range of the low IFI category. The ARMM
stands out registering the lowest IFI value for all dimensions. On the other end, theRegion XII recorded a 0.293 IFI value, very close to transcending to the middle IFI
category. The region’s index for banking penetration is significantly higher than theoverall average, however, the availability of banking services and the usage index fall
below the overall average. This could suggest that while the population is relatively
banked, utilization of financial services is limited, which perhaps could be related to thephysical barriers to financial access. In contrast, MIMAROPA has a relatively high index
for availability of banking services, only a little below the average, but its banked
population is quite low and the use of financial services is at the minimum. This could
imply that while there are many available bank outlets in the region, for some reason, thepopulation remains relatively unbanked and financial services are not quite utilized.

	
  

11	
  

`	
  

Table	  4:	  IFI	
  values	
  for	  the	  17	  regions	
  in	
  the	
  Philippines,	
  2012	  Legend:	
  

	
  

12	
  

`	
  
Table	  5:	  Low	
  IFI	
  Regions	  	
  Regions

IIIIV-B
VIXXIIVIIIXIII-Bank
Penetration

Availabilityof Banking
Services

0.065
0.000
0.007
0.09
0.087
0.47
0.085
0.024
0
0.092

0.127
0.362
0.428
0.213
0.101
0.224
0.153
0.292
0
0.211

IlocosCagayan Valley
MIMAROPA
Bicol
Zamboanga Peninsula
SOCOSKSARGEN
Eastern Visayas
CARAGA
ARMM
Average

The subsequent discussions explore
the potential factors that could help
shed light on the degree and variationof IFI values across region. Table 6
presents the correlation between the
IFI values and selected socioeconomic indicators. While several
otherfactors(e.g.
inflation,
unemployment, and average familysize) were considered in the study, thefollowing factors showed the strongest
correlation. Before proceeding further,a cautionary remark is in order. It is
important to keep in mind that
correlation only implies association
but not necessarily causality.

	
  

Usage

0.125
0.341
0
0.063
0.172
0.16
0
0.17
0
0.115

IFI

0.108
0.252
0.185
0.129
0.123
0.293
0.089
0.175
0
0.142

Table 6: Correlation with IFI Values
2012 (unless otherwise stated)
Per Capital Gross Domestic
Product (Current Prices)
0.742
Average Family Income
0.827
Poverty Incidence-0.739Subsistence incidence-0.650Level of Urbanization, 2010
0.846
Main Source of Income
Salary/Wage
0.738
Entrepreneurial-0.606
Other Sources
0.024
Functional Literacy Rate, 2008
0.692
Share of Agriculture in Total
Employment-0.809Source: National Statistical Coordination Board,
National Statistics Office, and Author's calculations

13	
  

`	
  
Regional GDP Per Capita Income and Average Family Income 2

ARMM	
  

CARAGA	  SOCOSKSARGEN	
  	
  Zamboanga	  Peninsula	
  Eastern	  Visayas	  Bicol	
  Mimaropa	  	
  Cagayan	  Valley	
  Ilocos	  Davao	
  Region	
  Central	
  Visayas	
  Calabarzon	  	
  Central	  Luzon	
  	
  

NCR	
  

The most common reason cited Chart	
  3:	
  	
  GDP	  Per	  Capita,	
  by	
  Region	  (High	
  vs.	  by the respondents in the CFS Low	  IFI	
  Regions),	
  2012	  (2009) for not having a bank 350,000	
  	
  
	
  
account is the lack of money or 300,000	
  250,000	  	
  insufficient income. The results 200,000	
  	
  
	
  
of this study comport with the 150,000	
  100,000	  	
  said statement, GDP per capita 50,000	
  	
  
0	
  	
  
is positively correlated to the
IFI values at 0.742. Chart 3
presents the regional per capita
income of high and low IFI
regions. Based on the chart,high IFI regions are associatedwith significantly higher perIII	
   IV-­‐ VII	
   XI	   I	   II	
   IV-­‐ V	
   VIII	
   IX	
   XII	
   XIII	
   	
