Human capitalism and trends in US income inequality
I would be the first to admit that I have an absolutely fantastic job. Sure, it is cathartic and sometimes strategic to complain about university bureaucracy and the endless search for funding. However, the reality is that to be paid to read, write and speak about ideas, data and policy is a privilege not available to many.
I had occasion to reflect on this recently when I was reading a new book just published by Princeton University Press titled Human Capitalism: How Economic Growth Has Made Us Smarter – and More Unequal. The author, Brink Lindsey, is a senior fellow at the Cato Institute, a think-tank in the US that markets itself as being ‘dedicated to the principles of individual liberty, limited government, free markets and peace.‘
Before getting to the main substance of the book, it is worth outlining a few trends in the US. The first is that most evidence would suggest that inequality in the US has been rising over the last few decades, especially at the upper end of the distribution. For example, the Congressional Budget Office (CBO) found that ‘between 1979 and 2007, income grew by 275 percent for the top 1 percent of households, 65 percent for the next 19 percent, just under 40 percent for the next 60 percent, and 18 percent for the bottom 20 percent.’
In another summary finding, the authors of the CBO paper demonstrated that most of that growth was due to a widening level of inequality in market income (that is, before taxes and benefits are taken into account). Using the Gini coefficient, a summary measure of income inequality that ranges from 0 (perfect inequality) to 1 (perfect inequality), inequality in market income rose from 0.479 in 1979 to 0.590 in 2007. This ‘great divergence’ is in contrast to the ‘great convergence’ in incomes that took place in the immediate post-war period.
So, what is Lindsey’s explanation for this rising inequality? In essence, it is an interesting version of ‘skills-biased technological change’ which is a ‘shift in the production technology that favours skilled over unskilled labour by increasing its relative productivity and, therefore, its relative demand‘ and ‘assortative mating’ where individuals with similar characteristics (in this case education) are becoming more likely to partner with each other. Where Lindsey differs somewhat in his explanation is a focus on the role of complexity in explaining these trends. The following few quotes should hopefully summarise his argument:
- ‘today the primary determinant of socioeconomic status is the ability to handle the mental demands of a complex social environment’ – p.3
- ‘economic growth breeds complexity, complexity imposes increasingly heavy demands on our mental capabilities, and people respond by making progressively greater investments in human capital’ – p. 4
- ‘Among families with college-educated parents, marriages have been growing more stable, parents have dramatically increased the time they spend with their kids, and they are spending that time in a dedicated effort to foster the intellectual, social, and personal skills needed to thrive in an ever more complicated world.’ – p.66
Although I am not completely convinced by all the policy prescriptions at the end of the book, it is an interesting argument. Which got me thinking about what is happening in Australia with regards to education and income inequality.
Changes in income inequality in Australia
The first thing to note about the changes in income inequality in Australia over recent decades is that they are less dramatic and, unfortunately, less consistent across data sources than they are in the US. It would appear from an analysis by Roger Wilkins of the Melbourne Institute that measured inequality using the Australian Bureau of Statistics’ income surveys that the Gini coefficient in Australia rose from a little over 0.30 in 1993/94 to a little under 0.34 in 2008/09. However, there have been a number of revisions to the methodology used by the ABS to collect income which may be driving some of these trends.
When you use a more consistent set of income measures (albeit over a shorter time period) in the Household Income and Labour Dynamics in Australia (HILDA) survey, income inequality seems much more stable. This is demonstrated in the following figure which gives the Gini coefficient for personal disposable income (that is, after taking into account taxes and benefits) as well as household equivalised disposable income (after also taking into account household pooling of income).1
Household surveys like the HILDA are a little restrictive as to what they can say about the extremes of the distribution. It is important to note, therefore, that Wilkins also shows using Tax and National Accounts data that the share of income going to the top 1% of the income distribution rose from at little over 6% in 1989/90 to around 8.5% in 2009/10. Nonetheless, it would appear that Australia has not experienced as large a rise in income inequality over the last few decades as the US.
The contribution of education to income inequality in Australia
What then can we say about the contribution of variation in education in Australia to income inequality? Well, the first thing to note is that according to the HILDA survey (and all other datasets), education levels in Australia increased substantially between 2001 and 2010. This is demonstrated in the following figure which gives the weighted per cent of the 2001 HILDA sample by their education level (in black) alongside the same figure for the 2010 HILDA sample.
