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Occasional Paper

Modelling Census Under-Enumeration - A Logistic Regression Perspective

Results - Odds Ratios

The table below gives the odds ratio estimates derived from the coefficients in the model. Odds ratios are more easily interpretable in comparison with the logistic regression coefficients. For example, it can be concluded that residents in income deprivation group 3 (upper quartile: least deprived) are 1.1 times more likely to be under-enumerated compared to the reference category (group 0: most deprived). However, for residents in group 1 the comparative value is 0.9 i.e. a reduction. Thus the greatest amount of imputation occurred in the lowest and highest income groups.

Table 4.6.1 (12 Kb PDF file) shows the odds ratio estimates of the variables in the final model.

Overcrowding and non-car ownership still remain significant even with the addition of the Deprivation Indices. As stated earlier, the Scottish Index of Multiple Deprivation was not exhaustive in the number of domains considered. It did not consider housing deprivation – of which overcrowding is an indicator. Car ownership was also not used in any of the indices because of the ambiguity over whether the non-ownership of a vehicle is indicative of deprivation or a reflection of personal choice. Therefore, this could be a possible explanation why these two variables are shown to be still significant in the final model. 

Secondly, single parentage has a comparatively large negative coefficient - meaning that the fewer single parents there are in an area, the higher the number of “synthetic individuals” imputed. This seems peculiar, until we look at the One Number Census methodology. The Census Coverage Survey identified certain household structures into which imputed individuals were placed – for instance a large number of female single parent households with live-in boyfriends (particularly in the Glasgow region). The imputation process placed young males into these households, hereby changing their structure.

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