Impact of High Inflation on Household Livelihoods in Urban South Sudan

Using panel data, this paper analyzes the impact of high inflation on household livelihoods in urban South Sudan. Our findings, based on the difference-in-difference approach, suggest that inflation negatively impacted various household livelihood indicators. Inflation had a strong negative impact on urban poverty between 2015 and 2017, and this was mainly driven by the outburst of non-food prices. We also find that food price inflation had a negative and statistically significant impact on girls’ primary and secondary school attendance. A particularly revealing finding is that proximity to school is very important for school attendance, for girls. In the face of inflation, long distance to school is a key reason why South Sudanese girls are dropping out of school. The results also show that increases in food prices led to a decline in labor force participation, increasing unemployment among urban residents. Our results also show that inflation is exacerbating food insecurity and hunger, particularly for the poorest households who are more vulnerable to hunger. We further find that inflation has negatively affected households’ perceptions of welfare. Our findings suggest that addressing the issue of high inflation must be at the center of efforts to cut down poverty and hunger and improve the welfare of the South Sudan people.


BACKGROUND
South Sudan became independent on July 9, 2011 after a six-year transitional period (2005 -2011) that followed the signing of the 2005 Comprehensive Peace Agreement (CPA).South Sudan's macroeconomic situation is near collapse with output contracting, a parallel exchange market spiraling, a significant fiscal deficit, burgeoning debt distress, and risks of hyperinflation.About one year after it acquired independence from (North) Sudan, a disagreement between the two countries (about transit fees) led to an oil shutdown and the closure of their shared border.This is contributing to dramatic losses in government revenue, which is the world's most oil dependent nation and one of the largest beneficiaries of international aid (Figure 1).Oil production is estimated to have decreased to about 127,000 barrels per day in 2017 down from 165,000 barrels per day in 2014, itself less than half of the peak production before independence in 2011 (World Bank Macro Poverty Outlook, 2018).Declining oil production and prices have pressured an economy already weakened by the 2012 oil export shutdown and the civil war.Following the move to a more flexible exchange rate arrangement in 2015, the South Sudanese Pound (SSP) has depreciated on the parallel market.The SSP depreciated from SSP 18.5 per U.S. dollar in December 2015 to SSP 70 per U.S. dollar by August 2016 and SSP 172 per U.S. dollar by August 2017.Political events plunged the volatility of the SSP.The SSP initially appreciated on the parallel market and converged with the commercial rate when the Government of National Unity came into place, but it later depreciated steadily, in particular after the new fighting erupted in July 2016 (Figure 2).It has continued to depreciate steadily since as instability across the country continues.Although the spread between the official and the parallel market rates is narrowing, the divergence between the two rates reflects that demand for hard currency continues to outweigh the limited supply of foreign exchange given unresolved fiscal and monetary issues as well as challenges in the interbank market for foreign exchange.Without exporting oil, South Sudan's foreign currency earnings will be highly diminished.Unless foreign currency earnings are re-established, further depreciation of the SSP seems inevitable.A depreciation of the currency will make imports more expensive and lead to higher prices.Consistent with exchange rate changes, South Sudan is going through a period of high and volatile inflation.The annual Consumer Price Index (CPI) increased by 426 percent from February 2016 to February 2017 (Figure 3).On annual basis inflation this is not currently considered hyperinflation, but very close.As the fiscal situation is worsening, a monetary expansion may exacerbate inflationary pressures and further depreciate the parallel market exchange rate.The monetization of the fiscal deficit, accelerated inflation from 187 percent in June 2016 to 550 percent in September 2016 before declining to 362 percent in June 2017.As the pace of money printing slowed in recent months, inflation decelerated to 125 percent during January 2017-January 2018 (Consumer Price Index for South Sudan published by the National Bureau of Statistics).Inflation has been high, but variable, across all categories of goods and services.Figure 4 breaks down annual price increases in broad categories of goods based on CPI data collected by the National Bureau of Statistics.Non-food items experienced price increase between June 2015 and June 2017.However, food prices also increased substantially during this period.This is a concern since food inflation typically hurts the poor disproportionately, due to the higher share of food in the poor's consumption basket.Given the already widespread poverty, such high food price inflation can be critical in the case of South Sudan.Although some poor rural households may be net producers of food (producing more than they consume), and thus less impacted by the high food price inflation, the very limited agricultural sector in South Sudan and the unusually high reliance on imported food suggest that the poor are also dependent on foreign imports, which have been severely affected by inflation.The high-inflation environment continues to put many households in both urban and rural areas (that rely largely on market purchases for consumption) under extreme financial stress, as they are faced by increased prices without compensatory increase in income (at least in the short-term), and many are unable to afford the minimum food basket.An important and inevitable question is how inflation is affecting household livelihoods in South Sudan, particularly the poor. 3It has been documented that high inflation can have negative impact on household livelihoods (Malin, 2016).
3 In 2015 nearly 66 percent of the population in South Sudan was poor, based on the $1.90 2011 PPP poverty line (excluding Jonglei, Unity, Upper Nile, and Warrap due to insecurity), which is a considerable increase in poverty from an already high level of 52 percent in 2009.In this paper, we assess the impact of high inflation on household livelihoods in urban South Sudan.Repeated time varying data for a sample of households is invaluable in understanding the changes they undergo during such difficult periods as between 2015 and 2017.Households that produce goods like food are usually less affected by high inflation as they are shielded from market prices.In fact, they can benefit from inflation if they sell products in the market.However, other characteristics like product types and market access can influence how much a household loses or benefits.Also for non-agricultural households, type of employment, level of education and other factors can render households more resilient against shocks.The paper also tries to identify households that were more resilient against the price shock to inform programs and policies to make households more resilient.
The next section of this paper proceeds by discussing the sources of data used in this analysis.
Section 3 presents descriptive statistics on inflation in urban South Sudan and livelihood indicators.Section 4 discusses the regression model used to examine the connection between rising prices and household livelihoods in urban South Sudan, and section 5 provides some concluding discussion and recommendations.

