Multidimensional Poverty Analysis of the Rubber Farming
Households in Makilala, Cotabato, Philippines

Cheryl L. Eusala1, Josefina T. Dizon2, Rowena T. Baconguis2, Maria Ana T. Quimbo2,
and Merlyne M. Paunlagui2

1 Ph.D. in Community Development Graduate, University of the Philippines Los Baños and Assistant Professor IV, Central Luzon
State University
2 Professors, College of Public Affairs and Development, University of the Philippines Los Baños,

国連SDGsは2030年までに地球上から貧困を解消することを目指し、貧困を多元的に理解するために多次元貧困指数（MPI）
を提唱している。本稿ではMPI算出ツールを用いてフィリピンのゴム栽培農家の状況を検証した。

Abstract

The central challenge for policymakers is to reduce poverty and deprivations, and the nature of poverty reduction
policies is influenced by how poverty is being framed. The official measurement and analysis of poverty in themunicipality of Makilala are based on the monetary approach; accordingly, farmers are the poorest sector in the
country. Within these premises, this study was conducted to bring in a new approach to measuring poverty basedon a multidimensional analysis. Generally, it aimed to analyze the multidimensionality of poverty of rubber farming
households in Makilala, Cotabato, using the Multidimensional Poverty Assessment Tool. A total of 167 rubber-farming households with an area of one hectare and below were randomly selected as respondents to the study. The
dashboard approach of the Multidimensional Poverty Assessment Tool (MPAT) analyzes the multidimensionality ofpoverty. Results revealed that the Non-Farm Assets and Exposure and Resilience to Shocks were the most deprived
dimensions. The study recommended a results-based policy and programs for local implementation toward poverty
reduction.
Keywords

multidimensional poverty, poverty analysis, MPAT, rubber farming households, Makilala, Cotabato

Introduction

Poverty is one of the most prevalent and pervasive
social problems in underdeveloped countries (Mss et al.,2017). It has also become a common social problem,
even in most developed countries (Gale, 2007). That is
why its reduction is one of the highest priorities of international development (Bonfiglioli, 2003). The
Sustainable Development Goals (SDGs) primary aim is
to “end poverty in all forms everywhere” by 2030 withthe clear pledge that “no one will be left behind.
Over the past decades, progress has been marked in
reducing poverty globally. Nevertheless, extreme poverty remains a global concern and continues to be arural phenomenon (Anriquez, 2007). In the Philippines,
data from the Philippine Statistics Authority (PSA)
revealed a poverty incidence of 26.3 % and 21% in thefirst semester of 2015 and 2018, respectively, based on
Family Income and Expenditure Survey (Bersales,2019). It corresponds to about 22 million Filipinos, or
3.8 million families were officially income-poor.
Suansing (2017) argued that the poorest regions in the
country are located in rural and agricultural areas. Thisis due to low farm productivity and a lack of non-farmor off-farm opportunities (Dy, 2015).
The municipality of Makilala, Cotabato in Mindanao,
is an agricultural and rural area with a total land area of
34,356.53 hectares, of which 71% is devoted to agricultural production. Rubber is the leading crop planted inabout 10,565 hectares recorded in 2014; thus, the majority are rubber farmers. In 2010, Makilala recorded an
alarming 44.47% poverty incidence (MPDC, 2017). The
poverty incidence was calculated in terms of income or059

Cheryl L. Eusala, et al.

consumption expenditure. Albeit special anti-poverty
programs implemented by the local government, thepoverty rate remained high in 2017, 39.5%, much higher
than the national average of 21% in 2018.
However, income alone is not a reliable indicator of acomprehensive picture of poverty (Laderchi, 1997). It
does not account for other necessary well-being variables. It often does not give holistic information about
the poor (Israel & Hakim, 2015) due to its severe limitations that only lead to a partial understanding of poverty(Adenuga et al., 2013). Through the years, uni-dimensional approaches to poverty have come under critical
scrutiny. Thus, the birth of the multidimensional
approach by the United Nations in 1990. The development of the Human Development Index (HDI) marked
the beginning of a shift from monetary poverty measurements to the multidimensional approach (Donohue& Biggs, 2015). The multidimensional approach considers multiple indicators of well-being aside from insufficient income (Adenuga et al., 2013) and develops a
multifaceted approach to solving rural poverty (Israel &Hakim, 2015).
Central to this study is to bring in a new area of poverty measurement based on a multidimensional approachfor the rubber-farming sector in Makilala, Cotabato, as
no study has ever been conducted measuring theirwell-being status as rubber farmers experienced difficulties due to price fluctuations of raw rubber products.
The price volatility of raw rubber products is believed tohave contributed to the high poverty incidence in themunicipality. As per the record, it went as high as
P100.00 per kilo in 2010, and in 2010 it went down to
Php18.00 to 25.00 per kilo.
Primarily, this study adopted a new tool for locallevel rural poverty assessment, the Multidimensional
Poverty Assessment Tool (MPAT), developed by theInternational Fund for Agricultural Development(IFAD). The MPAT has ten fundamental dimensions
related to rural poverty and human well-being. Theseare (1) food and nutrition security, (2) domestic watersupply, (3) health and healthcare, (4) sanitation andhygiene, (5) housing, clothing, and energy, (6) education, (7) farm assets, (8) non-farm assets, (9) exposure060

and resilience to shocks, and (10) gender and social
equality. Thus, this study aimed to analyze the poverty
status of rubber farming households in Makilala,
Cotabato multidimensionally and recommend policies,
programs, and projects to address the poverty problem.

