Making cool bubble-charts in R: structural unemployment edition

A problem with highly aggregated unemployment statistics is that they mask big differences in the work fortunes of different groups of people. In the ideal world, people made redundant in shrinking sectors can find work quite easily in growing sectors. Unfortunately, that doesn’t appear to be the case—the skills of a worker in a bike shop or shoe factory are different to the skills required to work in a growing sector, like mining or hospitals.

To visualise this, I pulled the ABS’s unemployment data by industry, employment by industry, and the 2006 Census’s education levels by industry, to make this pretty plot. On the horizontal axis we have median quarterly growth in employment from 2001 through 2012 (changing the metric here doesn’t greatly affect the chart). The vertical axis has the median unemployment rate for each industry over the period—again, this is pretty robust to changes in definition. The area of the bubbles represents the amount of employment in 2001. Finally, the colours are darker for those industries with a greater share of workers with a bachelor education or higher.

The code and data to make these plots are here (if you want to make them, you’ll need to change the working directory in the R script).

As I posted the other day, we know there are big differences in the unemployment rates in different sectors, and so it’s not really a surprise to see that unemployment rates tend to be higher in slowly-growing industries. Indeed, the relationship could be spurious: most unemployment observed at a point in time is short-term, though most unemployment (in terms of man-days not working over a period) is long-term. So it could be that we’re repeatedly measuring people just laid off from declining sectors. I’d not bet on that. People in the lagging sectors are less trained than people in low-unemployment sectors, and can’t easily shift industries.

All of this points to something quite sad: while we’ve all heard stories of Cashed Up Bogans in construction and mining making a motza with little  formal education, there are other people with a fairly low level of education who haven’t done so well out of the boom. While their unemployment rates have been quite low over the last decade (especially when we compare them to unemployment rates in Europe or the US), any slump in the future would shift all the circles up—especially the circles with less education. Then, it’s far from clear that displaced aluminium smelter workers will be able to find work in professional services or education.

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