where ln(��,�� is the log of the real value of per capita crop income of household _i_ at time _t._ ��,� is a set of variables representing production practices, containing the average plot area harvested by household<sup>12</sup> _i_ at time _t_ , and an indicator variable if the household owns or owns and rents plots (only renter is the excluded category), dummy variables for inputs such as fertilizer, pesticide, seeds/seedlings and hired labor, and the amount of family labor spent on the farm.<sup>13</sup> ��,� includes the distance in kilometers of household � to the nearest market selling agricultural inputs at time _t_ and whether extension services were provided to any household in the community. ��,� is a set of variables capturing the external environment. It includes the prices of maize and beans at the nearest major urban market to household � at time �, the WRSI weather measure experienced by household � at time �, and the number of fatalities in proximity to household � at time �.<sup>14</sup> Household fixed effects are denoted by ��.

Equation (2) is also applied to other sources of income and consumption in order to assess the importance of drivers of crop income in affecting total income and household consumption.

Finally, the following specification is used: in which household characteristics, ���, are interacted with ��� order to identify which households were better able to take advantage of (or protect themselves from) changes in the external environment during this time. This sheds some light on what factors may help to further reduce poverty and vulnerability in Uganda.

# V. Drivers of agricultural income growth

The results from the first model are presented in Table 6. As expected, per capita crop income is significantly higher among those who farm more land and apply more labor, fertilizer and pesticides. Households that used fertilizer and pesticides had crop incomes that were 12 and 19 percent higher than households that did not. The correlation between the use of these inputs and crop income is higher for the households that were in the bottom 40 percent: for these households crop incomes were 22 percent higher for those that used fertilizer and 14 percent higher for those that used pesticides. Crop income is not significantly higher among those using improved seeds. Although there was some increase in input use (at the extensive margin) among panel households during this period, the increase was relatively marginal. The proportion of households using fertilizer increased from 17

> 12 Self-reported by the household.

> 13 An alternate specification was also run using the value of purchased inputs rather than a series of indicator variables in an attempt to capture the quantity of inputs used. The results are qualitatively unchanged.

> 14 A variation of specification (1) and (2) where a measure of whether the household was affected (i.e. was within a 5 km radius) by a flood episode identified between November and December 2011, and between August and September 2012 was also included in E, but results are not statistically significant from zero. Flood episodes were calculated by digitizing flood imagery downloaded for flood events recorded in the Emergency Database (EM-DAT). These data are available from 2011 onwards, so they only cover the last wave of the UNPS used in our analysis (2012), which might explain the insignificant results obtained.

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