Tag Archives: inequality

The Australian Inequality Index

The public policy think tank, Per Capita, has just released a new multidimensional measure of inequality, the Australian Inequality Index. The index combines various measures of inequality in seven areas: income, wealth, gender, ethnicity, disability, and intergenerational and First Nations inequalities. The measures are weighted and give a measure of inequality for each category, and for overall inequality.

Given my ongoing critique of the use of mono-dimensional and often misleading household income statistics as the primary measure of inequality, I welcome Per Capita’s initiative. In previous posts I have tried to develop consistent indicators of inequality in a number of similar areas, mainly in terms of shares of total income. However, the Per Capita inequality index is broader than simply income (or economics) and includes a range of social measures – something that makes its methodology bolder, and more fraught (more below).

Results

Because the Inequality Index combines different types of measures (for instance, gender inequality includes political representation ratios, crime victimisation rates, and the gender wage gap), it is difficult to get a common language and measure. Per Capita solves this partly by using indexes with values between 0 (perfect equality) and 100 (the furthest distance from equality). So, for instance, if wealth inequality was rates at 70, this would mean that the wealth of the highest income group would need to decrease by 70% for equality to be achieved. These index numbers are then tracked in each area (and combined) for the years since 2010.

The outcome is summed up in the graph below from the Summary Report. It shows stability in the immediate years after the GFC, followed by a bumpy decrease in inequality from 2013 to 2018, primarily due to improvements in gender and ethnic equality, and in some measures of equality for First Nations’ people. However, the first two of these indexes turned around late in the decade and, coupled with rises in income and wealth inequality, the index shows a resurgence in inequality.

Line graph showing the Inequality Index from 2010 to 2021, hovering just under 44 until 2015, declining to 40.5 in 2018 and increasing again to just under 44 by 2020.

Interestingly, alongside these index numbers, there is also an estimate of the time that it would take to reach equality – at the current rate of progress, and with a 1% per annum rate of catch-up. I have created the table below from the Index to sum up the key findings in each area of inequality.

Measure2021 Index NumberChange since 2010: Index PointsYears to Equality based on trend over last 10 yrsYears to Equality at 1% p.a. Catch Up
Income45.4-4.286 yrs45 yrs
Wealth64.47.7Never85 yrs
Gender21.0-9.818 yrs19 yrs
Intergenerational28.51.2Never29 yrs
Ethnicity45.9-4.137 yrs45 yrs
Disability63.416.8Never63 yrs
First Nations36.5-6.751 yrs63 yrs
Overall Inequality43.60.1361 yrs40 yrs

Statistical Issues in the Inequality Index

Given the Inequality Index was put together by a relatively small think tank rather than a government statistical agency, I think there are some minor anomalies in a few places, but this should not distract from the usefulness or ambition of the project. However, there is still devil in the detail.

The full methodology and assumptions have not yet been published, so what follows is based on the information in the Summary Report.

The bringing together of multiple dimensions of inequality requires weighting the relative importance of the various components – otherwise a few areas of harsh inequality potentially impacting relatively few people may overwhelm the index. Yet weighting is tricky: how do you weigh the relative importance of women’s political representation, with the gender wage gap – let alone those issues with rates of Aboriginal incarceration or numbers of people with disability reporting discrimination?

The process is inherently subjective, but all statistics are subjective in that their definitions reflect subjective or theoretical assumptions. The bigger question is whether the weighting and the subsequent index is statistically robust. That is, would the index or the trends be significantly changed by minor changes in the weighting or categories. I don’t have the data (or the statistical skill) to make that judgement, but I am prepared to take the Inequality Index on face value – not least because the important thing about indexes is not so much how they are constructed, but their ability to show trends over time. In some senses, as long as they capture key elements well enough it becomes more important to maintain consistency over time then to continually adjust to political nuances.

