we can incorporate this behavior into our estimates using demand and supply elasticities<sup>89</sup> . The dotted line in the left hand panel of Figure 13 does exactly this – showing very slightly reduced welfare gains<sup>90</sup> across the distribution of expenditure<sup>91</sup> . Notice that below a certain level of expenditure, households experience average welfare gains equivalent to more than 2 percent of total expenditure. Compare this to the wealthier end of the distribution, where equivalent income gains are below 0.5 percent. Clearly, a decline in maize prices will substantially benefit the poor.

**Figure 13: Equivalent income effects of a 20 percent price decrease in maize: With zero or estimated elasticity (left panel), Rural/Urban with estimated elasticity (right panel)**

_Source: KIHBS (2005/06), author calculations. Elasticities are taken from Mghenyi et al (2011). For supply this is an elasticity of 0.4. For demand the largest elasticity is used (for the sake of conservatism); -0.55 across the whole sample. Note: ‘Equivalent income effect’ reflects the estimated equivalent change in real income (compensating variation)._

**Separating the welfare effects of rural and urban groups suggests that impacts** **_by income level_ are generally very similar.** The right hand panel of Figure 13 shows the estimated welfare impact curves (including elasticities<sup>92</sup> ) separated into rural (solid line) and urban (dotted line).   The only point where the two lines diverge is just below the boundary of the bottom decile of expenditure (as indicated by the solid line on the left hand side).  This divergence is explained by the fact that the poor in rural areas typically grow quite a lot of maize, reducing their net purchases, as opposed to the urban poor, few of whom grow maize. This suggests that the _poorest_ of the urban population have the most to gain from lower maize prices.  However, the portion of the population in this group is small (only 0.04 percent of urban households are in the bottom expenditure decile).

> 89 As with sugar we use a second order Taylor series expansion to estimate the effect.  The only difference here is that we include supply effects, which we ignored in the case of sugar.

> 90 Deaton (1989) argues that the first order impacts are good enough for the purposes of policy, since the difference made by elasticity’s in most cases would be small and estimates of these difficult to produce in a defensible way. These results are consistent with this view.

> 91 We use elasticities from Mghenyi et al (2011).  For supply this is an elasticity of 0.4.  For demand we follow their largest elasticity (for the sake of conservatism) of -0.55 across the whole sample.  Inspection of the size of the effect in the graphs suggests that the magnitude of assumed elasticities will not substantially change the results. 92 Extending these results with the use of elasticities for all of the different kinds of maize, that differ across rural/urban and poor/rich areas is not practical with the data available.  In addition, based on the analysis of Figure 2.6, this would only marginally change our estimates. The fact that maize has been and continues to be by far the dominant staple in Kenya, despite the large price swings of recent years, there is no reason to suspect that elasticities would be particularly large, nor that differences

27