I acknowledge the Ngunnawal people, on whose lands we meet today, and pay respects to all First Nations people present. My thanks to Peter Siminski for organising this important workshop on inequality and mobility, and to the Australian National University for hosting us.
There is a certain irony in speaking at a mobility workshop at the same institution where I was a professor 16 years ago. As my continued presence on this campus suggests, mobility is easier to theorise than to demonstrate.
In 1901, a young woman from the drought‑stricken New South Wales bush published a novel about a girl intelligent enough to see exactly why she was trapped, and powerless to escape. Miles Franklin called her book My Brilliant Career. The title was a joke. The career, at least in the novel, never comes. Sybylla Melvyn is clever, ambitious, full of force. Yet she inherits her father's debts, her mother's bitterness, and a social order that offers her one respectable ladder out: marriage to a wealthier man. She refuses it. So she remains where she began, with her eyes wide open, which is perhaps the cruellest version of the story.
Ninety years later, Tim Winton published Cloudstreet, with its 2 working‑class families in post‑war Perth, the Pickles and the Lambs, and in many ways he was asking the same question in a different key: does where you come from determine where you end up? Australian literature has worried at that question for as long as we have had a literature. Empirical economics arrived later, armed with big data and computing power, but it is still grappling with the same old problem.
That is the question I want to take up this morning. How much should a childhood predict?
For a politician, that is a question about fairness. For an economist, it is also a question about efficiency. If talent is spread widely but opportunity is packed tightly, then low mobility is unfair and wasteful. It means a society is failing twice. It is failing children whose prospects are narrowed by circumstances they did not choose. And it is failing itself, because it leaves ability half‑used and ambition underdeveloped.
My argument this morning is straightforward: mobility is a productivity policy.
Let me signpost where I am going. First, I want to sketch how economists have thought about intergenerational mobility, as the literature has developed over the past 40 years. Second, I will explain why mobility belongs in a conversation about productivity rather than being left in a separate moral compartment. Third, I will briefly discuss how the literature has widened from income alone to a richer account that includes health, place, networks and innovation. In doing so, I will briefly nod to some of the work that will be discussed later today. Finally, I want to draw out what that means for public policy in a country such as Australia.
1. Measuring mobility
In the Becker and Tomes (1986) tradition, intergenerational mobility is shaped by the interaction of family resources, parental investments, inherited endowments and the opportunities available to children as they move through education and the labour market. That framework remains influential because it treats persistence across generations as the result of mechanisms rather than fate. Families invest, institutions filter, luck intrudes and outcomes emerge from the interaction of all 3.
There are different measures of mobility. One concerns absolute mobility: do children earn more than their parents did at a comparable age? The other concerns relative mobility: how tightly are children's outcomes tied to their parents' position in the distribution? A society can have strong absolute mobility during a period of growth while still exhibiting sticky relative positions. The escalator can rise while families remain stubbornly close to the same rung.
The modern empirical literature also became more careful about measurement. Solon (1992) showed that earlier estimates of intergenerational mobility in the United States had often been biased upwards by measurement error and short income windows. Later work by Mazumder (2005), Haider and Solon (2006), and others reinforced the importance of measuring outcomes over an appropriate part of the lifecycle. That theme, incidentally, reappears in the newer wellbeing literature as well. Davis, Deutscher and Mazumder (2025) emphasise that different measures of wellbeing have different lifecycle profiles, so mobility estimates depend crucially on when and how those outcomes are observed.
Improved measures of mobility helped bust some popular myths. Free‑market advocates had argued that inequality was nothing to worry about because the same system also delivered high levels of social mobility. In fact, the opposite pattern prevailed. Economist Alan Krueger came up with the term 'the Great Gatsby Curve' for the fact that countries with higher income inequality tend to have lower intergenerational mobility (Krueger 2012; Corak 2013). More inequality, less social mobility (Andrews and Leigh 2009). As the rungs on the ladder stretch further apart, it becomes harder to climb up or down during a lifetime.
The Australian literature has developed rapidly, thanks to brilliant scholars (many of whom are in the room today) and high‑quality data. Early estimates using the HILDA dataset suggested that Australia was relatively immobile by international standards (Leigh 2007; Mendolia and Siminski 2016). More recent analysis, based on administrative data, paints a more optimistic picture. The Productivity Commission (2024), following the methodology of Deutscher and Mazumder (2020), looks at the birth cohort of 1976-82, and estimates an intergenerational earnings elasticity of 0.197 and a rank‑rank slope of 0.176. These are better figures that in most other advanced nations. They imply that in Australia, income is hereditable, but only about half as hereditable as height.