The per cent of the (weighted) sample who had completed Year 9 or less fell over the 9 years between Wave 1 and Wave 10 of the survey. This fall was greatest amongst those who also did not have any qualifications. There was also a fall amongst those who had completed Year 10 or 11 but did not have any qualifications. At the other end of the distribution, there were large increases in those who had completed Year 12, as well as those with a bachelor or post-graduate degree.2
Between 2001 and 2010, all education types experienced an increase in average income. This is demonstrated in the following figure which gives the percentage change in average disposable personal income over the period (in black) as well as the percentage change in average disposable household equivalised income (in grey).
Between 2001 and 2010, average personal disposable income for those in the HILDA who had completed Year 9 or less increased by only a small amount (7.7 per cent). Increases were greater for all other education groups, particularly for those with Year 12 and a non-degree (other) qualification (22.8 per cent), those with Year 10 or 11 and a non-degree qualification (21.3 per cent) and those with a post-graduate degree (20.3 per cent).
One way to look at the net effect of such changes is through a decomposable measure of income inequality (the Theil index) which apportions income inequality into the amount of inequality within education levels and income inequality between education levels. I used such a technique in a recent post on inequality within and between urban areas.
In the case of education, the within component is each education group’s inequality level, weighted by their contribution to total income and summed across the population. When I decompose inequality within and between education groups in HILDA, I find a very stable within component. In 2001, it was 0.297 for disposable personal income and 0.155 for disposable equivalised household income. In 2010, the figures were 0.301 and 0.152 respectively. Despite rapid income growth between 2001 and 2010, variation in income within a particular level of education does not seem to have changed too much.
There was, however, a slight increase in inequality between groups. Here, the between component can be thought of as what the overall inequality level would be if there was no variation in income within each education grouping. In 2001, it was 0.053 for disposable personal income and 0.020 for disposable equivalised household income. In 2010, the figures were 0.057 and 0.021 respectively. The increase in between-group income inequality over the decade suggests a small but important widening of education-based income inequality. It may not be as large as what has occurred in the US and documented in Human Capitalism, but it is noticeable in the HILDA survey.
Income inequality in a counterfactual Australia
In a paper that I had published recently in Education Economics (paywall), I showed that youth appear to respond to localised returns to education and are more likely to participate in education in areas where predicted returns are highest. For all its flaws, one of the great insights from economics is that individuals respond to incentives. Responses may not always be completely rational, but incentives matter. It would appear from the data presented here that this is also occurring at the national level, thereby minimising the effect of skills-biased technological change on inequality in Australia.
To highlight this, it is worth exploring what would have happened to inequality in the absence of such a change by setting up a counterfactual scenario. Specifically, what would income inequality in Australia be like in 2010 if the observed changes in income had occurred, but they did so alongside a static education distribution? The answer appears to be that inequality would have increased at a much faster rate than it actually did.
Focusing on personal disposable income, total inequality in 2001 according to the Theil index was 0.350. In 2010, it was 0.358, a small increase. However, if the education distribution had stayed the same (but income increased as it did), then inequality in 2010 would have been 0.369, a much greater increase than was actually observed over the period. Furthermore, this increase in inequality would have been driven by a 13.2 per cent increase in the between inequality component.
Taken together, two major trends in Australia seem to be mostly cancelling out, thereby leading to relatively stable income inequality. Although income was growing at a faster rate at the upper end of the education distribution, more people in the most recent data have those higher levels of education.
Despite trends being quite different in Australia, there are still a number of important insights from Human Capitalism. The first, which I haven’t touched on, is the impact of inter-generational income inequality. We don’t have good data on this across the lifecourse, but analysis clearly shows that those children in Australia from relatively disadvantaged backgrounds tend to have worse educational outcomes. The argument made by Lindsey is that this is in part due to complexity. It is worth exploring this in Australia because, even if overall inequality has stayed reasonably constant, if your parent’s income or education level is the major determinant of your own income or education, then this is a clear policy issue.
The second key point from Human Capitalism is that while important, income isn’t everything. It is true that the tax and transfer system in Australia reduces income inequality quite considerably. For example the Gini coefficient for gross personal income in Australia (0.493) was much higher than for gross disposable income (0.444) when the progressive tax system is taken into account. But public policy has fewer levers when it comes to autonomy and satisfaction in a person’s work. That is why I raised how much I love my job at the start of this post.
My education has given me the opportunity to do interesting work which makes me feel like I contribute to a small extent to society and our intellectual knowledge of the world. The unequal distribution of work fulfillment is an interesting and important avenue of future inquiry.
- In the analysis of HILDA, population weights are used and negative and zero incomes are set to $1 (to enable their inclusion in log-based inequality calculations). Varying this has no effect on overall conclusions.
- A small proportion of the sample had a degree without having completed Year 12. They were grouped with other degree holders for convenience.