Kenya than between cities in Tanzania, the additional price differences at the border will reflect both a border effect and the difference in the relative price distribution between cities of these two countries. Therefore, when within-country price distributions are very different, it is only possible to estimate a lower and an upper bound for the border effect. In the example of Kenya and Tanzania, the upper bound will reflect the border effect from the Tanzania’s perspective (γKEN-TZA-γTZA-TZA) and will be much bigger than the lower bound, computed from the Kenya’s perspective (γKEN-TZA –γKEN-KEN).

In addition to trade impediments, border effects can reflect different degrees of price stickiness between markets on both sides of the border. Indeed, if the price of a good in local currency does not change when the nominal exchange rate changes, cross-border relative prices would fluctuate with the nominal exchange rate and these variations will be captured by the border dummies. Yet, price stickiness may be related to border barriers, as a seller can resist cutting the price of their good when markets are imperfectly integrated. To separate this effect from the border effects, we introduce as an explanatory variable a measure of monthly nominal exchange rate variations ( ∆ ln _ERijt_ ). If prices perfectly adjust to nominal exchange rate variation, the coefficient of ∆ ln _ERijt_ will not be significant.

# **3.3. Data and Impediments to Market Integration in the Region**

Detailed consumer price data for 3 commodities (Maize, Sorghum, and Rice) was compiled from FEWSNET and FAO for 150 towns in 13 countries in East and Central Africa on a monthly basis. Because of data availability, we restrict our analysis to the common period May 2008-Oct. 2009.<sup>11</sup> In the raw data, prices of commodities are sometimes available in different units (e.g. kilograms, tons, etc) and for different varieties (e.g. maize or white maize). In order to keep the largest possible number of observations, we transform the price units to dollars per kilogram using the monthly average exchange rate and assimilate a variety (e.g. white maize) to the main commodity (e.g. maize) when the latter is not available. In appendix B, we compare the relative prices of our expanded sample with a more restricted sample where we exclude goods that are labeled differently than the main good (e.g. white maize) and find that the distributions of relative prices are similar, therefore our choice is unlikely to affect the results.

We match the commodity price data with a detailed data set on the road network in these countries compiled from GIS. The rich road network data not only includes the length of the road minimizing the travelling time between any pair of towns in our sample, but also includes detailed information about the road quality between any two towns.

> 11 The original database includes price data for several cities and countries from 2000 to 2011. Two main reasons lead to the decision of limiting the period. First, Data for Congo (D.R.) are only available over May 2008 and September 2009 and there are no observations for most countries before 2006. Second, most city prices in the original sample are available only for some periods. Therefore taking a larger period would reduce drastically the number of cities and countries. This could be appropriate to study the evolution of levels of market integration of a few cities over time but in this paper, we choose to give priority to the impediments to market integration between and within a large number of countries in the region during a recent period.

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