within‐survey multiple imputation (Pape and Mistiaen, 2018, 2015). The lower amount of time spent collecting consumption data allowed the HFS to devote more time to collecting complementary data. Indeed, the HFS questionnaires contained additional modules covering asset ownership, education, labor market outcomes, perceptions of government performance and provision of public goods and services, psychological well‐being, perceptions of violence and safety, allowing a well‐rounded depiction of welfare and livelihoods.

The rapid escalation of the conflict in the summer of 2016, including several violent incidents affecting international humanitarian and development staff, led to the closure of the World Bank Office in South Sudan, disrupting the implementation of the third wave of the HFS. Therefore, the NBS implemented the third wave of the survey more independently relying mainly on remote support. A multitude of challenges had to be met, including large inflation, fuel unavailability, electricity shutdowns, insecurity, delay in payment of staff salaries, high NBS staff volatility, and cash flow limitations. Even though the NBS and the World Bank project team managed to mitigate a number of those challenges, the final sample reached only about 50 percent of the intended sample size. Nevertheless, this paper will argue that despite the enormity of challenges faced during fieldwork and the slight methodological departures from established approaches to poverty estimation (e.g. Deaton and Zaidi, 2002), the data collected by the HFS provide the best‐possible insights on welfare and livelihoods during a critical period of the country’s history.

The data from the HFS are complemented by video testimonials providing a glimpse of the lives of the people of South Sudan. At the end of the interviews, respondents are offered to provide a short video testimonial where they can share their views and give a sense of their lives. The testimonials capture the dire situation on the ground and provide a much richer qualitative picture that accompanies and complements the quantitative data. While the data may help the government fine tune its policies, the videos may reach a broader audience and depict the sense of powerlessness, the pain of hunger, the stress of hopelessness and the feelings of disappointment that express people’s experiences. Overall, this helps to create a more rounded perception of the situation on the ground in South Sudan.<sup>7</sup> The levels of deprivation documented by the HFS are staggering. As mentioned above, more than 4 in 5 people across the seven states covered in 2016 were living under the international poverty line of $1.90 USD PPP (83 percent). Such breadth of poverty places South Sudan among some of the poorest countries in the world. The depth of poverty is just as important as its breadth, with the average poor household consuming about one‐half of the international poverty line (a poverty gap index of 47 percent). The incidence of poverty is much more widespread in rural areas compared to urban areas, with the rural poverty headcount reaching up to 86 percent compared to 65 percent in urban areas (p<0.001). The rural poor also to experience a deeper poverty than urban residents, with a poverty gap equal to 50 percent compared to 31 percent in urban areas (p<0.001). Widespread fighting and large‐ scale displacement over several consecutive planting seasons have disrupted many households’ normal agricultural activities, resulting in increasingly large production deficits each year and widespread food insecurity. This has had a devastating effect on livelihoods, given that except for a few oil enclaves the productive structure of South Sudan is one of a rural pastoralist society where more than 4 in 5 people practice subsistence agriculture (World Bank, 2018, 2016).

Despite initial intentions to expand the HFS across the entire country, continued insecurity made it impossible to reach the former states of Jonglei, Unity, and Upper Nile. To account for this gap in coverage and obtain countrywide poverty rates, a statistical model imputes poverty in inaccessible areas. The resulting poverty predictions are intended as supplemental to the survey estimates and serve as a proof‐of‐concept for using geo‐spatial information alongside on‐the‐ground data collection. A growing body of research has emerged leveraging the increasing availability of alternative data sources such as satellite imagery and other geo‐spatial characteristics. The estimates are derived by exploring the potential correlations between existing spatial data sets as well as custom‐derived spatial data with geo‐referenced poverty estimates obtained in the HFS. Once a set of spatial correlates were

> 7 The translated testimonials are available at: http://www.thepulseofsouthsudan.com.

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