  	
  	
  	
  	
  	
  
GDP capita income while low
A	
  
B	
  
IFI regions are associated withlower per capita income. The Legend:	
  Blue	  –	
  High	  IFI	
  regions,	
  Green	
  –	
  Low	
  IFI	
  high IFI and high income regions	
  region is led by the NCR Source:	
  National	
  Statistical	
  Coordination	
  Board	  posting 312,137 Php, almost
triple the national level of 110,314 Php. In stark contrast is the ARMM which has thelowest IFI value and the lowest per capita income at 27,819 Php. Probing further using
the regional average family income, two notable trends can be observed: 1) high IFI
regions generally have higher average family income; and 2) regions in the Luzon, eventhose low IFI regions, generally have higher average household income compared to theircounterparts in Visayas and Mindanao. In particular, the three high IFI regions in Luzon
rank in the top three nationwide, significantly exceeding the national average of 235,000
Php. The two other high IFI regions located outside Luzon, Region VII and XI, haverelatively lower average family income at 209,000 and 194,000, respectively. Moreover,while low IFI regions are generally associated with lower average family income,Regions I and II in the Luzon island registered moderate levels of average family income
at 204,000 and 195,000, respectively. These levels are almost at par with the high IFI
regions outside Luzon. The lowest average family income was recorded at the ARMM at
130,000, substantially lower than the national average. These results are consistent with
Sarma’s (2012) findings that income and level of financial inclusion moves in tandem.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
2	
  Data	  sources:	
  2012	
  Family	
  Income	
  and	  Expenditure	
  Survey,	
  NSO	
  and	
  NSCB.	
  
	
  

14	
  

`	
  
Poverty Incidence and Subsistence Incidence 3

ARMM	
  

CARAGA	  SOCOSKSARGEN	
  Davao	
  Region	  Northern	
  Mindanao	
  Zamboanga	  Peninsula	
  Central	
  Visayas	
  Eastern	  Visayas	  Bicol	
  Western	  Visayas	  	
  Mimaropa	  	
  Calabarzon	  	
  Central	  Luzon	  	
  Ilocos	  Cagayan	  Valley	
  

CAR	
  

NCR	
  

In	
  percent	
  Regions with high poverty incidence and subsistence incidence translate to higher
number of families with inadequate income to even support their basic needs. Thus, thesefamilies will most likely fall under the unbanked segment of the population withinsufficient income to open deposit accounts. These segments are also unlikely to obtain
credit from formal institutions because of their relative riskiness and their inability tocomply with the documentary requirements. Regions with high IFI values are associatedwith lower levels of poverty incidence among its population, especially the three high IFI
regions in Luzon: NCR, Central Luzon and CALABARZON. The NCR records thelowest poverty and subsistence incidence at 3.9 and 0.5 percent, respectively. As for thehigh IFI regions outside Luzon, their poverty and subsistence incidence are comparable,
even higher, compared to some of the low IFI regions located in Luzon. This is parallel tothe earlier findings on the average household income. Meanwhile, the low IFI regions in
Visayas and Mindanao havethe highest poverty and Chart	
  4:	
  Poverty	
  and	  Subsistence	  Incidences,	
  2012	  subsistence incidences amongits population. The ARMM has
Poverty	
  Incidence	  Subsistence	  incidence	
  
60	
  	
  
50	
  	
  
the highest poverty incidence
40	
  	
  
55.8 percent, more than half of
30	
  	
  
the population in the region.
20	
  	
  
10	
  	
  
Based on Chart 4, all the low
0	
  	
  
IFI regions, except for Regions
I and II in Luzon, areassociated with poverty andsubsistence incidences that are
well above the national
incidence. Table 6 indicates
	
  	
  	
  	
   	
  	
  	
  	
  	
  	
  	
  	
  	
