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This paper indicates that deprivation and certain household, demographic, economic and social characteristics appear to have some association with the level of under-enumeration in an area. For some characteristics, however, the associations are very modest and it is clear that, while there may be a variety of factors that affect whether or not an area suffers from under-enumeration, much of the variability remains unaccounted for by the deprivation and socio-economic factors alone.
However these factors can parsimoniously be split into transience or deprivation characteristics. By definition an area is said to have a transient population if it has a one (or more) of the following characteristics:
These findings appear to be in line with previous research establishing that under-enumeration is higher in areas characterised by certain social, economic and demographic characteristics. However, this paper quantifies how the different characteristics affected under-enumeration, by the successful use of logistic regression models.
The paper did not however look at all possible variables. There are a number of other factors (e.g. enumeration strategy) known to have an effect on under-enumeration. Additionally the paper’s emphasis is on associations; only an experimental study can provide evidence for causality. It is however, possible that other confounding variables, not considered, could account for the associations.
While a selection of demographic, social and household variables was found to predict under-enumeration, these variables were obtained from the post-imputation Census database and thus any associations could be due the underlying One Number Census methodology. More definitive results could be achieved by using the pre-imputation database.
The following future analysis may be justified:
The One Number Census methodology was principally concerned with identifying and adjusting for under-enumeration and assumes that over-enumeration (people being counted twice in the Census e.g. students counted both at home and at place of study) is negligible. An investigation into this assumption would be worthwhile.
Further investigation into whether using the pre-imputation database yields similar results would help verify the validity of the models.
The UK is the only country (currently) with a fully adjusted Census individual-level database. This makes comparison of the accuracy of the One Number Census project with other countries difficult. As other countries adopt similar approaches, the logistic models developed could be applied to their under-enumeration.
The Census variables chosen in the modelling process were by no means exhaustive. Further investigation could examine other attributes not considered.
The Scottish Executive has recently developed a new level of geography – the datazone. Datazones are fixed small areas, created from 2001 Census Output Areas, which avoid the homogeneity and confidentiality issues posed by other small areas such as postcodes or settlements. They have been designed to be compact in shape and contain households with similar social characteristics. Future work could ascertain if the logistic regression results are replicated at the datazone level.
Public perceptions of a Census would have been a good indicator to examine in the modelling process. Has a decline in individual commitment to civic duty – or an increase in apathy – been a factor? The last general election registered the lowest voter turnout post-war. Future work might investigate if voter turnout rates are associated with the level of under-enumeration.
Page last updated: 17 October 2006
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