Household behavior and vulnerability to acute malnutrition in Kenya

Abstract

Anticipating those most at-risk of being acutely malnourished significantly shapes decisions that pertain to resource allocation and intervention in times of food crises. Yet, the assumption that household behavior in times of crisis is homogeneous—that households share the same capacity to adapt to external shocks—ostensibly prevails. This assumption fails to explain why, in a given geographical context, some households remain more vulnerable to acute malnutrition relative to others, and why a given risk factor may have a differential effect across households? In an effort to explore how variation in household behavior influences vulnerability to malnutrition, we use a unique household dataset that spans 23 Kenyan counties from 2016 to 2020 to seed, calibrate, and validate an evidence-driven computational model. We use the model to conduct a series of counterfactual experiments on the relationship between household adaptive capacity and vulnerability to acute malnutrition. Our findings suggest that households are differently impacted by given risk factors, with the most vulnerable households typically being the least adaptive. These findings further underscore the salience of household adaptive capacity, in particular, that adaption is less effective for economic vis-à-vis climate shocks. By making explicit the link between patterns of household behavior and vulnerability in the short- to medium-term, we underscore the need for famine early warning to better account for variation in household-level behavior.

The impact of organic fertilizer on maize, field beans, and cabbages within the agricultural landscape of Kenya: An assessment of efficacy on sustainable development goals

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Association between the RTS,S/AS01 malaria vaccine and under-nutrition in children aged 10 to 59 months in Siaya County, Kenya

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Trajectories of resilience to acute malnutrition in the Kenyan drylands

“…Prior work operationalizes resilience as a household-level characteristic, capturing the ability of individual households to cope with shocks and stressors. The approach we develop integrates measurements at different units of analysis (Brown et al, 2020;Bakhtsiyarava and Grace, 2021;Grace et al, 2022;Wang and Do, 2023), tracing how household behavior shapes resilience at the ward-level while accounting for feedback loops and nonlinear scaling processes (Béné et al, 2015;Béné, 2020;Bhavnani et al, 2020Bhavnani et al, , 2023. In a given ward, we measure how many children across households fall above/below the malnutrition threshold and to what extent.…”