   I-­‐	
   II-­‐	
   III-­‐	
   IV-­‐A	
  IV-­‐B	
   V-­‐	
   VI-­‐	
   VII-­‐	  VIII-­‐	
   IX-­‐	
   X-­‐	
   XI-­‐	
   XII-­‐	  XIII-­‐	
   	
  	
  	
  	
  
that poverty incidence andsubsistence
incidence
are Note:	
   Poverty	
   incidence	
   and	   subsistence	
   incidence	
   for	   the	  highlyandnegatively Philippines	
  are	
  25.2	
  and	
  10.4	
  percent,	
  respectively.	
  correlated with IFI values at - Source:	
  Family	
  Income	
  and	  Expenditure	
  Survey	
  (FIES),	
  National	
  Statistics	
  Office	
  0.739 and -0.650, respectively.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
3	
  “Poverty	
   incidence	
   proportion	
   of	
   families/individuals	
   whose	
   income	
   is	
   below	
   the	   poverty	  threshold	
  or	
  whose	  income	
  is	
  not	  sufficient	
  to	
  buy	  the	
  basic	
  food	  and	
  non-­‐food	
  requirements,	
  ,	
  while	  the	
  subsistence	  incidence	
  is	
  proportion	
  of	
  families/individuals	
  whose	  income	
  is	
  below	  the	  food	
  threshold	
  or	
  whose	  income	
  is	
  not	  sufficient	
  to	
  buy	
  food	  items	
  that	  will	  meet	  the	
  basic	
  food	  requirements.”	
   (NSCB	
   Resolution	
   No.	
   2,	   series	
   of	
   2007)	
   The	   annual	
   per	
   capita	
   poverty	  threshold	
   Php	
   18,935,	
   while	
   the	   annual	
   per	
   capital	
   subsistence	
   threshold	
   is	
   Php13,232	
   in	
  2012.	
  

	
  

15	
  

`	
  
Level of Urbanization 4	
  Chart	  5:	  Level	  of	
  Urbanization,	
  2010	
  

ARMM	
  

CARAGA	  SOCOSKSARGEN	
  Davao	
  Region	  Northern	
  Mindanao	
  Zamboanga	  Peninsula	
  Central	
  Visayas	
  Eastern	  Visayas	  Bicol	
  Western	  Visayas	  	
  Mimaropa	  	
  Calabarzon	  	
  Central	  Luzon	  	
  Cagayan	  Valley	
  

CAR	
  

Ilocos	
  

NCR	
  

Most of the high IFI regions arelocated in Luzon, the largest and 120	
  
100	
  
considered as the center of economic100	
  and political activities in the80	  Philippines. Meanwhile, Regions VII
59.7	
  
59.3	
  
51.6	
  
and XI include the two major
60	
  
46.5	
  
43.7	
  
41.3	
  
34.7	
  
33.9	
  
economic
powerhouses
and
40	
  
27.5	
  
26.3	
  
22.3	
  
relatively highly urbanized cities,
15.3	
  
13.7	
  
12.7	
  11.6	
  
8.7	
  
20	
  
Cebu City and Davao City,0	
  respectively. Chart 5 presents thelevel of urbanization by region forthe year 2010. On one hand, it may
be observed that all of the high IFI
regions are above the national levelof 45.3, except for Region VII,
which is slightly lower at 43.7. This
	
  	
  	
  	
  	
  	
   	
  	
  	
  	
  	
  	
  	
   I	
   II	
   III	
   IV-­‐ IV-­‐ V	
   VI	
   VII	
  VIII	
  IX	
   X	
   XI	
   XII	  XIII	
  	
  	
  	
  	
  	
