Multidimensional Poverty in the Philippines Trend, Patterns, and

Document Sample
Multidimensional Poverty in the Philippines Trend, Patterns, and Powered By Docstoc
					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?
                                  Medium human % difference
 Indicator          Philippines    development
                                     countries

 Per capita GDP       4,170           4,269          -2.3


 Adult literacy        92.6           80.4           15.2

 Combined
                        81             64            26.6
 enrollment ratio

 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

                   survey held twice in last
Literacy           15 years, with definition
                   changing
                   held thrice in last 15
Nutrition          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*
                                                                     FLOL        Official
                                              MBN'
  Indicator     HDI       HPI      GRDI                   QLI       poverty      poverty
                                              Index
                                                                  incidence** 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            -0.84         0.39          -0.83         -0.59      -0.53    1
incidence**                                                                            .
Official
poverty            -0.80         0.55          -0.81         -0.77      -0.65   0.74   1
incidence***
*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


                                                              FLOL      Income
                                      MBN'
 Indicator      HDI         HPI                    QLI       poverty    poverty
                                      Index
                                                           incidence   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)*
                                                                            FLOL        Income
                                                       MBN'
                    HDI        HPI         GRDI                    QLI    Poverty       Poverty
     Region**                                          Index
                   (2000)     (2000)      (2000)                 (1999) Incidence*** Incidence****
                                                      (1994)
                                                                           (2000)        (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
     2
   R                                              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
      2
    R                                                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