|_Northern States_|0.154***|0.218***|0.222***|0.364***|0.365***|0.157***|0.0202|
|---|---|---|---|---|---|---|---|
||(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|(0.03)|(0.04)|
|_Border Closure *_||||||||
|_Northern States_|0.0783|-0.122|-0.113|-0.0714|-0.0607|-0.169***|-0.103|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.04)|(0.06)|
|_Constant_|0.249***|0.278***|0.180***|0.0314|0.0606|0.167***|0.257***|
||(0.04)|(0.04)|(0.03)|(0.04)|(0.04)|(0.02)|(0.03)|
|_Observations_|250|371|273|166|237|503|304|
|_R-squared_|0.095|0.056|0.094|0.358|0.23|0.063|0.018|

Standard errors in parentheses Source: Authors’ calculations

# **3.3. Price Volatility**

High price volatility, and in particular that of food products, is costly for the poor. It makes it difficult to plan ahead and to adjust to fluctuating market signals. Episodes of extreme volatility, especially when these imply food prices spikes are an important threat to food security in a country like South Sudan, in which the average share of food in total expenditures is 80 percent. The negative impact is exacerbated by little access to savings or credit that would allow households to smooth consumption decisions over time (e.g.: by buying and storing food when prices are low to be consumed later when prices are high). As a consequence, in this context, food spikes result in hunger and malnutrition in the absence of other interventions, making it a crucial issue for policymakers. In addition to the spatial dimension of price differences discussed above, food and energy prices in South Sudan have fluctuated substantially within each state capital over time.

Has price volatility increased after the imposition of trade restrictions with Sudan? To answer this question, we first measure the volatility associated with the prices of each product in a given state capital by calculating the standard deviation of the monthly rates of change of prices for each product for the period pre-border closure, and post-border closure. Second, we test for a difference in the standard deviation of price changes before and after border closure.<sup>14</sup> Figure 28 to Figure 34 show the pre and post border closure price volatility for those food products for which there was available price data spanning both sub-periods. We report the state capitals in which the differences in prices volatility were statistically significant.<sup>15</sup> Whenever there was a significant change in price volatility after the border closure, this change implied an increase in volatility. For the case of broad beans, for example price volatility more than tripled in Bor, it doubled in Juba, and increased by almost 10 percentage points in Malakal. Sorghum is the product for which the increases in price volatility were more widespread spatially (similarly for groundnuts). Volatility roughly doubled in Aweil, Bor and Rumbek; increased by 75 percent in Juba, and by 17.5 percent in Wau.

> 14 Statistically, the test results from the comparison of the sample variance of price changes pre-closure with the sample variance of price changes post-closure. Both sample statistics are distributed Chi-Squared, and its ratio is distributed as an F.

> 15 Given the short sample period, a cut off of 20 percent significance is considered. The full set of results for this volatility comparison as well as figures showing the evolution of the standard deviation of price changes for each state capital and product are presented in the Appendix.

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