Literature Review“Ending poverty means addressing its multiple
dimensions” (Alkire, OPHI, & UK, 2013)
There has been a “widespread agreement that poverty is a multifaceted phenomenon” since the pioneering
works of Bourguignon & Chakravarty (2003) and Tsui(2002). Several kinds of literature are rapidly growing
now, including Alkire & Foster (2011a), Chakravarty,
Deutsch, and Silber (2008), Deutsch and Silber (2005),
Duclos, Sahn, and Younger (2006), and Maasoumi and
Lugo (2008), and Ferreira & Lugo (2013), among others.
Accordingly, poverty has many dimensions and acomplex of deprivations such as health, nutrition, education, services, housing, and assets, among others. It is
not just about money. Nowadays, the view of poverty as
multidimensional is widely supported by poor communities, as well as governments and development agencies (Alkire et al., 2013). Although most countries
around the world define poverty as a lack of money, thepoor people themselves consider poverty as multidimensional. Focusing on income alone is not enough tocapture the actual reality of poverty as poor people
suffer various deprivations in their daily lives — such as
poor health, lack of education, inadequate living standards, disempowerment, poor quality of work, the threatof violence, and environmentally hazardous areas,among others (Oxford Poverty and Human Development
Initiative [OPHI], 2019). OPHI (2019) presented somereasons why to use a multidimensional approach to poverty, such as:• M onetary-based poverty measures can miss a lot— Studies revealed that the overlapping between
monetary and non-monetary measures of poverty
is not perfect. Mostly, not all individuals who areincome poor are multi-dimensionally poor, andnot all multidimensionally poor individuals areincome poor. Thus, the need to consider both6th AFC Best Papers (2022)• G
monetary and non-monetary measures of poverty
in policies to address the needs and deprivations
faced by poor populations is essential.
• Economic growth does not always reduce poverty
or deprivation. Many studies have found that economic growth is not strongly associated with
reducing other deprivations, such as child malnutrition or child mortality.
• Poor people describe their experience of poverty
as multidimensional. Poor people describe ill-being as poor health, nutrition, lack of adequate sanitation and clean water, social exclusion, low
education, bad housing conditions, violence,shame, disempowerment, and much more.
• The more policy-relevant information available
on poverty, the better-equipped policymakers will
be to reduce it. For instance, an area where mostpeople are deprived of education requires a different poverty reduction strategy from an area inwhich most people are deprived of housing
conditions.
Thus, an alternative lens through which poverty may
be viewed and understood is through multidimensional
measures (Alkire & Foster, 2011b).
Many scholars from different countries used a variety of dimensions for the multidimensional poverty
approach. Batana (2013) uses four indicators (1) assets,(2) health, (3) schooling, and (4) empowerment in the
study of multidimensional poverty measurement among
women in 14 Sub-Saharan African countries. Yu (2013)
studied multidimensional poverty in China and selected
five dimensions such as (1) per capita household income,(2) access to water, (3) body mass index as a measure ofhealth, (4) having completed primary education, and (5)access to medical insurance as an indicator of social
security. The papers of Trani, Biggeri, & Mauro (2013)
on the multidimensionality of child poverty inAghanistan consider eight dimensions, namely: (1)health, (2) material deprivation, (3) food security, (4)care and love, (5) social inclusion, (6) access to schooling, (7) freedom from economic exploitation, and (8)shelter and environment. Another multidimensional
poverty study conducted in Bhutan by Santos (2013)that served as a source of happiness in the 2007 Gross
National Happiness Survey selected six dimensions,
namely: (1) health, (2) education, (3) access to electricity, (4) safe water, (5) improved sanitation, and (6)enough room per person in dwelling. Two additional
dimensions are included for the rural areas, such as (1)access to roads and (2) land ownership.
Recently, a new multidimensional poverty measurement was developed intended to measure local-level
rural poverty using ten dimensions that measure six
fundamental needs such as (1) food and nutrition security, (2) domestic water supply, (3) health and healthcare, (4) sanitation and hygiene, (5) housing, clothing
and energy, and (6) education); and four central aspectsof rural livelihoods and well-being such as (7) farm
assets, (8) non-farm assets, (9) exposure and resilienceto shocks, and (10) gender and social equality. This iscalled the Multidimensional Poverty Assessment Tool
(MPAT), developed by the United Nations International
Fund for Agricultural Development (IFAD). The MPAT
was developed with the understanding that the mostfundamental and essential needs of people, as defined indimensions 1-6 of MPAT, must first be addressed before
they can effectively tackle long-term goals. The dimensions 7-10 of MPAT, which go beyond basic human andpsychological needs that sometimes constrain the capacity of rural people to step away from poverty, are part ofthe enabling environment.
This study considered the new multidimensional
poverty measurement from IFAD, which is ideallysuited for the study’s purpose, location, and respondents.
The MPAT assesses local-level rural poverty by aggregating people’s perceptions on ten different indicators
based on data collected from household or village surveys. It captures fundamental human needs, endowmentwith assets, exposure to risks, which helps understandsustainability aspects, and social equality, including
gender, which serves as a proxy for the social dimensionof development.

Methodology

The study was conducted in Barangay Biangan,
Barangay Luayon, and Barangay Sta. Felomina in061

Cheryl L. Eusala, et al.

Makilala, Cotabato. These barangays were randomly
selected from the top 10 barangays with high poverty
incidence in the municipality.
The two-stage sampling technique and Cochran’s
formula were employed to generate 167 respondents
who were allocated proportionally and randomly
selected from the three barangays.
The study’s respondents were the rubber farming
households in Makilala, Cotabato, limited only to those
who owned at least a hectare of rubber farm.
This study used a survey research design andemployed quantitative-qualitative research methods toanalyze the multidimensional poverty of the rubber
farming households in Makilala, Cotabato, Philippines,
using the modified Multidimensional Poverty
Assessment Tool (MPAT) v.6 frameworks from theInternational Fund for Agricultural Development(IFAD).
A combination of techniques in data gathering was
employed in the study. These include face-to-face interviews, key informant interviews, focus group discussions, and secondary data from the Office of theMunicipal Planning and Barangay Government Unit.
Various analytical tools and procedures were
employed to answer the different objectives of the study.
Descriptive statistics using percentages, frequencies,
and means were used to describe the socio-demographic
and economic characteristics of the rubber farming
households in Makilala, Cotabato. The weighted arithmetic average and weighted geometric average formulas
were used to compute the subcomponents and component values using the formula shown in the mathematical equation below:
Weighted arithmetic average

Where:
yjk:		 score for household j in subcomponent k
Wik:	weight attached to survey question i in the subcomponent k
Xyk:	scaled score for household j in question i in subcomponent k

062

It holds that.