In terms of absolute mobility, the Productivity Commission estimates that 67 per cent of that Generation X cohort earned more than their parents did at a similar age. However, Kennedy and Siminski (2022) find that absolute income mobility in Australia was even higher in the past, with over 80 per cent of baby boomers enjoying higher incomes than their parents. They find that the drop in absolute mobility was due to rising inequality (one‑third) and falling growth (two‑thirds).
That mix of optimism and unfinished business is one reason mobility deserves sustained attention. We are not dealing with a closed caste system. We are dealing with a society that performs decently by international standards yet still leaves too much talent stranded.
2. Why mobility matters for growth
Why call mobility a productivity policy?
A new OECD synthesis on intergenerational social mobility (Causa, Nguyen and Tanaka 2026) argues that reducing barriers to mobility can be justified on equity grounds and efficiency grounds, because helping individuals reach their potential supports innovation and productivity. That is a useful corrective to the tendency of some commentators to suggest we have to choose between growth and fairness. In this case, there is not an equity‑efficiency trade‑off.
A productive economy discovers talent. It nurtures talent. It matches talent to the places and tasks where it can do the most good. When these mechanisms are weak, we see less mobility.
An immobile society weakens discovery because children from poorer backgrounds are less likely to be exposed to the environments in which ability is noticed and cultivated. It weakens development because unequal access to health, schooling, mentoring and stable housing alters the formation of capabilities over the life course. And it weakens matching because labour markets, housing markets and social networks channel able people away from the occupations and places where their talents would be most productive.
In simple terms, low mobility is an allocative failure. Economists are accustomed to thinking about misallocation across firms, sectors and technologies. Intergenerational mobility asks us to think about misallocation across people. How much potential is left dormant because a child doesn't get good healthcare, a good school, good mentors, good networks or simply a kind word when they need it most?
That question grows more important as economies become more knowledge‑intensive. In a world where value increasingly depends on ideas, judgment and specialised skills, a society that draws talent from a narrow social base is like a team running onto the field with half its players left behind in the changing rooms.
3. Mobility beyond the pay packet
One reason the field has become more interesting over time is that it has become less narrow.
For many years, the central focus was income, education and occupation. Those remain important. Yet the literature has moved well beyond them. Davis, Deutscher and Mazumder (2025) survey the growing work on intergenerational mobility in health, consumption and life satisfaction, arguing that mobility research has expanded outside the traditional outcomes and that this broader conception may reveal weaknesses in some of our inherited approaches. Since Nathan Deutscher and Bhash Mazumder will both be part of today's program, that expanded focus will be very much in the room.
That broader perspective is valuable in its own right, but it also bears on the productivity argument. Human capital is shaped by health, security and stress as well as schooling. A child who arrives at school with chronic illness, unstable housing or untreated trauma faces a harder path to skill development than a child with the same raw ability and fewer impediments. In that sense, a productivity agenda begins well before the labour market - and perhaps before the first lunchbox. It begins in the production of capabilities.
The literature on health mobility is still young, but it is already suggestive. The Davis, Deutscher and Mazumder survey notes that intergenerational persistence in health often appears lower than persistence in income, though measurement remains challenging. And later today, Bhash Mazumder will present Swedish evidence linking parental health, income and education to the health trajectories of children over the life course, with especially strong early‑life effects from maternal health and rising intergenerational health correlations later in adulthood (Fischer et al. 2026).
The literature on place has made a similar contribution. Following the same methodology as the US study of Chetty and Hendren (2018), Deutscher (2020) uses de‑identified tax data to analyse outcomes for Australian children born between 1978 and 1991. Studying more than 300,000 instances in which families moved during childhood, he concludes that where an Australian child grows up has a causal effect on their adult income, education, marriage and fertility. The effect of exposure to place is typically largest during the teenage years, and there is suggestive evidence that peers play a key role. A feature of both Chetty and Hendren (2018) and Deutscher (2020) is the role of childhood exposure. Moving earlier to a better neighbourhood yields larger gains than moving later. This suggests that opportunity is not merely a property of individuals or households. It is also a property of local environments.
We parents sometimes tell our children the maxim that they are the average of their closest friends. And the data seem to bear this out. Analysing 21 billion Facebook friendships, Chetty et al. (2022) find that economic connectedness, meaning the extent to which low‑income and high‑income people are friends with one another, is a particularly strong predictor of upward mobility. Neighbourhoods with more cross‑class connection generate higher rates of mobility for children from poorer families.
