Poverty Cluster, World Bank Office Jakarta 26 September 2012
FORUM KAJIAN PEMBANGUNAN
Poverty Targeting in an Urban Setting: Indonesia’s Evolution
Poverty has been falling in both urban and rural areas in the last decade
0
5
10
15
20
25
30
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Urban
Rural
National
Poverty Headcount Rate in Indonesia (Percentage of Population)
Source: Susenas (BPS)
Poverty increased in 2006 due to high international food prices
With nearly 70 percent of Indonesians will be urban dwellers by 2025, poverty will be increasingly urban
0
20
40
60
80
100
2000 2010 2025
Urbanisation Rate (Percentage of Population)
Note: * 2025 urban share of poor assumes same poverty reduction in both urban and rural areas from 2010 to 2025, but with urbanisation rate increasing from 50 percent in 2010 to 70 percent in 2025. Source: 2000 and 2010 Population Census (BPS), Indonesia Population Projection (2005, Bappenas-BPS- UNFPA), Susenas (BPS)
0
20
40
60
80
100
1996 2000 2010 2025*
Urban Share of Total Poor (Percentage of Poor)
High urbanisation and populations mean most urban poor are currently concentrated in Java and Sumatra
0
10
20
30
40
50
60
Urbanisation and Poverty, 2010
Source: Susenas (BPS)
Regional Distribution, 2010
1
2
3
4
5
6
7
8
9
National Urbanisation Rate
Percent
National Urban Poverty Rate
Millions Of People
67.6%
20.4%
6.0% 2.9% 2.4% 0.4% 0.3%
Share of National Urban Poor
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Household Consumption Percentile
As poverty continues to fall, it will be harder to lift the remaining poor out of poverty
2010 Poverty Line
Average Consumption by Percentile, 2010 (Percentage of Poverty Line)
The remaining poor are increasingly far below the poverty
line
Effectively targeting social programs at the urban poor will
be key to eliminating poverty in Indonesia
Source: Susenas and World Bank calculations
2010 Poverty Rate
AGENDA
Historical Targeting Outcomes
Improving Targeting for the Urban Poor
Targeting Different Dimensions of Urban Poverty
Indonesia has a range of social assistance programs targeted at all poor households
Raskin (Rice for the Poor)
Jamkesmas (Health Insurance for the Poor)
BSM (Scholarships for the Poor)
PKH (CCT)
BLT (temporary UCT)
Different urban and rural development needs currently targeted through PNPM, not
household-targeted programs
Half of all poor are excluded from these programs, while half of the benefits go to non-poor
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
Perc
enta
ge R
ecei
vin
g B
enef
its
Household Per Capita Consumption Decile
BLT Raskin Jamkesmas
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10Perc
enta
ge o
f To
tal B
enef
its
Rec
eive
d
Household Per Capita Consumption Decile
BLT Raskin Jamkesmas
Sources: Susenas and World Bank calculations
Coverage of Program Benefits, 2010 Incidence of Program Benefits, 2010
Target Target Non-target Non-target
However, poor urban households are less likely to receive benefits than poor rural households
0
20
40
60
80
100
1 2 3
Household Per Capita Consumption Decile
Urban Rural
Notes: BLT data are for 2009 Sources: Susenas and World Bank calculations
Program Benefit Coverage of Target Deciles, 2010 (Percentage of Consumption Decile Receiving Benefits)
BLT
0
20
40
60
80
100
1 2 3
Household Per Capita Consumption Decile
Urban Rural
0
20
40
60
80
100
1 2 3
Household Per Capita Consumption Decile
Urban Rural
Jamkesmas Raskin
Greater leakage of benefits to non-poor households in urban areas
The rest of the presentation focuses on improving targeting of the poor, and whether this is sufficient
Are we visiting the right households?
Are we assessing the households accurately?
Question 1: How can targeting of the urban poor be improved? 1
There are a number of dimensions of welfare
Should consumption-based poverty targeting be used for all social programs?
