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Multidimensional Poverty in the Philippines: Trend,
Patterns, and Determinants
Geoffrey Ducanes and Arsenio Balisacan
Multidimensional Poverty - Philippines
There is government awareness that focus should be on poverty’s many aspects not just income povertyThis is evident in the Medium-term Philippine
Development Plan of every president since 1992 which refers to human development goals and not just income poverty targets.
Due mainly to effective lobbying by NGOs like the Human Development Network
Multidimensional Poverty - Philippines
e.g. KALAHI-CIDSS acronym for current government’s flagship
poverty project (roughly translatable to Arm-in-arm Against Poverty)
involves funding support for likes of road, water, health and day care projects for selected towns/municipalities
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, choosing eligible municipalities based on a composite index of income level, food consumption, clothing consumption, quality of shelter, disaster vulnerability, and citizen participation
3. etc.
Multidimensional Poverty - Philippines
Still, the literature in the country on multidimensional poverty is lagging compared to income poverty. Two main reasonsIncome poverty, rightly or wrongly, is seen to
be the more pressing problem. Justification for this may take the following form, for instance.
Income poverty more pressing?
Indicator PhilippinesMedium human
development countries
% difference
Per capita GDP 4,170 4,269 -2.3
Adult literacy 92.6 80.4 15.2
Combined enrollment ratio
81 64 26.6
Life expectancy 69.8 67.2 3.9
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 are obtained either at long intervals of time or irregularly
Data frequency
Life expectancy every 10 years
Infant mortality every 10 years
Literacysurvey held twice in last 15 years, with definition changing
Nutritionheld thrice in last 15 years by different agencies
Multidimensional Poverty - Measurement
Multidimensional indices have been constructed at the level of provinces. Important particularly in making local leaders and the people more accountable for their performance. HDI – real per capita income, primary and secondary
enrolment rate, high school graduate ratio, and life expectancy
HPI – probability at birth of not surviving to age 40, functional illiteracy rate, % not using improved water sources, and % of underweight children under 5
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 (%)
Multidimensional Poverty - Measurement
Table 1. Spearman's Rank Correlations of Provincial Welfare Measures*
Indicator HDI HPI GRDI MBN' Index
QLI FLOL
poverty incidence**
Official poverty
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 . .
FLOL poverty incidence**
-0.84 0.39 -0.83 -0.59 -0.53 1 .
Official poverty incidence***
-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
Multidimensional Poverty - Measurement
Table 2. No. of provinces identified in common among 20 poorest
Indicator HDI HPI MBN' Index
QLI FLOL
poverty incidence
Income poverty
incidence
HDI 20 . . . . . HPI 12 20 . . . . MBN' Index 12 13 20 . . . QLI 10 10 9 20 . .
FLOL poverty incidence 13 9 9 6 20 .
Income poverty incidence 15 11 10 8 11 20
Multidimensional Poverty - Measurement
Multidimensional Poverty - Patterns
Table 3. Regional Welfare Indicators (2000)*
Region** HDI
(2000) HPI
(2000) GRDI (2000)
MBN' Index (1994)
QLI (1999)
FLOL Poverty
Incidence*** (2000)
Income Poverty
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. **CAR – Cordillera Administrative Region; NCR – National Capital Region; ARMM – Autonomous Region of Muslim Mindanao ***Based on fixed level of living poverty lines and per capita expenditure. ****Based on per capita income
Multidimensional Poverty - Patterns
The most glaring pattern is that regardless of which welfare indicator is used Provinces (or regions) adjacent to and including Metro
Manila, the country’s capital, have the most favorable levels, almost without exception
The provinces in one region, the Autonomous Region of Muslim Mindanao, performs most poorly in almost all indicators. This is the region where majority of the country’s Muslim population is found and the base of a long standing armed conflict between secessionist groups and the government.
Multidimensional Poverty - Determinants
We examine multidimensional poverty in relation to
a. geographical/topographical factors,
b. infrastructure, and
c. political economy variables
Geographical/topographical factors
Climate and topography, for instance, affect livelihood patterns, food production, and shelter ,
Climate is also intimately related with disease burdens (such malaria in tropical areas, meningitis in mountainous areas) and health
Difficult terrain, as well as frequent inclement weather also makes children’s access to school more grueling.
In our regressions, geography is represented by dummies for climate type, as well as a dummy for whether a province is predominantly mountainous and a dummy if it is coastal.
Infrastructure
Infrastructure facilitates trade and travel, raising income levels
Infrastructure, say in the form of a good road network also facilitates the construction of, and transport to, further infrastructure such as markets, school buildings, and health centers.
Infrastructure is represented by road density and an indicator variable for the presence of international ports in the province. In addition, the population density, which is closely linked to the level of urbanization in an area, is included as an additional proxy infrastructure variable.
Political economy variables
Good governance, for instance, should lead to better welfare for the constituents
The presence of armed conflict in an area, insofar as it represents a direct threat to life and health, impedes access to 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 provincial per capita budget expenditure on education. To represent conflict, we include a dummy for significant presence of communist armed insurgence (CPP-NPA) in the area and also a dummy for the Autonomous Region of Muslim Mindanao, a historically contentious region and the main base of Muslim insurgents.
Regression Results
Table 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 0.80 Communist 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 White’s adjusted standard error estimates. Diagnostic tests on multicollinearity, omitted variables, and normality of residuals were made and except in the case of the normality of residuals in the HDI regression, all were passed at the 5% level.
Regression Results
Table 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 White’s adjusted standard error estimates. Diagnostic tests on multicollinearity, omitted variables, and normality of residuals were made and all were passed at the 5% level.
Regression Results
Regression results show in the case of Philippine provinces Geography, infrastructure, and political factors are robustly
related to multidimensional welfare levels. For policy, geographical features maybe made one basis for
targeting, although a closer study must be made to trace the exact path/paths through which geographical factors are transmitted to welfare levels, and then design interventions appropriately.
Infrastructure investment, good governance, and a quick and peaceful resolution to the armed conflicts must all be pursued to improve multidimensional welfare in the lagging provinces.
End