Category Archives: Inequality Research

Highlighting income and wealth inequality data, issues with inequality data, or research (mine or others’) on income and wealth inequality.

Inequality between Australian States: Household Income

This is the first of a series of posts on inequality, with a particular focus on South Australia. The series begins with income inequality between states, but will then consider inequalities within South Australia. Unless otherwise stated, the data is drawn from the Australian Bureau of Statistics’ Household Income and Wealth series, but the posts often use different categories and ask different questions to those presented in the ABS data.

The primary analytic used here is often the share of total income. It is an aggregate measure used because the more usual “average” figures (mean or median) tend to individualise social and structural issues. Further, the average figures do not account for the changes in numbers of people or households within a category. This is a parallel argument to one I made in relation to the gender wage gap, but in the case of the inequality highlighted in this post, the share of total income takes account of for both differences in household income and population changes. As will be seen, this makes a difference in the story told by the data.

The share of total income as a measure of inequality between states is also important in its own right because it highlights the relative resources available to different communities. A millionaire recluse living on their own island may have a high average income, but that is the only resource available to them. By contrast, a larger community may have much lower average incomes but far more resources which can be taxed and mobilised for the community.

Inequality between States: 2020 data

In 2020 South Australian households accounted for 6.3% of all household income in Australia, while constituting 6.9% of the population. This may appear to be a small difference, but is actually quite significant. This 0.6 percentage point difference is approximately 10% of the income share. It equates to around $130m per week or $6.7bn per year which would be in the South Australian economy if the state’s share of income matched its population share.

Putting the data in this form highlights the wicked dilemma of this inequality between states. South Australia (and other lower-income states) do not have the same income resources to drive the development which could see it catch up to the other higher-income states. If there are no other national financial redistributions, the inequality is perpetuated. This potential cycle is one reason why federal government support and the distribution of the GST pool in favour of the poorer states is crucially important.

As the graph below shows that SA is not alone in having a lower share of income than population. Queensland has the largest gap between income and population shares at 0.8 percentage points, but this is on a relatively large base (equating to just 4% of the income share). Tasmania’s income share is 0.3% below its population share, but this is particularly significant when the population share is only 2.3% of the whole in first place.

By contrast, WA has the highest gap with income share greater than their population share. Their 10.7% share of national household income is 0.5 percentage points above their population share. This equates to about 5% of their population share. Again, the percentage differences may appear small, but constitute significant amounts of money and represent significant inequalities between Australian states.

Australian State and Territory Share of National Household Income plotted alongside share of population to show inequality between states.

It is noteworthy that these figures do not simply reflect differences in household income. Average (gross mean) household income in South Australia was $1,989 per week, which was 85.4% of the national figure ($2,329). Tasmania’s average household income was 75% of the national average. But both SA and Tasmania also have smaller households on average, which pulls their income averages lower.

By contrast, the average gross household income in the Northern Territory was $2,711 – the second highest in the country and 16.4% above the national average. This would seem surprising, but the territory data excludes remote areas. The result is also a product of larger household sizes (average of 2.9 people per household in the Territory, as opposed to 2.6 nationally). The NT’s share of national income roughly reflects their population share.

Of course some of these demographic differences would be captured had I used the ABS equivalised income data sets (which are adjusted to take account of household size). However, using the data on shares of national income to measure inequality between states also provides important insights on changes over time.

Changes over Time: South Australia

While South Australia’s share of national household income is currently below its share of population, its income share has also declined over the last 20 years. As shown in the graph below, the state’s share of national household income reached a high in 2003-04 at 7.4%, but dropped below 7% in 2007-08 and has not recovered.

South Australian share of total national household income 2001 to 2020, showing long term decline.

In all years the SA share of national household income was below its population share, while average household incomes were also consistently below the national average. However, (again) it was not simply about lower average household incomes. As the graph below shows, the South Australian average household income as a percent of the national average fluctuated over the period. It ranged from a high of 91.6% of the national average in 2003-04 to a low of 81.3% in 2013-14, before returning by 2019-20 to close to its 2000-01 value around 85%. In that sense, while changes in household income provide short term fluctuations, what is really evident in the graph below (which plots the three variable as indexes with the same starting point) is that the overall decline in South Australia’s share of income has been driven much more by the decline in population share.

Index of Changes in South Australia's Income and Population showing volatility of average income in SA as % of national, but steady and same decline of population and income share.

Again, the fall of 0.7 percentage points in South Australia’s share of national household income over the period may seem minor, but it is actually a drop of around 10% of South Australia’s share of national income. It equates to well over $8bn annually in current dollars that would be in SA if income share had been maintained its share of national household income over the period.

While the COVID pandemic has changed some migration and population patterns, the longer-term trends remain to be seen and the wicked problem of maintaining income and population shares is likely to remain for a while yet.

