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

Presentation outline Introduction Methodology Survey design and data Results Conclusion

Introduction Low-income households lack health insurance and adequate health care services. CBHI has been recognised as an efficient mechanism to finance the need for healthcare of the low-income households in developing countries (DC). CBHI is a kind of insurance which is designed for low-income households who are totally excluded from formal insurance.The demand aspect of CBHI is important to policymakers. Many studies conducted in DCs had revealed that the low-income households 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)Ataguba et al., 2008 Bärnighausen et al., 2007Dong et al., 2004Dong et al., 2004b Dror et al., 2007Wang et al., 2005

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.

Methodology The contingent valuation method (CVM) was used to assess the demand for CBHI. Elicitation format used: Closed-ended question Open-ended question Spatial interaction was integrated in the two elicitation 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.Anselin and Bera, 1998 Testing the existence of spatial interactions Closed-ended question Open-ended question : OLS Tobit Will you be willing pay X $? 1. Yes 2. No Moran tests (Moran's_I) What is your maximun amount ? __$ Spatial autoregressive Probit (Bayesian approach) SAR or SEM Bayesian spatial Tobit (SARBT)

Survey design and data Government in Cameroon (Central Africa): 40% coverage with CBHI by 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 survey was the scenario CBHI and their benefit was presented to the head of the households. The monthly premium that they must pay.

Results

Results (cont.)

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.

Thank you for listening Your comments are welcomed.