Tag Archives: gender wage gap

Inequality Alarm Bells for South Australia

The data presented in my earlier “Snapshot of Inequality” shows that levels of inequality in South Australia are lower than the national average in a number of areas, but there are alarm bells ringing in the background – and they are getting louder.

The First Warning

At first glance it looks like good news for croweaters. Income distribution between households is slightly more even in South Australia with proportionately fewer households on very high incomes. Compensation for workers in South Australia is higher as a proportion of the state economy than the equivalent national figure (the labour share). The “official” gender wage gap is around half the national average, and households in regional South Australia earned closer to the average Adelaide household than the regional/capital city divide nationally.

However, the apparently good South Australian outcomes come against a background of South Australia having lower average incomes and wealth than the national averages. For instance, the gender wage gap is lower in South Australia – but so are women’s wages with average full-time ordinary time earnings for women $68 per week lower than the national average for women. Similarly, the average income of households in regional SA is closer to the Adelaide average, but $217 a week lower than the national average for regional households.

Wellbeing is about quantum as well as relativity to others (equality). However, the picture here is also complicated by differences in housing costs, service provision and the need for a fuller accounting of income (alluded to in a previous post, but beyond the scope of this work).

The Alarm Bells for South Australia

In the context of this study, what is ringing the alarm bells loudest for South Australia is not the current gap to the national averages, but the general decline in the position of South Australia relative to the rest of the country over the last 30 years. The South Australian share of the national economy (GSP as a proportion of GDP) has fallen from 7.71% in 1990 to 5.7% in 2021. The data in the graph below shows the impact of this relative decline on inequality statistics. Cue the alarm bells: South Australia’s share of national household income, SA labour’s share of the national economy and even the SA female share of the national wage pool, all declined over the period.

Cue the alarm bells: time series (2001-2021) showing downward sloping lines (i.e. declines) of SA shares of national population, household income, labour share, and female wage share.

It is important to note that this is not, or not necessarily, about average incomes declining relative to the rest of country. Equivalised household disposable income in SA was 92% of the national average in 2001, and the same in 2020. Similarly, female full-time total earnings in South Australia were 96% of the national equivalent in May 2001 and the same in May 2022.

Population Impacts

A key factor driving the data, but which is not evident in the average income data is the decline in South Australia’s population share. This decline is evident in the top line of the graph. The drop was less than 1 percentage point (from 7.8% in 2001 to 6.9% in 2020), but this is still significant.

Had SA retained its population share (that is, grown at the same rate as the rest of the country) over the period, there would be around 220,000 or 13% more people in the state. On current averages of household size and income, those population differences equate to around $9.7bn of household income per year extra that would have been in the SA economy (not counting any multipliers arising from extra population).

Clearly the relative loss of population has a major impact on the SA economy. Combined with the changes in the various average incomes, it gives the downward trajectories in most of the data (even where the income share actually increased in relation to the SA economy). Capturing both population and income changes is the whole point of using income shares rather than household averages, and in this case it highlights several concerns for South Australia.

Firstly, with a declining share of national household income, the gap between South Australia and much of the rest of the country is growing. SA is becoming poorer as a community, even if that is not reflected in the individual household data. That is the alarm bell ringing in the background. As I noted in relation to regional South Australia (where this process is heightened with a further decline in regional communities), this impacts on the ability of communities to provide infrastructure, growth and the ability to attract and retain people. It becomes a self-reinforcing cycle, and the alarm bells become a symphony.

Secondly, as the data above shows, the relative decline of South Australia as a whole means that inequalities within South Australia are also made worse – not internally, but in relation to the rest of the country. Put another way, the more egalitarian income spread and distributions within South Australia may ameliorate, but do not overcome the growing inequality between South Australia and the average of the rest of the country (hence the downward sloping graphs).

As a whole, the South Australian data provides an example of how different inequalities interact with eachother. In this case the geographic inequalities undermine greater gender and class equalities (or alternatively, greater gender and class equality ameliorate increasing geographic inequalities).

Policy Response

South Australia is not alone in this predicament. The same is probably true of other smaller jurisdictions (although I have not done the numbers), and it is certainly true of regional South Australia where the decline of local communities is truly alarming. Clearly a policy response is needed: the current policy settings are not working as South Australia and the regions are being left behind. However, the interactions of different forms of inequality make the policy response complicated.