Household survey data
This paper makes use of three waves of panel survey data from the High Frequency South Sudan Survey (HFSSS).The first wave was carried out from February to September of 2015, in six out of 10 states covering both rural and urban areas (Table 1).The four missing states are the ones most affect by the conflict and were excluded because of insecurity.Thus, the poverty estimates from the survey are a lower bound.The second wave was fielded from February to June of 2016.
An additional state was surveyed in this wave, making it seven out of 10 states, revisiting urban households from Wave 1. Conducted from September 2016 to March 2017, Wave 3 covered rural and urban households that ae different from previous waves.Wave 4 was carried out from May to August of 2017, revisiting urban households interviewed in Waves 1 and 2. Repeated time varying data for a sample of households is invaluable in understanding the changes they undergo during such difficult periods as between 2015 and 2017.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 (hence wave 3 is excluded from the analysis).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, wave 1, 2 and 4 provide household panel data.The panel data will be used to analyze within household dynamics in times of high inflation.The models will be applied to changes in livelihood and determinants of the impact mainly at the household level.
Since different causes affected livelihoods in this period of instability in South Sudan, the difference-in-difference approach will identify the effect of inflation on livelihoods by correlating changes in prices with changes in livelihood indicators.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.The datasets contain information on security, economic conditions, education, employment, access to services, and perceptions.They also include comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption Survey methodology as detailed in Pape and Mistiaen (2018)4 .

2.2.Price data sources
There are three sources of price data that can potentially be used for the analysis in this paper.The following paragraphs describes each of the sources as well as their strengths and weaknesses.
(i) Consumer Price Index (CPI) High-Frequency Price Index (HPI) In addition to collecting household data with high frequency, the HFSSS also collected weekly market price data (as well as daily exchange rate of USD in SSP -buying, selling and midpoint prices).The Market Price Survey (MPS) was expanded to 15 towns in South Sudan (World Bank, 2016). 6The weekly market price data is aggregated as price index (HPI), and is comparable to monthly CPI from the National Bureau of Statistics.It allows to observe relative changes in the price index (monthly and annual) and shortages of products in markets.The MPS collects weekly price data for 20 consumer items in South Sudan using handheld tablets and uploaded directly to a cloud-based server.The precise weight of the products is determined with a digital scale allowing for the calculation of comparable unit prices.Market traders are asked the prices they are offering for a typical quantity of their goods.Unlike the CPI (monthly data collection), for the weekly MPS the goods are not purchased, and thus money does not change hands as part of the MPS.This will induce an upward bias for the HPI since the first price asked is often considerably larger than a bargained price at the time of purchase (World Bank, 2016).
The HPI only includes 20 items representing 55 percent of the CPI weights.The HPI ignores price movements in the other products, making it more volatile than the CPI which includes a larger number of substitutes and reflects better substitution effects.The HPI adopts the weights from the CPI and then adjusts them to account for items in the CPI that are not included in the MPS.Therefore, price data from the MPS are aggregated to create a High-Frequency Price Index (HPI) similar to the CPI.This explains why the HPI resembles the CPI.A detailed description of the HPI methodology, how to construct weights and the cleaning procedure for outliers can be found at www.thepulseofsouthsudan.com.