  	
  	
  
suggests that regions that are highly
A	
   B	
  
financially inclusive are associatedwith higher levels of urbanization. Note:	
  The	
  national	
  level	  of	
  urbanization	
  is	
  45.3.	
  Financial institutions are likely Source:	
  National	
  Statistics	
  Office	
  prefer to invest in areas that are more
populous, progressive, and with higher income levels. Correspondingly, higher thepercentage of population living in the urbanized area translates to more people having
wider access to said financial institutions/services. This potentially explains the positive
relationship between the level of financial inclusion and highly urbanized regions.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
4	
  The	
  NSO	
  defines	
  the	
  level	  of	
  urbanization,	
  “as	  the	
  proportion	
  of	
  the	
  urban	
  population	
  to	
  the	  total	
  population	
  for	  a	
  specific	
  period,	
  which	  is	
  calculated	
  by	
  dividing	
  the	
  urban	
  population	
  by	
  
the	
  total	
  population	  and	
  multiplying	
  the	
  quotient	
  by	
  100.”	
  (http://www.census.gov.ph/sites/default/files/attachments/hsd/article/Explanatory%20Tex
t.pdf)	
  	
  

	
  

16	
  

`	
  
Functional Literacy Rate 5

ARMM	
  

CARAGA	  Davao	
  Region	  SOCOSKSARGEN	
  Northern	
  Mindanao	
  Zamboanga	  Peninsula	
  Central	
  Visayas	
  Eastern	  Visayas	  Bicol	
  Western	  Visayas	  	
  Mimaropa	  	
  Calabarzon	  	
  Central	  Luzon	  	
  Cagayan	  Valley	
  

CAR	
  

Ilocos	
  

NCR	
  

This study finds that high IFI Chart	
  6:	
  Functional	
  Literacy	
  Rate,	  2008	  regions are generally associated 	  with functional literacy rates that 100	
  
90	
  
are above the national level of80	
  86.4 percent. The NCR leads the
70	
  
60	
  
pack at 94 percent. Chart 6
50	
  
40	
  
presents the functional literacy30	
  rate for each region. For the
20	
  
10	
  
medium IFI group only the CAR,0	
  which has the highest IFI value
in the group, has a functional
literacy rate above the national
standard. Correspondingly, thelow IFI regions generally havefunctional literacy rates that are
	
  	
  	
  	
  	
  	
   	
  	
  	
  	
  	
  	
   I	
   II	
   III	
   IV-­‐ IV-­‐ V	
   VI	
   VII	
  VIII	
  IX	
   X	
   XI	
   XII	  XIII	
  	
  	
  	
  	
  	
  	
  	
  	
  
generally lower the national
A	
   B	
  
functional literacy rate. The tworegions with the lowest IFI, Notes:	
  1)	
  Includes	
  10-­‐64	
  years	
  old	
  
ARMM and Region VIII, also has 	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  2)	
  The	
  national	
  literacy	
  rate	
  is	
  86.4.	
  Source:	  Functional	
  Literacy,	
  Education	  and	
  Mass	  Media	  the lowest literacy rate at 71.6 and Survey	
  (FLEMMS),	
  National	
  Statistics	
  Office	
  72.9, respectively. This issubstantially below the national standard. Lehrer and West (2014) label literacy as “thehidden hurdle” in attaining financial inclusion. The article explains, “systems that should
work in theory breakdown when poor people are unable to learn how to use them....” Afunctionally literate person means that the individual cannot only read and write but alsoable to compute or completed at least a high school level of education, as such, they are
possibly more receptive or at ease in dealing with financial institutions. This could
explain the positive correlation between IFI values and the region’s functional literacy
rate. (NSO, 2010)

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
5	
  The	
   NSCB	   defines	
   functional	
   literacy	
   as	   a	   significantly	
   higher	
   level	
   of	
   literacy	
   that	   includes	  reading,	
  writing,	
  and	
  numeracy	
  skill.	
  These	
  skills	
  must	  be	
  sufficiently	
  advanced	
  to	
  enable	
  the	  individual	
   to	
   participate	
   fully	
   and	   effectively	
   in	
   activities	
   commonly	
   occurring	
   in	
   his	   life	
  situation	
   that	
   require	
   a	
   reasonable	
   capacity	
   beyond	
   oral	
   and	   written	
   communication.	
  (http://www.census.gov.ph/content/technical-­‐notes-­‐1994-­‐functional-­‐literacy-­‐education-­‐and-­‐mass-­‐media-­‐survey-­‐flemms)	
  	
