Actual numbers, an update

Our last post drew attention to the publicly available WGEA reports, in which large Australian employer organisations (including Australian universities) report their staffing mix as part of a national commitment to collecting and reporting data on workplace gender equity. This data doesn’t have casualisation as its focus, but casualisation is one of the factors reported.

It’s very detailed, and you can search for the reports from your own university here.

In our original post we also shared a work-in-progress spreadsheet that we’ve been working on, pulling together information reported by different universities to see how they compare. The comparisons were so extraordinary that we took another look, and we had a follow up conversation with researchers at the NTEU who are looking at the same data. The problem is that the very poor fit between the WGEA categories and university staffing is likely to create inconsistencies in category interpretation.

Today we’ve done some further checking against other sources, including Annual Reports, and it turns out we’d guessed wrongly where universities were reporting the bulk of their senior academic staff. So we’ve taken the spreadsheet down to have another think, as we’re still chasing this up. In one case at least most Level D/E academics are reported as Senior Managers; and a very small number are reported as Managers, while most (and most casuals) are below that level. But we don’t know if this is consistent across the sector. In fact, we now know we know less than we did before we started, despite this lovely tweet from the Research Whisperers yesterday:

Does your uni have the worst casual/adjunct/sessional ratio in Australia? @KateMfD and @Acahacker know. #securework https://t.co/bwZfPogPLF

— Research Whisperer (@researchwhisper) December 2, 2015

In terms of our analysis, the spread of academic staffing data across categories certainly shifts the apparent ratio of casual to permanent staff—the very thing that we’re all curious about. And collapsing academic and professional managers into the same WGEA category makes clarifying this much, much harder to do.

The other question that we’re asking since looking at the WGEA data: what does that reported number of casual staff itself represent, and do all universities report according to the same assumptions? Universities have a very complicated relationship to casual hiring in all categories, and in terms of academic staffing we deal with the slippery category of “adjunct” in the Australian context: professionals in other fields who take up some kind of university role, including in medical and other trainee supervision. Should we expect this to be a factor?

Overall, these complications suggest a significant correction to our optimism that we’d found a publicly available source of data that could be used by people like us—people without direct access to HR or organisational research divisions—to get a clear look at casualisation. And it’s strongly confirmed our suspicion that the common sense questions that people are asking about casualisation have generated no simple answers.

Working this out in public is a project we’re committed to. We want to demonstrate something about how Australia both counts and hides casualisation, which we have argued since we began CASA is a critical measure of three things: business sustainability, staff wellbeing and student quality of learning. As qualitative researchers, we really want to know: why is it so hard to get an answer to the question of casualisation as a proportion of the staffing experience in Australian universities?

So we’re reporting an adjustment to our first assumption about proportion, and we’ll be back with more reports on what we learn. This week’s key message: universities have tremendous capacity to collect and report data with clarity, so when they don’t, there’s a reason to be curious. The QILT dashboard is an excellent example of the way in which this kind of data can then be put to work in a public-facing way. If you want to compare performance in student satisfaction, graduate employability or other teaching indicators across a collection of up to 6 universities, right down to degree level, you can do so quite easily. So in the spirit of Christmas wishing, we’d really like to ask Australian higher education to put its data mining muscle to developing a public-facing comparison dashboard for university staffing so that we can all know what’s going on.

And on that note, no one has really bettered Donald Rumsfeld:

Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know.

If you shared our post this week, please pass on this update.

Thanks!