(iii) Household-reported prices
The consumption section of the household survey (HFSSS) collects information on items' unit prices and quantities.As with all data collected from sample surveys, the household-reported prices are subject to sampling errors.Item non-response and measurement error will also lead to biased estimates (Dahlhamer et al., 2003;Garner et al., 2009).However, household-reported prices have a key strength: knowing precisely the prices paid by households who make expenditures themselves has an advantage in that it captures the parallel exchange rates, showing households' real purchasing power.This is particularly important in the context of South Sudan with a strong parallel exchange market.

2.3.Calculating inflation
We have the luxury of being presented with three options for price data to choose from, which is not typically the case for many poor countries, talk less of countries with ongoing conflicts.Based on the strengths and weaknesses of the three price data sources, we decided to use householdreported prices because it covers the entire sample, and has prices information for all items consumed by the household.Thus, for our analysis, inflation is calculated based on unit price household survey data (using Laspeyres price index).In addition to using the total inflation variable, we also break it down into food price inflation and non-food price inflation to explore which of the two might be driving the results.

Outcome Indicators
To analyze the impact of inflation on household livelihoods, our dependent variables are household level (and individual level) outcome indicators.The variables cover a range of household social and economic indicators, which can be calculated based on the panel data (waves 1, 2 and 4).The outcome variables are selected from the following five categories: poverty, education, labor, hunger, and perceptions of welfare (Table 2).The unemployment rate is the number of persons in unemployment as a percentage of the total labor force.Outside the labor force/or inactivity A person is outside the labor force (or "inactive") if he/she is of working-age and neither employed nor unemployed, according to the preceding definitions.An inactive person is not necessarily idle, especially in the context of a developing economy.The data breaks this group down into those who are inactive because they do household work, those who are enrolled in education, those who are discouraged, etc. Hunger Hunger How often households lacked food or lacked resources to buy food at least once in the past month

Perceptions of welfare Satisfaction with life
The extent to which households are satisfied with life Living conditions Households views about their present and future living conditions Economic conditions Households views about the present, past and future economic situation of South Sudan.

Control over life
The extent to which households feel that they have control over their life Future of South Sudan Households biggest fear about the future of South Sudan Note: The labor force refers to the sum of persons in employment and in unemployment.It is the counterpart of the group of inactive persons, i.e. the labor force plus the inactive sum up to the entire working-age population (ILO, 2013).

DESCRIPTIVE STATISTICS
This section presents the descriptive statistics of inflation and the outcome indicators, showing levels and evolution over time.The panel analysis covers the six out of 10 states of South Sudan that were surveyed in wave 1 (2015) and revisited in waves 2 and 4 (2016 and 2017, respectively): Western Equatoria, Central Equatoria, Eastern Equatoria, Northern Bahr El Ghazal, Western Bahr El Ghazal, and Lakes state.Warrap state was surveyed in waves 2 and 4 but excluded from the analysis because it was not covered in wave 1 of the HFSSS.

Changes in price index (inflation) between 2015 and 2017
Consistent with the CPI data above, the HPI shows that inflation exploded between 2015 and 2017.The mean price index increased substantially from 1.30 in 2015 to 8.24 in 2016 and exploded to 30.97 in 2017 (Figure 5).The results show that non-food price inflation is drastically higher than food price inflation during this period.We use an adjusted Wald Test to test the significance of the differences in the means across the three years.The test confirms that the differences are statistically significant (p<0.01),meaning that inflation has increased significantly over time.The increase in prices varies across states and have been much higher in the Eastern Equatorial, Central Equatorial, and Northern Bahr El Ghazal states than in other states (Figure 6).This is mainly driven by the fact that food price inflation increased much more in these states.Since the price developments have been so different across the country, it is important to consider this significant geographic variation when analyzing the impact of inflation on livelihoods (as we do below: including location fixed effects in the regression analyses).