I have previously noted the importance of government redistribution through mechanisms like the sharing of the GST pool. The data here clearly shows the ongoing need for a sharing from the high-income states to the rest of the country. I have also noted that traditional responses in economies at the periphery (be it regional areas, struggling Australian states or developing countries) has been to attempt to intensify the use of natural resources. However, academic literature suggests that this “extractivism” offers no guarantee of genuine development and may exacerbate inequality and environmental degradation.

In South Australia’s recent history, the Rann/Weatherill Labor governments, like the Liberal state government before them, were desperate to develop new mining operations in the regions and defence technologies in Adelaide. The Marshall government offered a high-tech space future, and the current government spruiks a hydrogen future. Whatever we make of these ideas, the obvious point is that they did not stop the relative decline of South Australia and regional South Australia in particular, and there was little consideration of the multiple layers of inequality in those policies. A far more comprehensive approach is needed.

There are no easy answers here, and there are contradictions in dealing with ininequality. For instance, as my post “the super-rich don’t live here” indicated, the biggest areas of geographic inequality between South Australia and the rest of the country, and between regional SA and Adelaide, exist at the higher end of the income spectrum. This creates a contradiction because policies aimed at creating high paying jobs in particular areas would be a step towards reducing inequality between geographic areas, but would increase inequality within those same areas (as there would be a greater spread of incomes). For instance, policies which pay highly skilled workers loadings for working in regional or remote areas may increase incomes at higher levels (and may be necessary to ensure service provision), but they will increase the income gap between those people/households and most other households in that area. Similarly, regional development based on extractive industries may increase regional incomes and geographic equality, but is also likely to favour male workers and exacerbate the gender wage gap.

Conclusion

The contradictions of development and inequality are difficult to navigate, and the focus on multiple dimensions of inequality (in this case, how the inequality between South Australia and the nation as a whole intersects with inequalities within South Australia) complicates the picture of inequality. However, such a focus is important.

The examples above and the use of data on gender and class inequality alongside household income remind us that inequality is built in to economic activity. In turn, this suggests that addressing inequality can’t simply be a distributional after-thought where we drive policy for narrow economic goals, then catalogue how the results are distributed and perhaps seek to address inequalities at that point through welfare provision.

At best, with that approach we will always be playing catch-up – looking for payments and services to paper over fundamental cracks. More likely, we will simply lose. The macro-dynamics will overwhelm any welfare provision.

I will say more about that in a future post, but from the inequality data presented here and in previous posts, it is clear that greater economic intervention is essential. If left to themselves, market forces and demographic patterns will continue to channel money and population to existing growth areas, and continue the decline of South Australia. And if intervention and governance is done without a focus on inequality, or with too narrow a focus only on some inequalities, we will see people or groups within South Australia (and elsewhere) left behind.

The alarm bells are ringing – we need to pay attention.

Snapshot of Inequality – South Australia and National

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.

 AustraliaSouth 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.

Time series snapshot of inequality: share of household income and wealth captured by the bottom two (equivalised) income quintiles, 1994-2020.

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.

Time series snapshot of Inequality: Share of national and SA income captured by households in respective regional areas, 2000-2020.

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.

Time series snapshot of inequality: labour compensation's share of the economy, SA and Australia, 1990-2020.

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.

Time series snapshot of inequality: female share of total wage pool, SA and national, 10094-2022.

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.

South Australia’s Small Gender Pay Gap: Is it Good News?

The gender pay gap in South Australia is around half the national figure – but is this really good news?

This is the sixth post in the series on inequality in South Australia, with the early posts dealing with inequality between households and the later posts looking at structural inequalities. This post continues the focus on structural inequality, and looks at gender inequality – or at least one aspect of gendered economic inequality.

Gender Pay Gaps – Official

The “official” gender pay gap figures used by the government’s Workplace Gender Equity Agency show that the gender pay gap is significantly narrower in South Australia than in the country as a whole. At 7.4% the gender pay gap in South Australia is around half the national average, and is the lowest in the country. However, there is devil in the detail of this official figure.

While the gender pay gap is lower in South Australia, this gap is on lower wages overall and does not necessarily reflect women doing relatively better. ABS Labour Force data shows that the $1541 per week full-time ordinary time earnings for women in South Australia was 95.8% of the equivalent figure at the national level. For men in SA, full-time ordinary time earnings were 89% of the national figure. In theory, this difference could mean that SA has proportionately more women in higher paying jobs, but more likely it reflects labour market segmentation where proportionately more women are in jobs with wages set nationally (e.g. minimum wages or modern awards), while men are overly represented in non-award industries where wages may differ more across the country.

