Constructing such tables can thus give<sup>a goodindication of how groups of households</sup> differentiated by dimensions other than<sup>incomewill beaffected by reforms.</sup> Once such tables have been constructed<sup>the basic methodology outlined above can be</sup> applied to calculate the impact on<sup>each household.This has been done using data from</sup> several different countries and a wide<sup>variety of policy experiments.'3Table 5 provides</sup> an example of a typical set of results<sup>based on the simulation of a 10percent increasein</sup> the price of rice in Cambodia.

**Table 5: Effect of a 10 per cent increase in**<sup>**the price of rice, by docile, asper cent of**</sup> **total household expenditures, Cambodia**

|Urban|_1_|2|3|<br>_4_|_Decile(7_<br>_5_<br>|_6_<br>|7<br>|_8_<br>|_9_<br>|_10_<br>|**_TOTAL_**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|NetSellers|1.9<br>|2.8<br>|3.2<br>|3.5<br>|2.9<br>|2.9<br>|1.7<br>|2.2<br>|1.8<br>|5.7<br>|2.5<br>|
|NetBuyers|-2.2<br>|-1.5<br>|-1.7<br>|-1.5<br>|-1.5<br>|-1.3<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.6<br>|-1<br>|
|TotalUrban<br>Rural|-1.3|0.5|-0.4|-0.3|-0.5<br>|-0.1<br>|-0.1<br>|0.3<br>|-0.2<br>|-0.3<br>|-0.2<br>|
|NetSellers|2.2<br>|2.6<br>|2.4<br>|2.4<br>|2.9<br>|2.8<br>|2.4<br>|2.3<br>|1.4<br>|1.9<br>|2.4<br>|
|NetBuyers<br>|-1.9<br>|-1.4<br>|-1.1<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.7<br>|-1.3<br>|
|TotalRural|-0.4<br>|0.5<br>|0.7<br>|0.8<br>|1.1<br>|1.0<br>|0.9<br>|1.0<br>|-0.1<br>|-0.5<br>|0.6<br>|
|NetSellers|2.2|2.6<br>|2.4<br>|2.5<br>|2.9<br>|2.8<br>|2.4<br>|2.3<br>|1.5<br>|3.8<br>|2.5<br>|
|NetBuyers|-1.9<br>|-1.4<br>|-1.1<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.7<br>|-1.2<br>|
|Total|-0.5|0.5|0.7|0.7|1.0|1.0|0.8|0.9|-0.1|-0.4|0.5|

(*) Deciles by per capita total consumption _Source:_ Nicita, Olarreaga and Soloaga<sup>(2002)</sup> The results show that the main losers<sup>from a rice price increasewould be the poorest net</sup> buyers in urban areas (because they spend<sup>a large proportion of theirincome on rice),</sup> whilst the largest gainers would be the<sup>netsellers in the top decileof the urbanareas.In</sup> rural areas the gains among net sellers<sup>are moreevenly distributed,but the lossesarestill</sup> concentrated among the poor due<sup>totheir largeexpenditureshare on rice.</sup> As noted above, there are a large number<sup>of different simulationswhich can be done with</sup> the above framework. For example, Ajwad,<sup>Duygan and Sivri(2002)simulate the impact</sup> of a 25% reduction in transportation<sup>costs for farmer householdson household</sup> consumption in Armenia. They estimate<sup>thatrural households gain on average0.95%of</sup> total per capita expenditure, compared<sup>to 0. 1% for urbanhouseholds,but the gainsare</sup> reasonably evenly spread among the poor<sup>andthe non-poor.They also simulate the</sup> impact of a 10% increase in irrigation<sup>charges;this hurts both urbanand rural households</sup> by roughly the same extent on average,<sup>but poorer rural householdsare hit much harder</sup> than well off rural households because<sup>a much highershareof their expenditureis on</sup> irrigation. Similarly Ajwad, Aksoy and<sup>Sivri(2002) simulate the poverty impact of a</sup> 30% increase in tobacco and maize<sup>yields in Malawi. Thesimulatedincrease in tobacco</sup> yields raises incomes of the poorest quintile<sup>by 1.5%compared to0.99%for the top</sup>

> _1'_ See poverty chapters of the Diagnostic<sup>Trade IntegrationStudies for Cambodia,Mauritania,Madagascar</sup> and Senegal and drafts for Malawi, Lesotho,<sup>Yemen and Armenia.</sup>

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