Broader Critique

The question then is whether the Inequality Index does actually capture the key elements of inequality well enough – and here I do have some questions and critique. For instance,

  • The income inequality data is based on standard measures household income, which I have argued previously are misleading as they fail to take account of housing incomes, social-transfers-in-kind, capital gains and capital income.
  • The intergenerational inequality index focuses on current differences between age cohorts in rates of poverty and intended retirement age. It is too easy to dismiss these as life-cycle effects, and to me, the bigger intergenerational issues are long-term: what sort of economy, infrastructure, natural resource base and environment are we bequeathing the next generation? Is the next generation going to be better or worse off than the current or previous ones at same point or overall in their lives? These aspects of intergenerational inequality are not considered in the index.
  • Both the ethnicity and disability inequality indexes are based on rates of reported discrimination and labour force participation, but there is no accounting for the income that comes from that participation (or not). I wanted to know the share of income and wealth held by those groups.
  • Similarly, the First National inequality measure has 12 different components, but not one relating to income.

There are all sorts of good reasons for the choices about what to include and leave out, including the challenge (or impossibility) of getting robust and continuous public data on some of the issues above. For that reason, my main critique of the Inequality Index is not the points above (although they remain important), but two areas where I think the issues are of a much greater scale – the omission of class and geographic inequality.

Class

Despite the Summary Report’s Introduction acknowledging the importance of Thomas Piketty’s work, the Index uses the very bald income quintiles which Piketty criticises, and it does not examine the top 10% and top 1% where Piketty sees inequality growing at its most obscene. More importantly, the distribution of (some) income across a stratified income spectrum arbitrarily divided into quintiles does not capture class inequality or the structural inequality of the distribution of income between labour and capital.

The labour share of GDP is a much more robust measure of class inequality, and one for which there is robust ongoing data. While capital and labour incomes eventually land (differentially) in households on the household income spectrum, so to do the wage differentials of the gender wage gap and the differing incomes of varying labour force participations of other groups. This is no reason to exclude class inequality or assume it is covered by household inequality. I would have liked to have seen class, measured by the labour share of GDP included as an eighth sub-index, separate to and alongside the household income data.

Regions

The other significant omission from the Inequality Index is geographic inequality. The Australian population, income and wealth is concentrated in a small number of cities, and the data is clear that residents of some states (SA and Tasmania in particular) and people in regional and remote communities have significantly lower average incomes than those in the capital cities.

Beyond simply income, geography matters in terms of inequality in access to services. Many services cost significantly more (e.g. telecommunications) or are simply unavailable in many regional areas. Differences in access to health, education and other services can be seen as inequalities in the social wage, but they also have direct impacts on quality of life and the sustainability of communities. To ignore the geographic dimensions of inequality is a major oversight in measuring inequality in Australia.

Conclusion

Despite these queries and critiques, I still regard Per Capita’s Inequality Index as a bold and important initiative – a significant step beyond the narrow and flawed income measures used in much inequality analysis. I hope that in time the Index can be revised to incorporate some of the measures noted above, but either way, if Per Capita can sustain the methodology and index, it will be a valuable tool for understanding whether (and where) we are becoming more, or less, equal.

However, any socio-economic index is a tool, not an end in itself, and I suspect the greatest challenge for the Index is not its construction but its use. There are other indexes (e.g. the UN HDI, the Genuine Progress Indicator, and most recently, Wellbeing Budgets) that also reflect multiple dimensions of equality and wellbeing, but they pale in comparison to the use and status of economic statistics like GDP, the unemployment rate and CPI. Those official measures are sometimes misused, misunderstood or politically dubious, but they dominate economic discussion. They do so, not because they are the best scorecards of well-being or economics, but because they are causal variables (within ruling economic theories) used in economic management.

Accordingly, to be truly effective, the Australian Inequality Index will need to be not just a scorecard, but an active instrument of policy. Whether it has the theoretical framework and the mobilising power to play that policy role remains to be seen, but it is a start to build upon– and given my statistical efforts at measuring inequality, I am slightly jealous!

Extended Income and Inequality: Different Data, Surprising Results

In a previous post, I suggested that the treatment of housing in the official income distribution data massively underestimates inequality. This is because it fails to account for imputed rent (the non-monetised value of housing services enjoyed by owner-occupiers) and for capital gains. My calculations in that post were illustrative of why this extended income was important (renters became relatively worse off, homeowners better off), but those illustrative calculations were not analysis of real data. What I wanted to see was an income spectrum which included monetary income, imputed rent and capital gains, but also including 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, the standard income inequality questions could be asked to properly analyse inequality in this extended income.

