Transcript Document

Small Area Estimation for Monitoring the MDGs
at the Subnational Level
by
Candido J. Astrologo, Jr.
Jessamyn O. Encarnacion
Director, National Statistical Information Center
National Statistical Coordination Board
Chief, Social Sectors B Division
National Statistical Coordination Board
Workshop on MDG Monitoring
14-16 January 2009, Bangkok, Thailand
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Workshop on MDG Monitoring
CJA_JOE/14-16Jan2009
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Outline of Presentation
I.
II.
III.
IV.
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Introduction
Small Area Estimation (SAE) Methodology
Other Applications of SAE
Concluding remarks and recommendations
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I. Introduction
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•
Philippine official poverty statistics are released every 3
years at the regional and provincial levels of disaggregation.
•
All official regional poverty estimates (for 2000, 2003 and
2006) are reliable (having coefficients of variation (CVs) of at
most 10%).
•
In the case of the official provincial poverty estimates, 28 out
of 84* or 33% of the provinces are reliable with CVs less than
10%, while 46% have acceptable CVs between 10 and 20 and
21% have CVs over 20%.
•
No official municipal or city level estimates are generated
Workshop on MDG Monitoring
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I. Introduction
•
In 2005, the NSCB with funding assistance from World Bank
ASEM Trust Fund, conducted a poverty mapping project using
small area estimation methodology as part of the Philippine
Statistical System’s continuing effort to respond to the growing
need for lower level disaggregation of information on the poor.
2000 Family Income and
Expenditure Survey
2000 Labor Force
Survey
2000 Census of
Population and Housing
•
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2000 City and
Municipal Level
Poverty
Statistics based
on SAE
2000 poverty estimates for all the municipalities in the country
were released in November 2005 by the NSCB.
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II.
Small Area Estimation
Methodology
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II. Small Area Estimation Methodology
Aim
• Produce provincial-, municipal- and city-level
estimates of poverty incidence, gap and severity
based on official income-based provincial
poverty lines by merging information from
census and surveys
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II. Small Area Estimation Methodology
Data Requirements
• Survey
containing
target
independent variables (X)
variable
(Y),
• Census containing X (but not Y)
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II. Small Area Estimation Methodology
Two types of data sources:
1.
Household surveys
- include a detailed income and/or expenditure module
- however, due to relatively small sample size,
collected information is usually only representative for
broad areas of the country, e.g., regions
Data sources for the Philippines:
2000 Family Income and Expenditure Survey (FIES) and Labor
Force Survey (LFS)
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II. Small Area Estimation Methodology
Two types of data sources (cont’d):
2.
Census data
- available for all households and can provide reliable
estimates at highly disaggregated levels such as
cities and municipalities
- however, census data do not contain
income/expenditure information necessary to
estimate poverty
Data source for the Philippines:
2000 Census of Population and Housing (CPH)
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II. Small Area Estimation Methodology
Main idea
• Merge information from the two types of data sources to
come up with small area poverty estimates
• “Borrow strength” from the much more detailed coverage
of the census data to supplement the direct
measurements of the survey
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II. Small Area Estimation Methodology
Basic procedure
• Use the household survey data to estimate a model of
per capita income (Y) as a function of variables that are
common to both the household survey and the census
(X’s).
• Use the resulting estimated equation/model to predict
per capita income for each household in the census.
• The estimated household-level per capita income are
then aggregated for small areas, such as cities and
municipalities.
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II. Small Area Estimation Methodology
Candidate variables (X’s)
a. Common variables from FIES/LFS and CPH (18)
- Household dwelling characteristics (7)
- Family characteristics (11)
b.
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Municipal-level census means (25)
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II. Small Area Estimation Methodology
Modeling
• Regression
Yij  X ij   hi  eij
Regression models were constructed that estimated the
income of households based on household level and
community-level characteristics.
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II. Small Area Estimation Methodology
Production of small area estimates
2000 poverty estimates for each city/municipality,
province (urban and rural):
- poverty incidence
- poverty gap
- severity of poverty
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II. Small Area Estimation Methodology
HOW to update these city and municipal level estimates?
