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This research seeks to assess the impact of a credit-linked insurance bundle in Zambia, in terms of the inputs used and the amount of maize subsequently produced and sold.

To estimate the impact of a credit-linked insurance bundle, this research relies on a natural field experiment. A cross-sectional survey, conducted among 409 households that enrolled in a credit-linked insurance program prior to a drought and adverse market conditions, revealed that 252 households dropped out of the program. Of these, 113 households left for an exogenous, involuntary reason (i.e. group loan was not repaid on time).

A comparison of households that used the program and those that dropped out reveals that smallholders who lost the credit-linked insurance bundle purchased less fertilizer (−36%), and this input restriction resulted in diminished harvests (−27%) and less quantity sold (−31%).

Risk-exposed smallholders tend to be severely credit constrained, so they cannot invest in sufficient inputs to increase their yields. A credit-linked insurance scheme provides such risk-exposed smallholders, who lack or have only limited collateral, with commercial agricultural credit services and greater access to input. The current analysis cannot specifically attribute the impact of individual components of the bundle (i.e. credit, insurance and input supply), but the overall impact is substantial. The implication of this research is that policy initiatives which support a credit-linked insurance system should not only encourage upscaling but also resilience of the scheme.

Aligning government and public support programs with private initiatives suggests opportunities for greater benefits.

African farmers are mainly smallholders, so they face inherent production risks. They also tend to be severely credit constrained, with few means to mitigate these risks, so they suffer from a limited capacity to invest in improved farm technology systems that might increase their productivity. Insured input bundles could help farmers to cope better with adverse risks and facilitate increase productivity.

This research design exploits the peculiarity of the data, including group liability and a strict time window for loan repayments to remain eligible in a forthcoming growing season. This impact assessment approach is rigorous in controlling for self-selection bias and thus offers opportunities to establish how households in each sub-sample (eligible or not) are (un)able to ride out a lean season, following a drought and adverse market conditions.