Weighted geometric average

Where:
yjk:		 score for household j in subcomponent k
wik:	weight attached to survey question i in the subcomponent k
Xyk:	scaled score for household j in question i in subcomponent k
It holds that.
It adopted the pre-programmed excel spreadsheetthat automatically does all the calculations based on thesurvey information. The MPAT results were presented
in a dashboard using a radar graph to quickly assess themost deprived dimension that needs to be prioritized.
The MPAT scores in the range of 0 -100 were classified
into four color-coded categories:
1.	Scores of 80-100 were classified as high scores,
represented in green color;
2.	Scores of 60-80 were classified as medium-highscores in orange color;
3.	Scores of 30-60 were classified as medium-low
scores, represented in yellow color; and4.	Scores of 0-30 were classified as low scores and in
red color.

Results and Discussion

Socio-demographic and Economic Characteristics of
Rubber Farming Households
Table 1 presents the socio-demographic and economic characteristics of the rubber farming households(RFHs) and shows that the majority (65%) of the headsof the RFHs were in the age bracket of 25-54 years oldhaving a mean age of 46 and classified as prime working-age. The majority were males (67%), married (90%),
with an elementary level of education, with an averageof 5 household sizes,
Regarding the rubber farmers’ ethnicity, the majority
were (51%) Bagobo/Tagabawa, with an average farm
size of 0.59, engaged in rubber farming for almost 16

6th AFC Best Papers (2022)

Table 1. Socio-demographic and economic characteristics of the respondents
FREQUENCY(n=167)PERCENTAGE

Age (years)
15-24
25-54
55-64
65 & above

8
109
35
15

5
65
21
9

46.23

GenderMale
Female

112
55

67
33

Male

150
8
5
2
2

90
5
3
1
1

49
32
40
32
10
4

29
19
24
19
6
3

110
54
3

66
32
2

CHARACTERISTICS

Civil Status
MarriedWidowedSingleLive-in
Separated
Highest Educt’lAttainment
Elem Level
Elem Grad
High School Level
High School Grad
College Level
College Grad
Household Size
1-5
6-10
>11

MEAN /MODALRESPONSE

MarriedElementary
Level

5

years and practiced monocropping or planted rubber
trees only in their farm. For marketing of raw rubber(cup lumps), the majority of the RFHs (57%) claimed tohave sold their cup lumps less than 1 km from theirrubber farms due to the presence of buyers from theseven rubber processing plants in the municipality. Dueto the low price of cuplump, RFHs’ average net income
solely from rubber farming is only Php2,547.71 permonth.
Multidimensional Poverty Analysis of Rubber
Farming Households
The study found out that two (food and nutrition
security and domestic water supply) out of the six fundamental needs obtained high scores of above 80 points.
Meanwhile, the other four fundamental dimensions(health and health care; sanitation & hygiene; housing,clothing & energy; and education) obtained scores of

FREQUENCY(n=167)PERCEN
TAGE

85
54
22
2
2
2

51
33
13
1
1
1

28
82
48
9

17
49
29
5

101
66

61
39

Monocropping

62
105

37
63

0.59

Farm Distance to the Market
Outlet (km)<1
1.1 - 2
2.1 – 3
>3

96
39
21
11

57
23
13
7

Net Income from Rubber
Farming (Php)
Less than 1,000
1,000 – 3,000
3,001 – 6,000
Above 6,000

12
102
51
2

7
61
31
1

CHARACTERISTICS
Ethnicity
BagoboCebuanoBoholanoIlonggoIlocanMuslimNumber of Years in Farming
<10
10-15
16-30
>30
Cropping System
Monocropping
Intercropping
Farm Size<0.50
0.50-1.0

MEAN /MODALRESPONSE

Bagobo/Tagabawa

15.8

1.45

Php 2,547.71

60-80 points or medium-high scores. Notably, the ‘quality’ sub-dimensions of almost all the fundamental
dimensions were the common deprivation denominatorsthat the rubber farming households experienced, whichscored from medium-high to even medium-low scores.
Similarly, two new rurality dimensions (farm assets andgender & social equality) obtained above 80 points orhigh scores. However, two dimensions (non-farm assets
and exposure and resilience to shocks) also scoredlowest at 30-60 points, which means that these dimensions need appropriate action in addressing rural poverty problems among the rubber farming households inthe study area (Figure 1).
Furthermore, the MPAT results revealed that among
the ten dimensions, the ‘non-farm assets’ dimension
was the most deprived, having the lowest average scoreof 44.2. It implies that rubber farming households lack063

Cheryl L. Eusala, et al.

Dimensions

Sub-dimensions

1

Food & Nutrition
Security

2

Domestic Water
Supply3

Health & HealthCare

4

Sanitation &Hygiene5

Housing, Clothing &Energy6

Education

7

Farm Assets

8

Non-Farm Assets

9

Exposure &Resilience to Shocks

10

Gender & Social
Equality

Gender &Social…
Exposure&…

Food &
Nutritio…
100
80
60
40
20
0

Non-Farm
AssetsFarm
Assets

Education

83.5
92.9
72.9
76.6
83.1
90.1
84.1
75.5
57.9

Toilet Facility

83.1
55.6
98.4
81.7
97.4
54.9
62.0
96.1
79.3
82.7
80.8
77.6
29.7
58.8
58.5
35.6
70.4
68.6
97.3

Access to Health Care

76.9

Health &Health…
Sanitation& Hygiene

Average Scores

Consumption
Access Stability
Nutrition Quality
Quality
Availability
AccessHealth Status
Access & Affordability
Quality
HH Waste Management
Hygiene Practices
Housing Structure Quality
Clothing
EnergyQuality
Availability
AccessLand Tenure
Land Quality
Livestock Inputs
Employment & Skills
Financial Services
Fixed Assets & Remittances
Degree of Exposure
Coping Ability
Recovery Ability
Access to Education

