Lisa Grace S. Bersales Divina Gracia L. del Prado Mae Abigail O. … · 2019. 8. 4. · Divina...
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The Multidimensional Approach to Measuring Poverty
Lisa Grace S. Bersales1
Divina Gracia L. del Prado2
Mae Abigail O. Miralles3
Philippine Statistics Authority and University of the Philippines
Abstract
Keywords: multidimensional poverty, MPI, poverty, headcount ratio, intensity
1. Introduction
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2.
3. Methodology for the Multidimensional Poverty Index (MPI)
3.1. Unit of Analysis
3.2. Dimensions and indicators
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Table 3.1. Dimensions, Indicators and Description of DeprivationDimension Indicator Description
1. Education School
school
school2. Health and
NutritionHunger
Health Insurance
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3. Housing, Water and Sanitation
Assets
Toilet
Tenure
Housing
Electricity
asset
there is no electricity in the housing unit
4. Employment
not in School
3.3. Weighting Scheme
a. Proposed Weighting Scheme
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b. Alternative Weighting Schemes
Nested inverse incidence weights
Subjective welfare weights
Table 3.2
Table 3.2. Weights by Dimension and Indicator, and Weighting Scheme
Total 0.250 0.165 0.331
NutritionHunger
Health InsuranceTotal 0.250 0.188 0.255
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Assets Toilet
Tenure
Electricity Total 0.250 0.195 0.280
School Total 0.250 0.453 0.134
3.4.
a.
b.
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3.5. Aggregation
a.
i
b. Headcount Ratio
nH
n
i
)(iI
nH
1
1
c.
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cA
n
ii
1
i
n
i
)(i
n
i
)(i
I
II
A
1
1 1
3.6. Share of each Dimension to MPI
3.7. Data Source, Survey Weights and Measures of Precision
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4. Results and Discussion
4.1. Incidence of Poverty
Figure 4.1
4.2. Annual Rate of Change of Poverty
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Table 4.1
Table 4.1. MPI and Average Annual Percent Change of MPI
Year
k = 1/3
k=1/4
k=1/5
4.3. Choice of Weights: Nested Uniform Weights versus Alternative Weights
a.
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Figure 4.2
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b.
Figure 4.3
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c.
Figure 4.4
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Notes:1) The region with the highest value is assigned a rank of 12) Arrangement of regions is based on their ranks using nested uniform weights
(Appendix 5)
4.4.
a.
Figure 4.5
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b.
Figure 4.6
c.
Figure 4.7
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(Appendix 6)
Notes:1) The region with the highest value is assigned a rank of 12) Arrangement of regions is based on their ranks using nested uniform weights
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5. Summary, Conclusion and Recommendations
5.1. Summary of Findings
a.
b.
c.
d.
e.
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5.2. Conclusion
5.3. Recommendations
a.
b.
c.
d.
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References
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Appendices
Nested Uniform WeightsPoverty
YearMPI
Estimate SE CV Estimate SE CV Estimate SE CV
Nested Incidence WeightsPoverty
YearMPI
Estimate SE CV Estimate SE CV Estimate SE CV
Subjective Welfare WeightsPoverty
YearMPI
Estimate SE CV Estimate SE CV Estimate SE CV
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Appendix 2. Percent Shares of Dimensions to MPI by Weighting Scheme
Nested Uniform Weights
Poverty Year Education Health and Nutrition Employment
Housing, Water and Sanitation
Nested Inverse Incidence Weights
Poverty Year Education Health and Nutrition Employment
Housing, Water and Sanitation
Subjective Welfare Weights
Poverty Year Education Health and Nutrition Employment
Housing, Water and Sanitation
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Nested Uniform Weights, k= 1/3
RegionMPI
Estimate SE CV Esti-mate SE CV Esti-
mate SE CV
IIIIII
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Appendix 4. Percent Shares of Dimensions to MPI Using Nested Uniform
Region Education Health and Nutrition Employment
Housing, Water and Sanitation
IIIIII
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