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Multidimensional Poverty

in the Philippines: Trend,

Patterns, and Determinants

Geoffrey Ducanes and Arsenio Balisacan

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Multidimensional Poverty - Philippines

There is government awareness that focusshould be on povertys many aspects notjust income poverty

This is evident in the Medium-term PhilippineDevelopment Plan of every president since1992 which refers to human development goalsand not just income poverty targets.

Due mainly to effective lobbying by NGOs likethe Human Development Network

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Multidimensional Poverty - Philippines

e.g. KALAHI-CIDSS

acronym for current governments flagship

poverty project (roughly translatable to Arm-in-arm Against Poverty)

involves funding support for likes of road,

water, health and day care projects forselected

towns/municipalities

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Multidimensional Poverty - Philippines

e.g. KALAHI-CIDSS

steps in town selection

1. Choosing 20 poorest provinces out of 78 total in

terms of official income poverty

2. Within each of these 20 provinces, choosingeligible municipalities based on a composite indexof income level, food consumption, clothing

consumption, quality of shelter, disastervulnerability, and citizen participation

3. etc.

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Multidimensional Poverty - Philippines

Still, the literature in the country on

multidimensional poverty is lagging

compared to income poverty. Two mainreasons

Income poverty, rightly or wrongly, is seen to

be the more pressing problem. Justification for

this may take the following form, for instance.

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Income poverty more pressing?

Indicator Philippines

Medium human

development

countries

% difference

Per capita GDP 4,170 4,269 -2.3

Adult literacy 92.6 80.4 15.2

Combinedenrollment ratio

81 64 26.6

Life expectancy 69.8 67.2 3.9

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Multidimensional Poverty - Philippines

Data constraints. Many important non-income

indicators such as literacy rates, mortality rates,

life expectancy, and nutrition status of children,

access to health and education facilities areobtained either at long intervals of timeor

irregularly

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Data frequency

Life expectancy every 10 years

Infant mortality every 10 years

Literacy

survey held twice in last

15 years, with definition

changing

Nutrition

held thrice in last 15

years by different

agencies

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Multidimensional Poverty - Measurement

Multidimensional indices have been constructed atthe level ofprovinces. Important particularly inmaking local leaders and the people more

accountable for their performance.HDIreal per capita income, primary and secondaryenrolment rate, high school graduate ratio, and lifeexpectancy

HPIprobability at birth of not surviving to age 40,functional illiteracy rate, % not using improved watersources, and % of underweight children under 5

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Multidimensional Poverty - Measurement

Quality of Life Index (QLI) under-5 nutrition rate,

attended births, elementary cohort survival rate,

Minimum Basic Needs Index (MBN)

# of families

below the official poverty line (n), incidence of official

poverty in the province (%), cohort non-survival rate

(%), population illiteracy rate (%), infant mortality

rate (per 1,000 livebirths), malnutrition rate (%),households without access to safe water (%),

households with no sanitary toilets (%)

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Multidimensional Poverty - Measurement

Table 1. Spearman's Rank Correlations of Provincial Welfare Measures*

Indicator HDI HPI GRDIMBN'Index

QLIFLOL

povertyincidence**

Officialpoverty

incidence***

HDI 1 . . . . . .

HPI -0.53 1 . . . . .

GRDI 0.98 -0.57 1 . . . .

MBN' Index 0.62 -0.76 0.65 1 . . .

QLI 0.65 -0.66 0.68 0.78 1 . .

FLOLpovertyincidence**

-0.84 0.39 -0.83 -0.59 -0.53 1

.

Officialpovertyincidence***

-0.80 0.55 -0.81 -0.77 -0.65 0.74 1

*Using provincial level data as unit of analysis**Uses fixed-level-of-living poverty lines and per capita expenditure***Uses government computed poverty lines and per capita income

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Multidimensional Poverty - Measurement

Table 2. No. of provinces identified in common among 20 poorest

Indicator HDI HPIMBN'Index

QLIFLOL

poverty

incidence

Incomepoverty

incidence

HDI 20 . . . . .

HPI 12 20 . . . .

MBN' Index 12 13 20 . . .

QLI 10 10 9 20 . .

FLOLpovertyincidence 13 9 9 6 20 .

Incomepovertyincidence 15 11 10 8 11 20

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Multidimensional Poverty - Measurement

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Multidimensional Poverty - PatternsTable 3. Regional Welfare Indicators (2000)*

Region**HDI

(2000)HPI

(2000)GRDI(2000)

MBN'Index(1994)

QLI(1999)

FLOLPoverty

Incidence***(2000)

IncomePoverty

Incidence****(2000)

CAR 0.620 19.5 0.574 0.57 0.71 20.1 44.2

1 0.639 12.8 0.602 0.72 0.8 20.2 43.7

2 0.567 14.7 0.539 0.72 0.78 29.6 36.2

3 0.634 11.7 0.591 0.73 0.78 16.4 23.0

NCR 0.830 9.6 0.732 . . 5.6 12.1

4A 0.669 12.1 0.621 0.77 0.78 14.7 24.8

4B 0.535 15.3 0.51 0.64 0.59 39.2 60.2

5 0.523 17.8 0.503 0.56 0.59 49.7 62.9

6 0.587 20 0.552 0.59 0.6 28.1 51.5

7 0.563 17.7 0.537 0.67 0.75 39.3 44.0

8 0.519 18.4 0.495 0.61 0.60 46.8 51.6

9 0.530 23.6 0.505 0.47 0.61 49.0 54.9

10 0.606 16.6 0.558 0.59 0.71 31.2 49.3

11 0.594 21.7 0.553 0.58 0.59 23.1 45.0

12 0.569 20.5 0.538 0.51 0.57 32.5 59.2

13 0.520 17.4 0.499 0.54 0.59 33.9 56.7

ARMM 0.395 31.1 0.381 0.37 0.55 58.9 72.6

*Regional figures are population-weighted averages of provincial figures in Appendix Table 1.**CARCordillera Administrative Region; NCRNational Capital Region; ARMMAutonomous Region of

