UNICEF ILO S Social Protection Floor Costing Tool i l P t ti Fl - - PowerPoint PPT Presentation

UNICEF‐ILO S i l P t ti Fl C ti T l Social Protection Floor Costing Tool

ODI Conference on Financing Social Protection in LICs 26‐27 May, London 26 27 May, London

Jenn Yablonski

Presentation Outline

• Background
• Costing Tool Overview

Costing Tool Overview

• Application of the tool
• Senegal example

Background to development of tool

• Part of overall work on SP Floor ‐ strengthening and

expanding social protection systems

• Debates on affordability often held in abstract – not

useful in looking at concrete options and cost/fiscal implications

• Tool developed to support governments in assessing

Tool developed to support governments in assessing costs of different SP options

• Also for those advocating for particular programmes to
• Also for those advocating for particular programmes to

estimate costs I t d d i t i t i iti ti d b d ti

• Intended as one input into prioritization and budgeting

process

• Builds on earlier ILO tool

Tool Overview

• Designed to be user‐friendly
• Support cost estimates of different types of benefits
• Support cost estimates of different types of benefits

(planned or existing) over 20 year period:

• Pensions
• New Birth Registration Lump‐Sum
• Pensions
• Child Benefits
• Disability Benefits
• Orphan Benefits
• New Birth Registration Lump Sum

Benefit

• Youth Labour Market Programme
• Unemployment Benefits
• Education Stipend
• Other SP components (e.g. health insurance) not

tl i l d d ith ti t l l d i t currently included – either costing tools already exist, or national specificity makes generic tool difficult

• Tool manual to help users step‐by‐step

Tool Overview – Tool Components

Tool Overview

Tool Overview: Data Inputs

• Users can use internationally available data

– Population: DESA Population Projection Population: DESA Population Projection – Labour Market Data: ILO – Economic Data: World Bank Economic Data: World Bank

• AND/OR national statistics, including historical data

All t dif t t f

• Allows users to modify parameters, e.g. amount of

benefit, target population (gender, age), admin costs, i ti coverage expansion over time

Tool Overview: Results

• Automatically produces graphs on inputs and results
• Results include:

– Expenditure, expressed in absolute cost, GDP, government expenditure and revenue – Amount of benefit in local currency, per capita GDP – Male and female coverage g – Impact on poverty headcount and poverty gap

How the costing tool is being used

• Country offices using primarily in dialogue with

h l d d l government partners where already engaged in social protection

• Different levels of depth – ranging from quick estimates

to ongoing exercise with multiple partners

• Pairing with other tools – e.g. Rapid Assessment

Protocol, Adept , p

• Egypt, Thailand, Vietnam, Argentina, Madagascar,

Senegal Senegal

How the costing tool is being used: Senegal Example

• Costing exercise as part of ongoing partnership with

Senegal Example

g p g g p p government

• Collaboration with other partners including ILO and

Collaboration with other partners, including ILO and World Bank

• Based on previous research and discussion with
• Based on previous research and discussion with

government, introduction of a cash transfer program is a priority priority

• Children under the age of 5, in rural areas remain highly

l bl vulnerable.

• Study explores the design options of a cash transfer

program for Senegal targeted to children under 5.

Senegal Example: Design Options

• Scenario 1: Selection of beneficiaries is based on location and

Scenario 1: Selection of beneficiaries is based on location and number of family members.

• Scenario 2: All households with a child under the age of 5

Scenario 2: All households with a child under the age of 5 living in the 15 poorest districts are deemed eligible.

• Scenario 3: All households with a child under the age of five

g that have 14 or more members and that leave in the 20 poorest districts are deemed eligible.

• Scenario 4: All children under the age of five residing in rural

areas are deemed eligible. Using the costing tool and ADEPT SP , the following simulation results are arrived at:

Senegal Simulation results : Coverage, Distribution Inclusion/exclusion error Distribution, Inclusion/exclusion error

All scenarios show to be highly progressive and benefit more the poorest quintiles

• f the wealth distribution. For example, scenario 1 would provide almost 80% of

p , p the benefits to the bottom 2 quintiles of the income distribution, while 11% would go towards households in the top 40% of the welfare distribution.

Senegal Simulation results : Cost and Cost Benefit Cost and Cost‐Benefit

Senegal Simulation results : Conclusions Conclusions

• Given the Senegalese context, the most appropriate option

f h l d ld b ll f l from the scenarios simulated would be to target all families living in the 15 poorest rural districts that have a child under 5 (scenario 2) (scenario 2).

• Such targeting would be covering 37% of all poor children under

5, and 56% percent of all extreme poor children. The leakage 5, and 56% percent of all extreme poor children. The leakage rate would only be 11%.

• When comparing scenario 2 with scenario 1, while the latter is

p g ,

• nly slightly more effective at reaching the poorest, the fact

that scenario 2 is considerably less costly to implement and does not require much administrative capacity makes it a much better option

Next steps

• Continue supporting work at national level
• Doc ment res lts and lessons learned th s far
• Document results and lessons learned thus far
• Calibration of tool, as necessary
• Update manual and provide ‘roadmap’ for using tool in

combination with others

• Possible addition of other SP components and/or

linking to existing costing tools g g g