In Australia, Anna Zhu's paper on peer effects in social housing, which will be presented this afternoon, builds on the international literature (Broadway, Humes and Zhu 2026). Using rich administrative data, it suggests that peers matter for labour market participation and earnings, and that information‑sharing about job search is one plausible mechanism, especially for people born outside Australia and those from non‑English‑speaking backgrounds. It is a reminder that opportunity is partly about income, but partly about who tells you which door to knock on.
The innovation literature delivers perhaps the strongest productivity case of all. Bell et al. (2019) show that children born to parents in the top 1 per cent of the income distribution are 10 times as likely to become inventors as those born to families with below‑median incomes. Those gaps persist even among children with similar mathematics performance in early life.
In Australia, evidence from the Startup Muster survey indicates that women comprise 1 in 3 founders and founders from lower socioeconomic backgrounds just 1 in 9 (Hurps 2026). We might think of those who miss out as lost Fiona Woods and Elizabeth Blackburns: children with the native ability to transform burns medicine or molecular biology, whose path never crosses the classroom, mentor or laboratory that would have let that ability flower. That's not just a gross inequity, it's a productivity loss, pure and simple.
That's why anyone who wants to boost productivity needs to read the mobility literature. Because it debunks the Horatio Alger myth that talent naturally rises. Sometimes it does. Often it does not. Exposure matters. Place matters. Health matters. Networks matter. A society that wants stronger productivity growth cannot afford to shrug at any of those channels.
A further strand of the literature examines how beliefs about mobility and inequality shape attitudes to redistribution. Alesina, Stantcheva and Teso (2018) show that perceptions of mobility influence policy preferences. Christopher Hoy and his co‑authors (Hoy et al. 2026), in a paper on today's program, add a new dimension: across 6 high‑income countries, they find that people tend to underestimate wage inequality. Correcting those misperceptions has limited average effects. But there is one exception: telling far‑right respondents the correct level of wage inequality causes their support for redistribution rises markedly. With right‑wing populism on the rise, this insight matters.
4. What a mobility agenda looks like
So what follows, if we take seriously the claim that mobility is a productivity policy?
The first implication is that the early years deserve economic attention, not merely social sympathy. In the Becker and Tomes (1986) framework, parental investments loom large. Later work on human capital formation deepens that point. Capabilities accumulate. So do setbacks. A serious mobility agenda therefore begins with maternal health, early childhood education, family support and preventive care. These are long‑run productivity investments because they shape the stock of human capability from which the economy will later draw.
The second implication is that housing and transport policy belong inside the mobility frame. Once we take place seriously, access to high‑opportunity neighbourhoods becomes economically salient. If good schools, useful networks and better jobs are concentrated in particular places, then the price of entry to those places becomes a price on opportunity itself.
Here again, today's program offers a useful Australian inflection. Siminski and Wilkins's housing paper argues that once imputed rent and accrued capital gains are counted in the income base, the apparent redistributive impact of income tax is reduced by around 40 per cent. That is a reminder that housing is not just shelter, or even just wealth. It is a mechanism through which advantage is stored, magnified and passed across generations. Building more homes is fundamental to improving social mobility.
The third implication is that education policy should be thought about more broadly than test scores and credential counts. Bell et al. (2019) point toward the importance of exposure to innovators. Chetty et al. (2022) point toward the importance of connection. That suggests a richer agenda: strong schools, certainly, but also mentoring, internships, enrichment, links between universities and communities, and deliberate efforts to widen the social map available to children from modest backgrounds. A highly able child does not benefit from talent alone. They also benefit from seeing a future self that feels imaginable.
That point is especially relevant in a migrant society. Cecilia Karmel's paper later today asks how intergenerational income mobility differs between children of migrants and those with Australian‑born parents, with attention to variation across groups and to gender (Karmel 2026). That is exactly the kind of work a serious mobility agenda needs: not a single national average, but a closer look at where persistence differs and why.
The fourth implication is that open markets and open opportunity sit comfortably together. Competition policy is rarely discussed in the same breath as intergenerational mobility, yet there is a kinship between them. Both are arguments against incumbency. Both ask whether entry is too difficult. Both worry about systems in which inherited advantage entrenches itself.
The fifth implication is that mobility research should make us more careful about what we infer from persistence alone. Peter Siminski and Nathan Deutscher's brilliant draft‑lottery paper is valuable here. It shows that veterans' children were more exposed to parental welfare receipt and were more likely to receive welfare as young adults, yet the authors find very few lasting effects on later‑life outcomes, with direct estimates of causal intergenerational welfare elasticities close to zero. This helps debunk the critique that providing a decent social safety net risks condemning children to a life of welfare dependency. It reminds us that correlation is not causation, and that policy should be built around careful identification rather than folklore.
