selected several models were trained and evaluated using a cross‐validation approach. The final model was used to predict poverty rates at the 100m*100m level into all settled areas of the country including where survey data were not available. To aggregate the estimates at the state and county level, the 100m*100m level are weighted using a newly developed data set of human settlements across South Sudan constructed by combining a variety of publicly available data sources.

This paper describes the design and analysis of the third wave of the HFS in 2016.<sup>8</sup> The paper is focused on Wave 3 of the HFS, conducted between mid‐2016 and early 2017, representing the most recent wave covering both urban and rural areas. Furthermore, the period between late 2016 and early 2017 was a critical period in South Sudan’s history, when the conflict and refugee crises were reaching their peak. In Section 2, the paper describes the survey design and implementation, including the deviations from the original sample frame presenting consistency‐checks used to evaluate potential selection issues that affect representativeness. Section 3 will detail the process of calculating consumption aggregates and estimating poverty using within‐survey multiple imputations, including calculating durables consumption flow and spatial‐time deflators. Section 4 gives a brief overview of the results of the poverty estimation, while a comprehensive assessment of poverty trends is available elsewhere (World Bank, 2018). Section 5 describes the estimation of poverty rates using satellite data as a proof‐of‐concept while Section 6 concludes the paper with a short discussion of main limitations.

# 2. Survey Design and Implementation

## Sample Design

The 2016 Wave of the HFS was conducted between mid‐2016 to early 2017 and consisted of the second nationally representative survey wave of the HFS. The survey covered rural and urban areas across 7 of the 10 former states of South Sudan. The regions covered include Greater Equatoria, Greater Bahr el Ghazal, and Lakes. The 10 former states are used in planning for the HFS instead of the 28 more recent ones because the sample was constructed based on the sampling frame derived from the 5<sup>th</sup> Sudan Population and Housing Census from 2008.<sup>9</sup> The survey was designed to be representative at the state level and employs a stratified two‐stage clustered sample design. Within each state the primary sampling units are enumeration areas (EAs) that were drawn randomly proportional to size. The EAs were drawn by the NBS for the 2008 Census (Southern Sudan Center for Census, Statistics, and Evaluation, 2010).<sup>10</sup> The number of EAs and households was equalized across states in order to balance the fieldwork across teams. Within the EAs, 12 households were drawn randomly as the unit of observation based on a listing exercise.<sup>11</sup> The EAs were allocated across urban and rural areas within each state to minimize the variance of indicators of interest across the strata while explicitly taking into consideration the design effect. The data used for the sample size calculations came from the NBHS 2009, and the indicator used for the sample size calculations was the real total per capita household expenditure.<sup>12</sup> While this variable is one of several that are of interest in the HFSSS, consumption/expenditure is generally strongly positively correlated with other indicators of interest. For the purposes of comparison, the relative standard error (complex standard error / mean) is used. The allocation was

> 8 The data from Wave 3 (2016) of the HFS and the code used to process these data can be downloaded from the World Bank MicroData Library at the following link: http://microdatalib.worldbank.org/index.php/catalog/9584/

> 9 The more recent states have largely been drawn based on the counties subdivision of the former states, the geographical boundaries have therefore largely remained intact.

> 10 Urban EAs were drawn to contain approximately 100 to 150 households, while urban EAs would generally contain between 200 to 300 households.

> 11 The number of households per EA was determined to be 12 to allow an equal split into 4 groups per EA to facilitate the implementation of the Rapid Consumption Methodology. The specific options of 8, 12, and 16 were considered. Eight households per cluster was deemed as too small as the number of EAs necessary and the associated travel time could not be done within the fieldwork calendar. Sixteen resulted in very high design effects, over 3 in most cases and as high as 5 for some strata, and was therefore deemed too large. Twelve households per EA was therefore selected as the ideal cluster size.

> 12 The top and bottom 1 percent of outlier observations were trimmed for the sample size calculations.

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