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

3

2

3

2.

3. Methodology for the Multidimensional Poverty Index (MPI)

3.1. Unit of Analysis

3.2. Dimensions and indicators

4

Table 3.1. Dimensions, Indicators and Description of DeprivationDimension Indicator Description

1. Education School

school

school2. Health and

NutritionHunger

Health Insurance

5

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

6

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

7

Assets Toilet

Tenure

Electricity Total 0.250 0.195 0.280

School Total 0.250 0.453 0.134

3.4.

a.

b.

8

3.5. Aggregation

a.

i

b. Headcount Ratio

nH

n

i

)(iI

nH

1

1

c.

9

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

10

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.

12

Figure 4.2

13

b.

Figure 4.3

14

c.

Figure 4.4

15

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

16

17

b.

Figure 4.6

c.

Figure 4.7

18

(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

19

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

25

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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