Testing Iterations of Anticipatory Cash Models to Improve Climate Resilience for Agropastoral Populations

Over the last decade, humanitarian and development actors have increased their use of anticipatory action programs, in which support is provided to climate vulnerable households ahead of forecasted shocks such as floods and drought. Evidence to date suggests that anticipatory action in the form of cash transfers can help households improve their food consumption, take precautionary measures ahead of shocks, and reduce negative coping strategies during and after shocks.

Cash transfers offer several unique advantages: they can be used to meet multiple needs and can be delivered physically or digitally in a relatively short time, which is critical when windows of action can be narrow ahead of shocks like floods. However, there is a lack of evidence on how we can leverage cash transfer’s flexibility or how to sequence it alongside other activities to maximize impacts on key outcomes, as well as understand how the same cash transfer can differentially impact diverse populations.

To date, most evidence has been produced in more stable development contexts or with non-displaced populations, with only a few studies coming from fragile or conflict-affected contexts or displaced populations. If humanitarian actors are to scale anticipatory action programs, it is imperative to ensure that fragile and conflict-affected settings and displaced populations are included in rigorous research on anticipatory action.

With support from the German Federal Foreign Office (GFFO), the IRC produced rigorous impact and cost research around various iterations of anticipatory cash transfer programs in three humanitarian contexts: Afghanistan, Somalia, and Kenya. Between 2024-2026, the IRC conducted a randomized experiment on cash transfer timing and values plus a cost-effectiveness study in Afghanistan, a quasi-experimental study on cash versus cash+ and livelihoods differences alongside a cost-effectiveness study in Somalia, and a cost analysis on “the cost of not triggering” in Kenya.