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Transcript of Geoff Ley
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8/13/2019 Geoff Ley
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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