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.
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.
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.