Equals sign with diagonal line through it and the words "Inequality - not what you think" - which relates to extended income.

My Wish Granted

Since writing my original post, my attention has been drawn to the work of Yuvisthi Naidoo, from the UNSW Social Policy Research Centre. Her PhD and subsequent articles calculate many of the above changes (and more). She starts with standard household disposable income and adds values for net imputed rent and social transfers in kind to produce a measure of “full income”. She then converts household wealth into income flows in the form of imputed lifetime annuities (that is, an income equivalent to drawing down on capital to leave zero at the end of life). This is done for household financial assets to produce a measure of “potential consumption”, and then more controversially for owner-occupied housing to get “adjusted potential consumption”.

The incorporation of an income stream from wealth is important because it overcomes a fundamental problem in standard inequality data. Simply focusing on standard money income (and considering wealth separately or not at all) gives a false picture of which households have the most and least economic resources, and the gap between them. For instance, ABS data tells us that 24% of low-income households have moderate wealth, and 9.8% have high wealth, while 60% of households in the highest income bracket do not have high wealth, yet these households are ranked solely on income in most analyses.

The use of annuities to calculate income flows from wealth holdings is not the capital gains accounting I was envisaging: a simple capital gain = income equation. However, it incorporates capital gains into the base on which the annuities are calculated. For instance, in the annuities model, a $50,000 capital gain is not considered income of itself, but the annuity is calculated on the increased asset value and for a 20 year annuity would add $2,500 to the income stream. The annuities approach probably under-estimates the value of capital gains income to households, but it is a better overall accounting of capital wealth and income than simply capital gains – and is certainly better than the nothing in the usual income accounts.

Results

The table below shows Naidoo’s calculations for median household incomes at each step in the process. Surprisingly (to me) the overall result is that extended income is more evenly spread than disposable (cash) income. This is evident in the final column which shows the ratio of median income in the lowest income quintile to the median in the highest quintile decreases. When just standard money income is considered, those in the top income quintile average 4.2 times the income of those in the lowest quintile, but when extended income is considered (as “adjusted personal consumption”), this figure falls to 3.14 times.

Median Incomes $p.aIncome QuintilesRatio Q5/Q1
 Q1Q2Q3Q4Q5
Disposable Income18200300214046152923763994.20
Full income33259446445443865677894872.69
Potential Consumption339824653857763717471030023.03
Adjusted PC360585069362728790061131543.14

The reason for this more equitable distribution in extended income is the impact of social transfers in kind, the benefits of which flow disproportionately to lower-income households. The step from disposable income to full income (that is, the inclusion of net imputed rents and social transfer in kind) produces an 83% increase in income for the lowest income quintile, but only a 17% increase for the highest quintile. By contrast, the inclusion of wealth annuities only adds a further 8% to income in the lowest quintile, but 26% in the highest quintile – so (unsurprisingly) these capital incomes increase inequality.

Naidoo focuses on older-age households, and she goes on to investigate other measures, but for me the impact of government services in reducing inequality (and the quantification of that) is the first standout political point coming out of the analysis of extended income distribution.

However, equally important is the fact that there are different households in each quintile once we incorporate extended income. As I argued previously, the inclusion of imputed rents as income moves homeowners up the income spectrum while renters will be even more clustered among those with low incomes. But it is not just housing tenure. Naidoo’s research showed that nearly a half of all older people (65+) were in the lowest standard (money) income quintile, but the inclusion of imputed rent and social transfers in kind reduced that to 22.5% (because older Australians are disproportionately more likely to own homes and benefit from health services). By the time imputed wealth annuities were included in the analysis, only 17% of older people were in the lowest income quintile, while 26% were in the highest quintile (up from 7.1% when only disposable income was taken into account) (Naidoo, Appendix Tables C8-11) .

Implications

The data in Naidoo’s PhD is now dated (2010 HILDA income data), but the two key implications highlighted above are clear and remain relevant:

  • expenditure on public services has a major impact on reducing inequality, and
  • a more comprehensive income analysis changes who we see and understand as being on the lowest (extended) incomes, and potentially the most vulnerable.

