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Understanding Poverty in AcehDuring the Tsunami and Early

Reconstruction PeriodJed Friedman,Ellen Tan,Enrique Blanco Armas,Sukma Yuningsih,The World Bank

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Overview These are the preliminary results of the poverty

assessment for Aceh – comments welcome! Aim: to deepen understanding of the poverty

impacts of tsunami and reconstruction in Aceh Overall, poverty in Aceh has increased slightly

for both urban and rural areas from 2004 to 2005 Relatively larger increases in tsunami-affected

and post-conflict areas than in less affected areas

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Context The tragic 2004 Sumatran-Andaman earthquake and

tsunami caused damage and loss, in both economic and human terms, of large proportions.

The global community, led by the Indonesian government, mobilized a reconstruction effort of unprecedented scale for a developing country.

Shortly after the tsunami, the Government of Indonesia and GAM reached an historic peace agreement.

The World Bank aims to assist the Government of Aceh in the formulation of a sustainable economic development strategy. This poverty assessment is the first study among several.

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Approach Individual and household welfare is multi-

dimensional – e.g. income, wealth, health, “happiness”

Traditional economic analysis focuses on consumption as a summary measure of household resources

The focus here will be on consumption, but alternative dimensions – assets, health – will also be considered

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Methods Two principal data sources are employed:

SUSENAS, a large scale socio-economic survey fielded annually by BPS – 10,200 in 2004 and 10,900 households in 2005.STAR, special purpose longitudinal survey that re-contacts a selection of 2004 SUSENAS households with an extensive questionnaire in 2005 – 5,300 households in total.

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Methods (cont.) SUSENAS records several key dimensions of socio-

economic welfare: household composition, characteristics, and consumption as well as access to health and education services.

STAR records the identical information and supplements with it with a household record of tsunami and early reconstruction experiences.

SUSENAS is representative of the entire province, STAR only samples from tsunami affected districts – not everywhere in Aceh.

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Methods (cont.) We start with poverty lines in 2004 that yield the BPS

2004 estimates of poverty. For 2005, in order to provide a welfare consistent

update to the poverty line we:– 1. determine a consumption bundle representative to the

poor in urban and rural areas,– 2. apply these consumption weights to the monthly BPS

urban price series. Limitations of this approach: price changes are

measured only in Banda Aceh and Lhokseumawe. To what extent are these changes accurate to the rest of the province?

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Summary results

2004 2005headcount se headcount se Δ 2005

– 2004Aceh Province 28.4 0.2 29.6 0.2 1.2

Urban 17.6 0.3 18.8 0.3 1.2

Rural 32.6 0.3 32.8 0.3 0.2

Sumut Province 14.9 0.1 12.4 0.1 -2.6

Urban 12.0 0.2 7.5 0.2 -4.6

Rural 17.2 0.2 16.2 0.2 -0.9

Indonesia 16.7 16.0 -0.7

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Summary results

2004 2005headcount se headcount se Δ 2005

– 2004Aceh Province 28.4 0.2 29.6 0.2 1.2

High tsunami* 28.0 0.4 34.9 0.5 6.9

Low tsunami* 32.4 0.3 29.6 0.3 -2.9

High conflict* 33.6 0.4 36.1 0.4 2.5

Low conflict* 28.1 0.3 26.0 0.3 -2.1

STAR districts 25.8 0.6 27.0 0.6 1.2

Note: *Only includes Kabupaten not Kota

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2004 Aceh Poverty by District

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2004-2005 change in poverty, by district

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2005 Aceh Poverty by District

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Summary results (cont.) A slight increase in poverty while the national

and neighboring (Sumut) trends were declining.

Larger increases in tsunami- and post-conflict (rural) areas areas suggest particular vulnerability to changes (disaster; price increases) in those areas.

Are these findings robust to different reference levels of consumption welfare (i.e. different poverty lines)?

