Dina C. Magnaye, PhD, EnP School of Urban and Regional ... · PDF fileof national and local...

Paper presented at the 8th CBMS Philippines National Conference held at SMX Convention Center, Pasay City, Philippines on March 19-21, 2012

Dina C. Magnaye, PhD, EnPSchool of Urban and Regional Planning

University of the Philippines

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BackgroundBackground Poverty is central to any planning activity of

human settlements with poverty reduction as the overarching goal

Integration of poverty reduction efforts and inequality improvements in the formulation of national and local development plans

Prioritization of the poor in development interventions to increase their income and improve access to basic services

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General ObjectiveGeneral Objective

Develop a framework to mainstream pro-poor planning integrated approach in local development planning

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Examine the existing poverty-based information systems;

Identify the core dimensions of poverty and characterize the degree of poverty;

Determine the relationship between geographical or spatial poverty targeting and investment programming;

Define the factors affecting the likelihood of being income poor and capability poor; and

Identify the mechanisms to address the needs of the income and capability poor.

Specific ObjectivesSpecific Objectives4

Tarlac Province has 17 municipalities, one city, and 511 barangays (411 rural and

100 urban).

The Study AreaThe Study Area5

Tarlac Province has 17 municipalities, 1 city, and

511 barangays (411 rural and 100 urban)

6 Type of Settlement

Sample Municipa-lity/City

Income Classification

(2007)

Number of Sample

Households

Small Town San Manuel Pura

4th

4th4,9474,658

Medium Town

Gerona 1st 17,506

Large Town Capas 1st 23,100

Small City Tarlac City Component 59,933

Total 110,144

Sample Municipalities and City

Conceptual FrameworkConceptual Framework

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Poverty Landscape Characterization

Spatial Poverty

Analysis &Mapping

Poverty Targeting

Results and DiscussionA. Poverty Landscape Characterization and Poverty Mapping

Poverty Dimensions

Findings

Survival Needs

Favorable health and nutrition condition

Child death among 0-5 years old children<1.0%Maternal mortality rate <1.0%Low proportion of malnourished children = 1.44%

Low level of poverty in water and sanitation

Households with access to community water system = 93.75%Households with access to sanitary toilet facilities = 92.30%

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Poverty Dimensions Findings

Security Needs

Adequately satisfied housing needs

HH living in makeshift housing = 3.0%HH living in informal settlements = 3.46%

Generally a peaceful province

Almost nil proportion of victims of crime

Low participation in community organization

Low membership in community organizations=1.36%

High participation of voters in electoral process

Participation rate in electoral process = 82.67%

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Poverty Dimensions Findings

Enabling Needs

Relatively high magnitude of capability poverty in education

Children 6-12 years old not attending elementary school = 23.53% Children 13-16 years old not attending high school = 39.98%

High income poverty among households

HH with income below poverty thresholds = 43.94%HH with income below food thresholds = 29.45%

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Poverty Dimensions FindingsEnabling NeedsLow unemployment level

Unemployed members of the labor force = 3.80%

Deprivation in basic human needs in almost all towns and one city

47% of households have unsatisfied capability needs

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Results and DiscussionB. Spatial Distribution of Poverty

Spatial Distribution Measure

Findings

Moderate degree of spatial inequality

Dissimilarity Index (D) = 0.49

Random pattern of capability poverty

Capability poor has almost attained maximum spatial concentration possible

Non-capability poor are more concentrated than capability poor

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Results and DiscussionB. Spatial Distribution of Poverty

Spatial Distribution

Measure

Findings

Relatively weak local economy

Majority of the municipalities and one city of Tarlac Province have LQ<1.0 indicating less specialization in most of the local economic activities, excluding agriculture, mining and forestry; and community, social or personal services.

Deficits in local employment given negative Minimum Requirement Technique (MRT) values

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Results and DiscussionC. Poverty Targeting

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Results and DiscussionC.1 Area/Location-Based

Poverty Targeting

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• Capability deprived towns are concentrated in the western portion of the province

• Characterized with steep slopes,hilly portions and forested lands

• Areas are severely eroded and lahar prone;

• Relatively large number of indigenous settlers are located

First Level Location-Based Poverty Targeting Scheme

Results and DiscussionC.1 Area/Location-

Based PovertyTargeting

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Second Level Location-Based

Poverty Targeting Scheme

• Selection of capability deprived barangays

Results and DiscussionC.2 Beneficiary Targeting

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Capability Deprived

Municipalities

Capability Deprived

Barangays

Target Number of Household

s

FirstPriority

Households (≥5.0 SCI)

Second Priority

Households (3-4 SCI)

ThirdPriority


Sta. Ignacia NambalanBaldios, Sta. Ines East,Pugo-Cecilio,Pilpila, PoblacionWest,San Francisco 2,470 62 911 1,497

San Jose Maamot, Sula, David, Labney 1,488 391 493 631

Results and DiscussionC.2 Beneficiary Targeting

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Capability Deprived

Municipalities

Capability Deprived

Barangays

Target Number of Households

FirstPriority

Households (≥5.0 SCI)

Second Priority


ThirdPriority

Households (1.0-2.0 SCI)

San Manuel San AgustinLegaspiLanatColubot 1,208 67 497 644

Mayantoc LabneyGayonggayongMamonit 731 74 230 427

Capas Sta. JulianaBuenoMaruglu 1,146 318 473 355

Gerona Sta. Lucia 166 37 109 20

Results and DiscussionC.3 Priority Development Sector Targeting

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In terms of Annual Investment Program Allocation and Utilization


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In terms of Average Expenditures per Capita by Sector


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In terms of Share of Government Expenditures from the IRA


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In terms of the Principal Component Analysis

Results and DiscussionC.4 Program, Project and Activity (PPA) Targeting

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Results of the Logit Regression where Model p= Probability that a Household is Income Poor


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Results of the Logit Regression where Model p= Probability that a Household is Income Poor

Results and DiscussionC.4 Program, Project andActivity (PPA) Targeting

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Logit Regression Results where Model p= Probability that a Household is Income Poor


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Results of the Logit Regression where Model p= Probability that a Household is Capability Poor by Proximate Poverty Determinant and by Type of Barangay-

Based Core Local Poverty Indicator


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Results of the Logit Regression where Model p= Probability that a Household is Capability Poor by Proximate Poverty Determinant and by Type of Barangay-

Based Core Local Poverty Indicator


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Results of the Logit Regression where Model p= Probability that a Household is Capability Poor by Proximate Poverty Determinant and by Type of Barangay-Based Core Local Poverty Indicator


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Results of the Logit Regression where Model p= Probability that a Household is Capability Poor by Proximate Poverty Determinant and by Type of Barangay-Based Core Local Poverty Indicator


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Proposed Program, Project, and Activities (PPAs)

Results and DiscussionC.4 Summary Results of Poverty Targeting

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Results and DiscussionC. 4 Summary Results of Poverty Targeting

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Results and DiscussionC.4 Summary

Results of Poverty Targeting

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Results and DiscussionC. 4 Summary Results of Poverty Targeting

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Conclusion and Recommendation

Pre-Conditions on the Application of PPIA Model in Local Planning and Development

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Applicability

Poverty data representing both income and capability indicators by basic human need should be made available.

LGU options: a) utilize the available CBMS data; b) adopt CBMS to generate poverty data; c) gather poverty data through local government agencies; or d) collect data through survey of sample households.



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Transferability

• Human Resources and Skills (Capability Building)

• Financial Resources

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