Domestic
Water…

Housing,
Clothing…

Overall

Ave.
82.8

82.5

72.4

78.2

76.5
77.7

80.5

44.2

54.5

86.5

Social Equality

93.4

No. of MPAT
scores10Components

30
SubcomponentsAbove 80 points

4

14

Between 60 and 80

49Between 30 and 60

2

6

Below 30 points

0

1

Figure 1.	MPAT components and subcomponents scores of
the rubber farming households in Makilala,
Cotabato

non-agricultural income-generating ability, access tocredit, and household wealth. This finding corroborates
the findings of Cerio (2018) that the non-farm assets
dimension is the most deprived dimension among theupland farming households in Goa, Camarines Sur,
Philippines. On the other hand, the gender and social
equality dimensions were the most sufficient, with thehighest score of 86.8. It connotes that RFHs in the study
area claimed that their household members have equal
access to children’s education, health care, and social
equality.
The other three dimensions with high (80-100) scores064

were food and nutrition equality (82.8), domestic watersupply (82.5), and farm assets (80.5). Four dimensions
that scored medium-high (60-80) were sanitation andhygiene (78.2), education (77.7), housing, clothing &energy (76.5), and health & health care (72.4). Exposureand resilience to shocks obtained a medium-low score of
54.5.
Discussed below are the detailed results of eachdimension across the RFHs in the study area.

1. Food and Nutrition Security

This dimension measures the stability and availability of sufficient quantities of adequately nutritious foodto the household. It assessed consumption, access stability, and food and nutrition security. It ranked second
with the highest average score (82.8) among the ten
dimensions. Among the three sub-dimensions, accessstability obtained the highest average score of 92.9, followed by the consumption sub-dimension (83.5) andnutrition quality sub-dimension (72.9).
The study revealed that the majority of the RFHs
claimed that they had sufficient food to eat, almost all
RFHs (95%) claimed that no member of their households had slept hungry, and they mostly had rice, bread,cereals, pasta, potatoes, and cassava. Thus, this dimension revealed that most of the RFHs showed high foodand nutrition security levels. However, there was still aminimal percentage of RFHs who also experienced
food shortages. With this, Makilala is still in the fight tofulfill SDG No. 2, which is to obtain “zero hunger.”

2. Domestic Water Supply

This dimension measures the quality, stability ofsupply, and household access to the domestic watersupply. It ranked third with a high average score of 82.5
among the ten dimensions. Among the three sub-dimensions, accessibility got the highest average score of
90.1, followed by availability (83.1) and quality sub-dimension (76.6).
This revealed that most of the RFHs have their own
installed water source inside their homes or backyards
that they are afforded to pay for and supply whole yearround and meet their required water needs. This means

6th AFC Best Papers (2022)

that accessibility and availability of domestic watersupply are not a problem for RFHs. For the water quality
sub-dimension, the majority (82%) of the RFHs believedthat the water was safe with excellent quality. Thus,
treatment is not necessary before drinking.

3. Health and Health Care

Health and health care (HHC) dimension measures
the quality of health care based on health status, people’s
access to health care, and the quality of care provided. It
ranked 8th among the ten dimensions, with an averagescore of 72.4. Among the three sub-dimensions, thehealth status got the highest score of 84.1, access andaffordability (75.5), and the quality sub-dimension witha medium-low score of 57.9.
The health status sub-dimension revealed that almost
half (47%) of the RFHs had family members who rarely
got sick with non-serious illnesses (once or twice a year).
Fortunately, more than three-fourths (77%) declaredthat they never had any household members with severe
illnesses in the past 12 months. Meanwhile, the overall
health status in the study area has improved moderately,as perceived by the three health practitioners interviewed during KII.
In terms of health access and affordability sub-dimension, the majority (81%) of the RFHs claimed that it
would take less than 30 minutes of travel to reach thenearest health centers that can diagnose and treat simple
illnesses and minor injuries since all barangays in themunicipality have established health centers withtrained BHWs on duty under the supervision of the LGU
health personnel. Moreover, for the affordability of theprofessional treatment of severe illnesses, about 41%
claimed to afford hospital fees if they borrowed money.
In comparison, 32% declared to afford treatment fees
provided that the government or employer will help
them out.
The healthcare quality sub-dimension has the lowest
overall score of 57.9 among the three sub-dimensions of
the health and health care dimension. Despite the established health centers with trained health practitioners(midwife and barangay health workers or BHW) at thebarangays, most of the RFHs (84%) claimed that

medical supplies in the health centers were insufficientand about (89%) of the RFHs regarded barangay healthcenters with low service quality because of the insufficiency of medical supplies, particularly medicines.
Thus, adequate healthcare services were rarely provided
to the community.

4. Sanitation and Hygiene

Sanitation and hygiene (SH) measure the quality of
the household’s sanitation (toilet facilities), food waste
management, and personal hygiene. SH has three
sub-dimensions, namely, (1) toilet facility, whichassesses the general quality of the toilet facilities thehousehold uses, (2) waste management which assesses
how the households manage their waste materials and(3) hygiene practices sub-dimension, which assesses thequality of the household’s general hygiene practices.
The SH dimension ranked fifth among the ten dimensions, with an average score of 78.2. Among its three
sub-dimensions, hygiene practices (98.4) and toilet
facility (83.1) obtained high scores, respectively, while
waste management obtained a medium-low score of
55.6.
Regarding hygiene practices sub-dimension, themajority (92%) of RFHs brushed their teeth twice or
thrice a day, about 95% washed their hands before
eating, and 97% of RFHs cleaned their hands after defecating. Hence, proper hygiene was being practiced by
the majority of the RFHs. For the toilet facility, 74% of
the RFHs owned enclosed-flush and pour-flush toilets,
15% had enclosed-pit toilets, and the other 9% usedcommunal toilet facilities. Unfortunately, 4% of the
RFHs had no toilet facilities; thus, they would defecate
anywhere, making the place unsanitary. Meanwhile, theRFHs claimed that their toilets were usable very often(50%) and always (48%) whole-year-round. The waste
management sub-dimension revealed that 99% of the
RFHs used food waste materials to feed their pets or
guard dogs. For their non-food waste materials, morethan half (58%) of the RFHs burned them, while theothers (29%) composted their non-food remains.
In general, the majority of RFHs practiced proper
sanitation and hygiene. However, a few still needed065

Cheryl L. Eusala, et al.

immediate interventions on the importance of cleanliness and sanitation to their health and nutrition.

