The 1995/1996 Household Income, Expenditure - (PDF, 101 mb ...

1.11
decreases average household sizes. Note also that the unweighted estimates
produce a smaller design effect, meaning that weights raise the sampli¥ error
due to the use of a more complex and less statistically efficient design.
Weighting changes estimate magnitudes in ways small enough to forego
weighting itself, especially given the imprecision of the components which went
into the construction of the weights.
Some of the reluctance to "inflate the sample to national estimates" for
the present study comes from a concern for aggregation bias. For example,
people should not be led to believe that each household's income rises 10%
when GNP rises 10%. Similarly one cannot predict consumption patterns as
national income rises using cross-section Engel curves. Non-linearities enter
into distributions with temporal change. Without complex simulation of the
entire system, microeconomic statistics remain outside the purview of
macroeconomic dynamics.
There is a problem of making comparisons over time. The 1990/1991
HIECS sample was only 42.3% rural. CAP MAS sampling staff oversampled in
urban areas in an effort to reduce field costs, and because they believed that
rural areas were "more homogeneous." Oversampling is a fairly common
practice and justified for those reasons, but only if it is followed up by
representative weighting. Unfortunately there is little to be done about this
now because no weights were constructed for the 1990/1991 survey.
Inferences are sensitive to this change in representation. Readers are
strongly cautioned that the effect of different sampling percentages for the
two survey periods becomes more marked the larger the domain of estimation.
One interesting characteristic of the sample selection method for the
1995/1996 HIECS comes from the third stage of sampling, the systematic
selection of 30 households within the sample "chunk." These 30 sample
households are randomly halved so that 15 of the households are enumerated
in the first half of the survey year, then the remaining 15 households are
interviewed in the second half of the year. In other words, all areas are
represented in the first half of the year as well as the second half of the
year. Besides controlling for seasonal effects, such an "interpenetrating"
division permits comparison by half-samples, and any number of sampling and
non-sampling comparisons might be made to detect changes in variability and
bias over the year.
The actual target of the enumeration was not the household itself but
the "dwelling unit," the physical space which the household occupied based on
the 1993 listing. The enumerator interviewed the present and usual occupants
of the dwelling units, and updated the information about the household head if
the listing was different from the facts on the ground.
LB. Survey Instruments
5 The design effect for an estimate is the ratio of the variance of the estimate
given a complex design and the variance of the estimate under a simple
random sample design. In many cases the standard error reported in these
pages assumes a simple random sample, and should be inflated by the square
root of the design effect to obtain the standard error based on the stratified
cluster sample (Le., complex) design.
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