Question 2: Is effective urban targeting sufficient to address urban welfare issues?
2
Targeting can be improved in two fundamental ways
Data Collection If not all households are being
assessed, need to decide which households to visit
Beneficiary Selection Households visited need to be
assessed accurately
1
2
TWO MAIN COMPONENTS OF TARGETING
Determining which households to visit in the past was not optimal
Sub-village heads nominated potentially poor households
Previous Method of Data Collection and Beneficiary Selection (2005)
If household not nominated, cannot be selected by model
Weaknesses of Data Collection
Households surveyed with pseudo-Proxy Means Test (PMT)
Possibility of elite capture
Could be less effective in urban areas
– Friends and family nominated instead of the poor
– Urban communities tend to be less well-defined
– Rural-urban migrants even more disadvantaged
– Not all households known to head – Not all households included in some neighbourhood
– Less likely to be known to head – Poverty status less clear to head
The new National Targeting System being developed by TNP2K will address this issue
National Targeting System
Improved Methodologies Benefitting Urban Targeting
Use of Geographic Targeting to determine: Number of households to survey
for database in each district Number of beneficiaries for each
program in each district
1
Use of Census-based Poverty Mapping to pre-list households for assessment: Census data combined with
Susenas to make small-scale poverty map
2
Unified registry of 25 million households, and over 100 million people
Data collected in 2011 by BPS
PMT models constructed by BPS and TNP2K
Household rankings constructed by TNP2K
Program beneficiary lists extracted by TNP2K
However, improving urban targeting also requires accurately identifying households as poor
2009 Outcomes
Poor* Non-poor*
BLT Beneficiary
8.3 m 11.0 m
BLT Non-beneficiary
11.7 m 80.7 m
Urban households can be poor or non-poor, as well as beneficiaries or non-beneficiaries
Poor beneficiaries in 2009 were not much poorer than poor non-beneficiaries
100
200
300
400
500
600
700
Poor BLT Poor no BLT Non-poorBLT
Non-poorno BLT
Ave
rage
Mo
nth
ly P
er C
apit
a C
on
sum
pti
on
(R
p 0
00
s)
Notes: Poor are those in the poorest 30 percent of households nationally Sources: Susenas and World Bank calculations
Why did we mis-identify half of the urban poor in
2009?
The method for determining which households were considered poor in the past had limitations
Sub-village heads nominated potentially poor households
Previous Method of Data Collection and Beneficiary Selection (2005)
Only 14 indicators collected on each household
Weaknesses of Data Selection
Households surveyed with pseudo Proxy Means Test (PMT)
Scoring system for these indicators was ad hoc – Weights not calculated in reference to consumption
The National Targeting System has addressed some of these issues with best practice PMT selection models
National Targeting System
Improved Methodology Benefitting Urban Targeting
Use of international best practice Proxy Means Test (PMT) models:
Over 40 household and community indicators used
Household scores estimated from consumption regressions
1
However, further work needs to be done on identifying new indicators of the urban poor
The excluded urban poor look more like the urban non-poor than the included urban poor, on education
0
5
10
15
20
25
30
35
40
45
Poor BLT Poor no BLT Non-poor BLT Non-poor no BLT
Perc
ent
Illiterate None SD SMP SMA Tertiary
Notes: Poor are those in the poorest 30 percent of households nationally Sources: Susenas and World Bank calculations
Urban Head of Household Education, 2009
The same is true for sector and type of employment, as well as housing conditions
0
10
20
30
40
50
60
Poor BLT Poor noBLT
Non-poorBLT
Non-poorno BLT
Perc
ent
Agriculture Formal
Notes: Poor are those in the poorest 30 percent of households nationally Sources: Susenas and World Bank calculations