5. Housing, Clothing, and Energy

Housing, clothing, and energy dimension measures
the general construction quality of the household’s
home, the availability of adequate clothing, and thequality of the energy sources used in the home (concerning indoor air pollution and fuel efficiency). It
ranked sixth among the ten dimensions, with an averagescore of 76.5. Among the three sub-dimensions, clothing got the highest overall score of 97.4, followed byhousing structure (81.7) and energy with a medium-low
score of 54.9.
The results revealed that most of the RFHs claimed
to have enough clothing (94%) and adequate footwear(92%) for the whole family, either for the wet or dry
season. Regarding energy sources sub-dimension, most
(88%) of the RFHs used stable voltage electricity fromthe grid, and 87% used wood or other natural material
for cooking. For the housing structure quality sub-dimension, results revealed that more than half (56%) of
the RFHs’ houses had exterior walls made of cement
blocks, and 68% of the RFHs claimed that their houses
could withstand strong winds and severe rains.

6. Education

This dimension measures the quality of children’s
formal education, availability, and access. It ranked seventh among the ten dimensions, with an average score of
77.7. Among its sub-dimensions, the availability of education got the highest overall score of 96.1, followed byaccess with a score of 79.3, and the quality sub-dimension with a score of 62.0.
For the availability sub-dimension, three head teachers from the study sites who were interviewed revealed
that all teachers had adequate teaching supplies that areneeded for effective teaching. Also, two head teachers
claimed that all students have adequate school supplies
like books, paper, and pencils for effective studying.
Moreover, the school authorities accommodated all students, and not even a single child was denied admission.
Thus, all the children had an equal chance for education.

066

All 38 barangays in Makilala have complete elementary grade levels schools and implemented no collection
policy since free primary education is implemented in
the municipality. Thus, education accessibility is not a
problem. Many (34%) of the RFHs claimed that it took
less than 15 minutes for their kids to arrive at school,
while about 30% claimed about 15-30 minutes, and 8%
revealed that their children took beyond 30 minutes toarrive at school. Meanwhile, the education quality
sub-dimension was perceived to have low quality,
though a moderate improvement in the students’ overall
performance in the last two years was claimed.

7. Farm Assets

This dimension measures the household’s generalability to produce food and create agriculture-based
income. It ranked fourth among the ten dimensions,with an average score of 80.5. Among the three sub-dimensions, land tenure got the highest score of 82.7, followed by land quality with a score of 80.8, and livestock
inputs with a score of 77.6.
The land tenure sub-dimension measures the RFHs’
access to agriculture, the number of hectares intended
for agriculture, and its ownership status. This study purposively included RFHs who owned 1 hectare and below
rubber farm in its sampling universe. Thus, 100% ofthem legally owned their lands and had access to farming. The majority (90%) of the RFHs owned a hectare
and below, while the other 10% owned bigger than one
hectare planted with other crops. For soil quality, most(90%) of the RFHs perceived that their farm soil was a
mixture of loamy and clay. Only half (50%) of the RFHs
raised livestock for the livestock inputs dimension.
About 46% of them always had enough water to supply
their livestock. However, more than half (55%) had difficulty feeding their livestock, 27% can ‘always’ supply
enough feeds, and 18% supply sufficient feeds.

8. Non-Farm Assets

Non-farm assets measure the household’s non-agricultural income-generating ability, access to credit, andhousehold wealth. Results revealed that non-farm assets
ranked tenth and the most deprived dimension of the6th AFC Best Papers (2022)

RFHs with the lowest average score of 44.2. Among thethree sub-dimensions, the employment and skills
sub-dimension got the lowest score of 29.7. The othertwo sub-dimensions are among the lowest, with an average score of 58.8 for financial services and 58.5 for fixed
assets and remittances.
The employment and skills sub-dimension assessed
RFHs income earned from small businesses and skilledservices rendered. Results revealed that 87% of the
RFHs claimed that not anyone in their households managed or ran their own small business. Also, 92% claimed
that no one in their households provided others a skilledservice for livelihood. Hence, the majority of them weredependent on farming activities.
Regarding RFHs’ access to financial services, 31%
assumed they might not be granted loans, while 29%
were sure they could not avail of any bank loans. On theother hand, 27% assumed they might be granted a loan,
and only 13% were sure they could easily avail of a loan.
Meanwhile, 14% of the RFHs declared that they are
debt-free, while 58% claimed to have a little debt, 26%
said they were moderately indebted, and only 2% wereindebted a lot. Most loans were from financial institutions such as microfinance institutions (26%) and rural
credit cooperatives (25%). Others borrowed moneyfrom informal sources such as relatives (20%), friends(12%), and private lenders (2%). The result implies that
RFHs had low access to financial institutions thatoffered lower interest rates like banks. Instead, most of
their loans were from other credit facilities and informal
financing institutions with high-interest rates since
rubber would take almost seven years before it becomestappable or productive. Thus, banks do not consider
financing rubber production because of the long waiting
period for payment.
For the fixed assets and remittances sub-dimension,
results revealed that most (70%) of the RFHs do nothave adult family members working outside their households. Thus, the remittance opportunity is nil. With this,most RFHs were dependent solely on their farm income.
Meanwhile, almost all (99%) of the RFHs had suitableroofing materials (metal sheeting), and 69% owned atleast one television.