School attendance
Here, we report attendance rates for children of primary school Age (6-13) and secondary school Age (14-18).About 3 in 4 South Sudanese children were attending primary school in 2015.The primary school attendance rate remained stable in 2016, and increased to 80 percent in 2017 (Figure 7).Secondary school attendance dropped from 84 percent in 2015 to 78 percent in 2016, but increased to 81 percent in 2017 (Figure 8).These are relatively high rates of attendance considering the political turmoil, violence and insecurity in South Sudan.While primary educational outcomes among the poor have slightly improved from 2015 to 2016, school attendance of children in the bottom 40 percent poverty quintiles has declined in 2017.On the other hand, there is an increase in attendance for children in the top 60 percent poverty quintiles, with a notable 100 percent primary attendance for the richest quintile in 2017.Primary school attendance for the poorest quintile increased from 61 percent in 2015 to 78 percent in 2017 (p<0.05).The corresponding number for secondary school attendance remained stable at 71 percent during this period.Primary school attendance for boys and girls increased at about the same rate between 2015 and 2017 (Figure 9).For the older children, attendance rate for boys declined between 2015 and 2017 by 9 percentage points (Figure 10).The opposite is true for girls' attendance, which has increased slightly during this period.Perhaps older boys are dropping out of school to join the labor force.This is plausible as children of working age can be expected to join the workforce to help the household support its livelihood during times of economic hardship (World Bank, 2017).However, there is no evidence that this is already happening at a large scale in the states covered by the survey.This is because the difference in school attendance rate of boys aged 14 to 18 between 2015 and 2017 is not significant in a statistical sense.Labor South Sudan's economic instability led many of the working age to drop out of the labor force between 2015 and 2016.During this period, the urban labor force participation rate dropped significantly from about one half to about one third (Figure 1111).It is concerning to have only about one third labor force participation rate, particularly in urban areas.This low participation is likely due to the fluid political and economic conditions that caused many working age people to be outside the labor force.Poorer households experienced a larger decline in labor force participation.In 2015, the labor force participation rate remained relatively similar between poor and non-poor households and across expenditure quintiles.In 2016, the difference in labor force participation rate became more marked.There are no significant differences between men and women in labor force participation.This is true for both years.The 2017 labor force participation numbers are not entirely comparable with the previous years because of the changes in the questionnaire.But given the deteriorating political and economic crisis, it is possible that many urban South Sudanese remain outside the labor force.There was only a slight change in urban unemployment rate between 2015 and 2016, with about one in 10 urban South Sudanese unemployed.The urban unemployment rate was 8 percent in 2015 and 11 percent in 2016 (Figure 12).This number reduced substantially to 3 percent in 2017.It may not be very surprising to observe relatively high employment rates because like many other poor countries, South Sudan lacks social safety nets, which forces unemployed individuals to seek employment.The poorest quintile reduced unemployment the most, cutting it down by 35 percentage points between 2016 and 2017.

Unemployed
There have been marked changes in employment type between 2015 and 2017 (Figure 13).Running a non-farm business, followed by farming, hunting, or fishing at own account were the most common types of employment in 2015 and 2016.In 2015, the non-poor were much more likely to be employed as salaried workers, or engaged in non-farm business.In 2017, farming, hunting, or fishing at own account became the most common type of employment, followed by running a non-farm business.The share of those in farming, hunting, or fishing at own account increased by 13 percentage points from about 30 percent in 2016 (which was itself an 8 percentagepoints increase from 2015) to 43 percent in 2017.The main reason behind this shift in employment type is that more men, especially in poor are now involved in farming hunting, or fishing at own account.This shift in employment type is consistent with business income or wages and salaries becoming less reliable sources of income, forcing people to embark on farming hunting, or fishing at own account that would allow them to support livelihoods of their households.

