We present data in a way that makes sense for our clients in local government.
Question:
1) Why are the figures sometimes different on profile ID and the ABS?
I am looking at housing tenure in particular. I understand that they are categorised differently, however, I’m curious as to why the total numbers are different?
Answer from .id:
Hi - thanks for the email.
The reason the totals are different are in the ABS table footnotes.
(a) Excludes ‘Visitor only’ and ‘Other non-classifiable households.
The ABS table is not a count of all occupied private dwellings – it excludes households containing only visitors to the area (eg. short-stay rentals), and also excludes “Other non-classifiable” (ONC) households – which are primarily those who the Census collector couldn’t make contact with but believes are occupied on Census night.
We include both those categories – so that all tables counting households use the same total as those counting occupied private dwellings.
We find that this keeps things consistent, and ensures that there are no “gaps” in the numbers when our clients are using household and dwelling data from different tables. These dwellings are notionally part of your dwelling stock. You’ll notice that most of the difference is in the “Not Stated” category (ABS = 710, .id = 2,427), which will be mostly the ONCs, which have the majority of their answers set to Not Stated.
Neither way of looking at it is wrong, and the ABS do point out that these categories are missing from their table. But this is one example of how we do present the data differently to ABS to try to enable our users to tell the changing story of the area more effectively (these categories were also collected differently in some earlier Censuses, so including all dwellings makes it a bit easier to compare over time).
You’ll also see other topics where the numbers are different – eg. in dwelling type data, for some reason the ABS only shows this in their standard tables for Occupied Private Dwellings, but we show it for all dwellings. And in Educational Qualifications, we separate out under 15s from the “unqualified” data.