Sector and Type of Employment for Urban Head of Household, 2009
20
40
60
80
100
Poor BLT Poor noBLT
Non-poorBLT
Non-poorno BLT
Perc
ent
Not earth floor Brick wall
Private toilet Clean drinking water
Housing Conditions, 2009
Only in size and age are the excluded urban poor most similar to the included urban poor
0
1
2
3
4
5
6
Poor BLT Poor noBLT
Non-poorBLT
Non-poorno BLT
Nu
mb
er
of
Mem
be
rs
Notes: Poor are those in the poorest 30 percent of households nationally Sources: Susenas and World Bank calculations
Average Household Size, 2009 Average Age, 2009
0
5
10
15
20
25
30
35
Poor BLT Poor noBLT
Non-poorBLT
Non-poorno BLT
Year
s
Need to find indicators that better identify the urban poor
Targeting programs to the monetary poor may not ultimately address different dimensions of poverty
Outcomes
Monetary Income
Health
Education
Housing
Food Security
Opportunities
Health Access
Education Access
Transportation Access
Social Assistance Access
Program design and targeting will depend upon who is poor on each of the dimensions
Scenario 1: High associations between dimensions of poverty
Scenario 2: Lower associations between dimensions of poverty
Hardcore Poor
Consumption Poverty
Health Poverty
Education Poverty
Consumption Poverty
Health Poverty
Education Poverty
Targeting Implications: Target social protection programs
to the hardcore poor
Targeting Implications: Target different vulnerable groups
according to deprivations
For example, housing conditions might be improved through higher income, but not water and sanitation
Urban Housing Conditions Urban Water and Sanitation
Why do non-poor not have access to water and sanitation? Targeting may need to be
done at the community level
Substandard housing 52.5%
No water or sanitation
24.4%
Poorest 30% 22.2% Poorest 30%
22.2%
7.3%
14.9%
37.6% 12.4%
9.8%
14.6%
Sources: Susenas 2011 and World Bank calculations
Moreover, it is not the same urban households who have limited access to services
19.2%
Poor Physical Education Access 35.2%
Poor Physical Health Access 40.2%
Poor Transport Access 52.5%
1.1%
Poor Transport Access 23.9%
Poor Physical Education Access 6.2%
Poor Physical Health Access 9.1%
Rural Households Urban Households
Urban planning may need to account for changing populations to identify
infrastructure needs Sources: Podes 2011 and World Bank calculations
In addition, food security is not strictly a matter of income in urban areas…
Poor (national poverty line) 6.8%
Food-energy deficient (light activity) 7.2%
Food-energy deficient (moderate activity) 19.2%
Poorest 30% 22.2%
Did not receive Raskin 6.4%
Food-energy deficient (moderate activity) 19.2%
Sources: Susenas 2011 and World Bank calculations
Urban Food Poverty Urban Food Poverty and Raskin
3.4% 1.9%
1.5%
5.7%
10.1%
11.5%
2.5%
5.2%
3.9%
10.6%
Understand why non-income constrained households do not
consume enough food
…nor is urban school under-enrolment
Parents without SD 20%
Poorest 30 percent 28.8%
Not enrolled in SMP 28.3%
Not enrolled in SMP 28.3% No SMP
within 1 km 12.6%
Poorest 30 percent 28.8%
Urban households with children aged 13 to 15
Sources: Susenas and World Bank calculations
Require deeper analysis of what is driving under-enrolment, focusing on income and
physical access is not sufficient
17.2%
7.7%
17.0%
2.9%
6.2% 2.1%
1.3%
16.5% 2.8% 7.3%
6.0%
13.9%
3.9%
5.1%
Thus, while improvements to targeting of the urban poor are underway, there is more to do
Poverty increasingly urban in Indonesia 1
A range of programs exist to help the poor
2
Targeting outcomes need to be improved especially for urban poor
3
New unified registry will be important 4
Current State Further Work Required
Identify better indicators of the urban poor
1
Test new methods of identifying the poor e.g. targeting experiments
2
Unified registry needs to be dynamic grievances redress, updating
3
Develop different targeting instruments for different programs
4
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