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Michael Epprecht Centre for Development and Environment CDE University of Bern, Switzerland

September 1 – 4, 2013 Chulalongkorn University, Bangkok, Thailand. Targeting poverty in the Lao PDR : how well do current approaches reach the poor?. Michael Epprecht Centre for Development and Environment CDE University of Bern, Switzerland. Background of the SAE in the Lao PDR

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Michael Epprecht Centre for Development and Environment CDE University of Bern, Switzerland

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  1. September 1 – 4, 2013Chulalongkorn University, Bangkok, Thailand Targeting poverty in the Lao PDR: how well do current approaches reach the poor? Michael Epprecht Centre for Development and Environment CDEUniversity of Bern, Switzerland

  2. Background of the SAE in the Lao PDR • Results of the Lao poverty mapping analysis • Implications for targeting OUTLINE

  3. Mapping Poverty in the Lao PDR • Small-area estimation analysis using • Lao Expenditure and Consumption Survey 2003 (LECS III) • 8092 households (20% urban)in 540 villages (5%) • Population and Housing Census 2005Systematic sample of 75% of households: • 712,900 households • 4,123,988 individuals BACKGROUND

  4. SAE poverty mapping model applied in Lao PDR • Rural Urban • X X • X X • X X • . X X • X • X • . . • X X • X X • . X . • X X • X X • . X X • X X • X X • X X • Household size • Household size squared • Age composition of household • Female-headed household . • Education of head • Education of spouse • Occupation of head • Ethnicity . • Type of floor • Type of wall • Type of roof • Area of house . • Type of water source • Type of toilet • Type of cooking energy • Village cooking energy . • Village floor type • Village ethnicity • Agro-ecological region BACKGROUND

  5. Lao poverty mapping analysis • Reliability of results • Where are the poor? • Who are the poor? RESULTS

  6. Poverty estimates and confidence intervals RESULTS - RELIABILITY

  7. 1000 Frequency 500 0 0 200 400 600 800 # of sample households per village • Size of Lao villages (75% sample) RESULTS - RELIABILITY

  8. Spatial patterns of poverty in the Lao PDR • Poverty rates arehighest in the South East along the mountainous border with Vietnam • Rural poverty is low in • Xayaburi Province:high value agric. for export • Mekong corridor:irrigated rice and trade with Thailand • Boloven plateau:fertile basaltic soils - coffee, tea, & cardamom RESULTS WHERE ARE THE POOR?

  9. Spatial patterns of poverty in the Lao PDRPoverty incidence and poverty density

  10. Lao poverty mapping analysis • Where are the poor? • Who are the poor? RESULTS WHO ARE THE POOR?

  11. Poverty rates amongmale- and female-headed households:

  12. Poverty among ethnic groups RESULTS WHO ARE THE POOR?

  13. Poverty among ethnic groups RESULTS WHO ARE THE POOR?

  14. With the poor a bit of everywhere: IMPLICATIONS FOR TARGETING

  15. With the poor a bit of everywhere: IMPLICATIONS FOR TARGETING • What are theimplications… ? …for targeting of poverty alleviation efforts?

  16. Where to reach the poor? IMPLICATIONS FOR TARGETING

  17. MDG 1: Reducing the number of poor people by 50 % in 2015 •  50% of the poor live in districts classified as not poor •  1/3 of the poor live in poor priority districts IMPLICATIONS FOR TARGETING

  18. MDG 1: Reducing Laos’ number of poor by 50% IMPLICATIONS FOR TARGETING Highest poverty density Poorest areas

  19. Most people are poor in remote, and sparsely populated areas high ‘access’ costs per person costly service provision little potential ‘leakage’ to non-poor IMPLICATIONS FOR TARGETING • Most poor people live in comparatively densely populated areas low ‘physical access’ costs per person, but potentially higher targeting costs cheaper service provision high ‘leakage’ to non-poor

  20. Balancing targeting (and implementation) costs vs. ‘leakage’ • Considerations on type of assistance in targeting(household vs. community/ geographic) • Data needs, and updatability of key data •  Regional development approach in sparsely populated poor remote areas •  Household or population segment targeting in densely populated areas IMPLICATIONS FOR TARGETING

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