This suggests that much of the difference in the national and SA gender pay gaps is not about women’s pay, but rather because men’s wages in SA are disproportionately lower than the national average. Put another way, rather than the labour market in SA bringing women’s wages up closer to men’s, it is an “equalling down” based on relatively lower men’s wages.

This creates particular challenges for the left and the union movement: how to increase wages in South Australia to closer to national averages without also increasing the gender wage gap? Obviously, a focus on gender segmentation and increasing wages in highly feminized industries like childcare is a start, but it should also be a factor in changes to enterprise bargaining currently under consideration.

Bigger Gender Pay Gaps Beyond the Headline Figure

The “official gender pay gap” based on full time ordinary time figures is only one measure of gendered pay inequality. The table below shows a range of key gender pay gap and wage share data for Australia and South Australia and is necessary because the full-time ordinary time data is limited. It ignores (and arguably institutionalises) gender differences in access to overtime and bonuses.

The first line in the table is the official full-time ordinary time data, but as can be seen in the second line of the table, when overtime, bonuses and other extras are taken into account in total full-time weekly earnings the gender pay gap is much larger. It is still less of a gap in South Australia, but the difference in the national and SA figures is narrower.

However, even these full-time figures ignore the over-representation of women in part-time and casual work. As evident in the third line in the table, the gender pay gap jumps markedly when the average weekly earnings of all employees are included. The gap between South Australian and national figures is much lower here, presumably because of a higher proportion of women in part-time jobs in South Australia. This is confirmed by the gender wage share data in the bottom line of the table, where the difference between the national and SA figures is lowest.

Gender Pay Gaps, May 2022

 AustraliaSA
F/T Ordinary Time Earnings14.1%7.4%
F/T Total Earnings16.5%9.6%
Average Earnings29.7%27.7%
Women’s Participation Rate62.2%58.4%
Gender Wage Share39.0%40.0%
Source: Calculations from ABS Average Weekly Earnings and Labour Force

Gender Wage Share

I have set out elsewhere a rationale for considering, alongside the traditional average pay gap data, the gender wage share – that is, the female share of the total wage pool. The importance of that wage share data can be seen here.

Both nationally and in South Australia, the female half of the population takes home around 40% of the total wage pool. In actual dollars, the May 2022 figures show that men as a whole in South Australia take home $221m more than women each week, for an annual return to gender of $11.5bn. To put that in the context of the local economy, the total sales revenue of South Australia’s biggest company, SANTOS was around $6.9bn in 2021, around 60% of the gender wage share differential.

As I argued in an earlier post about the corresponding national figure of a $200bn annual wage differential, the aggregate gender wage share difference is a significant economic flow that not just reflects but contributes to the reproduction of that inequality.

Changes Over Time

The 2022 data above shows, in short, that while the official gender pay gap is much lower in South Australia, the benefit of this is diluted by lesser participation in paid work by women (both relatively lower participation in the workforce, and relatively fewer hours by those engaged in paid work). As we will see below, these patterns are not new.

The following graph shows the full-time (all earnings) gender wage gap in Australia and South Australia over the last 25 years. While the data on South Australia is more volatile, the gender pay gap in South Australia has generally been below the national figure. In both data sets, the gender pay gap shrinks in the late 1990s, but grows again from 2000 nationally and from 2004 in South Australia through to 2014. The gap then shrinks over the next few years before flattening out in the last few years.

Time series of gender pay gap in full-time total earnings in South Australia and Australia.

However, the gender wage share data tells a slightly different story. There is little difference in the time-series data on South Australian and Australian women’s share of their respective total wage pools. In both data sets there is a small but sustained increase in women’s share (apart from a small dip after the Global Financial Crisis in 2008) (see the black SA line in the graph below). In South Australia women’s share of the total wage pool increased from 35.1% in November 1994 to 40% in May this year. The national figures showed an increase from 33.1% to 39% over the same period.

Crucially though, this story is set against the backdrop noted in a number of my previous posts of a general decline in South Australia’s economy relative to the national economy. This was evident in SA’s share of household income and the labour share of the economy. The graph below shows that this decline also impacts on the gender wage share. While the top line shows that women have been increasing their share of the South Australian wages pool, with that pool in relative decline, South Australian women’s share of the national wages pool has actually shrunk from 2.6% in 1994 to 2.41% this year.

Time series (1994-2022) of female share of total wages pool in South Australia, and South Australian female wages as proportion of Australian wage pool.

