# IV. Analytical approach

Given the relevance of agricultural income, we now turn to examine the drivers of crop income and consumption growth in Uganda for the period 2006-2012.<sup>11</sup> More specifically, we examine the correlation between crop income growth and changes in production practices, access to markets and services. We also examine the degree to which changes in the external environment affected crop income.

To capture the impact of changes in production practices on crop income growth, data on the area and ownership of the plot being harvested; the use of fertilizer, improved seeds and pesticides and household labor inputs (both hired labor and family labor) are used. Data on changes in market access and extension services are also considered.

Changes in the external environment that may have impacted crop income are analyzed by looking at the effect of prices, weather shocks and conflict fatalities. Changes in the external environment can have an impact on crop income directly and/or indirectly through the way that households produce. For example, good weather has a direct impact on crop income by determining production quantities but it can also impact crop income indirectly through the household’s decision to apply inputs as a response to weather. Good prices for crops increase crop income but they also increase the incentives to produce and may encourage increased input or labor use. As a result, when production practices are included in the regression along with variables that reflect the external environment, they are likely to capture some of the impact of the external environment on incomes.

For this reason, we run two regressions. The first regression includes variables on production practices, market access, extension services and the external environment. The panel nature of the data allows the inclusion of household fixed effects to control for time-invariant household characteristics. However, it is possible that omitted time-invariant characteristics influence both production practices and market access, and agriculture income. For example, it is possible that changes in distance to market and provision of extension services in the community are not fully exogenous, with investments in infrastructure and services being targeted to communities that are more (or less) agriculturally productive. For this reason, the coefficients on these variables cannot be considered causal. The results of this regression provide an indication of the correlation between changes in income and changes in production practices, markets access and services. Although the variables describing external shocks are exogenous to household behavior, the coefficients from this regression only pick up their direct impact on income (not its indirect impact through household behavior) and do not represent the full impact.

In a second regression, only those variables that considered to represent the external environment are included along with household fixed effects. This captures the full impact of the external environment on income. As Dell et al. (2014) discuss, the inclusion of fixed effects ensures that time-invariant aspects of the environment (such as the propensity for weather shocks to occur) are captured. This allows the coefficient to properly identify the impact of the change in external factors on agricultural income.

The two models estimated are given by equations (1) and (2) below:

> 11 Crop income has represented 65 percent or more of the total agricultural income (not including wages) at the household level for the period studied.

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