  

	
  

17	
  

`	
  
Main Source of Income 6

Chart	  7:	  Share	  of	
  Microenterprises,	
  2012	
  	
  

ARMM	
  

CARAGA	  Davao	
  Region	  SOCOSKSARGEN	
  Northern	
  Mindanao	
  Zamboanga	  Peninsula	
  Central	
  Visayas	
  Eastern	  Visayas	  Bicol	
  Western	  Visayas	  	
  Mimaropa	  	
  Calabarzon	  	
  Central	  Luzon	  	
  Ilocos	  Cagayan	  Valley	
  

CAR	
  

NCR	
  

The research also tests for the6%	
  correlation between the family’s 100%	
  
5%	
  
95%	
  
main source of income, by region,4%	
  and the IFI and uncovers an
90%	
  
3%	
  
interesting observation that regions
85%	
  
2%	
  
wherein the family’s main source
80%	
  
1%	
  
of income is entrepreneurial are
0%	
  
75%	
  
associated with low IFI values.
Taking a step further, the researchlooked into the composition of theentrepreneurial activity in theregions and gains support on its
initial proposition that these
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  I-­‐	
   II-­‐	
   III-­‐	  IV-­‐A	
  IV-­‐B	
  V-­‐	
   VI-­‐	
  VII-­‐	
  VIII-­‐	  IX-­‐	
   X-­‐	
   XI-­‐	
   XII-­‐	  XIII-­‐	
   	
  	
  	
  	
  
entrepreneurial activities could be
Share	  of	
  Microenterprises	
  to	
  Total	
  Number	  of	
  Establishments	
  (LHS)	
  dominated by microenterprises.
Share	  of	
  Microenterprise	
  loans	
  to	
  Total	
  Loans	
  (RHS)	
  Chart 7 shows the share of
Note:	
  Classification	
  of	
  the	
  size	  of	  establishments	
  is	
  based	  microenterprises in the totalon	
  number	
  of	
  employees.	
  Enterprises	
  with	
  1-­‐9	
  employees	
  number of establishments as wellare	
  categorized	
  as	
  a	
  microenterprise.	
  as the share of outstanding
Sources:	
  Bangko	  Sentral	
  ng	
  Pilipinas	
  and	
  List	  of	
  microloans to outstanding total
Establishments,	
  Department	
  of	
  Trade	
  and	
  Industry	  	
  loans, by region. Microenterprises
in high IFI regions account for about 82.4 – 91.1 percent while for low IFI regions
microbusinesses comprise about 92.2 – 96.0 percent. Running a correlation test on the IFI
values and the percentage distribution accounted by microenterprises shows a stronger
negative correlation at -0.797. Taking these into account, this study surmises that thesemicroenterprises could be largely home-based businesses with income sufficient only tocover day-to-day operations and possibly modestly supplement the family income. As
such, there may be none or very little savings, which could potentially be used to open abank deposit. Consequently, given the size of these businesses it could be that there islittle motivation to apply for credit for business expansion and undergo the perceptibly
cumbersome loan application process. Similarly, banks may not be very keen on lending
to these markets considering the potential risks and the relatively small potential of
earning significant returns. The share of loans to microenterprises is highest in Region II
and XIII, however, it only accounts for six percent of the overall level of outstandingloans in the region.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
6	
  Data	  Source:	
  2012	
  Family	
  Income	
  and	  Expenditure	
  Survey,	
  NSO	
  

	
  

18	
  

`	
  

Share of Agriculture in Total Employment
Another interesting result of thestudy is that most regions withlow IFI values are associatedIn	
  Thousands	
  
80%	
  
with relatively high employment70%	
  shares in the agricultural sector,
60%	
  well above the overall share of50%	
  agriculture in total employment40%	
  of 32 percent. The medium IFI
30%	
  regions broadly follow a similar
20%	
  
