**Table 8: Multiple Imputation results.**

||**(I)**|**(II)**|**(III)**|**(IV)**|
|---|---|---|---|---|
|**Region**||**Povert**|**y rate**||
|Mogadishu (Urban)|73.67% (69.45%, 77.9%)|72.25% (67.83%, 76.64%)|73.74% (69.54%, 77.94%)|72.28% (67.81%, 76.58%)|
|North‐east Urban|58.78% (43.17%, 74.38%)|56.93% (40.45%, 73.68%)|59.01% (43.54%, 74.49%)|57.21% (40.44%, 73.72%)|
|North‐east Rural|62.46% (62.1%, 62.81%)|64.97% (64.3%, 64.88%)|63.59% (52.36%, 75.21%)|65.01% (64.3%, 64.88%)|
|North‐west Urban|62.71% (51.81%, 73.62%)|61.5% (50.93%, 72.24%)|62.7% (51.83%, 73.68%)|61.48% (50.97%, 72.27%)|
|North‐west Rural|77.3% (67.07%, 87.53%)|75.29% (64.66%, 86.04%)|76.48% (65.52%, 87.4%)|75.41% (64.75%, 86.48%)|
|IDP Settlements|75.62% (62.35%, 88.88%)|74.55% (61.43%, 88.1%)|75.62% (62.31%, 88.86%)|74.45% (61.4%, 88.03%)|
|Central regions Urban|59.18% (47.46%, 70.9%)|58.21% (46.2%, 70.24%)|59.18% (47.42%, 70.85%)|58.24% (46.25%, 70.32%)|
|Central regions Rural|65.06% (27.44%, 102.7%)|64.77% (27.28%, 102.6%)|65.01% (27.41%, 102.5%)|64.81% (27.28%, 102.5%)|
|Jubbaland Urban|53.34% (42.4%, 64.29%)|59.33% (54.81%, 63.53%)|53.85% (42.51%, 64.31%)|48.81% (44.01%, 54.32%)|
|South West Urban|62.72% (43.1%, 82.35%)|60.8% (40.43%, 80.96%)|62.39% (42.62%, 82.22%)|60.91% (40.57%, 80.88%)|
|South West Rural|74.94% (61.43%, 88.44%)|73.61% (59.25%, 88%)|75% (61.52%, 88.45%)|73.53% (59.17%, 88.02%)|
|Nomadicpopulation|71.61%(63.1%,80.12%)|70.86%(62.27%,79.54%)|71.71%(63.18%,80.22%)|70.87%(62.28%,79.53%)|

Source: Authors' calculations.

Note: (I) final model used to impute consumption and poverty; (II) sensitivity test without income quartiles in imputation model; (III) sensitivity test with Jubbaland urban combined with Central regions instead of South‐West; (IV) as (III) but without income quartiles. 95% confidence interval in parentheses The results from the imputation process are stable and robust considering these different specifications. The imputation process and these results were judged the best alternative to overcome the issues experienced in data collection.

# 5. Imputing poverty in inaccessible areas using geo‐spatial data

Prevalent insecurity and conflict meant that parts of Somalia remained inaccessible for the SHFS field teams (Table 1). In the 10 least accessible urban and rural strata, less than 50 percent of the population could safely be reached.<sup>19</sup> The survey poverty estimates in these regions are therefore insufficiently representative of the regions’ entire urban and rural populations. Hence, poverty in each region was predicted making use of correlations between geo‐spatial information and survey estimates. The resulting poverty predictions are supplemental to survey estimates and serve as a proof‐of‐concept for using geo‐ spatial information alongside on‐the‐ground data collection. This section describes selection of geo‐ spatial variables and the model used to impute poverty.

## 5.1. Selection of variables for poverty predictions

Spatial variables expected to predict poverty well were drawn from three types of sources. First, a custom‐ derived global database of over 300 spatial covariates from the WorldPop research group at the University of Southampton (see Stevens et al., 2015).<sup>20</sup> Second, spatial variables were computed from geo‐tagged data from publicly available sources such as ACLED conflict data or FEWSNET food security data, and OpenStreetMap. Third, population and population type data drawn from a novel population density map using recent data from OpenSteetMap, BMGF / Digital Globe spatial data, UNFPA survey and SHFS data.

> 19 Of these 10 strata, 4 were urban and 6 were rural. The survey of IDP and nomadic populations was not subject to similar accessibility problems, so that survey results are considered representative for these populations.

> 20 WorldPop “Global high resolution population denominators”, project funded by the Bill & Melinda Gates Foundation (OPP1134076).

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