Although these considerations have not gone entirely unrecognized (see for example Atkinson, 1991; Atkinson and Micklewright, 1992; Deininger and Squire, 1996)3, conventional poverty analysis tends to rely only on welfare measures which are as _comprehensive_ as possible, without going to the same lengths to establish perfect _comparability._ As a result, little is known about the biases in poverty comparisons caused by departures from comparability. Taking consumption as our measure, we use data from Ecuador and Pakistan to illustrate that changes in calculated poverty rates across consumption definitions can be very substantial.<sup>4</sup> We also present an example of the use of the robust procedure to deal with a problematic survey design used in El Salvador. In this example, a different consumption definition was used for different households within the same survey.

While we show that robust comparisons of poverty are possible across datasets with different definitions of consumption, this is at the cost of restricting oneself to the headcount measure-with its well-known drawbacks.<sup>5</sup> However, it can be argued that moving beyond simple counts of the poor to the development of _poverty profiles_ will ultimately be of greater use to those concerned with the relative poverty of population subgroups. By virtue of their relation to poverty rates, profiles based on the aggregation consistent poverty indicator are similarly robust to different consumption definitions.

The latter fact gives us a baseline to explore the possible tradeoffs involved in aggregation when there is measurement error in the data. When measurement errors are not perfectly correlated, aggregation can be beneficial. However, non-food expenditures are often thought to be particularly poorly measured, and this might make more comprehensive consumption measures less reliable than, say, food alone. Typically, the net impact of aggregation is impossible to assess because we do not observe true expenditures. Using our robustness result we show that, in these data, aggregation is beneficial. The more comprehensive the consumption definition, the sharper the poverty profile, with more clearly defined differences between poor and non-poor population sub-groups.

The following section explores how poverty measured in a variety of ways will change as the definition of consumption expenditure becomes more comprehensive. Section III gives empirical evidence from Pakistan and Ecuador. Section IV discusses possible solutions to the problem of comparing data with different degrees of comprehensiveness, and demonstrates a use of the robust approach. Section V turns to a poverty profile for Ecuador. It details the tradeoff involved in aggregating noisy measures and uses the robustness result to determine that, in these data, aggregation improves the welfare measure. The final section offers some concluding comments.

3 In another context, Ravallion and Chaudhuri (1996) illustrate that conclusions from the recent literature on household consumption smoothing in rural India are also sensitive to the precise definitions of consumption which are employed.

4 Note, the problems associated with non-comparability of consumption measures are not confined only to poverty rate calculations. Recent advances in "robust' poverty measurement, which apply stochastic dominance results, are subject to the same difficulties because within this framework distributions are compared at the same poverty line (although the comparisons may be carried out, in turn, over a number of poverty lines). See for example, Atkinson (1987).

The headcount "has little but its simplicity to recommend it" (Watts, 1968, quoted in Atkinson, 1987).

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