The first point (and the quantification of that impact) is important because it not only makes a further case (beyond the direct health, education and other outcomes) for funding public services, but it may also add to our perception of tax-and-transfer policies. Progressive tax is usually understood as taxing high income earners at higher rates than those on lower incomes. This is a key instrument for limiting income inequality. However, the inclusion of social transfer in kind shows that spending this tax revenue on health, education and community supports is a further transfer from rich to poor.

That said, the second point above confounds the impact of progressive taxation. Since income taxation is largely based on money income, the amount of tax paid will depend partly on the type of income rather than the amount. Those with relatively higher cash incomes may pay much more income tax than those with significantly higher extended incomes but lower cash incomes. The money-based tax system does not follow households as they shift up and down the extended income spectrum and so some progressivity is lost (or mistargeted).

There are of course issue around taxing non-cash income as it would require a conversion of some wealth to cash to pay the tax. This itself may cause hardship – unless of course the wealth was maintained and simply taxed at realisation or end-of-life. Another argument for inheritance taxes – but I digress!

There is a further complication in the progressive tax story. A flat rate tax like the GST is generally regarded as regressive because it impacts disproportionately on lower-income households (who spend a great proportion of their income on GST-taxable consumption). While this is undoubtedly true, the fact that social transfers in kind disproportionately benefit those on low incomes, and that as a state tax the GST goes fairly directly to the provision of those services, provides something of an offset against its regressiveness – although this is probably only partial and not an argument for increasing or broadening the GST.

Finally, the movement of people up and down the extended income scale suggests that when we focus services and concessions on those in the lowest money (disposable) income brackets, we may in some cases be providing services and concessions to those who are relatively better-off, and missing out on people with fewer economic resources (just because all their resources are cash incomes). However, this is not as straightforward as the similar argument above about tax because in some cases it is the provision of concessions and services (i.e. social transfers in kind) which lifts households out of the lower income brackets. There may be a circularity in targeting based on extended incomes, but targeting services and concessions based only on money incomes is equally flawed.

The extended income analysis is not a policy panacea, but I think it does provide a better window on income distribution from which to do policy analysis.

Conclusion

My previous post asked for an analysis of inequality based on a broader understanding of income, one that combined income and wealth into one metric. Having now got data thanks to Yuvisthi Naidoo’s great work, I can see that my first estimate of increased inequality was wrong (because the impact of wealth inequality is offset by the progressive impact of social transfers in kind). But the analysis throws up a whole range of questions and challenges for progressive tax and transfer policies. The discussion above is only the beginning of an analysis, and it is fraught in an era when we must fight for even the basic principle of progressivity. In that sense, my take-home message from all this is: be careful what you wish for!

And yet …

I continue to believe that categories and statistics are socially/politically constructed, not neutral reflections of reality. Uncritically using definitions and statistics that are designed for other purposes or other theories limits our vision and the potential for change.

“Alarm Bells” or “Business as Usual” state budget

Today, Adelaide’s online newspaper, InDaily published an opinion piece from my boss, (SACOSS CEO) Ross Womersley. The piece is titled ‘Business as usual’ state budget won’t cut it, and is an analysis that basically says that SA’s economy is in trouble, and that we are becoming poorer as a community.

Cover photo and link to InDaily article "'Business as Usual' state budget won't cut it".

The article walks through the data that shows the state’s relative economic decline, and concludes that we need a 2023-24 state budget with a bold and interventionist approach, a budget that “provides a vision, strategy and, most importantly, investment on a new scale in industry and regional development, skills development (including raising levels of digital competency and inclusion), and population retention and attraction.” The alternative, as the conclusion makes clear, is decline and inequality.

It does not take a close reading of the InDaily piece to see echoes of my previous post on “Inequality Alarm Bells for South Australia“. It is ok. It is not plagiarism, or theft of intellectual property. More a ghostly presence at my workplace.

But remember, you heard it here first! 🙂

Industrial Relations, Income Flows and Inequality

With Australia’s new industrial relations law now through the federal parliament, there is talk of increased wages, or at least a hope that real wage increases will be possible with workers having better bargaining tools to try to secure them. After years of wage stagnation, obviously any increase in wage levels is welcome, but much of the public debate has focused on the need for wage increases to help households, especially low-income households, with increased cost of living. But increasing wages is also fundamentally important to macroeconomic income flows and equality.