Caveats

There are of course a number of caveats to the above data and analysis. In previous posts I have been critical of these household income figures: (here) for not taking account of capital gains and non-cash housing income, and (here) noting Piketty’s critique of the categorisation and data sources. The ABS does publish some data on non-cash income (imputed rents, and “social transfers in-kind” [i.e. provision of free services like health and education which do not appear in household budget]). This provides a fuller account of household income, but it is still without capital gains and is not published at the state level.

Without that state data on total income, the analysis is incomplete. It is likely that imputing rent for owner-occupied dwellings would reflect higher rental prices in eastern states and increase the differences between South Australia and some of those states. By contrast, the social transfers in-kind are likely to disproportionately benefit the lower-income states and reduce inequality. However, without the data I can’t be sure or estimate the extent of impact.

Conclusions and Implications

Even with these caveats though, the income share data does show significant geographic inequality between states. Alarmingly, it also shows the situation is getting worse for South Australia and points to a vicious cycle of falling relative incomes leading to shedding population which itself leads to lower incomes shares and a decreasing ability to generate the things that could build/maintain population and income shares.

It is also coincidental but noteworthy that the period studied here is the period since the GST was introduced. That is important because the formula for the distribution of the GST is explicitly designed with an equalisation objective to “provide states with the opportunity to provide their residents with comparable services” (Commonwealth Grants Commission). The formula is based on a complex range of metrics (not income shares) and the distribution has been controversial. Western Australia in particular in recent times has complained of not getting their fair share. However, the data in this post suggests not only an ongoing need to support a redistributive approach to states with weaker income and revenue, but indeed that more needs to be done (within and/or beyond the GST).

The alternative is greater inequality between states driven by some states capturing a greater and greater share of national income and population, leaving the weaker states in their wake.

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).

Massively Underestimating Inequality? The Problem of Housing Incomes

Just before Christmas the Australian Bureau of Statistics (ABS) published statistics on Personal Income in Australia for 2018-19. The data, based on income tax returns, provides a window on inequality – and unusually for the ABS, the data is individual rather than household-based.

The ABS summary data tells us that the ACT has the highest median income and Tasmania the lowest. The top 1% of income earners accounted for 9.5% of all personal income, while the top 10% took one-third of all income. As the graph below shows, it also tells us which local government areas have the highest median income.

Income Inequality by LGA

I was also interested to see some quirky South Australian patterns. Roxby Downs, with miners’ incomes and few pensioners, had a stand out median income ($95,196) – way more than the second highest local government area (Walkerville). SA and Tasmania also had fewer people in the highest income bracket (quartile), which partly accounts for the lower average income in those states.

Problems in the Data

However, despite such insights, this headline ABS data is incomplete and is arguably significantly underestimating inequality.

Most obviously the data is incomplete because it does not include those on very low incomes or government payments who don’t have to file a tax return. Accordingly, it is a very truncated income spectrum. Further, household income may look very different to personal income depending on whether there are one or more breadwinners. As the ABS notes, their Household Income and Wealth survey data is a much better base for analysis.

But there is a more fundamental problem (in both ABS data sets) that leads to underestimating inequality, and that is the treatment of housing income.

Housing is important in income terms because different housing tenures create differences in effective (actual purchasing power) household income. This flows through to very different living standards and chances of wealth accumulation. The two key issues, which are not captured in the basic income data, are imputed rents and capital gains.

Imputed Rent

Tenants pay rent to landlords for the use of a house (the provision of housing services) and this appears as income for the landlord. However, a home owner-occupier pays no rent, yet is clearly in receipt of a “housing service” – the provision of shelter and amenity. This issue has long been recognised as a problem in economic statistics. In national accounting, it is a problem because renting out an existing house would suddenly add income and “product” to the market, when in fact nothing more is produced – the house provides the same amount of housing service regardless of whether it is rented or owner-occupied.

The economist’s solution to this issue is to impute a value for rent – that is, assign a value of income as if the home-owner pays rent to themselves. This recognises the value of the housing service being enjoyed as in-kind income by the owner-occupier. In Australia’s national accounts, some 7.7% of Gross Domestic Product is the value of rent imputed to home owner-occupiers (my calculation from ABS data).

Theoretically, the accounts could go further and impute a value for the other services produced within that house (meals, cleaning, counselling, sex) – but that is another story (and a PhD).

However, in terms of housing income, the imputed value of the rent should be added to the income of home owner-occupiers to better reflect the differences in the purchasing power of income between renters and home-owners. This is similar to the common practice in poverty and inequality research of using “after-housing” income as the benchmark. In this case though, the adjustment happens on the income rather than consumption side of the household budget.

We will see below the potential difference this can make to inequality data.