9. Exposure and Resilience to Shocks

Exposure and resilience to shocks measure thehousehold’s exposure to natural and socio-economic
shocks and its ability to cope and recover from suchshocks. This dimension obtained the second-lowest
average score of 54.5 among the ten dimensions. It
implies that the RFHs were highly exposed to naturaland economic shocks with less resilience.
Among the three sub-dimensions, the degree of
exposure has the lowest overall score of 35.6. Historically,
Makilala had been exposed to natural disasters such as
flash floods, earthquakes, drought, and rain-induced
landslides. Thus, the RFHs perceived that they wereprone to natural and socio-economic shocks in the next
12 months. Of all the possible adverse events that mightoccur, the earthquake was ranked first by the RFHs
(62%) with high-major damage severity (90%) andlikely (46%) to occur in the next 12 months.
The second adverse event ranked by the RFHs was a
local conflict (22%). The majority (63%) believed it
would ‘likely’ occur in the next 12 months with ‘high tomajor’ (67%) damage severity since peace and order
have been a pressing concern as insurgent groups likethe New People’s Army thrive in the municipality. The
third was the low market price (16%) of natural rubber
products (cuplump), which is ‘likely’ (48%) to occur inthe next 12 months with high to major (58%) damage
severity. This economic shock or price fluctuations of
raw rubber (cup lumps) was recorded in the municipality. At one time in 2010, the price of raw rubber (cuplumps) reached as high as Php100 per kilo, and today theprice of 15-day-old rubber cup lumps is Php 20-25 per
kilo which distressed the rubber farmers. Accordingly,
rubber price volatility is due to international market
forces. Thus, other farming strategies and skills development for rubber farmers must be developed andintroduced.
Other identified socio-economic and natural shocks
were drought, flood, and family sickness. During thefirst half of 2016, El Nino dry spell (drought) hit
Makilala, which affected the farmers’ agricultural production and created social unrest, resulting in a street
demonstration demanding food support. Flash floods

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Cheryl L. Eusala, et al.

were recorded in 2006 and 2009, affecting four barangays, with seven deaths and hundreds of households.
Despite the high degree of exposure perceived by theRFHs, their coping ability scored a medium-high level(70.4). However, the three coping strategies identified
by the RFHs primarily depend on government andfamily aid if adverse events occur. They said that theirprimary strategy is to rely on local government (62%),
second is to rely upon the national government’s help(22%), and third is to borrow money from relatives(16%). These coping mechanisms connote dependence
on agencies and people, which could be critical if thehelp they were expecting could not be delivered.
Further, the study revealed a low recovery ability of
the RFHs in the event of a disaster. The majority (76%)
perceived that they could recover from damage andreturn to a satisfactory situation within a year. Moreover,in an extreme disaster that could destroy their houses,
49% claimed that it would take 12 months to rebuild
their houses, 39% for about 24 months, and the rest
would take 3 to 4 years to rebuild their houses.
Meanwhile, if a disaster will occur in the next 12months, most (59%) of the RFHs relied on government
assistance, particularly from the local government and
28% from family/relatives.

10. Gender and Social Equality

This dimension measures the quality of access to
education and health care for female and male children
and adults and the degree of social equality in the barangay. It ranked first among the ten dimensions and thesufficient dimension as perceived by the RFHs, with anaverage score of 86.8. Among the three sub-dimensions,access to education has the highest average score of
97.3, followed by social equality with a score of 93.4,
and access to health care with a 76.9 score.
Access to education sub-dimension got the highest
average score of 97.3 among the three sub-dimensions.
Results showed that most of the female (52%) and male(56%) children of the RFHs were likely to achieve college level, while almost the same percentage (15% &14%) of both females and males will likely achieve
vocational or senior high school level. The RFHs were068

confident enough that their children could attain ahigher level of education since primary and secondary
education in Makilala is free of charge. Furthermore,
the establishment of the Makilala Institute of Scienceand Technology and Makilala Vocational and Technical
Skills Training Center offered free tertiary educationand skills development for free. With these, the RFHs
claimed that access to education for both females andmales is assured, and there are no reasons their kids
could not proceed to higher education.
For the social equality sub-dimension, the majority(71%) of the RFHs claimed they had equal economic or
political opportunity regardless of religion and ethnic
affiliation. Also, the barangay leaders claimed that economic and political opportunities were equal for everyone in the community.
Meanwhile, access to health sub-dimension revealed
that more than half (53%) of the RFHs claimed that both
men and women received about the same or equal healthcare when needed, while 46% claimed that women
received more health care compared to men. Similarly,
more than half (51%) of the RFHs claimed that healthcenters could ‘sometimes’ provide adequate health careservices for women due to the limited medical supplies
and, at the same time, the lack of funds intended for
health services in the barangays. Nevertheless, 42%
declared that women received adequate health carewhen needed.
Policy/Program Recommendations to Reduce Poverty
among the Rubber Farming Households
As discussed above, the MPAT revealed that therubber farming households were significantly deprived
in the new rurality dimensions such as the non-farmassets and exposure and resilience to shocks, which only
scored 44.2 and 54.5, respectively. Special attention will
also be needed for the other four dimensions, such as
health and health care, housing, clothing and energy,
education, and sanitation and hygiene, which scored at60-80 points or the medium-high level. Despite being
sufficient on food and nutrition, domestic water supply,
farm assets, and gender and social equality dimensions,which scored between 80 and 100, the RFHs were still6th AFC Best Papers (2022)

vulnerable to poverty. Their deprivations on non-farmassets and exposure and resilience to shock were thecritical factors that would significantly affect theirsources of income (rubber farming). Rubber farming,
when exposed to socio-economic and environmental
shocks such as low prices, local conflict, drought, andearthquakes, would mean loss of their farming livelihood. Further, their deprivation of non-farm assets ortheir inability to earn income from skilled work or businesses (non-farm assets) makes them more miserable
when shocks hit them.
With these findings, a policy, program, and projects
are recommended to address the multidimensional poverty experienced by the rubber farming households in
Makilala, Cotabato. The focus is on improving the mostdeprived dimensions with average scores of 30-60, suchas the non-farm assets and exposure and resilience toshocks (Figure 2).