Hunger
Between 2015 and 2016, hunger incidence deteriorated severely for households in the poorest quintile, with the likelihood of experiencing hunger 'often' (more than 10 times per month) increasing from 4 percent to 10 percent (Figure 14, p<0.05).Households in the second poorest expenditure quintile also experienced an increase in hunger during this period, but to a lesser extent.That hunger worsened for poorest people is probably not surprising given that food prices increased drastically during that time.Due to rising prices without compensatory income increases, especially the wage-dependent urban population lost real purchasing power.The severity of hunger has reduced since then, with only 2 percent of the poorest households experiencing hunger 'often' in 2017.It is probably a result of interventions to curb hunger.However, food insecurity and hunger remains a serious issue for South Sudan.For the poorest households, the likelihood of experiencing hunger 'sometimes' (3-10 times per month) has been rising from 29 percent in 2015 to 40 percent in 2016 and 43 percent in 2017.This confirms that poorest households are more vulnerable to hunger than richer households in the face of rising food prices.This is because richer households are much more likely to adjust their diets to cope with a lack of food, while the poorest households cope with a lack of food by going entire days without eating.This may pose serious health issues, and affect children education outcomes, with both short-term and long-term adverse effects on poverty.Resorting to more moderate strategies, households in the top 4 poverty quintiles

Perceptions of welfare
The deterioration of economic conditions in South Sudan, as indicated by continued high inflation, is well echoed by households' perceptions.In 2017, almost all (97 percent) South Sudanese residing in urban areas felt that economic conditions in their country were bad or very bad (Figure 15).This is a drastic increase compared to the previous years.This figure stood at almost two thirds of urban South Sudanese in 2015, increasing to almost 9 in 10 (63 vs. 86 percent respectively, p<0.001).However, there seems to be a growing sense of optimism among the urban residents about the future.In 2015, nearly half of households (46 percent) believed that economic conditions will be better or much better in 3 months' time.While people became less optimistic in 2016 (29 percent), the figure increased considerably to two thirds in 2017 (66 percent).It should also be noted that a sizable share of residents remains pessimistic about the future, with 21 percent of households of the view that economic conditions will get worse or much worse in 3 months' time.The deterioration of economic conditions is also well reflected in households' perceptions of their own living conditions.In 2015, almost half (45 percent) of urban households felt that their living conditions were fairly bad or very bad (Figure 16).This figure increased significantly to 78 percent in 2016 and 80 percent in 2017.There does not seem to be much hope for many households who believed that their personal living conditions will deteriorate in the next 3 months.Between 2015 and 2016, the share of households that believe living conditions will get worse or much worse increased significantly from 25 percent to 46 percent (p<0.001).The figure decreased to 31 percent in 2017, though it is still high.Nevertheless, as with economic conditions, there seems to be growing optimism about the future for one half of urban households.The share of households believing that living conditions will get better or much better increased from 39 percent in 2016 (10 percentage points less, compared 2015) to 53 percent in 2017.There seems to be a correlation between having control over people's lives and the extent to which they are satisfied with life.The share of urban residents who felt that they have no control over their lives increased from 26 percent in 2015 to 37 percent in 2016, but decreased substantially to 16 percent in 2017 (Figure 17).Feeling much more in control of their lives, 32 percent of households strongly agreed that they are satisfied with life (Figure 18).Note that in 2016 there was a general decline in life satisfaction relative to 2015, which reflected a growing feeling among urban folks that they were powerless in the face of deteriorating political and economic conditions.South Sudanese in urban areas are increasingly worried about the future of their country.When probed about their greatest fear for the future of South Sudan, respondents in 2017 were much more likely to cite concerns about civil war and ethnic violence than they were in 2016 (Figure 19).It is disturbing that fears about civil war and ethnic violence, which declined by about one half between 2015 and 2016, doubled in 2017.Other notable concerns include insecurity, poverty and lack of jobs and opportunities for youth, as well as an overall bad economy.

