required enumeration areas to be segmented into smaller enumeration blocks using satellite imagery. Enumeration blocks are small enough for enumerators to list and select households immediately before conducting the interview (section 2.2). Himelein et al. (2016) compare this methodology with other second‐stage sampling strategies designed for use in fragile and time‐sensitive settings.

Complete food and nonfood consumption modules result in an overall questionnaire length that is prohibitive in areas with high insecurity. The length of consumption modules can be reduced by removing rarely consumed items from the module or to combine categories of items (e.g. vegetables) and ask aggregates rather than individual items. Beegle et al. (2012) and Olson Lanjouw and Lanjouw (2001) provide evidence that both approaches lead to an underestimation of consumption and hence an overestimation of poverty. Fujii and Van der Weide (2013) propose an alternative approach which could be adapted for use in fragile settings, by assigning a full consumption module to households in areas without a binding security and time constraint, with only the covariates of consumption administered to households in insecure areas. Consumption and poverty could then be imputed based on those covariates. This approach, however, potentially leads to biases as the assignment of the two different modules depends on security and is not necessarily random. Instead, the Rapid Consumption Methodology (Pape and Mistiaen, 2018) was used to significantly reduce the length of the survey’s consumption modules. The Rapid Consumption Methodology used in the SHFS relies on a set of core consumption items administered to all households. The remaining items are algorithmically partitioned into optional modules distributed systematically across households, with multiple imputation techniques used to impute total consumption and poverty. Pape and Mistiaen (2018) show that this design yields reliable poverty estimates (section 4).

Third, the SHFS relies on correlates derived from satellite imagery and other geo‐spatial data to estimate poverty in areas that remained completely inaccessible as a result mainly of insecurity. A growing field of research is dedicated to predicting a range of outcomes based on a diverse set of such data sources. Early applications use night‐time lights data to predict economic activity. These data are particularly successful at predicting GDP at the country‐level (Henderson et al., 2012; Pinkovskiy and Sala‐i‐Martin, 2016), but appear less well‐suited for measuring income and when variation in welfare is desired at a highly disaggregated level (Engstrom et al., 2017; Mellander et al., 2015). More recently, deep learning techniques applied to daytime imagery in order to classify such objects as roof types, roads, tree coverage, and crops has led to advances in measuring welfare at more disaggregated levels (Krizhevsky et al., 2012). Jean et al. (2016) use a convolutional neural network based on daytime satellite features to predict per capita consumption at the level of the enumeration area from living standards measurement surveys. Their model is successful in predicting consumption and explains 46 percent of variation on average across four countries and out‐of‐sample. Engstrom et al. (2017) provide a recent overview of the state of the literature and use high‐resolution satellite features to estimate poverty at the village‐level. In the SHFS, estimating poverty in inaccessible areas relied on a linear model with the objective of creating reliable and transparent poverty measures (section 5).

The remainder of this paper proceeds as follows. Section 2 discusses the sampling strategy. Section 3 provides an overview of the data collection process. Section 4 describes the derivation of the consumption aggregate, including the Rapid Consumption Methodology. Section 5 describes the imputation of poverty in inaccessible areas, and section 6 gives an overview of poverty in Somalia.

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