Moreover, the EAs sampled were spread across a vast territory and mostly in remote areas. They were far from each other, and far from urban centers. NE teams who covered rural areas had to travel up to two days to reach some EAs, longer than teams in any other region. Team leader reports from the field indicate that these large distances and conditions created fatigue among enumerators. Further, direct monitoring of field teams by supervisors was limited due to poor connectivity, and thus sending frequent and timely feedback was more challenging that for other teams. As a result, the performance of teams did not improve as in other regions.

Finally, the consumption profile of most core food items was different to other rural areas, including nearby and ostensibly similar areas covered by other teams (Table 7). Hence, the consumption data (food, non‐food and assets) were discarded, and poverty was estimated from a multiple imputation process.

# Consumption imputation process

Consumption data in North‐East rural and Jubbaland urban were imputed in Stata with Multiple Imputation (MI) techniques. The same multiple imputation process and model described to estimate the consumption of non‐assigned optional modules from equation (4) were used to obtain the four consumption components, and thus the total consumption expenditure for households in these regions.

The dependent variable of the model is total consumption expenditure per capita with data from North‐ East rural and Jubbaland urban set as missing and to be imputed.<sup>18</sup> The independent variables were chosen based on explanatory power with respect to household consumption: household size, share of children in household, share of seniors in household; household head gender, employment, and education; dwelling type, dwelling drinking water access, dwelling floor, and dwelling ownership; household experience of hunger and receipt of remittances; population type (urban, rural, IDP, nomadic) and a region‐population type interaction, as well as consumption quartiles. With an R‐Squared of 71 percent, this model had high explanatory power.

The model for imputing consumption had two caveats: first, each value or category of the right‐hand‐side variables of the model must overlap with some non‐missing values of the dependent variable. Otherwise, there is no basis for simulating the relationship between consumption and these explanatory variables. This means that the region‐population type interaction variable must be modified, as North‐East rural and Jubbaland urban are two categories of that variable without overlap with any non‐missing consumption values. To do this, the North‐East rural category was combined with North‐East urban to form a general North‐East category. Jubbaland urban was combined in the final specification with adjacent South‐West urban (Table 8, column I). Various other specifications were tested in which Jubbaland urban was combined with Central Regions urban, as an assessment of the sensitivity of the final estimates to this choice (Table 8, column III and IV). Second, the model contains consumption quartiles as a key right‐hand‐ side variable. Since the consumption data for North‐East rural and Jubbaland urban were inconsistent, consumption quartiles were calculated for North‐East rural and Jubbaland urban separately, to include this variable in the final specification. Other specifications excluding the quartile variable were assessed as a sensitivity test as well (Table 8, column II and IV).

> 18 A logarithmic transformation is not feasible in this case due to its singularity at zero. As the core module was constructed to capture maximum consumption shares, many optional modules – almost by definition – obtained zero consumption especially among the poorer households, which have a less diversified diet.

24