Model specification
This section models the impact of inflation on household livelihoods in urban areas of South Sudan.As inflation escalated in 2016 relative to 2015 and continued to rise in 2017, this presents us with a natural experiment to analyze its impact.As noted earlier, we are using panel data collected in 2015, 2016 and 2017.We use a difference-in-difference (double difference) approach to exploit both the time dimension and differences in the exposure to inflation.This approach is powerful as it can distinguish between secular effects and the impact of inflation on the outcome variables, although it relies on sufficient variation of exposure to inflation.This identification will eliminate preinflation differences in the outcome variable and controls for anything that also changes over time and affects both groups.Hence, the assumption will be made that changes in outcomes from households in areas with high and in areas with low inflation would have been the same in the absence of the inflation shock: More specifically, the difference-in-difference estimator β1 is computed by comparing the firstdifferenced values of the outcome for the high-(H) and low-inflation (L) groups.Hence, the outcome differences for the low-inflation group are differenced from the high-inflation group after taking the simple difference, which gives us the difference-in-difference estimate.The purpose of a difference-in-difference approach is to analyze whether the estimate β1 is statistically and significantly different from zero.
To estimate the difference-in-difference effect, we use an ordinary least squares (OLS) regression model including the control vector: Where   is an outcome measured for the individual or household i living in Boma s at time t ; postt is a binary variable indicating time period t (pre-or post-inflation); Postt = 1 for each of waves 2 and 4 and zero otherwise (i.e.we treat waves 2 and 4 as having occurred at different times, with wave 1 being the reference period).inflations is a continuous variable measuring the inflation rate of the Boma s; Inflation is computed as the first difference of the log price index at the Boma level.
To avoid an omitted variable bias (as there are other confounding factors affecting the given outcome variables besides time-period and exposure to inflation), a control vector   for household i living in Boma s at time t is introduced; γs and δt are respectively the Boma fixed effects and the time fixed effects.Standard errors will be clustered at the Boma level to allow for within cluster correlation9 .β1 is the difference-in difference estimator.

Results
The model formulated in equation [1] is used to generate regression results for the different outcome variables presented earlier.We run the regression for each outcome variable using the inflation variable and a range of control variables.The complete difference-in-difference estimates are presented in the appendix (Tables A1-A7).A summary of the regression results of the impact of inflation (β1 coefficients, difference-in difference) is provided in Table 3.

Impact of inflation on poverty and consumption
We find strong evidence of the negative effect of inflation on household consumption and poverty.The inflation impact is entirely from non-food price inflation (the coefficient on food price inflation is both qualitatively and quantitatively not significant).Poverty increases with the severity of exposure to inflation by the households.If inflation increases by 1 percent, the share of poor urban population (living below USD 1.90 per day PPP) increases by 0.353 percent.
The results also show that the conflict variable is associated with slight decreases in consumption, with a marginal impact on poverty.We also observe that being employed on itself does not matter for poverty reduction.This is probably not surprising given that urban unemployment rate has been low in South Sudan (as per the descriptive statistics).What matters for poverty reduction is the sector of employment.Households whose heads are employed in the services sector are less likely to be poor compared to those in the agriculture sector.This probably reflects higher wages for those in the services sector.Some common findings in other poor countries are also observed here: household head being a female, and larger household size are associated with low consumption and the likelihood of being poor.On the other hand, education and land ownership help to reduce poverty.We also run a regression that includes an interaction term between inflation and land ownership.The coefficient on the interaction term is not statistically significant for both food price inflation and non-food price inflation.This suggests that the impact of inflation on poverty and real consumption is the same for both households that do own land and those that do not.
Finally, university education seems to be important for poverty.It increases consumption and reduces poverty.The impact of inflation on real consumption is significantly less for households whose heads do have university education than those who do not (the coefficient on the interaction term is 0.985, p<0.01).No significant differences in education level exist when it comes to the inflation impact on poverty.

Impact of inflation on school attendance
We modelled the impact of inflation on whether or not children were attending primary and secondary school at the time of the survey.The results indicate that food price inflation had a negative and statistically significant impact on girls' school attendance (but no effect on boys).For girls, the likelihood of attendance diminishes with a rise in food prices.The distance to the nearest school is also important for school attendance.The chances of girls attending school diminish with increases in the distance they would have to walk to the nearest school.We run a regression for girls' attendance that includes an interaction term between inflation and distance to school.The coefficient on the interaction term is statistically significant and negative (-0.712; p<0.01).This means that the impact of food price inflation on school attendance is greater for girls who take more than 5 hours to walk (one way) to the nearest school from their homes compared to girls who take less than 30 minutes to do so.One explanation for this result is that when faced with an economic shock such as inflation, households become poorer (as noted above), and tend to sacrifice the education of their female children whose schools are far away from their homes as they may be able to afford the costs related to living far away from school.In this regard, bringing schools closer to households will help to mitigate the adverse impact of inflation on girls' school attendance.The results also suggest that school attendance increases if the household head is a woman and has secondary or university education.Designing programs to promote female education will help to improve education outcomes in general, and for girls in particular.

