Safety Nets and Index-Based Insurance: Historical Assessment and Semiparametric Simulation for Northern Ghana

This article considers index-based safety nets aimed at assuring participants a minimum income at the village level, set by a prespecified poverty line. The participating villages collectively manage a pooled budget, offering an index-based per capita indemnity to the villages financed from a uniform per hectare premium, possibly supplemented by an external subsidy. The scheme involves implicit cross-subsidies in favor of the poorest villages. Otherwise, it focuses on covariate risk, leaving it to mutual arrangements to deal with intravillage idiosyncratic risk. Its design is obtained from a risk-minimizing model for the collective of villages with constraints of two kinds. The first is the budget that ensures that payments receive adequate finances. The second concerns the set of admissible schedules requiring that payments are triggered by a flexible function of only a few variables. For this we propose a semiparametric form from kernel learning, noting its capacity to combine a priori on the impact of shocks via the parametric term, with a flexible fit to the data at hand via the nonparametric term. To test the approach, we estimate and simulate an index-based safety net for northern Ghana, constructing a pseudovillage panel from four rounds of household surveys and assembling all villages in a single risk pool. Results of a fully self-financed arrangement indicate a reduction by 20 percentage points of the poverty incidence from an initial level of 63% and are robust under resampling. Yet, for the given typology of villages, this arrangement requires an unrealistically high premium and large cross-subsidies. We show that both can be reduced significantly when a moderate external subsidy is allowed for. Nonetheless, in all cases, the basis risk remains considerable, reflecting the limited capacity of the selected price and weather variables to eliminate poverty. (c) 2010 by The University of Chicago. All rights reserved.