Is it all about Money? A Randomized Evaluation of the Impact

of Insurance Literacy and Marketing Treatments on the Demand for Health

Microinsurance in Senegal

Jacopo Bonan (Milan-Bicocca), Olivier Dagnelie (Namur), Philippe LeMay-Boucher (Heriot-Watt)

and Michel Tenikue (CEPS)

Thanks to: ILO/Bill and Melinda Gates Foundation, Carnegie Trust of Scotland, Fonds National de la Recherche Luxembourg and the GRAIM, Thies.

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IntroductionHealth providers in Senegal:• Organized according to a tiered system:

1) health huts (staffed by community workers)2) health posts (nurses and certified midwives)3) health centres (with medical doctors, etc.)Survey: Thiès (2nd largest city in Senegal)Thies: 1 public hospital and 1 private hospital

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Introduction• High health costs

-Cost in health huts and posts are small-Hospitals: Xray (6000FCFA); Blood analysis (2000); Consultation (10-25000); Night (>25000)(sample median income 112500FCAF/month)

→ Often too ExpensivePoor people face difficulties to access ‘modern-medical’ health care

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Introduction1) health shocks → direct expenditures (drugs & treatment) : out-of-pocket payments (OOP)2) indirect costs → reduction in productivity

• OOP in Senegal : 70% of total expenditure in health (WHO)(5% in GBR, 11% in France)

• at low levels of income : strong link between health and income

→ Scope for more health insurance

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Health Microinsurance Available

• no universal coverage in Senegal No state insurance (ill-functioning: CESAME)

• informal insurance(bilateral transfers; networks :De Weerdt et al.(2006); ROSCAS: Dagnelie et al. (2012))

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Health Microinsurance Available

• IPM: treasury made up of employees’ compulsory contributions. (for large employers)

• Private health insurers (PAMECAS, etc)

• Mutual Health Organizations (MHO)

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MHOs expansion• Self or informally employed: no IPM

(>60% in our sample):ill-suited supply of health insurance

• Senegal 1990 in Fandene: first ‘mutuelle de santé’ or MHO

• Grass-root movement: managed by locals• MHOs in Senegal: 13 (1993) → 140+ (2007)

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MHOs structure and rules• Open to everybody• Membership fees (1000-3000FCFA/hh)

• Monthly premium 100-500FCFA/month/person

• Period of observation (3 months)

• MHO have contracts with all health providers

• cover 25 to 75% of consultation feescover 50% to 100% of medical exams and various inpatients cares fees

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Microinsurance take-up in Thies

• Out of our sample of 360 heads of hh:32% of have health insurance, (for 73% of all household members)

Of which:→ 19% with IPM (public or private)(several private IPM are not working…)→ 3% with private health insurance → 10% with MHOs

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Low MHO take-up• Overall MHO take-up rate in Thies region = 5% Smith et al. (2008) & Lépine et al. (2010).

→ Our Research Question:Why is MHO take-up rate so low despite being well established and having the potential to reach poor people?

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Why low MHO take-up?• From our sample, people who justified the

lack of membership to MHOs:1) lack of information about the product offered and/or MHOs existence (70% of hh)2) lack of means (16%)3) lack of interest (5%) 4) lack of trust (2%).

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Lack of Information• Cai et al. (2009): farmers in China refuse to purchase a heavily

subsidized insurance for sows → lack of awareness of the program.

• Giné et al. (2007); Cole et al. (2009) and Gaurav et al. (2009): → Limited understanding of rainfall insurance mechanisms in rural India.

• Jutting (2003): concept of insurance is alien to a large proportion of people in Senegal.

→ information campaign could have some impact

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Lack of Means• Jutting (2003) poorer hh in Senegal less member of MHOs. • Chankova et al. (2008) similar results in Ghana and Mali• Giné et al. (2008) : take-up rate of rainfall insurance increases

with household wealth in rural Andhra Pradesh. • Cole et al. (2009) low take-up rates of rainfall insurance :

insurance is expensive. • In our sample: only mentioned by 16% Why?

→ willingness to pay (WTP) = actual premiums → a banana is 100FCFA (!?!)

Bonan (2011) uses this data and the contingency valuation method to get WTP for MHOs premiums.

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Lack of Trust• Cai et al. (2009) low take-up by Chinese farmers: lack of trust

toward governmental institutions.

• Cole et al. (2009) endorsement of rainfall insurance in India from a third party ↑ purchase by 40%.

