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

 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

 Social assistance in Albania (Ndihma Ekonomike)  Current targeting mechanisms  Targeting performance  Results of simulations ◦ Block grant (geographic) allocations ◦ Household level identification  Conclusions

 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

CriteriaNE allocation scheme  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 Communes Households Block grants Household identification

Relative to neighboursImproving over time

Coverage of bottom quintileCoverage over time

Block allocations per poor person  Urban program: amount of transfer is fixed  Urban poverty rates are now high in coastal areas  But allocations are not adjusted accordingly

 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

CounterfactualSimulation Geographic allocation simulation Geographic targeting Proportional to actual weights Proportional to poverty index Household targeting Current NE recipients Household level targeting simulation Geographic targeting No geographical allocation: total budget/number of beneficiaries Household targeting Current NE recipientsBottom 7 percent of population as identified by PMT

Targeting accuracy with actual weights Targeting accuracy with poverty headcount weights Targeting accuracy with poverty severity weights Q Q Q Q Q Poor Non-poor

 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

Simulation resultsAssumptions Coverage of poorest decile (%) Share of benefits to poorest decile (%) Baseline PMT  No geographic targeting ◦ Benefit = budget / # beneficiaries  Constant overall budget  Constant overall share of beneficiaries

 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