Muslim Mindanao***Based on fixed level of living poverty lines and per capita expenditure.****Based on per capita income

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Multidimensional Poverty - Patterns

The most glaring pattern is that regardless ofwhich welfare indicator is usedProvinces (or regions) adjacent to and including Metro

Manila, the countrys capital, have the most favorablelevels, almost without exception

The provinces in one region, the Autonomous Regionof Muslim Mindanao, performs most poorly in almostall indicators. This is the region where majority of the

countrys Muslim population is found and the base of along standing armed conflict between secessionistgroups and the government.

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Multidimensional Poverty - Determinants

We examine multidimensional poverty in relation to

a. geographical/topographical factors,

b. infrastructure, andc. political economy variables

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Geographical/topographical factors

Climate and topography, for instance, affect livelihoodpatterns, food production, and shelter ,

Climate is also intimately related with disease burdens

(such malaria in tropical areas, meningitis in mountainousareas) and health

Difficult terrain, as well as frequent inclement weatheralso makes childrens access to school more grueling.

In our regressions, geography is represented by dummies forclimate type, as well as a dummy for whether a province is

predominantly mountainousand a dummy if it is coastal.

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Infrastructure

Infrastructure facilitates trade and travel, raising incomelevels

Infrastructure, say in the form of a good road network also

facilitates the construction of, and transport to, furtherinfrastructure such as markets, school buildings, andhealth centers.

Infrastructure is represented by road densityand an indicator

variable for thepresence of international portsin theprovince. In addition, thepopulation density, which is closelylinked to the level of urbanization in an area, is included as anadditional proxy infrastructure variable.

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Political economy variables

Good governance, for instance, should lead to betterwelfare for the constituents

The presence of armed conflict in an area, insofar as it

represents a direct threat to life and health, impedes accessto education and health facilities, and represents a grave

psychological burden, should be detrimental to well-being.

As measures of good governance, we include a measure for the

extent of local political dynasty and also provincialper capitabudget expenditure on education. To represent conflict, weinclude a dummy forsignificantpresence of communistarmed insurgence(CPP-NPA) in the area and also a dummyfor theAutonomous Region of Muslim Mindanao, ahistorically contentious region and the main base of Musliminsurgents.

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Regression ResultsTable 4. Regression Results

HDI 2000 HPI 2000

Variable Coeff p-value Coeff p-value

Climate type 2 -0.08 0.00 *** 1.86 0.25

Climate type 3 -0.05 0.01 *** 3.48 0.02 **

Climate type 4 -0.07 0.00 *** 4.18 0.01 ***

Mountainous 0.01 0.80 0.58 0.59

Coastal 0.01 0.56 1.35 0.45

International port 0.01 0.69 0.20 0.86

Road density 1990 0.02 0.54 -4.64 0.02 **

Population density 1990 (000) 0.16 0.01 *** -2.05 0.44

Dynasty -0.06 0.02 ** 1.04 0.65

Educ expend per capita (P000) 0.04 0.17 0.00 0.80Communist insurgency -0.02 0.16 2.44 0.06 *

ARMM -0.15 0.00 *** 18.57 0.00 ***

Intercept 0.55 0.00 16.32 0.00

No. of observations 72 72

R2 0.673 0.668

*significant at the 10% level; **significant at the 5% level;***significant at the 1% level

****Regressions were done in Stata 8 using the robust method, which uses Whites adjusted standard errorestimates. Diagnostic tests on multicollinearity, omitted variables, and normality of residuals were made andexcept in the case of the normality of residuals in the HDI regression, all were passed at the 5% level.

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Regression ResultsTable 4. Regression Results

MBN 1994 QLI 1999

Variable Coeff p-value Coeff p-value

Climate type 2 -0.09 0.00 *** -0.05 0.08 *

Climate type 3 -0.09 0.00 *** -0.07 0.01 **

Climate type 4 -0.11 0.00 *** -0.06 0.07 *

Mountainous -0.02 0.48 -0.04 0.03 **

Coastal -0.08 0.01 *** 0.04 0.15

International port 0.08 0.03 ** 0.05 0.02 **

Road density 1990 0.05 0.19 0.14 0.00 ***

Population density 1990 (000) 0.17 0.01 ** 0.12 0.02 **

Dynasty -0.09 0.08 * -0.03 0.30

Educ expend per capita (P000) 0.29 0.01***

0.29 0.05*Communist insurgency -0.04 0.08 * -0.02 0.21

ARMM -0.22 0.00 *** -0.09 0.00 ***

Intercept 0.57 0.00 0.56 0.00

No. of observations 72 72

R2 0.70 0.79

*significant at the 10% level; **significant at the 5% level;***significant at the 1% level****Regressions were done in Stata 8 using the robust method, which uses Whites adjusted standard errorestimates. Diagnostic tests on multicollinearity, omitted variables, and normality of residuals were made andall were passed at the 5% level.

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Regression Results

Regression results show in the case of Philippine provinces

Geography, infrastructure, and political factors arerobustly related to multidimensional welfare levels.

For policy, geographical features maybe made one basisfor targeting, although a closer study must be made totrace the exact path/paths through which geographicalfactors are transmitted to welfare levels, and then designinterventions appropriately.

Infrastructure investment, good governance, and a quickand peaceful resolution to the armed conflicts must all be

pursued to improve multidimensional welfare in thelagging provinces.

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End