Poverty comparisons and household survey design by Steven Howes

By Steven Howes

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Footnote continued from previous page) to Jamaica as a whole would then depend on the (probably perfectly acceptable) judgement that the survey's sample frame differed little from the country as a whole. Page 9 3 Estimators of Totals and Means and Their Variances Appropriate for Complex Survey Designs Before turning to poverty measures, we consider, in this section, the estimation of means of variables. We focus on means not because they are attractive welfare measures, but on account of their simplicity.

In practice, random sampling without replacement implies that once a random number has picked out a household or cluster any subsequent random numbers which identify the same element are ignored. 7. The Jamaican Survey of Living Conditions (JSLC, described in PRDPH, 1994) provides another illustration of the individual features which surveys can have, for which there is no solution without approximation. The survey is drawn from the Jamaican Labor Force Survey (LFS), a stratified, clustered, two-stage sample.

1 Clustering One feature which most household surveys share is that they are clustered. That is, the first selection (from the population or sample frame) is not of households, but of some higher level units such as villages or street blocks, known as clusters. This is the case for all nationwide household surveys, though some very small surveys (of one or several villages, say) are not clustered. As we will see, clustering leads to higher variances. Its justification is purely practical. By concentrating sampled households in a small number of geographical areas, clustering drastically reduces survey costs per household.

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