Better outcomes with better data: the case of

social assistance in AlbaniaCaterina 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

Main messages

Social assistance in Albania (Ndihma Ekonomike)

Current targeting mechanisms Targeting performance Results of simulations

◦ Block grant (geographic) allocations ◦ Household level identification

Conclusions

Presentation outline

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

Overview of NE

NE targeting mechanism

Criteria NE 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

Good targeting accuracy

Relative to neighbours Improving over time

Serbia MOP

Kosovo SA

Albania NE

Montenegro FMS/MOP

FYR Macedonia SFA

BiH CSW

Serbia CA

ECA average (unweighted)

0 10 20 30 40 50 60 70 80 90

Share of benefits to poorest quintile

Q1 Q2 Q3 Q4 Q50.0

10.0

20.0

30.0

40.0

50.0

60.0

Share of benefits, by quintile

20022008

BUT Low coverage

Coverage of bottom quintile Coverage over time

Kosovo SA

FYR Macedonia SFA

Serbia CA

Albania NE

Montenegro FMS/MOP

Serbia MOP

BiH CSW

ECA average (unweighted)

0 5 10 15 20 25 30 35 40 45 Q1 Q2 Q3 Q4 Q50.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Coverage, by quintile

20022008

AND geographic variability in performance

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

Our approach

Counterfactual SimulationGeographic allocation simulation

Geographic targeting Proportional to actual weights

Proportional to poverty index

Household targeting Current NE recipients Current NE recipients

Household level targeting simulationGeographic targeting No geographical allocation:

total budget/number of beneficiaries

No geographical allocation: total budget/number of beneficiaries

Household targeting Current NE recipients Bottom 7 percent of population as identified by PMT

Simulations set-up

Targeting accuracy with

actual weights

Targeting accuracy with

poverty headcount weights

Targeting accuracy with

poverty severity weights

Q1 54.6 61.2 63.8

Q2 24.5 18.6 17.0

Q3 12.0 8.4 11.6

Q4 4.9 3.9 3.9

Q5 4.0 3.3 3.7

Poor 39.8 39.9 46.5

Non-poor 60.2 60.1 53.5

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

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

Improving 2nd step in allocation: household identification criteria

Improving 2nd step in allocation: household identification criteria

Simulation results Assumptions

Coverage of poorest

decile (%)

Share of benefits to

poorest decile (%)

Baseline 24.9 38.3PMT 36.9 50.4

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

Conclusions