10%	
  
trend. Meanwhile, high IFI0%	
  regions generally have relatively
lower shares of employment
engaged in agriculture, with
figures much lower than thebenchmark, except for Region
XI. This trend is shown inChart 8, which presents the share
	
  	
  	
   	
  	
  	
  	
  	
  	
  	
   I-­‐	
   II-­‐	
   III-­‐	  IV-­‐A	
  IV-­‐B	
  V-­‐	
   VI-­‐	
  VII-­‐	
  VIII-­‐	  IX-­‐	
   X-­‐	
   XI-­‐	
  XII-­‐	
  XIII-­‐	
  	
  	
  	
  	
  	
  
of
agriculturetototal
Legend:	
  Yellow	
  columns	
  –	
  High	  IFI	
  region;	
  Blue	  –	
  employment, by region, for the
Medium	  IFI	
  Region,	
  and	
  Orange	
  –	
  Low	
  IFI	
  region	
  year 2012. The test for
Source:	  Bureau	
  of	
  Agricultural	
  Statistics	
  correlation suggests that low(high) IFIs are largely associated
Table 7: Poverty Incidence for Basic Sectors
with high (low) reliance to2006, 2009 and 2012
agricultural livelihood. This
2006
2009 2012
finding becomes very logicalonce linked to average income
Philippines
26.6
26.3 25.2
levels and poverty incidence
Fishermen
41.2
41.3 39.2
Farmers
38.5
38 38.3
statistics. As shown in Table 7,Children
35.2
35.3 35.2
the farmers and fishermen, both
Self-employed and Unpaid Familyclassified
asagricultural
Workers
30.6
29.9
29
workers,arethetwo basic
Women
25.9
25.7 25.6
sectorswhich
consistently
Youth
21.1
21.6 22.3
Migrant and Formal Sector
1616.8 16.6
registered the highest poverty
Senior Citizens
16.9
16.1 16.2
incidence. Hence, a significant
Individuals residing in urban areas
12.6
12.6
13
chunk of the impoverished
Note: Basic sectors are not mutually exclusive
individuals or families comes
Source: Philippine Statistics Authority
from those engaged in the
agricultural livelihood. With insufficient income to barely cover their basic expenditure,
this segment of the population are not likely able to save or borrow from the formal
channels. This could explain why regions with high level of employment in the
agricultural sector are associated with low IFI values.

	
  

ARMM	
  

CARAGA	  Davao	
  Region	  SOCOSKSARGEN	
  Northern	
  Mindanao	
  Zamboanga	  Peninsula	
  Central	
  Visayas	
  Eastern	  Visayas	  Bicol	
  Western	  Visayas	  	
  Mimaropa	  	
  Calabarzon	  	
  Central	  Luzon	  	
  Ilocos	  Cagayan	  Valley	
  

