telephone networks are a good example, since they generally comprise relatively heavy infrastructure that is not easily destroyed through the type of warfare occurring in South Sudan. This is also a good indicator of sample selection favoring wealthier areas, especially in the context of South Sudan where only one in four households is covered. Access to electricity is a similar indicator given that it is exclusive to a few selected areas of South Sudan. Again, the latter two indicators do not seem different from 2015 and 2016. Finally, the share of households living far from schools, health centers, and markets, did not change significantly – this generally holds for various thresholds.

More importantly, the path of enumerators and geographic coverage of Wave 3 data was closely inspected to ensure that it remained broadly comparable to that of previous HFS waves and other sources of population data. This helped to control that entire areas were not systematically excluded. As an exception, the city of Yei was not surveyed at all in Wave 3 because it was the site of several large battles during fieldwork and subsequently experienced a massive wave of displacement. This was likely the most severe case, and in many other instances where fighting affected specific areas enumerators simply delayed fieldwork until it was safe to continue. This explains to some extent the prolonged duration of fieldwork relative to the low number of interviews conducted in total.

# 3. Measuring Poverty in a Fragile Context

## Calculating Consumption Aggregates

Poverty in the HFS was measured according to a standardized methodology best described in the seminal contribution by Deaton and Zaidi (2002). Poverty analysis consists of comparing a welfare measure to a predetermined poverty line. Therefore, the first step is to calculate a measure of welfare. The measure chosen for the HFS is the households’ consumption expenditure per capita.<sup>17</sup> The nominal household consumption aggregate consists of the sum of consumption expenditure per person on three primary components, i) total expenditures on food items, ii) total expenditures on non‐food items, and iii) the value of the consumption flow from the durable goods owned by the household.<sup>18</sup> The consumption aggregate is then deflated to reflect spatial and temporal cost of living differences.

Accurately measuring consumption in highly volatile environments is a complex task, primarily because insecurity and uncertainty severely restrict the time that can safely be spent by enumerators in certain areas and the time spent conducting each interview. Consumption modules tend to be bulky and take a long time to administer. At the very least, it requires asking information on quantities consumed, quantities purchased, and prices of purchase – including additional information on home production in a context such as South Sudan – for what is often upwards of 300 to 400 consumption items (Beegle et al., 2012). Reducing the length of the questionnaire is therefore a key strategy when designing surveys for fragile contexts. For example, it is common to remove rarely consumed items or to combine categories of items (e.g. vegetables). However, Beegle et al. (2012) and Olson Lanjouw and Lanjouw (2001) show that such approaches tend to result in underestimated consumption levels, and hence overestimate the poverty rate.

> 17 In the context of South Sudan using consumption as a measure of welfare is preferable to  a measure of income for two main reasons: (i) there exists no real reliable information on income given poor administrative record keeping, and (ii) employment is primarily irregular and informal in nature, with subsistence agriculture accounting for about two‐thirds of employment, non‐farm business ownership for one‐eighth, and salaried labor also only about one‐eighth (World Bank, 2018).

> 18 In some cases, housing is included in the consumption aggregate. However, calculating the consumption flow obtained from housing requires estimating rental values from the open market (Balcazar et al., 2014). Unfortunately, the housing market in South Sudan is highly underdeveloped, making such estimations impossible in any sort of accurate manner. Indeed, in the 2016 HFS, 91 percent of households were owned by the residents and fewer than 4 percent were rented. Thus, housing was excluded from the consumption aggregate.

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