Impact of inflation on labor
Next, we analyze the impact of the inflation on labor indicators: active participation in the labor force and unemployment.Similar to school attendance, food price inflation is found to have a strong impact on the labor market.Increasing food prices lead to a decrease in labor force participation with an increase unemployment.In urban areas, education level is a strong determinant of unemployment for both men and women, with people with secondary education less likely to be employed.This may be surprising as one may expect those with primary or no education to be more likely to be unemployed.Perhaps these groups took up low-skilled jobs that are easier to get, while those with secondary education continue to search for better paid jobs.

Impact of inflation on hunger
Hunger is measured by how often households lacked food or lacked resources to buy food at least once in the past month.Inflation increases hunger, and the combined effect of both food price inflation and non-food price inflation is very strong, with rising food prices having greater impact.Descriptive statistics showed that the poorest households are more vulnerable to hunger.The regression results concur this.While the pinch of inflation was felt by every household, the poorest ones were the worst affected.Rising food prices have led to growing food insecurity for the poorest households, for whom the incidence of hunger has increased sharply.The poorest households are in a vicious circle as they may become poorer due to consequences of hunger (including poor health, child malnutrition and education outcomes).The finding that rapidly rising food prices is a causal source of hunger and food insecurity is consistent with findings from other poor countries, including a recent study on Malawi (Jolliffe et al., 2016).
The analysis reveals other interesting findings.Households whose heads have university education experience less hunger than others.The coefficients on the poverty quintiles are significant and negative, and get larger in magnitude as one moves up the consumption distribution from Q2 to Q3, Q4 and largest for the richest quintile.This suggests that poverty also has a significantly impact on hunger, with hunger incidence declining as consumption increases.

Impact of inflation on perceptions of welfare
Finally, we model the impact of inflation on perceptions of welfare variables.It is found that increases in inflation are associated with less satisfaction with life.This effect is marginally significant.Regarding satisfaction with present living conditions from 1: very good to 5: very bad), the positive coefficient means that people are less satisfied in the face of inflation.However, people's views about future living conditions are positive, consistent with optimism noted from descriptive statistics.There is a strong feeling among urban residents who are exposed to inflation that they are powerless and have no control over their lives.