In his landmark Captial in the Twentieth Century, Thomas Piketty noted that in many western countries inequality was increasing to levels unprecedented since the turn of the last century because the growth of capital incomes was outstripping wage incomes. Capital incomes are concentrated at the high end of income distribution, so the relative share of society’s income going to capital and to labour was a crucial determinant of inequality.

However, in a really interesting exchange after the release of Piketty’s book, political economist Anwar Shaikh argues that Piketty’s work focuses largely on the final distribution of income, but a far more nuanced understanding of inequality could be gained by tracing the primary, secondary and tertiary income flows which lead into that final distribution.

Shaikh argues that in a capitalist economy, production is based on the harnessing of labour power to produce new value from which capital can make a profit. Accordingly, the primary income distribution is that between labour and capital in the production process. At the macro level this primary distribution is captured in the structure of the national accounts where the income side of Gross Domestic Product is divided into compensation of employees (labour income) and gross operating surplus (capital income). In June 2022, labour received 44% of GDP, and the historically low labour share was one of the driving forces of the government’s Future Work Summit and the push industrial relation changes (see for instance the ACTU Job Summit Paper: An Economy that Works for People).

However, this primary distribution does not tell us the full story because from this primary distribution there are secondary distributions. Wages (compensation of employees) is split between taxes and disposable “take-home” income – an important distribution as progressive taxation is a significant factor in equalising take home wages from what is a much more unequal original distribution between wage earners. But gross operating surplus is also split into rents, royalties, profits, interest and taxes. These distributions are the property claims of different types of capital on the surplus income. The relative amounts of the secondary flows between these types of capital reflect the structure of production and the balance of class power within capitalism – so that, for instance, it has been suggested that finance capital has claimed most of the gains of neoliberal economic growth over the last 20 years.

Finally, there is the tertiary distribution which is redistribution of the taxes (taken in the secondary distribution) to households through transfer payments and to capital via subsidies and industry support. This is important because these social security transfers are the most visible face of “redistribution” and efforts to over-come inequality (e.g. campaigns to increase income support payments, or to provide public services). However, as we will see below, it is also the smallest of the distributions. While interventions in this space are necessary – especially for those outside of the circuits of capital and production income – they are also necessarily limited as the size of the tertiary flow is inevitably determined by the primary and secondary income flows.

The focus on primary, secondary and tertiary income flows arises out of classical and Marxian political economy, but they are difficult to quantify because our national accounts are based on Keynesian and neoclassical principles. Accordingly, the accounts do not necessarily record these flows. However, some data is available and is captured in the table below.

Income Distributions, Australia, June Quarter 2022

Table showing primary, secondary and tertiary income flows: GDP $609,133m Wages $268,573m Surplus $182,263m Total Taxes $174,807m Social Security Transfers $36,991m

Source ABS, Australian National Accounts, June Quarter 2022, Tables 7, 22, 23.

These numbers are important because they show the magnitude of the different income flows and the potential impact of changes in them. For instance, a 2 percentage point increase in the labour share of the economy (compensation of employees), which would return labour to the levels of twenty years ago, equates to a $12bn or 4.5% increase in total wages for the quarter. By contrast, even a 10% increase in social security payments (personal benefit transfers), would only see a $3.6bn redistribution of income.

Again, this is not to say that arguments for social security increases are unimportant, but it does emphasise the importance of industrial contestation over the primary income distribution. Or put another way, it emphasises the importance of class (income flows based on relationship to the means of production) to understanding inequality.

A similar argument could be made around gender. Applying the proportion of the total wage pool noted in a previous post to the above national accounts data, a 2 percentage point increase in the female wage share would equate to a $5.3bn (5.1%) increase in women’s wages in the quarter. Again, that is more than the total of social security transfers (which also disproportionately go to women). Arguably then, closing the gender pay gap or increasing women’s labour force participation is a more direct route to gender equality than social security payments – albeit with application to different women.

Obviously these class and gender arguments reprise my previous arguments about the importance of a structural approach to addressing inequality, but they may be particularly important as the labour movement goes forward with the campaigns under the new industrial relation system.

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.