Identifying Vulnerable Households Using Machine Learning

Many Afghanistan households face food insecurity (FI), and this threatens sustainable development. Policymakers and international donors are trying to alleviate FI using food aid, development assistance, and outreach. This study identified household characteristics that discriminate between food-insecure and food-secure households, facilitating accurate assistance targeting in Afghanistan. We used machine learning classification models (classification decision tree and random forest model) and applied to a household survey. This was done using equal priors and 1.5:1 misclassification penalties. The resulting model is able to correctly identify 80% of food-insecure households. Characteristics in six major categories are found important. Unsurprisingly traditional key variables, such as (1) income and expenditure items, (2) household size, (3) farm-related measures; (4) access to particular resources, and (5) short term shocks are important determinants of food security level. We also found the relevance of long-term household characteristics, such as dwelling wall composition, which are not generally addressed in the existing literature. We argue that these are reflective of accumulated household wealth and this supports the idea that some factors determining food security are persistent. We also found that commonly used demographic variables were not important.