The analysis uses both consumption and income aggregates (crop income, livestock income, wage income -agricultural and non-agricultural-, and income from non-farm self-employment) calculated from the UNPS data. The consumption aggregate used is the aggregate constructed by the Uganda Bureau of Statistics to generate the official poverty statistics. The majority of the income aggregates come from the Rural Income Generating Activities (RIGA) database, which uses standardized protocols to generate gross income aggregates across waves. Where inconsistencies were noticed, they were communicated to the RIGA team and corrected in the protocols we used. The RIGA aggregates calculate crop and livestock income using information on the amount of goods produced by the household in the last 12 months and the price of these goods reported by the household. Prices are imputed from other sampled households when they are not collected. The measures of crop and livestock income calculated are gross income not netting out costs of land, labor or purchased inputs. Wage income is generated from data collected on wages earned in the last 12 months. The selfemployment income aggregate calculated all self-employment income earned in the last 12 months and nets out the cost of purchased inputs. Purchased inputs can be quite a large share of the income from self-employment activities such as petty trading or handicrafts and netting out the cost of these inputs is important. For more details on how these aggregates are constructed, see Carletto et al. (2007).

In order to assess real changes in incomes and consumption across time the analysis converts the nominal consumption and income aggregates into real aggregates. The national Consumer Price Index (CPI) is used to convert the consumption aggregate across years. Differences in the cost of living across space in Uganda are also accounted for by using a combination of the consumption aggregate provided by the Uganda Bureau of Statistics and the regional poverty lines. The regional poverty lines reflect differences in the cost of non-food requirements across regions and urban and rural locations in Uganda. Differences in the cost of food items are already incorporated in the consumption aggregate provided. For this analysis, the ratio of the poverty line across regions is used in order to correct for price differences across regions.<sup>4</sup> The consumption aggregate and the income aggregates are divided by this ratio in order to bring all aggregates to one national price. Descriptive statistics on the consumption and income aggregates are presented in the next section.

In addition to documenting changes in consumption and income aggregates, the paper explores the drivers of changes in these aggregates. It relies on data collected in the household survey on household demographic characteristics, distance to market, extension visits, and the type and quantity of inputs used in agricultural production. Table 2 provides descriptive statistics for the key household characteristics used in the analysis.

As Table 2 highlights, on average, there has been surprisingly little change in the use of agricultural inputs in Uganda; which is perhaps unexpected given that this was a time in which crop prices—and thus presumably the returns to using inputs— changed considerably. In general, input use is very low in Uganda in comparison to other countries in the region with data collected using a similar survey instrument (Sheehan and Barrett 2014, Binswanger and Savastano 2014).

> 4 This may be an imperfect measure of regional price differences if regional food price differences are quite different from regional non-food price differences. Ideally the income aggregates would also correct for food price differences across regions, however in the absence of having these data, using non-food price differences appears to be a reasonable approximation.

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