Taken together Tables 1 and 2 give us a good indication of<sup>the likely initialimpact of a</sup> given price shock. Clearly changes in rice prices will<sup>have the largest impactgiven their</sup> large share-in both income and consumption. Of course<sup>what mattersis the households</sup> _net_ consumption position; the tables suggest that the<sup>poorest decile will be net consumers</sup> on average (28.4% of their consumption is on rice, 21.4% of their<sup>incomeisfrom rice)as</sup> are the top two deciles, whilst all other deciles are net<sup>producers.Thusa sharpincreasein</sup> the rice price may hurt the poorest, but help the not quite so poor<sup>atthe same time.The</sup> tables also give us insight into issues which might<sup>not otherwise be apparent.For</sup> example, decreases in livestock prices could hurt<sup>the poorto a significantextent,but</sup> would be of much less significance for the upper deciles<sup>of the population.Similarly</sup> shifts in wage income will matter more to the<sup>poorest decilethan toslightly less poor</sup> households in deciles 2 to 5. And increases in<sup>housing,fueland transportationcosts will</sup> hit the richest hard, but will have a much smaller<sup>effectupon the poorest80%of the</sup> population. Thus a careful examination of the income sources<sup>and expendituresharesof</sup> the population can provide much of the "story"<sup>aboutthe potentialimpact of price shocks</sup> upon the poor.

The ability to tell a relevant story about who is affected<sup>by variousreformsalsodepends</sup> upon the ways in which households are grouped.<sup>Tables1 and2 grouped householdsinto</sup> deciles of per capita consumption expenditure, which is a<sup>naturalgroupingif one is</sup> interested in determining the impact upon<sup>the poor.However,deciles of consumption</sup> expenditure are rarely a relevant group for<sup>policy purposes;rather policymakers tendto</sup> be interested in what the effects of a reform<sup>arefor functionalor geographicalgroupings</sup> e.g. rice farmers versus informal urban workers or<sup>Western provinceversus Eastern</sup> Province. Indeed there are an infinite variety of possible<sup>groupingse.g.educationallevel,</sup> gender of household head, ethnicity, location, principle<sup>activity,land ownershipetc.The</sup> particular combinations of grouping which are relevant<sup>willdependon theprecise</sup> context, but if the analysis is conducted at<sup>the householdlevel,then it is possible to put</sup> together any grouping for which the relevant variables<sup>are availablefrom the survey data.</sup> Furthermore, if one wishes to explore non-income "dimensions"<sup>of poverty(e.g.</sup> educational attainment, remoteness, health etc)<sup>and thesevariablesareavailablein the</sup> dataset, then it is possible to present the above tables in<sup>terms of deciles of these</sup> variables. Table 3 for example, breaks down households'<sup>incomesourcesin Nepal by</sup> deciles of time to get to the market. It shows for example<sup>that incomefrom own</sup> enterprises is only of real importance to households<sup>whoare relatively closeto markets,</sup> whilst consumption of own production increases as a proportion<sup>of totalincomethe</sup> further away one is from markets, rising to over half<sup>of total incomefor the most remote</sup> three deciles.

There are many different dimensions across which such tables<sup>may be constructed. For</sup> example, Table 4 shows household income share<sup>for each of the10 geologicaland</sup> economic regions of Nepal along with summary poverty<sup>and illiteracy figuresfor each</sup> region. It shows large differences in sources of livelihood<sup>betweendifferent regions.</sup> Income from own enterprises and non-agricultural<sup>wagesare key in Kathmanduand other</sup> 9