Capital Gains

The second major problem in the basic income distribution data is that it does not include capital gains. Total income includes wages and all employment earnings, income from unincorporated businesses, and investment and superannuation earnings, but not the increase in the value of capital assets.

In an earlier post, I noted a study by Lisa Adkins and others that argued that asset price inflation means that capital gains, capital income and inter-generational transfers are the preeminent drivers of inequality. Further, the authors argue that housing, more than any other asset class, is driving the dramatic increase in net wealth among high-income households. This is so important that they present a new categorisation of class in Australia based on asset ownership.

Of course, there are cash flow problems and costs/barriers to realising (spending) these non-cash incomes, but the fact remains that if renters and home owner-occupiers spent the exactly the same on non-housing items each week, the home-owner would be saving more and accumulating wealth which the renter would not. Their effective incomes are quite different.

Combining Income and Wealth Data

Again, we will see below the potential difference that including capital gains as income could make to inequality data, but including this (and imputed rent) also begins to address a key problem common in presentations which treat income and wealth inequality as different data sets. As I have previously posted, many low-income households do not have low wealth, and some high-income households do not have high wealth – and there are policy problems in shaping policy responses simply around low income.

The ABS did significant work on these issues in 2013 and developed a concept of low economic resource households (defined as those simultaneously in the lowest 40% of income and wealth) – and also included imputed rent in income calculations. However, it was a sideline to the main income data and the ABS has not really continued this work. Further, the concept is clunky to use and has not really been taken up by anti-poverty or inequality researchers (for instance, the World Inequality Database and the flagship ACOSS inequality report both largely present income and wealth data separately and do not include non-cash incomes).

That said, the ABS does still publish stand-alone estimates of imputed rent, and also household wealth from which capital gains can be calculated. Incorporating this non-cash housing income into income data may then be one step in integrating wealth and income inequality data into something more usable.

The Average Renter/Homeowner

The impact of the above can be seen in the following calculation, which is based on the baldest averages in the ABS Household Income and Wealth data for 2017-18.

The average household disposable income for homeowners (both mortgagees and full-owners[1]) was $1,972 per week, by comparison with $1,508 for all renters. In this standard comparison, the average homeowner earned 1.3 times more income than the average renter.

According to the ABS data, the average net rent imputation (that is, imputed rent minus housing costs) was $248 per week. This would take the average homeowner’s enhanced income to $2,220 per week – or 1.5 times that of the average renter.

The ABS data also shows that the average value of owner-occupied dwellings was $755,000 in 2017-18. Two years earlier, it was $673,000. Assuming that most of that $41,000 a year gain was price inflation rather than improvement in housing stock, that equates to $788 per week in capital gains income. This equates to 40% of the household income of the average home-owner (reinforcing the importance of asset price inflation).

The table below shows the impact of both the imputed rent and capital gains adjustments to the income of the average home owner-occupier. Total income for home-owners increases to $3008 per week, basically double that of the average renter.

Table showing Average Income + Net Imputed Rent + Capital Gain = Total Income

Clearly, the inclusion of non-cash housing income for owner-occupies significantly changes the relative incomes between the average home owner and average renter. It is another reason to argue that issues of housing affordability are felt most acutely by renters. While even on the raw ABS data it was possible to say that renters on average earned less than home-owners and spent proportionately more than home-owners on housing, the figures above show that the real situation is even more pronounced and more unequal.

Research Agenda

There is a caveat to the above because of the limitations of adding 3 separate averages (income, imputed rents, capital gains) and holding all other incomes/circumstances equal. It is order-of-magnitude approximation of the impact on inequality data, rather than a real estimation.

What is really required would be to use the ABS microdata to do this calculation for each household to produce a new total income data set. This could be made more complete by including asset price inflation of non-housing capital assets, and counter-balancing with an imputation for “social transfers in kind” – that is, the receipt of public services such as education, public health care, child care subsidies as well as a range of rebates and concessions.

From there a new income continuum could be constructed based on this total income, and then the standard income inequality measures could be applied to that. For instance, it would be possible to calculate a Gini coefficient, income percentile ratios and income shares for the top 1%, 5%, etc using the new income data.

Pretty clearly from the above, many home owner-occupiers would find themselves in much higher income brackets, while renters would be even more clustered in the lower income brackets. But I think that is the reality, that renters in effect have far lower incomes and much tougher cost of living struggles than home-owners (particularly non-mortgagee owners).

Obviously, the total spread of incomes would also be much greater, which I think would give a much truer picture of inequality in Australia.

Unfortunately though, while all this data is in the ABS Household Income and Wealth dataset, I don’t have the technical expertise to crunch the microdata numbers. Any volunteers?

[1]              The ABS presents this data separately, so I have combined them by multiplying the income by number of households in each category, and dividing the sum of both by the total number of households.

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.