CAR	
  

NCR	
  

Chart	
  8:	  Share	  of	
  Agriculture	
  in	
  Total	  Employment,	
  2012	
  

19	
  

`	
  
Special Focus: The Autonomous Region of Muslim Mindanao (ARMM)
Moulton and Dall (2006) used the term “fragile states” to characterize a phase of fragility
in a nation’s political or economic history, which stems from problems in governance.
According to case study, “under the current USAID fragile states typology, the case of
Mindanao in the Philippines is considered as a state in arrested development.” However,the Australian Agency for International Development (AusAID) (2012) points out that inreality there are two Mindanaos that exist, "one largely conflict free and growing
economically, and one that suffers from insecurity, extreme poverty, and a crisis ofgovernance." The ARMM appears front and center in the latter. The peace and ordersituation is surmised as a key element that explains the region's dismal state of financial
exclusiveness. Also, the conflict in the region contributed to its despondent economic andsocio-economic conditions.
The region’s development has been stunted because of the complicated and hostilesecurity and political conditions. The armed conflict in ARMM brought about by decadesof insurgency arising from the fight for autonomy by Moro groups as well asinternationally-linked terrorist activities. (World Bank, n.d.) Likewise, clan wars,
religion-related disputes, and violent political contestation also substantially contribute to
the conflict in the region. (AusAID, 2012) Moreover, the region suffers from poor
governance and weak rule of law. Table 8 summarizes the Good Governance Index(GGI) and Human Development Index (HDI) of provinces in the region. These figuresindicate undesirable living conditions in the area. 7 In addition, the region’s poor
infrastructure and unstable power supply have become a crucial impediment to its
economic development. (BSP, REDP, 2012)
The security concerns and political instability in the region make the struggle for a morefinancially inclusive system more challenging. On one hand, for the financial services
providers, the instability increases uncertainty in the market and its riskiness as aninvestment. The constant displacement of people makes the establishment of a permanentand stable client base in the area difficult, increasing the likelihood of loan
non-repayments. These conditions discourage financial institutions to invest in the area.
On the other hand, for the consumers, the constant displacement and uncertainty in theoverall environment compels them to resort to informal mechanisms, which are usually afaster and easier means to access to credit. (African Development Bank, 2013) The
constant threat of war also makes it difficult to maintain a stable livelihood and income
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
7

The NSCB developed the GGI to: “(1) draw attention to good governance outcomes… (2)determine and address the specific areas for improvement… and (3) improve the generation,
analysis and utilization of government statistics by addressing the most pressing informationneeds of the society...” The GGI is constructed using three (3) major indices: the Economic
Governance Index (EGI), the Administrative Governance Index (AGI) and the Political
Governance Index (PGI). (http://www.nscb.gov.ph/ggi/techNotes.asp) The HDI “attempts tomeasure the complex concept of human development.” The HDI takes the average of (1) life
expectancy; (2) weighted average of functional literacy and combined elementary and secondarynet
enrolment
rate;
and
(3)
real
per
capitaincome.
(http://www.nscb.gov.ph/technotes/hdi/hdi_tech_comp.asp)

	
  

20	
  

`	
  
stream as farms are abandoned and crops are destroyed amid the outburst of armed
conflicts. Thus, the overall flow of credit is significantly disrupted, placing both thepeople and the financial institutions at the losing end. Table 9 presents the impact ofconflict in region to credit availment in 2013.
Table 8: Good Governance Index and Human Development Index of the Provinces in the
ARMM, for the years indicatedProvince
Good
Rank
Political
Rank Human
Rank
Governance
Governance
Development
Index, 2008
Index, 2008
Index, 2009
Basilan
104.38
65
91.08
56
0.460
62
Lanao del Sur
130.6726
101.7213
0.416
70
Maguinadanao
79.06
79
91.46
51
0.470
78
Sulu
107.0
58
79.06
79
0.266
79
Tawi-Tawi
128.73
30
90.12
59
0.310
77
Notes: 1) For the ranking, 1 is the highest out of 79 provinces nationwide; 2) Political governanceindex is a component of the good governance index.
Sources: NSCB, and UNDP (2013), "Philippine Human Development Report 2012/2013"
Table	  9:	  Impact	
  of	
  Conflict	
  to	
  Credit	
  Availment	
  in	
  ARMM,	
  2003	  Source:	  The	  World	  Bank	  (2003).	
  "Social	
  Assessment	
  of	
  Conflict-­‐Affected	
  Areas	  in	
  Mindanao,"	
  p.	
  14.	  In addition, the inhabitants of the region are predominantly Muslims who have differentbanking practices in accordance to the Islamic Shari’ah principles. This is another viablereason for the weak financial penetration in the region, as Islamic banking still has a very
limited presence the Philippine financial system. To date, Al Amanah is the only Islamic
Bank in the country and it has persistently struggled financially, thus, the privatization by
the Development Bank of the Philippines. (Lema, K. and Vizcaino, B., 2013)

	
  

