Mogadishu, providing a baseline data set for monitoring poverty and contributing to other key statistical indicators. However, in addition to large inaccessible areas, the sample excluded nomadic population and households in insecure areas. Furthermore, the rural sampling frame had to be derived ad‐hoc with only limited representativeness. Wave 2 of the SHFS, implemented in December of 2017, significantly expanded coverage to urban and rural areas in central and southern Somalia and included the nomadic population for the first time, while a newly derived sampling frame enhanced overall representativeness.

The specific context of insecurity and lack of statistical infrastructure in Somalia posed a number of challenges for implementing a household survey and measuring poverty. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability‐based representative sample. Second, while some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. Third, poverty in completely inaccessible areas had to be estimated by other means. Finally, the non‐stationary nature of the nomadic population required special sampling strategies. This paper outlines how these challenges were overcome in wave 2 of the SHFS through methodological and technological adaptations in four areas: sampling strategy, survey design, fieldwork implementation, and poverty measurement. In line with the challenges outlined above, this paper contributes to several themes in the literature on poverty measurement and data collection in the context of conflict and fragility, involving hard‐to‐survey populations.

First, geospatial techniques and high‐resolution imagery were used in the SHFS to model the spatial population distribution, build a probability‐based population sampling frame, and generate enumeration areas in an effort to overcome the lack of a recent population census (section 2). The SHFS sampling strategy bears resemblance to the strategy proposed by Muñoz and Langeraar (2013), which relies on satellite imagery and grid cells to build a sampling frame in Myanmar. Wardrop et al. (2018) review various efforts to produce spatially disaggregated population estimates based on satellite imagery, in contexts where census data are absent or inaccurate. Barry and Rüther (2005) and Turkstra and Raithelhuber (2004) employ satellite imagery to study informal urban settlements in South Africa and Kenya, respectively, while Aminipouri et al. (2009) estimate various slum populations in Dar‐es‐Salaam, Tanzania. Himelein et al. (2016) compare the viability of various satellite and area‐based sampling methods in second‐stage sample selection in Mogadishu, Somalia.

Second, risks to the safety of field staff required spending as little time in enumeration areas as possible. One strategy to address this issue is to call or message respondents on their mobile phones and not visit dangerous areas at all. A growing body of literature explores the use of mobile technology in this context (e.g. Demobynes and Sofia, 2016; Dillon, 2012; Firchow and Mac Ginty, 2016). However, administration of necessary consumption modules to estimate poverty is not feasible via phone surveys.

To address security concerns, the SHFS adapted logistical arrangements, sampling strategy, and questionnaire design to limit time on the ground. In logistical arrangements, a detailed and timely security assessment ensured that the enumeration areas to‐be‐visited were safe on the day of fieldwork. The fieldwork protocol was designed such that teams would spend as little time as possible in any given region and draw little attention, ensuring enumerator and respondent safety (section 3.2). Concerning sampling strategy, it was not feasible to conduct a full listing of all households in an enumeration area, as this was too time‐intensive and may have raised suspicion. Instead, a micro‐listing approach was used, which 3