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Cumulative Distribution Function for Aceh0

.2.4

.6.8

1

0 100000 200000 300000 400000 500000Real 2004 Per Capita Expenditure

Aceh 2005Aceh 2004

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Rural Aceh

0.2

.4.6

.81

0 100000 200000 300000 400000 500000Real 2004 Per Capita Expenditure

Rural Aceh 2005Rural Aceh 2004

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Urban Aceh

0.2

.4.6

.81

0 100000 200000 300000 400000 500000Real 2004 Per Capita Expenditure

Urban Aceh 2005Urban Aceh 2004

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Robustness of findings to choice of poverty line Analysis of CDFs suggest that measured

poverty level is particularly sensitive to choice of poverty line.

Poverty lines within a broad range would still find a small increase or no change in poverty, especially in rural areas.

Households that were relatively well-off in 2004 did even better, on average, in 2005, especially for urban areas.

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Who were the poor in 2004?Poor Non poor

Rural (%) 82.8 67.9

Average HH size 6.0 4.8

Average number of children 2.4 1.5

Male households (%) 88.4 87.7

HH heads without completed primary education (%) 20.7 16.9

Agriculture (%) 60.7 42.6

Fishery (%) 8.6 6.7

Manufacturing Industry (%) 2.0 3.4

Construction (%) 3.8 4.5

Trading, Restaurant, and Accommodation Service (%) 12.9 20.1

Transportation, Storing, and Communication (%) 4.6 5.6

Public/Social/Personal Service (%) 6.8 15.2

Other (%) 0.6 2.0

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Who were the poor in 2004?- Relative Odds

Odds Odds

0 children in HH 0.25*** Male HH 0.55***

1 child in HH 0.60*** Female HH 1.00

2 children in HH 1.00 Rural 1.58***

3 children in HH 1.80*** Urban 1.00

4 children in HH 2.92*** Agriculture 1.00

> 5 children in HH 5.66*** Fishery 0.68**

HH without education 1.39* Manufacturing Industry 0.47***

HH with some primary education 0.75** Construction 0.48***

HH finished primary education 1.00 Services sector 0.42***

HH with some senior education 0.79** Transportation 0.62***

HH finished senior education 0.33*** Public services 0.58***

HH with higher education 0.14*** Other 0.31***

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Direct experience of tsunami for poor and non-poor Direct tsunami experience was more common for the non-

poor due to location at time of disaster. Among most heavily affected areas, little difference between poor and non-poor: Overall Most heavily damaged areas Poor Non-Poor Poor Non-Poor

Mortality6.5% 10.7% 19.3% 24.4%

Heard the water28.5% 34.5% 62.8% 63.7%

Saw tsunami come ashore19.2% 23.6% 47.5% 46.6%

See people search for loved ones24.7% 28.5% 51.2% 54.9%

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Direct experience of tsunami for poor and non-poor – immediate aftermath At time of STAR interview, roughly equal numbers of poor

and non-poor with similar aftermath experiences:

Overall Most heavily damaged areas Poor Non-Poor Poor Non-Poor

Living in temporary housing7.4% 8.4% 18.2% 18.3%

Reliant on aid-provided water5.9% 8.4% 21.8% 21.4%

Receipt of tsunami related aid from government43.7% 44.0% 68.0% 65.3%

Receipt of tsunami related aid from NGOs42.3% 40.5% 70.3% 67.6%

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Direct impacts of tsunami on poor and non-poor – asset lossPercentage of households experiencing property damage

(conditional on ownership) in heavily damaged areas: Poor Non-Poor

Housing: 60% 62%Land: 26% 28%Crops: 51% 64%Livestock: 48% 65%Household goods: 64% 61%Transport: 50% 56%Gold/Jewelry: 46% 53%

Damage rates range from:15-30% in moderately affected areas1-5% in unaffected areasfor both poor and non-poor

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Direct impacts of tsunami – mental health We already looked at assets – tsunami impacts substantial

regardless of poverty status Now look at one dimension of health: post-traumatic stress -

“Post Traumatic Stress Reaction Index” measured in STAR and varies from 0-21

Much higher level in damaged areas, but little difference between poor and non-poor within those areas

All By Degree of Tsunami Damage

Heavy Some None

PTSD Reaction Index (since tsunami) 6.6 8.7 7.0 5.2

PTSD Reaction Index (at interview) 4.2 5.6 4.8 3.0

- Only 8% of individuals with high scores had received counseling

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Who were the poor in 2005?Poor 04 Poor 05