1. Support for Non-Farm Assets
Development

Non-farm assets are the economic activities that generate income from employment and skills, fixed assets
and remittances, and financial services. Accordingly,
agriculture alone cannot provide enough livelihood
opportunities. Thus, non-farm employment and enterprise are potentially vital in reducing rural poverty,
especially during adverse shocks, since smallholder
farming households can less tolerate negative shocks.
However, RFHs were deprived of these non-farm assets,making them difficult to survive when adverse shocks
hit them. Technical skills development and microfinancing support are some of the identified intervention programs to address this problem.
1.1 Capacity Building through Technical Skills
Development
The majority (87%) of the RFHs claimed to have noother sources of income except farming, and 92%
declared to have no skills for potential off-farm income.
Thus, technical skills development is essential forrubber farmers to generate off-farm income. Some of
the identified skills development programs linked to
LGU-Makilala TESDA-accredited Vocational School(Makilala Vocational Technical Skills Training Center),which offers free technical skills development, are driving, small engine repair, automotive, and vulcanizing.
Also, the Municipal Agricultural and Services Office
could facilitate technical assistance on capability building of the rubber farmers’ other household members(wife or children), such as food or product processing
and soap making, among others. Hence, the need forpolicies, programs, or projects that reinforce the government’s focus on farmers’ capability building throughtechnical skills development.
1.2 Micro-financing Support Fostering
Entre-preneurship
Varied income sources are essential in the fightagainst poverty through the incorporation of more

Figure 2.	Policy framework to combat multidimensional poverty experienced by the rubber farming households in Makilala,
Cotabato, Philippines

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Cheryl L. Eusala, et al.

non-farm activities (Jatta, 2013). The majority (60%) of
the RFHs claimed they could never avail of loans frominstitutions with lesser interest rates. Hence, micro-financing from the local government is recommended toprovide capital to start a non-farm business of rubber
farming households to put into practice their developed
technical skills from vocational school (e.g., automotive,
driving, vulcanizing, or carpentry) or learned capabilities from training (e. g., food or product processing,cooking, and more). Because according to Gupta (2003)
and Khanam et al. (2018), micro-finance has shown apositive effect on poverty alleviation and increases borrowers’ living conditions by raising their income. Thus,
micro-financing is one way to achieve poverty reduction. LGU-Makilala could consider this strategy throughpolicy creation.

2. Mitigating Socio-economic andNatural Shocks

Most of the RFHs declared they were vulnerable tosocio-economic and natural shocks. They identified low
rubber prices and local conflict as the socio-economic
shocks and earthquakes and drought as the natural
shocks. Mitigating these aspects is extremely importantsince the respondents solely depend on rubber farming
as their primary source of income and livelihood. Hence,
it is highly recommended to the Municipal Disaster
Risk Reduction and Management Office of the LGUMakilala have a contingency plan specifically for thispurpose. Furthermore, the local conflict must be
included in the Municipal Peace and Order and Public
Safety Plan to be addressed.

Conclusion

This study concluded that the rubber farming households in Makilala, Cotabato were multidimensionallypoor, particularly on the aspects of new rurality components such as the ‘non-farm assets’ and ‘exposure andresilience to shocks. The rubber farming households in
Makilala, Cotabato, lack the non-agricultural
income-generating ability and do not have any available
skills for livelihood development such as for business or
skilled works. They have no remittances and could not070

avail of loans from the banks. Hence, the respondents
merely reckoned on farming activities for livelihood andsurvival. The rubber farming households claimed tohave been highly exposed to socio-economic shocks(such as the low price of cup lumps and local conflict,among others) and natural shocks (earthquake anddrought).
Given the above premise, it is crucial to develop nonfarm income opportunities through capacity building,technical skills development, and support microfinancing for rubber farmers to ensure survival duringsocio-economic and natural shocks. Furthermore, contingency plans to mitigate socio-economic and natural
shocks must be in place to support rubber farmers intimes of disaster.
Lastly, the Multidimensional Poverty Analysis Tool
(MPAT) analyzed using the combination of the dashboard approach, and counting approach of Alkire andFoster methodology captured the realms central tohuman well-being and, by extension, to poverty reduction in the 21st century congruently. While most of thepoverty measurement studies in the government used
income or consumption expenditures, this study contributes to the scant literature on multidimensional poverty assessment in the government, particularly in themunicipality of Makilala, specifically rubber farming
households. This study served as the municipal baseline
data of the rubber farming households’ poverty status
multidimensionally. MPAT gives a new avenue inassessing rural poverty to provide an enabling environment for the poor to escape poverty.
Note for the future MPAT User:
The MPAT is an open-source tool intended for use by
those concerned with rural poverty alleviation. All the
resources needed to use the MPAT, such as MPAT User’s
Guide (IFAD, 2014), MPAT Book (2009), MPAT Excel
spreadsheet, and the survey instruments, are available
at https://www.ifad.org/en/web/knowledge/publication/asset/39631564. To the future user of this MPAT, theresearcher recommends conducting cross-checking of
every survey question and choices that correspond to
the excel spreadsheet valuations to ensure the6th AFC Best Papers (2022)

correctness of the results. As the researcher found outthat the downloadable survey questions and choices
were different from that of the excel spreadsheet, whichsignificantly affects the whole analysis.

