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 poverty  This 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 reasons  Income 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? IndicatorPhilippines Medium human development countries % difference Per capita GDP4,1704, Adult literacy Combined enrollment ratio Life expectancy

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 Literacy survey held twice in last 15 years, with definition changing Nutrition held 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

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

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.

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