• In Thies:We asked the sample of non-members aware of the existence of MHOs: trust towards MHOs 1 to 10

Rescaled median score w.r.t. Trust in mother and family: 8/10

→ Large positive a priori from locals towards MHOs

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What if we inform and ↓ fees?• Factors at play: Information & Means

impact on membership of: -more info -↓ financial barriers

→ Randomized Control Trials• Design and implement two treatments: 1)Insurance Literacy module2)Marketing with 3 vouchers

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Our 2 treatments on 360 hh1) Literacy: 180 hh invited to:

3hour module on health microinsurance, MHOs and the concepts of risk and insurance. (given by GRAIM)

After2) Marketing: redeemable for 3 monthsVoucher 1: invitation to GRAIM (120 hh)Voucher 2: membership fees (120 hh)Voucher 3: memb. fees + 3000FCFA obs period

(120hh)16

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Randomized Evaluation • Old technique:

The first published RCT appeared in the 1948 paper entitled:"Streptomycin treatment of pulmonary tuberculosis“which described a Medical Research Council investigation. -Austin Bradford Hill is credited as having conceived the modern RCT

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Randomized Evaluation examples (I)

• Very active field in recent years:impulse from MIT and Poverty Action Lab

• Ex.: PROGRESA Mexico (1998)506 communities (half randomly selected)Treatment: cash grants to women conditional on school attendance and preventive health measures.

Results: Gertler et al. (2001)↓ illness, ↑ height, ↑enrollment

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Randomized Evaluation examples (II)

• Ex: Kenya, free breakfast program effect on school participation.Results: Vermeersch (2002)↑ school participation

• Ex: Kenya, program providing uniforms and textbooks (to 7 randomly selected schools)Results: Kremer et al. (2002)↓ Dropout rates

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Randomized Evaluation Ex: avg test scores (Duflo et al. 2007)Schools with textbook (T) – Schools without (C)

D = treatment effect + selection bias→ with successful randomization : We can pretend selection bias = 0

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CYETYETYYED

CYETYEDCi

Ci

Ci

Ti

Ci

Ti

|||

||

^^ DTY olsii

Our data: Thies June 2010• Second city of importance in Senegal

population of 240,000 (2002 census)-its MHOs are the oldest in Senegal: well established supply of MHOs.

• 360 randomly selected households• Urban area: 20 km2• Baseline survey : housing information,

household composition, info on head hh.• Uptake decision: head of hh

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Survey timeline:

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May-June 2010:Survey of 360hh

September 2010: deadline for vouchers

Our dependent variable: subscribe or not to MHO between May-Sept

Invitation to Module is made followed by Marketing treatment

Greedy enumerators constantly asking for an increase in salary…

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• ‘Strongly risk averse’ based on Voors et al. (2010)takes value 1 if individuals always opted for the certain outcome ‘A’ when presented with :

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• ‘Patient’We elicit discount factors (Voors et al. (2010))→ discount factors at one month of:5%, 10%, 25%, 50%, 75%, 100%, 150%, 200%. dummy if head is more patient half of our sample.

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•Aside: experience with real money gave very different results…

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•No significant difference if we look at the assignment across vouchers 1-2-3 (not shown)

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CYETYETYYED Ci

Ci

Ci

Ti |||

Not good newsSelection bias is clearly not equal to zero.Treatment group:-less insured (↑ intake)-less rich (↓ intake)-less knowledge about insurance (minor)-less public servant (↑ intake)

Bias can be + or – difficult to guess

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Empirical Strategy• Our model:

-M takes value 1 if hh subscribes to a MHO following our treatments-E takes value 1 if hh was invited to module -Voucher takes value 1 if hh was given either voucher 2 or 3

• Results similar with probit (shown) and OLS

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Our estimates• E measures the ‘invitation effect’

- does not measure the actual participation effect - α :‘intend to treat’ effect

• Low compliance rate (58%)we compute also ‘treatment on treated’ or ‘average treatment effect’ (Imbens-Angrist 1994) IV (instrument attendance by invitation)

• Both techniques give similar results31

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Randomized Evaluation related to insurance

• Rainfall insurance intake1)Gaurav et al (2009): Gujarat, India (600hh)Treatments: insurance educ module & Marketing→ module ↑ intake; little impact from marketing

1)Cole et al. (2009): India (2000hh)Treatment: insurance educ module

→ no impact from module

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Conclusion• Literacy module has no significant impactWhy?- Representative present (not being the head)- Health insurance is a simple product (relative

to rainfall insurance): no need- Quality of our module delivery?• Vouchers 2-3 have strong and positive impact-efficient to only distribute voucher 2

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