Rural (%) 82.8 85.9

Average HH size 6.0 6.0

Average number of children 2.4 2.4

Male households (%) 88.4 86.3

HH heads without completed primary education (%) 20.7 33.0

Agriculture (%) 60.7 62.5

Fishery (%) 8.6 7.5

Manufacturing Industry (%) 2.0 2.7

Construction (%) 3.8 5.2

Trading, Restaurant, and Accommodation Service (%) 12.9 9.7

Transportation, Storing, and Communication (%) 4.6 5.6

Public/Social/Personal Service (%) 6.8 5.1

Other (%) 0.6 1.8

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Who were the poor in 2005?- Relative Odds

2004 20050 children in HH 0.25*** 0.34***

1 child in HH 0.60*** 0.51***

2 children in HH 1.00 1.00

3 children in HH 1.80*** 2.02***

4 children in HH 2.92*** 3.09***

> 5 children in HH 5.66*** 6.92***

HH without education 1.39* 1.78***

HH with some primary education 0.75** 1.06

HH finished primary education 1.00 1.00

HH with some senior education 0.79** 0.72***

HH finished senior education 0.33*** 0.32***

HH with higher education 0.14*** 0.09***

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Relative Odds in 2004 and 2005 (cont.)

2004 2005Male HH 0.55*** 0.72*

Female HH 1.00 1.00

Rural 1.58*** 1.51***

Urban 1.00 1.00

Agriculture 1.00 1.00

Fishery 0.68** 0.53***

Manufacturing Industry 0.47*** 0.45***

Construction 0.48*** 0.68**

Services sector 0.42*** 0.36***

Transportation 0.62*** 0.53***

Public services 0.58*** 0.33***

Other 0.31*** 0.76

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2004 2005

High tsunami 0.80*** 1.24***

Low tsunami 1.00 1.00

High conflict 1.30*** 1.60***

Low conflict 1.00 1.00

Relative Odds in 2004 and 2005 (cont.)

Note: Only includes Kabupaten not Kota

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Predictors of poverty transitions over 2004-5 The poor is not a static group – some people exited

poverty over 2004-5 and slightly more people entered

What are the factors significantly associated with the transition out of poverty?

– Small household sizes: an increase in one household member reduces the odds of escape by 32%

– Education: an increase in one year of schooling for the household head increases the odds of escape by 10%

– Productive assets: having a non-farm business increases the likelihood of escape by 100%

– Disaster assistance: the receipt of government aid increases the likelihood of escape by 42%

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Predictors of poverty transitions over 2004-5 (cont.) What are the factors significantly associated with

protection from the transition into poverty?– Small households: an increase in one household member

raises the odds of entry by 38%– Productive assets: having a non-farm business reduces the

likelihood of entry by 50%, and one year of education by 10%

– Crop diversification: Agricultural households that only grow rice are more vulnerable to entering poverty (30% more likely) while those that grow perennials and other crops are 32% less likely to enter poverty

– Disaster assistance: the receipt of NGO aid reduces the likelihood of entering poverty by 23%

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Concluding remarks The slight rise in poverty masks a good deal of

underlying heterogeneity Most poor are rural and work in agriculture - the

overall size of this group may have increased over the period

Both poor and non-poor in tsunami areas were seriously affected in a variety of dimensions

However the growth in urban areas under reconstruction has led to relatively faster income growth for the better-off (and most likely better educated)

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Concluding remarks (cont.) Tsunami aid appears to help poverty transitions

but there are questions over targeting Crop diversification and non-farm enterprises

seem to be important for beneficial poverty transitions

Two vulnerable groups: the structurally poor (poor before tsunami) and the “shocked”, who suffered loss of private goods and assets however many retained key endowments.

Given limited public funds for poverty alleviation, what is the most efficient group to target?

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Next steps

Investigate possible other sources of local price information and discuss inclusion into the analysis

Investigate other dimensions of welfare, most importantly access to public services such as health and education