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The role of networks in insuring health shocks. The case of poor urban communities in Accra
1  Centre For Development Research, Bonn University


Health risks are increasingly threatening the welfare of households in urban areas and the economic effects of sickness carry consequences on the resilience capacities. The empirical evidence in household ability to smooth consumption is mixed, while the general policy trend has been towards promoting publically funded health insurance to provide financial protection. However, the demand for health insurance is surprisingly low, and the scholarship has not been able to solve the puzzle of scant demand despite the benefits offered.

A promising strategy for understanding the factors that influence the uptake of health insurance is accounting for network effects, given that the social connections have a powerful impact over decisions and they usually offer the only means of protection over shocks.

The understanding of the way in which customary risk pooling arrangements contribute to the formalization of insurance can only be addressed by using a holistic approach, since insurability of consumption may be driven by different responses to different shocks. The aim of this work is to detect the influence of social networks in individual choices regarding risk preferences. The study site is the Accra Metropolitan Area, and as many other urban areas in the global south, is experiencing a rapid urbanization dynamic, increasing health risks, increasing urban poverty, and changing food systems occurring along with demographic and epidemiological transitions.

Data obtained from the Socioeconomic Panel Survey and household level interviews will be used to recover the parameters of a general network formation model. To identify boundaries and efficiency levels of the risk reduction strategies in place, a Pareto risk efficient allocation model at community and household level will be used. Moreover, to estimate how the probability of adopting certain risk reduction strategies is affected by the social networks, a series of probit models for each strategy will be employed.

Keywords: Social Networks; Health Risk; Health Insurance; Food Security