Tag Archives: imd

older aussie well-being

A tweet just alerted me to this report on the well-being of older Australians. I haven’t had time to read in detail but a quick skim seems to indicate it did all the “usual” things of checking correlations, doing PCA, etc and then “The final index was then calculated by averaging the five domain index_log”.

Oops.

I cannot but help feel a little frustrated. I gave a talk on this subject 6 years ago in Sydney when working at UTS. Many of my top publications from my time in academia concerned the development and Australian usage of the ICECAP-O instrument (see chapter 12) as a measure of the well-being of (primarily but not just) older people. Advantages it has over the research in the report I’ve just read are the following:

  1. ICECAP-O doesn’t use variables that must be collected from official sources or be part of (say) the SEIFA. The five variables came from extensive qualitative work that established what it is about (say) housing uncertainty that really contributes to/takes away from well-being. We wanted the underlying key conceptual attributes of well-being. So whilst health (for instance) is valued, it is what it gives you in terms of independence, security, enjoyment of activities that really matters.
  2. ICECAP-O is an individual-level one A4 page questionnaire. Four response categories per question means 4^5=1024 distinct “states” you could be in, each with its own percentage score. So slice and dice the data in far more flexible, disaggregated ways than what’s out there so far.
  3. The five domains are NOT simply averaged, nor do the response categories across domains be equally valued – e.g. the 2nd to top level of “love and friendship” is more highly valued, on average, than the top level of ANY of the other four other domains. They don’t all matter equally to older people. There is even a fair degree of heterogeneity AMONG older people as to the relative importance of these, heavily driven by factors such as marital status and gender. We used choice models to find this out and the findings are based on robust well-tested theoretical models.
  4. You can compare with (say) the SEIFA – we did this with the UK Index of Multiple Deprivation in one of my papers looking a British city – and get far better insights. So for instance, measures like the IMD/SEIFA can be misleading when they fail to capture measures of social capital or connectedness.

It’s a shame when disciplines don’t talk to one another. Things could move forward a lot more quickly. And as long as we use SEIFA/IMD type measures in policy, we’re going to be directing resources to the wrong people.