employment, level of education and other factors can render households more resilient against shocks.

The theoretical causes and impacts of hyperinflation are well known, and provided in the seminal work of Cagan (1956) and Nordhaus (1973). A more recent review and update was conducted by Fischer et al. (2002). A historic overview can be found in He (2017). Recent studies focus on the causes and policy options (for example, Acemoglu et al., 2003 and Reinhart & Savastano, 2003), and historic dimensions often in the context of Zimbabwe (for example, Coomer & Gstraunthaler, 2011). Given the dearth of micro-data in countries with high- or hyperinflation, only very few studies look at the direct welfare impacts of high- or hyperinflation. Fajardo & Dantas (2018) study the impact of hyperinflation on investment behavior in Brazil. However, they do not touch on welfare or livelihood impacts. Larochelle et al. (2014) use a small-area-based approach with an asset index and find that rural poverty also increased in Zimbabwe’s hyperinflation period. In contrast, Kurasha (2021) uses micro-data from several years before and after hyperinflation, but finds that rural poverty fell while urban poverty increased, while asset inequality dropped during the hyperinflationary period. Health indicators worsened for both urban and rural as well as access to electricity, safe drinking water, improved toilets and health care.

In this paper, we assess the shorter-term impacts of high inflation on household livelihoods in urban South Sudan. Longitudinal micro-data for a representative sample of households is used to understand the changes in livelihoods between 2015 and 2017, accompanied by continuous price data collected across South Sudan. The novel data sets based on a set of innovative high-frequency surveys allow the use of a difference-in-difference approach providing a stronger identification than can currently be found in the literature. Furthermore, the paper identifies resilient households to draw conclusions for social protection programs and policies.

The next section of this paper presents the data and methodology in more detail. Section 3 presents descriptive results followed by results from the identification on inflation impacts on urban livelihoods in South Sudan. Section 4 concludes with a discussion and policy recommendations.

# **2. Data and Methodology**

# _2.1. Data_

This paper makes use of three waves of panel survey data from the High Frequency South Sudan Survey (HFSSS; Table 1).<sup>3</sup> Wave 1 of the HFSSS was conducted largely before prices exploded, while waves 2 and 3 were implemented in the period of high inflation, and wave 4 was conducted when prices had escalated. We use location- and time-specific price differences to quantify the impact of high inflation on poverty and other livelihood indicators. The panel analysis in this paper is restricted to urban households, as it aims to identify factors that make households resilient. While the restriction to urban areas limits the scope of this paper, the panel analysis allows to gain better understanding of the impact of inflation. For urban areas, waves 1, 2 and 4 provide household panel data. The panel data will be used to analyze within-household dynamics in times of high

> 3 The High Frequency South Sudan Survey, funded by DfID, was conducted by the World Bank in collaboration with South Sudan’s National Bureau of Statistics, to monitor welfare and perceptions of citizens in all accessible areas of South Sudan.

3