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

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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

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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)

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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

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10

20

30

40

50

60

Urbanisation and Poverty, 2010

Source: Susenas (BPS)

Regional Distribution, 2010

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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

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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

Historical Targeting Outcomes

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

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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

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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

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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

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40

60

80

100

1 2 3

Household Per Capita Consumption Decile

Urban Rural

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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

Improving Urban Targeting

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

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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

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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

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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

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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

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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

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Poor BLT Poor noBLT

Non-poorBLT

Non-poorno BLT

Year

s

Need to find indicators that better identify the urban poor

Different Dimensions of Poverty

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