While this decline in South Australian women’s share of the national wage pool is alarming, it is made even more problematic because, as noted in the previous post, the wage pool itself is declining as a proportion of the economy.

Summing Up

The official gender pay gap data shows South Australia doing relatively well with the smallest gap in the country, but a closer look at the data tells a less rosy story. The smaller gender wage gap is based on lower women’s participation rates and relatively lower male wages in South Australia. It is not a story of greater female agency in the labour market. The national/SA differences almost disappear when the share of the total wage pool is considered.

And regardless of the comparative story, the gender wage inequalities in South Australia remain significant – with a 27.7% gap in average earnings leading to an aggregate $11.5bn annual wage gap. Further, while women’s share of the South Australian wage pool has increased over the last 25 years, these gains are undermined by the shrinking of that wage pool relative to the national wage pool – a national pool which, with historically low labour shares of GDP, is itself shrinking relative to the economy as a whole.

Beneath the headline gender pay gap data, for South Australian women workers there is a particularly problematic intersection of gender, geographic and class inequalities.

The Gender Wage Share: History and Implications

This post traces the gender wage share and women’s increasing share of the total wage pool in Australia since the mid-1980s. Women as a whole currently earn just 38% of all wage earnings in Australia. This is a product of the aggregate of the gender wage gap and the difference in labour force participation, and by my calculation amounts to a difference of around $200bn a year.

In a previous post I argued that the magnitude of the difference constituted a significant macroeconomic flow with an important role in the reproduction of society – and of gender relations in particular.

The rationale for the use of gender wage share data and the conclusions drawn are set out in that previous post. Here I want to consider the changes over time in those gender economic aggregates, and the implications of those changes for our understanding of inequality.

Time Series

The graph below shows the female share of “total earnings” in the ABS Average Weekly Earnings data from 1984 to the present. This is a simple calculation based on average employment earnings multiplied by the number of workers.

It is important to note that “earnings” here refers only to employment income. In the ABS data it is called “total earnings” because it includes overtime – as opposed to ordinary time earnings (which is also in the data set). However, that should not be confused with a total of all earnings, which could include social security payments, investment income or other mixed income. The World Economic Database now has this all-earnings data for Australia, but the time series is more limited and contains a bold assumption that mixed income is shared in the same proportion as other income. In any case, the results are similar with the data for 2019 showing a female share of 36.6% of total earnings, while my data has the female share at 38%.

Line graph of Female Share of Total Earning 1984 - 28.6% 2020 - 38%

The graph shows three phases in a history of an overall increase in women’s share of the total wage pool over the last 36 years. From the mid-1980s through to 1992, there was a significant increase in women’s share going from 28.6% of wages to 33% of the total wage pool. This was based on a small narrowing (2 percentage points) of the full-time gender wage gap, but a more significant increase in women’s share of jobs – going from 37.9% of employment in 1984 to 42.6% in November 1992.

After 1992, women’s share of the wage pool continued to increase, but at a slower rate until 2012. There is a change in ABS data series here so some data discontinuity, but women’s share of total wages has grown significantly since then from 34.9% of all wage earnings in 2012 to 38.5% in May 2021. This has largely been on the back of a decrease in the full-time gender wage gap (4.5 percentage points) and a more modest (1.9 percentage point) increase in the share of jobs.

These drivers are shown in the following graph which creates indexes showing changes in the gender wage share alongside the proportion of the workforce who are women (participation) and the changes in proportionate remuneration (that is, average female full-time earnings as a proportion of male full-time earnings). This last index is just a different presentation of the commonly-cited gender wage gap.

As can be seen, the increased gender wage share tracks most closely with increased participation. However, between through the 1990s and early 2000s the wage share is dragged down below the participation rate increase by a stagnation of the remuneration gap (evident in the F-T wage proportion) from 1992 to 2007, followed by a widening of this gap after the onset of the global financial crisis in 2007. From 2014 this dynamic largely reversed with the decreasing remuneration gap accelerating the female wage share faster than the increase in participation.

Index of Gender Wage Share, Gender Wage Gap (F/T Ordinary) and Participation (female proportion of jobs).

The overall trend of an increase in women’s share of the earnings is not unique to Australia. The World Inequality Report 2022 data shows that women’s share increased between 1990 and 2020 in most regions of the world (with China being the notable exception).

Implications

This gender wage share data has implications for how we understand and speak about inequality. With the female share of the total Australian wage pool growing significantly and (relatively) steadily since the 1980s we have seen a move towards greater gender equality (at least in terms of labour market incomes). Yet, particularly following Piketty’s work, it is now a fairly standard claim on the left that inequality has increased since the early 1980s.