21	
  

`	
  
VI. Conclusion
The low ratio of financial inclusion in the Philippines reflects the predominance of low
IFI regions over the few high IFI regions in the country. While the lack of physicalpresence of financial institutions is an apparent constraint in the provision of financial
services in the low IFI regions, the more interesting finding is the notable extrication
between the availability of financial services and its actual utilization. The people in theseregions are not taking advantage of the availability financial services and transactions areusually limited to bank deposits. This disconnection appears to have dragged down thefinancial inclusiveness in the relevant regions. Thus, initiatives need to be undertaken andefforts need to be intensified to encourage more active usage of financial services.
This study likewise offers evidence that even on a micro-level, higher financial
inclusiveness is associated with higher levels of income, represented by regional GDP per
capita in this study, as in the cross-country research findings. The rationale of this result
is supported by the findings on other related variables—average family income as well as
poverty and subsistence incidences and their correlation to the IFI values. Similarly,
urbanization and literacy levels are also positively correlated with financial inclusiveness,comporting with the findings of other existing literature. On a more distinct note, theresearch finds that in the Philippines, low IFI regions are commonly associated withpredominance of entrepreneurial activity as a main source of income and high share of
employment to agriculture.
Looking at the overall structure of micro, small and medium enterprises (MSMEs) in the
country, the numbers for medium-sized enterprises remain low, resulting in a “hollowedmiddle” industry (Fajardo, 2013) Despite the dominance of microenterprises, the amountof outstanding credit extended to these enterprises remains modest compared to the total.
The lack of credit could be among the factors that constrain these enterprises to transition
to a bigger scale. Moreover, the relatively low credit provision could also be attributed totheir inability to obtain bank loans due to insufficient credit profile information and weakasset base to serve as collateral given their relatively small size. Hence, the relatively
weak link between the two markets could partly account for the low IFI value in theregions where entrepreneurial activities dominate the source of income.
The same can be inferred from the low IFI values in regions where employment is highly
contingent on the agricultural sector. Farmers and fisher folks have relatively lower skill
level at the same time face an inherent uncertainty in their livelihood and income stream,
which adversely influence their inclination and ability to avail loans. However, the morecrucial angle behind the financial exclusion of this segment could be on the supply side.
Lending institutions catering to this market face “high risk and thin profit margins”
because of the market’s modest borrowing capacity and high default probability. (IPA,2009) The inadequacy of the small farmers’ assets to fully cover the loan reinforces thedefault concerns thereby further discouraging loan extensions. As a result, formal lending
institutions are highly cautious in their lending activities to this segment. In addition,
farmers predominantly reside in the rural areas where banking presence is limited, as
described in the earlier section. (World Bank, 2014)
	
  

22	
  

`	
  

In conclusion, financial literacy can effectively tackle demand side issues, particularly inencouraging and easing the unbanked segment’s apprehensions in transacting withfinancial institutions.	
   Meanwhile, supply side constraints could be addressed byenhancing the institutional presence of formal fund providers and developing innovative
financial instruments that would better cater to the untapped segments. Likewise, thepresence of Islamic banking should be strengthened to reach out to the Muslim
population in the country. Majority of the Muslim population resides in the Mindanao
and thus, the expansion of Islamic banking services could generate significant
improvements in the state of financial inclusion in the island where several low IFIs
regions located. Furthermore, the glaring significance of income in financial inclusion
highlights the need for decentralization of development, which is concentrated mainly in
the NCR or in a sense largely centered in Luzon, in order to improve the economic
position and income/job opportunities in other regions. However, income seems to be notthe only important variable as seen in some low IFI regions in Luzon with relatively
moderate-income levels, urbanization and literacy also play a role in a region’s financial
inclusiveness. Lastly, further research on the area could include more parameters to better
capture the different dimensions, especially the usage dimension, as well as cover alonger time span to provide a more dynamic analysis, particularly in terms of assessing
the effectiveness of programs or initiatives targeted at promoting financial inclusion.

	
  

23	
  

`	
  
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Websites:www.bsp.gov.ph
www.census.gov.ph
www.nscb.gov.ph
www.bas.gov.ph
www.dti.gov.ph
www.undp.org

	
  

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