Much has been written about movements in the Participation Rate (PR) and the impact this has on unemployment rates; if participation increases then any decline in the unemployment rate is harder to achieve with more people entering the labour force (and vice versa). The discussion is certainly a valid one, but here I want to try and dig a little deeper into what is driving the changes in PR at a national, state and regional level.
PR is the percentage of the working age population (those 15 years and older) who are in the Labour Force (those either employed or counted as unemployed). Changes in the PR tell us about the proportion of the working population who opt to be in the Labour Force, but the number itself tells us nothing about why those changes might be occurring.
If we wish to understand more fully the structure of the Labour Force we need to look at the two factors at work determining changes in the net PR.
Fortunately, there is a way of splitting these two effects out from the PR data. The method was described and detailed in 2009 in a paper entitled Decomposing Changes in the Aggregate Labor Force Participation Rate written for the Federal Reserve Bank of Atlanta by Julie L. Hotchkiss.
The paper specifies the aggregate PR to be the population weighted average of the PR for different age groups. The difference in PR between different time periods (t and t-1) is therefore;
Where LFPR is the Labour Force PR; i are the age groups and p is the percentage of working age population.
The formula tells us that changes in PR between periods can be deconstructed into the change caused by shifts in PR within age groups (let us call this the “Propensity effect“) and change caused by shifts in the demographic make-up (which we shall call the “Demographic effect“).
Propensity effect can be caused by a range of issues. The most often cited is the impact that a weak labour market will have in discouraging people from entering the labour force; the reverse is true when a strong labour market encourages people to return to work. However, this is not the only thing causing the Propensity effect. More young people opting to go into tertiary education, or staying longer in secondary education, would also shift the PR within their age group. Likewise, more older workers opting for early retirement, or being forced to delay retirement, would impact on PR in that age demographic.
Demographic effect is a result of shifts in the age make-up of the population. An aging population, such as we are currently witnessing across much of the developed world, will tend to shift aggregate PR as a higher proportion of the population moves into the older age groups where PR (naturally) is lower than in middle-age.
In addition, there have been significant differences in the impact that these effects have had according to sex. We have seen a large increase in propensity to enter the labour force from the female cohort. In order to see those differences, we have calculated the effects for both male and female cohorts across the three age cohorts. We then sum these effects (when weighted for the proportion of males and females in the working age population).
In order to deconstruct changes in PR we need to have data for PRs and population proportions for the various age and sex cohorts considered. While the ABS provide us with aggregate s.a. PR levels at the National and State levels, we have had to construct our own Conus/CBC Staff Selection s.a. PRs (based on the original ABS regional data) for the chosen sex and age cohorts (youth 15-24 years; middle 25-44 years; older 45+ years) at the State and Regional level.
Doing so allows us to see not only how PR has changed over time but also what has been the relative importance of the factors driving that change. The table below considers PR changes in the period Oct ’98 (when regional data became available) to Feb ‘21 for QLD regions.
We should note that as we slice and dice a small data set at the regional level into ever smaller sub-sets (females aged 15 to 24 in Mackay for example) issues around volatility and the reliability of the data become problematic. For the larger data-sets (national, state and Capital/Rest of State) this issue is much less marked. Even allowing for this caveat the compiled data provides an interesting and useful insight to further understand regional labour force dynamics.
For our own region it’s interesting to note that Cairns is the only region in QLD which has seen a decline in all components. In particular it is one of only two regions where the female propensity effect has detracted from PR since Oct ’98.
Some regions, such as Brisbane Inner City, have managed to see an increase in both male and female demographic effects suggesting that this region has acted as something of a ‘youth magnet‘, unlike areas such as Moreton Bay – North which have seen an ageing population detract sharply for PR for both males and females.
Further analysis, including consideration of changes over different time-frames, and additional commentary are available by contacting me directly on firstname.lastname@example.org or 0439 490088.