Efficiency, equity and feasibility of strategies to identify the poor: an application to premium exemptions under national health insurance in Ghana

Caroline Jehu-Appiah, Genevieve Aryeetey, Ernst Spaan, Irene Agyepong, Rob Baltussen

Health Policy 2010;95:166-73

Saly, Senegal 2011

Background• Currently many sub-Saharan African countries

are exploring ways to replace user fees at point of service use with more equitable alternatives.

• Many are experimenting with social and community health insurance

• Regardless of what financing mechanisms are used, exemptions are needed for the poorest

• However there are challenges with effectively identifying and targeting the most vulnerable groups for exemptions.

Background cont’

• Ghana passed a National Health Insurance (NHI) act in 2003 and by 2010 66% enrolment (NHIA)

• Empirical evidence shows enrolment among the poor is low (Asante and Aikins 2008).

• Efforts to identify and enrol the poor through

premium exemptions at an early stage of the NHIS are required.

• Difficulties in identification and targeting of the poor and uncertainties as to the most cost-effective approaches to use.

Objectives •To identify potential strategies to identify

the poor, and assesses their feasibility, efficiency and equity

▫Estimate costs of each strategy, and present trade-offs between feasibility (defined as practical ability to

identify the poor in a given context), efficiency (defined by cost per exempted poor

individual, and equity (defined by error of exclusion)

Methods• A literature search in Medline. 62 articles selected and

classified into 4 broad strategies to identify the poor: (1) means testing, (2) proxy means testing (PMT), (3) geographic targeting (GT) and (4) participatory welfare ranking (PWR).

• To estimate the implementation costs, cost models were developed on the basis of a combination of empirical estimates and expert opinion, using the 2008 price levels and costs and errors of in- and exclusion of these strategies

• Sensitivity analysis was employed to assess the impact of varying assumptions on study results and study conclusions

emiumxPxPCostSurveyTC eligible Pr0

Identifying the Poor by Household Income - Means Testing (LSMS)

Pros of LSMS’s Cons of LSMS’s

• Benchmark in poverty assessment

• Objective , high quality assessment of HH income

• Both absolute and relative poverty analysis of welfare

• High cost

• Measure income defined poverty and not its broader dimensions

• Measure HH and not individual welfare

• Do not disaggregate beyond regional level

• Small sample size

Targeting the Poor by Household Indicators - Proxy Means testing (DHS,CWIQ)

Pros Cons

• Potential to provide alternative welfare ranking

• Lower administrative costs and information on the key indicators is more widely available.

• Effectiveness-correctly predicts poverty status from 80-84% of its participants (Johannsen 2006).

• Limited to relative analysis of welfare

• Exclusion of 16-20% of poor (Johansen,2006)

• Asset indices say nothing about absolute poverty

• Cannot be used to monitor changes in poverty over time

• Measured at HH and not individual level

Geographic targeting (poverty maps)

Pros Cons

• Relatively easy to implement

• Possible to map at the district, sub district levels

• Narrow targeting improves coverage of poor

• Village level targeting more effective than regional targeting at reducing leakages

• Leakages to non poor especially in urban areas

• Data availability

• Requires High level of Econometric Expertise

Poverty Mapping

• Maps out poverty incidence for whole country

• Allows for Blanket exemptions of whole districts based on incidence of poverty

• At no cost to MOH

Targeting by Participatory Welfare Ranking (PWR)Pros Cons

• Simple, transparent

• Low cost

• Widely accepted – community participation and ownership

• Effectiveness- 82%

• Useful in combination with Geographic targeting

• Captures new settlements, street children, ophans

• Ineffectieve in larger communities urban areas

• High level of skill and facilitation

• Risk of sampling and respondent bias

• Criteria differs from community to community therefore results are not comparable

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Accra Tema Ga Dangbe West

Dangbe East

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Gt. Accra Region

PWR

PROXY MEANS TESTING

GEO TARGETING

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Upper West Region

PWR

PROXY MEANS TESTING

GEO TARGETING

District Strategy Costs        Cost of survey (g)

Costs of premium exemptions (h)=(f)*φ

Total Cost (j)=(g)+(h)

Cost per identified poor individual (efficiency indicator) (j)=(i)/(f)

incremental cost per extra exempted poor individual

Nadawilli PWR 45,799 356,566 402,365 11.63

PMT 70.491 404,930 475,421 12.57 23

GT 536,189 536,189 11.63 7

Tema PWR 239,855 1,276,787 1,516,642 41.44

PMT 407,393 1,343,473 1,750,866 43.76 69

  GT   3,252,962 3,252,962 66.67 171

Discussion• PMT, PWR and GT achieve efficiency and equity objectives to

different degrees

• PWR appears the least costly and therefore most efficient strategy, but is also the least equitable.

• GT covers all (poor) individuals in a given area, and is therefore the most equitable but also the most costly.

• Incremental costs of exempting one extra poor individual range between US$7 and US$ 171.

• Choice highly dependent on poverty setting and feasibility of implementation

• Selection of a strategy therefore has to be contextualised, and it is not advisable to apply a single strategy across the entire country.

Policy dilemma • TC of paying for insurance premiums exemptions

for all poor households in Ghana is $22 million

• These costs consume 4% of total health resource envelope for 2008 ($513 million) and

• 16% of total NHI budget ($142 million)

• Policy dilemma and tough choices posed by the gap between the desired mandate and financing constraints

Study limitations

•Analysis is based on literature review and expert opinion

•Research is needed to verify assumptions

•Partial assessment of various strategies and excludes the perceptions of the community and policy makers on the acceptability of various approaches

•Thank you