This claim of increased inequality is certainly true based on the usual measures of household income (the data is well summarised by ACOSS/UNSW), but given the data on the female wage share we need to recognise that claims about increasing inequality are gendered, or at least gender-blind, statements. They are not wrong, but they are privileging particular data and the gender-blind category of the household over other standpoints and data which focus on women’s income.

Or to put it another way, such measurements of increasing inequality are based on (and promote) views of society as households stratified along a continuum, rather than as structured by gender (and other) inequalities.

Further, the gender wage share data puts a different light on the left critique of neoliberal or right-wing labour market policies. The standard argument is that the removal of labour protections, penalty rates and working conditions, and the increasing precariousness of work are likely to impact disproportionately on women who are in the most marginal and disempowered jobs. (See for instance Alison Pennington’s excellent critique of last year’s Industrial Relations Bill).

There is no argument from me with these critiques of the neoliberal reforms of the last 30 years. But what the gender wage data shows is that these neoliberal advances have been counterbalanced and ultimately outweighed by the movement of women into the labour market in greater numbers and some closing of the gender wage gap.

This is not an argument for complacency, but rather to argue for a more nuanced and multi-level analysis of our analysis of inequality. There are other forces beyond neoliberalism which are also shaping economic outcomes.

Caveats and Conclusion

While greater gender equality would (and should) normally be seen as a good thing, the increased female wage share is not unproblematic. Firstly, it should be recognised that this is an increasing share of a proportionately decreasing pie as the labour share of total income has been decreasing over much of the period. (See the Journal of Political Economy’s Special Issue on the Declining Labour Share).

Further, it must also be recognised that, in an era of stagnating wages and increasing cost of living (in particular, rapidly rising housing costs), one of the drivers of increasing female workforce participation is the need for households to have two incomes to stay afloat.

Unequal symbol with words "inequality - not what you think"

Ultimately though, regardless of these caveats on the increasing gender equality story, what the gender wage share data shows is the importance of standpoint and the questions we ask about inequality (and much else). Asking questions about structural inequality (such as gender, class, race, geography) provides different perspectives and different conclusions than the traditional focus on household income – a theme I will return to in future posts about other structural inequalities.

And of course, there is also the sheer size of the $200bn gap in the gender wage share, which is important in its own right (and not visible in the mainstream statistics).

The Gender Wage Share – a $200bn gap

It seems to me to be an important fact that men receive $4bn a week more than women in the Australian labour market. This is the aggregate of the gender wage gap, but at $200bn a year it represents more than a gap in individuals’ earnings. It is a significant macroeconomic flow shaping both the economy and gender relations.

The Gender Wage Share in the Australian Labour Market Men 62% Women 38%

The Gender Wage Gap

The gender wage gap is a common measure of the difference between the average earnings of women and men in the workforce. It is generally expressed as a percentage of men’s earnings based on average total remuneration for full-time workers (a gap of 20.1%), full-time base salary (15%) or full-time average weekly earnings (14.2%) (WGEA data).

These measures are all based on full-time work. This is useful for comparing like-with-like (i.e. full time work) and can be important for highlighting differences in pay rates with women on average in less senior jobs and clustered in low paid jobs and industries. However, the full-time data ignores the actual work of just under half of the female workforce where around 45% of female jobs are part-time. Further, given that only 19% of male jobs are part-time, the focus on full time work centres and normalises male-work patterns and underestimates the differences in wages actually taken home.

These problems are corrected somewhat by calculating the gender wage gap based on the average weekly earnings of all workers. This blows the gender wage gap out to 31.3% because women are not only being paid less by a straight comparison (full-time earnings), they are working fewer hours so their average earnings are lower. Yet this adjustment only goes part of the way to recognising differences in labour force participation because the average weekly earnings do not tell us how many men or women are earning that income.

It would be theoretically possible to have an economy with no gender wage gap, but very few women employed. Indeed, if there were no women employed there would be no gender wage gap!

Alternatively, (and more realistically), women’s participation in the labour force may increase over time, which would be important in terms of the income, independence and economic power of women. However, if that increased participation simply replicates existing patterns then the gender wage gap data would remain unchanged.

The gender wage gap does not tell us how many people are impacted by the gap.

Gender Wage Share

Alongside these disaggregated averages and the expression of the gender wage gap in individual terms, I think it is important to record and express the aggregate outcome of the gender wage gap. This can be done by calculating the share of the total wages pool taken home by men and women. This will be a function of both earnings and participation and is used in the United Nations’ Gender Development Index.