References
1. Adenuga A., Omotesho O. Ojehomon V., & Diagne, A. (2013).
Poverty Analysis of Rice Farming Households: A
Multidimensional Approach. Agricultural University of Tirana,
12 (4), 641–651.
2. Alkire, S., & Foster, J. (2011a). Counting and Multidimensional
Poverty Measurement. Journal of Public Economics, 95(7–8),
476–487. https://doi.org/10.1016/j.jpubeco.2010.11.006
3. Alkire, S. & Foster, J. (2011b). Understanding and
Misunderstandings of Multidimensional Poverty Measurement.
J Econ Inequal, (9), 289–314. https://doi.org/10.1007/s10888-0119181-4
4. Alkire, S., OPHI, & UK. (2013). How to Measure the Many
Dimensions of Poverty? In Development Co-operation Report:
Ending Poverty (pp. 43–51). OECD, 2013. Retrieved from https://www.oecd-ilibrary.org/docserver/dcr-2013-7-en.pdf?expires=1557216168&id=id&accname=guest&checksum=BA8F285906C448605C29BD2D0D5BE984
5. Alkire, S. & Santos, M. (2013). A Multidimensional Approach:Poverty Measurement & Beyond. Social Indicators Research,
112(2), 239–257. https://doi.org/10.1007/s11205-013-0257-3
6. Anriquez, G. (2007). e JADE Rural Development and Poverty
Reduction: Is Agriculture Still the Key? 4(1), 5–46.
7. Batana, Y. (2013). Multidimensional Measurement of Poverty in
Sub-Sahara Africa. Social Indicators Research. https://doi.
org/10.1007/s11205-013-0251-9
8. Bersales, L. (2017). Farmers, Fishermen and Children
Consistently Posted the Highest Poverty Incidence among Basic
Sectors — PSA. Retrieved from https://psa.gov.ph/poverty-pressreleases/nid/120251
9. Bersales, L. (2019). Proportion of Poor Filipinos Registered at
21.0 Percent in the First Semester of 2018. https://doi.org/
Reference No.: 2019-053
10. Bonfiglioli, A. (2003). Empowering the Poor: Local Governancefor Poverty Reduction. New York, NY 10017: United Nations
Capital Development Fund Two United Nations Plaza, 26th Floor
New York, NY 10017. Retrieved from http://unpan1.un.org/intradoc/groups/public/documents/un/unpan010168.pdf
11. Bourguignon, F., & Chakravarty, S. (2003). The Measurement ofMultidimensional Poverty. Journal of Economic Inequality,
1(25), 25–49. https://doi.org/10.1007/978-0-387-79253-8_6
12. Cerio, C. (2018). Multidimensional Poverty Assessment andInformal Ex-Ante Social Risk Mechanisms of Farming
Households in Upland Communities of Goa, Camarines Sur,
Philippines. Master’s Thesis. Master of Science in Rural
Sociology. University of the Philippines Los Baños.
13. Cohen, A. (2010). The Multidimensional Poverty Assessment
Tool: A New Framework for Measuring Rural Poverty.

Development in Practice, 20(7), 887–897. https://doi.org/10.1080/09614524.2010.508111
14. Donohue, C. & Biggs, E. (2015). Monitoring Socio-environmental
Change for Sustainable Development: Developing a
Multidimensional Livelihoods Index (MLI). Applied Geography,
62, 391–403. https://doi.org/10.1016/j.apgeog.2015.05.006
15. Dy, R. (2015). MDG 2015: Benchmarking Philippine Poverty
Scorecard. Retrieved from https://business.inquirer.net/199061/
mdg-2015-benchmarking-philippine-poverty-scorecard
16. Ferreira, F. & Lugo, M. (2013). Multidimensional Poverty Analysis :Looking for a Middle Ground, 1–16. https://doi.org/10.1093/wbro/
lks013
17. Gale, T. (2007). Poverty in Underdeveloped Countries — The
Poorest of the Poor. Encyclopedia.Com. Retrieved from https://w w w.encyclopedia.com /i nter nat ional /news-and- education-magazines/poverty-underdeveloped-countries-poorestpoor=
18. Gupta, A. (2003). The Role of Microfinance in Reducing Poverty.
Special Seminar in International Management — India. http://
www.microfinancegateway.org/sites/default/files/mfg-en-paperthe-role-of-microfinance-in-reducing-poverty-2003.pdf.
19. International Fund for Agricultural Development (2014). The
Multidimensional Poverty Assessment Tool (MPAT). Retrieved
from https://www.ifad.org/en/web/knowledge/publication/asset/
39631564
20. Israel, A. & Hakim, A. (2015). Business and Economics
Multidimensional Poverty Assessments in Rural FarmHousehold, South- West of Nigeria: Evidence from Oyo State
Farm Settlements. https://doi.org/10.4172/2151-6219.1000157
21. Khanam, D., Mohiuddin, M., Hoque, A., & Weber, O. (2018).
Financing micro-entrepreneurs for poverty alleviation: a performance analysis of microfinance services offered by BRAC, ASA,
and Proshika from Bangladesh. Journal of Global
Entrepreneurship Research, 8(1). https://doi.org/10.1186/s40497018-0114-6
22. Laderchi, C. (1997). Poverty and Its Many Dimensions: The Roleof Income as an Indicator. Oxford Development Studies, 25(3),345–360. https://doi.org/10.1080/13600819708424139
23. Municipal Planning and Development Coordinator (2017). Local
Government Unit of Makilala, Philippines
24. Municipal Planning and Development Coordinator (2019). Peace
and Order and Public Safety Plan. Local Government Unit ofMakilala, Philippines
25. Msss., A., Vu, C., Pintak, I., Lehning, A., Vu, C., Theories, I., &Pintak, I. (2017). Journal of Human Behavior in the Social
Environment Theories of Poverty Theories of Poverty: Findings
from Textbooks on Human Behavior and the Social Environment,
1359 (December). https://doi.org/10.1300/J137v16n01
26. OPHI (2019). What is Multidimensional Poverty? Retrieved
August 1, 2019, from https://ophi.org.uk/policy/multidimensionalpoverty-index/
27. Santos, M. (2013). Tracking Poverty Reduction in Bhutan:
Income Deprivation alongside Deprivation in other Sources ofHappiness. Social Indicators Research. https://doi.org/10.1007/
s11205-013-0248-4

071

Cheryl L. Eusala, et al.

28. Suansing, B. (2017). Philippines’ Poverty Rate: Rural
Communities Still Left Behind. Retrieved from https://borgenproject.org/philippines-poverty-rate/
29. Trani, J., Biggeri, M., & Mauro, V. (2013). The Multidimensionalityof child poverty: An empirical investigation of children of
Afghanistan. Social Indicators Research. https://doi.org/10.1007/
s11205-013-0253-7
30. Yu, J. (2013). Multidimensional Poverty in China: Findings
Based on CHNS. Social Indicators Research. https://doi.
org/10.1007/s11205-013-0250-x

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