produced locally, and the supply response is not elastic, their prices would also increase. Thus the extent of substitution following price changes may effectively be moderate.

A more important limitation of our estimates is the absence of data to compute S (sale) in equation (3), which will induce a potentially relevant upward bias in the estimate of cv. The size of the bias will depend on the amount that households could sell on the market for the various goods we consider. We shall keep that in mind when interpreting the results of the estimation.

This approach relies on three further assumptions. First, we assume that all of the tradable goods’ prices change by the same proportion following the shock. That is as reasonable an assumption as we can make in the absence of more disaggregated information. That is also reinforced by the fact that the coefficients in the above analysis are not statistically different across commodities. Second, due to data limitations we have also to assume no substitution between tradable and nontradable products and no effect on the prices of non-tradables from the tradables price changes. The second and third assumptions generate biases that under-estimate the poverty reducing effect of the induced price changes. In this sense we consider our estimation to represent a lower bound of the effects of the deepening of the market integration.

We first simulate the impact of upgrading all the state-level roads to the quality of primary roads. Taking the lower bound coefficient of -3.22 from column (7) in **Error! Reference source not found.** , a 10 percentage point higher fraction of primary roads in a state would reduce the price differential between the state capital and Juba by 32 percent (). With an average of around 30 percent of primary roads per state, a complete upgrade implies an increase of primary roads by about 70 percentage points. This translates into a price reduction of about 39 percent for tradable goods as the average prices in the states are 48 percent higher than in Juba. Applying this reduction in price to equation (3), we estimate that the average consumption of tradable goods increases by 63 percent on average. With about 45 percent of tradable consumption for the average household, this road improvement increases total consumption on average by 28 percent. The computations with the upper bound coefficient -6.61 (derived from column 9 in **Error! Reference source not found.** ) yields an average increase in consumption of 57 percent. Hence, the first-order impact of upgrading the quality of all roads to that of primary roads increases total consumption by between 28 percent and 57 percent for the average household.

A simulation confirms the results of the back-of-the-envelope calculation. Based on the NBHS 2009 data for South Sudan, the impact of the price reduction due to road improvement can be simulated. As Juba in Central Equatoria is the reference for the price differential, the impact of road improvement in Central Equatoria is simply estimated as the average price consumption effects per tradable item over the remaining states. We use the lower (-3.22) and the upper bound (-6.61) for the elasticity of reduction of the price differential with respect to road improvement . The simulation considers upgrading major roads connecting the state capitals assuming that the upgraded roads would yield a road quality of 100 percent for the corresponding states.

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