# **Introduction**

The collection of survey data from war zones or other unstable security situations provides important insights into the socioeconomic implications of conflict. Data collected during these periods, however, are vulnerable to error, because conflict often limits the options for survey implementation. For example, the traditional two‐stage sample design for face‐to‐face surveys in most developing countries first selects census enumeration areas as the primary sampling unit (PSU) with probability proportional to size and then conducts a listing operation to create a frame of households from which a sample is selected. Such an approach, however, may not be feasible in conflict areas. At the first stage, updated counts are often not available, making probability proportional to size selection inefficient. Also as the second stage requires that survey staff canvas the entire selected area, it may also be too dangerous in a conflict setting. As a result, many surveys of conflict areas are limited to qualitative work or resort to non‐probability designs.

This paper uses simulations to explore several alternative sampling approaches considered for the baseline of the Mogadishu High Frequency Survey Pilot (MHFS). The baseline was a face‐to‐face household survey in Mogadishu, Somalia, conducted from October to December 2014 by World Bank and Altai Consulting. A full listing (see Harter et al, 2010 for details) was deemed unsafe in Mogadishu because the additional time in the field and the predictable movements by interviewers would increase their exposure to robbery, kidnapping, and assault, and increase the likelihood that the local militias would object to their presence. The survey needed an alternative second‐stage sample design that would minimize the time spent in the field outside the households, but also could be implemented without expensive equipment or extensive technical training. In addition, international supervisors from the consulting firm could not go to the field, necessitating a sample design in which quality could be verified ex post.

The implementing partner originally proposed a random walk procedure. While this methodology has the benefits of fast implementation and unpredictability of movement, the method is non‐probability and literature has shown the procedure to give biased results, even if implemented under perfect conditions (Bauer, 2014). Intuitively, a random walk would only be unbiased if the paths taken during the selection crossed each household once and only once, which is extremely unlikely in the field. Therefore the team considered four alternatives for household selection. The first option was to use a satellite map (of which many high quality options exist, due the limited cloud cover and political importance of the region) to identify all structures in the PSU and select ten for the survey. The second option considered was to subdivide the selected PSUs into segments consisting of eight to ten households and ask enumerators to list and choose households from the segments.<sup>2</sup> The segments would be of roughly equal size in terms of number of households but are likely to have irregular outlines reflecting the irregular layout of structures in Mogadishu. The third option considered was to lay a uniform grid over the PSU and ask enumerators to list and choose households from selected grid boxes. The final option considered was to start at a random point in the cluster and walk in a set direction, in this case the Qibla, or direction in which Muslims pray, until the interviewer encountered a structure.

> 2 Interviewers had a selection application on their smart phones that they used whenever subsampling was needed.

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