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