The calculation in the UN index is complicated by the need to ensure international comparability, but in Australia the gender wage share can be calculated fairly easily. Using the ABS Labour Force and Average Weekly Earnings data, it is simply a matter of multiplying the average male wage by the number of male workers and the average female wage by the number of female workers. This gives an aggregate wage pool, and the male and female share of that pool. The table below shows the data for May 2021.

Data table showing female and make no. of employees, average weekly earnings, and difference and share of total wage pool.

There are caveats on this data in broader gender terms given that it relates to labour earnings only, and does not include investment or other income. Nor does it deal with the distribution of non-market production income, or the redistribution of wage income within households, or the range of other inequalities which are tied up in wage gaps.

However, wages are the main source of income for most Australian households, and the data above shows that women take home 38.5% of the total wages pool, a gap of 23 percentage points to the male share – or 11.5 points to an equal share.

But for me, the standout figure is the differential in total earnings of $4bn a week between men and women. That is, the ABS data shows that in any week men as a group earned around $4bn a week more than women as group.

To put that in perspective, it is over $200bn a year – an annual figure which is about the same size as the entire federal government expenditure on social security (Commonwealth Budget Paper No.1, Statement 6, Table 3).

In a recent submission to the Federal Government’s review of the Workplace Gender Equity Agency (WGEA), I contrasted this difference wage share with the annual budget of the agency. Obviously responsibility for addressing the gap in women’s wages and workforce participation does not lie solely with the WGEA, but it seems fairly optimistic to expect an expenditure of $6m to have much impact on a $200bn problem (quite apart from the limitations of the liberalism of the agency – which I did not mention in the submission!).

Macroeconomics

But beyond the individualism of liberal workplace strategies, understanding the quantum of the difference in the gender share of wages is important because of its macroeconomic reproductive role. I noted above that $200bn a year was roughly equivalent to the entire federal government expenditure on social security. It is also around ten times the size of the entire South Australian state budget.

It would seem fairly uncontroversial to say that the social security system and state governments have an important role in total income distribution, and in the stability, direction and reproduction of the economy and society. Could we not then also see that a $200bn a year income differential between men and women is an important income distribution in itself? And that it has a role in the reproduction of society – and specifically the gendered inequalities across society?

For those familiar with structural analysis, this should seem logical. But for those with a more individualist starting point, it may be challenging to posit gender groups as collective economic entities. After all, the social security system or a state government are institutions with formal rules, lines of authorities and someone in charge, whereas “men as a group” or “women as a group” are just collections of individuals making their own decisions with no governing force or collective interest.

However, in macroeconomics we happily talk about a $120bn tourism industry, when it is a myriad of small and large operators competing to maximise their individual business profits. We talk about flows of money to and from a “finance sector” which is a range of entities responding to the savings made available by and the financial needs of individual actors outside of its realm. At a more abstract level, we lionise the disparate decisions of many people into a collective entity called “the market”, so the concept of women or men as a group should not be too much of a stretch.

There are of course differences within gender categories, issues of intersectionality and critiques of such a binary construction of gender. These are big and important issues, but beyond the scope of this post. My point here is simply that if we are to talk about a gender wage gap, then it seems reasonable to also talk about it at a collective and aggregated level.

At this aggregate level, the shares of total wages going to men and to women, and the substantial difference between the two is important because money is not neutral, or simply a conveniently exchange mechanism. It is a store of wealth and an enabler of access to goods and services.

It matters to gender relations and the structures of society that men as a group have access to $200bn a year more cash than women as a group (or that women’s access to that extra money is mediated by men at home or by the state at large). That money enables men as a whole to do more things (or more economically rewarded things) than women, to occupy public space and power, and to command greater aggregate purchasing power to be met by market production.

A gendered labour market not only reflects inequalities, it begets further inequality.

Concluding Directions

I am not claiming that gender differences are limited or reducible to the economic sphere, or determined by economic forces. But I am trying to move away from individualist economics, and in this instance, mainstream theories of the gender wage gap as being a result of differences in human capital or household income maximisation strategies. I am arguing for a more macro-level and structural analysis.

In a future post I may use the gender wage share data to track changes over time (spoiler: the female wage share has increased with greater labour market participation, but tracks a bit differently to the more traditional gender wage gap data). For now though, I am simply putting the $200bn figure out there and challenging anyone to say that an aggregate flow of that amount of money is not important.