This snapshot of inequality summarises my series of recent posts on this subject. The series has been a journey. I didn’t completely know where the data would take me and I am now asking different questions than when I started. However, frustrated by the mono-dimensional analyses that often dominate discussion of inequality (including in my own work) I was keen to explore the multi-layered nature of the beast.
Inequalities in income and wealth distribution between households, across states and regions, and between structurally differentiated social groups all matter, so I was keen to analyse the data on all of these – even if in an iterative fashion. I also wanted to look particularly at South Australia, partly for local relevance and partly because state-level data is often not factored in to the analysis of inequality.
Snapshot of Current Inequality
Overall the data examined in this series (almost all it sourced from the Australian Bureau of Statistics) showed significant levels of inequality across the country, as summarised in the table below.
| Australia | South Australia | |
| Household Income (between states) (2019-20) | Australian average (mean gross) income $2329 per week, but state averages range from $1,736 (Tas) to $2,422 (NSW) | Average household income $1,989 p.w. = 85.4% of national average, although difference mainly at top end. SA receiving 6.3% of all household income (which is below its population share). |
| Household Income (within states) (2019-20) | Bottom 40% of households received 13.4% of the total income. High-income households (90th percentile) received 9 times the income of low-income households (10th percentile). | Inequality broadly reflects national patterns, but relatively lower incomes at the top end of the income spectrum |
| Household Income (Regional Areas) (2019-20) | Regional Australia accounted for 31% of the population, but received only 27% of income. | Regional SA share even smaller with 19.9% of population, but just 17.7% of income. |
| Household Wealth (2019-20) | Distribution more unequal than distribution of income: highest wealth quintile held 62.2% of all household wealth, while the bottom 40% of households held only 6.1% of wealth. | Distribution data not available, but wealth holdings in SA have a different structure (relatively less wealth in home ownership, more in financial assets). |
| Labour Share of the Economy (2021) | “Compensation of employees” at historically low levels (47.7% of GDP). | Labour share slightly higher (49.3% of GSP) |
| Gendered Wage Patterns (2022) | Gender pay gap of 14.1% in full-time ordinary-time earnings. Bigger gaps when all earnings and all employees included. | At 7.4%, f/t ordinary-time earnings gap is around half the national average, but on lower earnings and relatively lower male earnings. Difference between national and SA figures narrows when all earnings and employees included. |
Changes Over Time
The above snapshot of inequality is precisely that – just a snapshot at the current point in time. Arguably, a more important story is evident when changes over recent decades are considered.
That story is not straight-forward and has been explored more fully in the earlier posts. However, the short version is that, at the national level:
- income inequality between households has increased slightly,
- inequality between cities and the regions, between capital and labour, and inequalities in household wealth have all increased more markedly,
- gender wage inequality was the only measure where inequality decreased.
These trends are evident in the graphs below which trace changes in the share of the various pools of total income (or wealth, or production), alongside the same data for South Australia (i.e. share of SA total income/product). The time periods vary depending on data availability.
Household Income and Wealth
As can be seen, nationally, the share of total household income of the lowest two (equivalised) income quintiles has been relatively stable, peaking in 1996-97 at 21.4% and falling to 20% in 2019-20. However, while this fall in income share appears small, every 0.1% change represents over $22.6m (in 2019-20) going from the lower to higher income quintiles. Even more significantly, the share of national wealth held by the poorest two wealth quintiles fell more markedly, although the data is more limited, and is not available for South Australia.
Households in Regional Areas
The share of total income received by households outside Australian capital cities fell from 30.7% of the national total in 2000-01 to 27% in 2019-20. (Note: not all years are included in this data). The South Australian data is more volatile, and shows a lower share overall (with proportionately fewer households outside the capital), but the trend is similar.
Class
Nationally, labour’s share of the economy fell from 48.8% in June 1994 to 47.7% in June 2021, with each 0.1% change in this data set representing $2bn (in 2021) lost from labour payments. However, the South Australian data here is different, falling more swiftly from a higher share of the economy to a low point in June 2004, then recovering to 49.3% of Gross State Product in 2021 – a larger share of the economy than the labour share nationally.
Gender
As noted above, the gender wage share is the only indicator to see a reduction of inequality with women increasing their share of the national wage pool from 33.1% in November 1994 to 39% in May this year.
Caveats and Conclusions: Why the Numbers Matter
This snapshot of inequality focuses on shares of total pools of income/wealth, rather than the more traditional but disparate average income figures. My approach enables some consistency of analysis across the different data sets, but I make no claim of a causal relationship or that the inequalities are comparable in nature. Clearly, inequalities can contribute to each other and the data sets overlap, but they are analytically separate and the different trajectories show why it is flawed to simply focus on one dimension (usually household income) when considering questions like whether inequality is increasing or not.
In a future post examining the South Australian data in more detail I will explore more specific interactions between different axes of inequality, but the point of this snapshot of inequality is simply to summarise the data and note the importance of considering multiple forms of inequality alongside each other, rather than the usual mono-dimensional focuses.
In arguing for a broader focus on the multiple forms or layers of inequality, I am not calling for endless sets of data or the infinite division of society until we are left only with individuals (as in the neoliberal dream). Rather, my point is that statistics (and all research data) is a reflection of the questions we ask and the theoretical understandings underpinning the research. Our national economic statistics are a product of the neoclassical and Keynesian theories that gave rise to them (that was Chapter 1 of my PhD). So too, the data we use to describe inequality reflects particular theoretical standpoints.
More than that, the data can limit the way we see society and the policies we might pursue to address both inequality and political economy more generally. For instance, data on the distribution of income between households tends towards a tax-and-transfer redistribution (after the fact) to support households in the lowest income brackets. By contrast, labour share data begets industrial policies, while regional data inevitably leads to development debates.
In saying that, I am mindful that my analysis is not comprehensive. I am sure that people with better statistical programs and skills could provide more nuanced numbers, and not all structures of inequality have been examined. Most notably, there is no consideration of structures of racial inequality, although with relevant census data to be released later this year I may be able to add to the analysis later. Perhaps more importantly, I am also acutely aware that race, gender and class inequality is ultimately not reducible to numbers – or even to the economic.
All that said, the economic aspects of inequality are important, and the numbers do provide useful points of reference. At its most basic, it seems to me to be important to have some sense of the scale of inequality, whether things are getting better or worse, and in what areas.