CONCLUSIONS AND POLICY RECOMMENDATIONS
The high inflation in South Sudan continues to put many households under extreme financial stress, as they are faced with increased prices without compensatory increase in income.This paper contributes to available empirical evidence on micro-level impacts of inflation by analyzing the impact of high inflation on household livelihoods in urban South Sudan.We use panel data collected during the High Frequency South Sudan Survey (HFSSS) waves in 2015, 2016 and 2017.Increasing resilience to high inflation would allow to shift the inflation crisis management paradigm from a humanitarian to a development approach.The paper also attempted to identify household features that make them more resilient against the inflation shock to inform programs and policies to make households more resilient.
We start by presenting descriptive statistics showing the extent of inflation, which exploded between 2015 and 2017.Breaking down inflation by food price inflation and non-food price inflation reveals that the latter increased drastically more than the former during this period.Although there has been significant increases in both food and non-food prices, the observed high inflation in South Sudan is mostly driven by the escalation of non-food prices.We also find that inflation intensity varies across the states.Our econometric results, based on the difference-indifference approach, suggest that inflation negatively impacted various household livelihood indicators related to poverty, education, labor, hunger and perceptions of welfare.The multivariate regressions included a range of household and individual variables that may also affect the livelihood indicators, including conflict.The key findings are summarized as follows.
First, inflation had a strong negative impact on urban poverty between 2015 and 2017, and this was mainly driven by the outburst of non-food prices.The loss of purchasing power of wages and salaries has driven many of the South Sudanese residing in urban areas into poverty.Continuous increases in inflation will only worsen the already high poverty situation.Addressing the issue of high inflation must be at the center of efforts to cut down poverty in South Sudan.In addition, higher education has a key role for poverty reduction because the impact of inflation on consumption is significantly lower for households whose heads do have university education than those who do not.
Second, we find that food price inflation had a negative and statistically significant impact on girls' primary and secondary school attendance.The probability of a girl attending school diminishes as food prices increase.A related and interesting finding is that proximity to school is very important for school attendance.Interacting food price inflation and distance to school reveals that school attendance is less likely for girls who take more than 5 hours to walk from their home to the nearest school from compared to girls who take less than 30 minutes to do so.This corroborates earlier reports that long distance to school was one of the most commonly cited reasons by for dropping out of primary and secondary school in South Sudan (EMIS, 2016).While the cost of schooling is a major constraint for school attendance of both boys and girls, it disproportionately affects girls.In the face of limited resources parents prioritize boys for schooling over girls (World Bank, 2018b).Investing in female education is very important for poverty reduction and development, especially as we also find that school attendance increases if the household head is a woman and has secondary or university education.One important policy implication from this study is that bringing schools closer to households will help to mitigate the adverse impact of inflation on girls' school attendance.In terms of building resilience to mitigate the impact of inflation on household livelihoods, investing in education, particularly in fragile contexts like South Sudan will be a smart move.Without boosting education levels, it will be difficult to reduce poverty and improve the welfare of the South Sudanese people.
Third, another consequence of the observed increases in food prices is a significant decline in labor force participation and a surge in unemployment among urban folks.Employment programs with a focus on poverty reduction should, therefore, consider ways to mitigate the impact of rising food prices.For example, providing on-site meals for employees and take-home rations (targeted food transfer) may push people to re-join labor market through helping to reduce hunger.
Fourth, inflation is exacerbating food insecurity and hunger, particularly for the poorest households who are more vulnerable to hunger.This will most likely be stretching the government's finances, as well as pose challenges to humanitarian relief given the predicted worsening food-security situation of the most vulnerable groups.Households adopt various strategies to cope with hunger (including eating less preferred food, skipping entire days without eating, selling assets, among others).However, these coping strategies may put them at increased risk for future stints of food insecurity.This is because, the coping strategies employed by the poor (selling productive assets such as livestock) typically put them at an even greater disadvantage in the future (Barrett 2002).
Finally, inflation has negatively affected households' perceptions of welfare.Urban residents who are exposed to inflation strongly feel that they are powerless and have no control over their lives.This has led to less satisfaction with life and present living conditions.
A key economic priority for the Government of South Sudan should be to implement urgent macroeconomic measures to reduce high inflation.Addressing the problem of high inflation will help to curb poverty (as our econometrics results indicate), which is very crucial for progress towards achieving the first Sustainable Development Goal (SDG 1) to end poverty by 2030.In addition, for South Sudan to achieve SDG 2 (to end hunger and ensure access to food by all people, including the poor by 2030), the issue of rising inflation has to be contained very quickly as it is exacerbating hunger and food insecurity.This paper has shown that inflation has had adverse effects on the livelihoods of urban households.Because our analysis focus on urban areas, some of the results may not directly be generalized to the entire country.Even if the inflation crisis improves livelihoods of thepredominantly ruralhouseholds producing food, the rapidly increasing prices of non-food items is likely to have increased rural poverty and hunger as well.

Figure 1 :
Figure 1: Projections of Oil Revenues

Figure 3 :
Figure 3: Trends in CPI inflation, year-on-year

Figure 4 :
Figure 4: High inflation in all categories of goods between June 2015 and June 2017

Figure 5 :
Figure 5: Recent trends in price index

Figure 11 :
Figure 11: Labor force participation rate

Figure
Figure 12: Employment and enrollment status

Figure 14 :
Figure 14: Hunger incidence over the past 4 weeks

Figure 15 :
Figure 15: Perception of economic conditions

Figure 16 :
Figure 16: Perception of living conditions

Figure 17 :Figure 18 :
Figure 17: Feeling in control over own life

Figure 19 :
Figure 19: Fear for the future of South Sudan

Table 1 :
High Frequency South Sudan Survey (HFSSS), survey dates and coverage Source: High Frequency South Sudan Survey

Table 2 :
Outcomes variables Labor force participation rate The ratio of the active in the labor force to the total working age population (15-64 years) Employment rate A person is employed if he/she is of working age and has engaged in one form of employment activity. 8The employment rate is the number of persons in employment as a percentage of the total labor force.Unemployment rate A person is unemployed if he/she is of working age, is not in employment during the reference period, and has been seeking employment over the past 4 weeks.
more likely to deal with a lack of food by reducing the number of meals or portion size, or consuming less preferred food than the poorest households. are

Table 3 :
Summary of regression results for each outcome indicator and inflation variable