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Better outcomes with better data: the case of social assistance in Albania

Better outcomes with better data: the case of social assistance in Albania. Caterina Ruggeri Laderchi , Ramya Sundaram , Natsuko Kiso and Alexandru Cojocaru World Bank International Conference “Poverty and Social Inclusion in the Western Balkans” Brussels, Belgium, December 2010.

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Better outcomes with better data: the case of social assistance in Albania

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  1. Better outcomes with better data: the case of social assistance in Albania Caterina Ruggeri Laderchi, RamyaSundaram, NatsukoKiso and AlexandruCojocaru World Bank International Conference “Poverty and Social Inclusion in the Western Balkans” Brussels, Belgium, December 2010

  2. Main messages • Better household level information can improve performance of targeted programs at no cost • Criteria anchored in a poverty measure can be useful even when sudden change is not feasible • Clear and objective criteria improve transparency and support for a program

  3. Presentation outline • Social assistance in Albania (NdihmaEkonomike) • Current targeting mechanisms • Targeting performance • Results of simulations • Block grant (geographic) allocations • Household level identification • Conclusions

  4. Overview of NE • Largest non-contributory social assistance (cash benefit) program in Albania • Over 100,000 HH in 2008 (7% of population) • BUT budget of only 0.3% of GDP • Administered by local governments • Block transfer from central government • Centrally defined identification rules • Local approval of eligibility and distribution of benefits

  5. NE targeting mechanism • Block grants • Regional poverty estimates (LSMS 2008) and municipal population estimates (Census 2001) • # of NE beneficiaries in municipalities in previous year • Household identification • Means-test, implemented through multi-layered filters • Different across urban / rural areas NE budget Block grants Communes Household identification Households Criteria NE allocation scheme

  6. Good targeting accuracy Relative to neighbours Improving over time

  7. BUT Low coverage Coverage of bottom quintile Coverage over time

  8. AND geographic variability in performance • Urban program: amount of transfer is fixed • Urban poverty rates are now high in coastal areas • But allocations are not adjusted accordingly Block allocations per poor person

  9. Our approach • Two counterfactual simulations • Geographic targeting: through a poverty map • Household level targeting: through a PMT • Main features of the counterfactuals: • We focus on one feature at the time • We simplify by using • Per capita allocations (no equivalence scales) • No differences between rural/urban amounts

  10. Simulations set-up

  11. Improving 1st step in the allocation: geographic allocations (block grants)

  12. Improving 2nd step in allocation: household identification criteria • Replacing filters with household eligibility based on a proxy means test (PMT) • Household composition • Type of dwelling • Asset ownership • Identify the (predicted) bottom 7% of population • Allocate within current NE budget envelope

  13. Improving 2nd step in allocation: household identification criteria • No geographic targeting • Benefit = budget / # beneficiaries • Constant overall budget • Constant overall share of beneficiaries Simulation results Assumptions

  14. Conclusions • Advantage of improving geographic targeting with the poverty map • Improved targeting • Improved transparency – even if not jumping to a new system • Further improvements likely with new Census data • Additional improvement possible in the long run with a centralized national criterion for identifying beneficiaries

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