Hermann Pythagore Pierre Donfouet CREM, UMR CNRS 6211 University of Rennes I

1. Accounting for Spatial Interactions in the Demand for Community-Based Health Insurance: A Bayesian Spatial Tobit Analysis Hermann Pythagore Pierre Donfouet CREM, UMR CNRS 6211 University of Rennes I Pierre WilnerJeanty Kinder Institute for Urban Research Rice University Eric Malin CREM, UMR CNRS 6211 University of Rennes I, France

2. Presentationoutline • Introduction • Methodology • Survey design and data • Results • Conclusion

3. Introduction • Low-incomehouseholdslackhealthinsurance and adequatehealth care services. • CBHI has been recognised as an efficient mechanism to finance the need for healthcare of the low-incomehouseholds in developing countries (DC). • CBHI is a kind of insurancewhichisdesigned for low-incomehouseholdswho are totallyexcludedfromformalinsurance.Thedemand aspect of CBHI is important to policymakers. • Manystudiesconducted in DCshadrevealedthat the low-incomehouseholds are willing to pay for CBHI (Ataguba et al., 2008; Bärnighausen et al., 2007; Dong et al., 2004; Dong et al., 2004b; Dror et al., 2007; Wang et al., 2005)

4. Introduction (cont.) • Lack of spatial interactions in the previous studies. • Spatial dependence can be ascribed to the situation where observations on the dependent variable (or the error term) at one location is correlated with observations of the dependent variable (or the error term) at other locations. • To the best of our knowledge, no previous studies have examined the factors determining the demand for CBHI while allowing for the spatial interactions. This present study is an attempt to fill this void.

5. Methodology • The contingent valuationmethod (CVM) wasused to assess the demand for CBHI. • Elicitation format used: • Closed-ended question • Open-ended question • Spatial interaction wasintegrated in the twoelicitation formats by defining a social network spatial weights matrix (Anselin and Bera, 1998) as follows: households are neighbors if they live in the same village. • Testing the existence of spatial interactions • Closed-ended question • Open-ended question : • OLS • Tobit Will youbewillingpay X $? 1. Yes 2. No Whatisyourmaximunamount ? __$ Moran tests (Moran's_I) Spatial autoregressiveProbit (Bayesianapproach) SAR or SEM Bayesian spatial Tobit (SARBT)

6. Survey design and data • Government in Cameroon (Central Africa): 40% coveragewith CBHI by 2015 • 6 villages in Bandjoun (November 2009) by a two-stage sampling • Face-to-face interviews sponsored by the International Labour Organisation (ILO) • The most important part of the CV surveywas the scenario • CBHI and theirbenefitwaspresented to the head of the households. • The monthly premium thatthey must pay.

7. Results

8. Results (cont.)

9. Results (cont.)

10. Results (cont.)

11. Conclusion • The test of spatial interactions in the SARBT reveals that there are spatial interactions in the demand for CBHI. These spatial interactions thus affect the WTP for CBHI • As provided by table 4, the intensity of the spatial interactions is positive (rho>0) , implying that households buying behavior are strategic complements. • This externality (imitation effects) in the demand for CBHI may be explained by the social norms that rule many rural areas in developing countries. • Policymakers must be conscious that space matters a lot in the demand for CBHI and must take this in account when designing health insurance packages for rural households and their premium as well.

12. Thank you for listening Your comments are welcomed.