2000 SAE
2003 SAE
2000 Family
Income and
Expenditure
Survey
2003 Family
Income and
Expenditure
Survey
2000 Labor
Force Survey
2000 Census of
Population and
Housing
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2003 Labor
Force Survey
2000 Census of
Population and
Housing
Time-invariant
(i.e., variables
that may be
considered
“stable” over
time)
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II. Small Area Estimation Methodology
Features of the 2000 and 2003 SAE methodologies used
Features
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2000 SAE
2003 SAE
1. Data used
2000 FIES
2000 LFS
2000 CPH
2003 FIES
2003 LFS
2000 CPH
Identifying timeinvariant variables
2. Variables used
Consistent across all
data sets
Consistent AND TIMEINVARIANT across all
data sets
2. Models
developed
National model
Regional models
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IV. Other Applications of SAE
Other indicators where SAE technique was
applied in the Philippines
• Proportion of households not meeting energy adequacy
at the provincial level
• Provincial prevalence of underweight among 6-10 year
old children
• District (or barangay) level estimation of the proportion
of underweight Filipino children aged 0-5 years
• Proportion of stunted 0-5 year-old children at the
provincial level
• Provincial prevalence of hypertension among adults
• Labor and employment statistics at the provincial level
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IV. Other Applications of SAE
Relevance/Actual policy use of 2000 SAPE
• The 2000 small area poverty estimates released by NSCB in
2005 were already used by DSWD in their Pantawid Pamilyang
Pilipino Program.
• The Department of Social Welfare and Development (DSWD)
used the municipal poverty incidences in identifying priority
municipalities for KALAHI-CIDSS (e.g., Samar)
• NNC and DSWD used the data in December 2007 to identify
priority households for the Pamaskong Handog of GMA.
• The SAE were used by the Department of Agriculture (DA) as
one criterion in the identification of target sites of the
Cordillera Highland Agricultural Resources Management
Project (CHARMP II).
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IV. Other Applications of SAE
Relevance/Actual policy use of 2000 SAPE
• Regional KALAHI Convergence Group (RKCG) used the
estimates to serve as one of the bases in identifying its
convergence municipalities throughout the region (e.g.,
MIMAROPA).
• National Nutrition Council (NNC) Region VIII used the SAE in
assessing the nutritional situation of municipalities in the region
in October 2007.
• Results were used as input to determine target enrolment for
health insurance sponsored programs of PhilHealth in 2007.
• Leyte:
SAE results were used to determine priority
municipalities in Leyte in May 2007 for: (i) sponsorship program
for schooling of indigent children; and (ii) for micro-enterprise
development (MED) projects.
• The Department of Energy also expressed interest in the SAE
results as a possible reference for the installation of bio-diesel.
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IV. Other Applications of SAE
Relevance/Actual policy use of 2003 SAPE
• The 2003 intercensal small area poverty estimates was also
used by the DSWD as basis for prioritizing target
households for their proposed National Household
Targeting System for Poverty Reduction (NHTSPR)
• The Department of Energy also expressed interest in the
SAE results as a possible reference for the installation of
bio-diesel.
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IV. Concluding remarks and recommendations
• Small area estimation techniques can be used successfully
to produce poverty estimates at the provincial and
municipal levels.
• The estimates at provincial level were in general consistent
with, but more precise than the direct estimates obtained
from the survey data alone (official methodology), with an
average SE (CV) of less than 2% (5%)
• The precision of the municipal level estimates was more or
less similar to that of the official provincial level estimates
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IV. Concluding remarks and recommendations
• Possible generation of SAE for other indicators critical in
decision- and policy-making such as:
a) Unemployment – not available for city/municipal levels
b) Infant and maternal health
c) Post-census populations (alternative pop’n. projections)
d) Non-income component indicators of the HDI (i.e., life
expectancy, functional literacy, and basic education
participation rate)
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IV. Concluding remarks and recommendations
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•
Generation of poverty statistics for basic sectors – not
available for: 1) city/municipal levels and 2) some
sectors, where direct estimation of poverty statistics is
not possible due to data constraints.
•
Generation of poverty maps at lower levels of
disaggregation - poverty estimates overlaid and/or
combined with information on education, health,
access to infrastructure, environment, crime, among
others.
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Maraming Salamat po!
URL: http://www.nscb.gov.ph
e-mail: [email protected]
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