The final implication is about measurement. Much of today's workshop would have been impossible without the huge advances over the past decade in de‑identified panel datasets such as the Person Level Integrated Data Asset (PLIDA). Investment in these data assets is a classic public good and has delivered huge returns to the community in terms of how we understand topics such as inequality and mobility. Much of it has been driven by the Australian Bureau of Statistics under the leadership of David Gruen, engaging with researchers in this room.
To give you a sense of the scale of the enterprise, as at 31 March 2026, there were 443 active projects in the DataLab accessing integrated data and standard microdata. There were 2,058 unique active researchers on DataLab projects, including 19 international researchers. PLIDA's 70,000‑plus variables link things you do not usually see in one place: visa pathways and traveller movements; apprenticeships, university study and payroll records; tax returns, rental property income and superannuation; Medicare claims and medicine purchases; disability supports, aged care and Centrelink; and even births, deaths and early childhood development. Work is underway to link more housing, wealth and other financial data to enable analysis of expenditure patterns. Integrated data also lowers the cost of running randomised trials by allowing researchers to track long‑term outcomes in administrative data rather than conducting expensive surveys.
That point matters because the public debate can be too blunt. We often ask whether Australia is a fair country in a general, sentimental way. A better question is more precise: how much does family background predict adult outcomes in this domain, through this mechanism, in this place? Once the question is posed that way, policy becomes less rhetorical and more exacting.
5. Conclusion
Let me return, in closing, to Sybylla Melvyn.
What gives My Brilliant Career its power is not poverty alone. It is the spectacle of thwarted capacity. Sybylla can see the size of her own mind. She can also see the bars around it. Like so many literary heroines, her story is one of brilliance, trapped.
Economists now study that problem with administrative panel data and clever identification strategies. Yet the basic question is much the same. How often does a society place bars around bright minds? How much talent goes missing because childhood circumstances press too hard on adult life?
My answer today is that this is not merely a question for moral philosophy or social policy. It is also a question for productivity analysis. A country is more productive when it is better at discovering ability, developing ability and deploying ability. Every barrier that keeps talent from rising also keeps the economy from growing.
Mobility is a productivity policy because wasted ability is inefficiency with a human face.
It is a productivity policy because a society that draws on the full range of its people will usually be fairer, and usually be richer too.
And it is a productivity policy because the distance between a clever child and a flourishing adult should depend less on parental income, postcode or social network, and more on effort, imagination and the institutions that help talent find its way.
References
Alesina A, Stantcheva S and Teso E (2018) 'Intergenerational mobility and preferences for redistribution', American Economic Review, 108(2):521-554.
Andrews D and Leigh A (2009), 'More inequality, less social mobility', Applied Economics Letters, 16(15):1489-1492.
Becker GS and Tomes N (1986) 'Human capital and the rise and fall of families', Journal of Labor Economics, 4(3, Part 2):S1-S39.
Bell A, Chetty R, Jaravel X, Petkova N and Van Reenen J (2019) 'Who becomes an inventor in America? The importance of exposure to innovation', Quarterly Journal of Economics, 134(2):647-713.
Broadway B, Humes H and Zhu A (24 April 2026) 'Peer effects in social housing: Evidence from Australia' [conference paper], Inequality and Intergenerational Mobility Workshop, Australian National University, Canberra.
Causa O, Nguyen M and Tanaka T (2026) 'Intergenerational social mobility across OECD countries: Does the apple fall far from the tree?', OECD Economics Department Working Papers, No. 1858, OECD Publishing, Paris.
Chetty R and Hendren N (2018) 'The impacts of neighborhoods on intergenerational mobility II: County‑level estimates', Quarterly Journal of Economics, 133(3):1163-1228.
Chetty R, Jackson MO, Kuchler T, Stroebel J, Hendren N, Fluegge RB, Gong S, Gonzalez F, Grondin A, Jacob M, Johnston D, Koenen M, Laguna‑Muggenburg E, Mudekereza F, Rutter T, Thor N, Bailey M, Barberá P, Bhole M, Wernerfelt N, Townsend W and Zhang R (2022) 'Social capital I: Measurement and associations with economic mobility', Nature, 608:108-121.
Corak M (2013) 'Income inequality, equality of opportunity, and intergenerational mobility', Journal of Economic Perspectives, 27(3):79-102.
Davis J, Deutscher N and Mazumder B (2025) Intergenerational mobility in measures of wellbeing: Consumption, health and life satisfaction, NBER Working Paper No. 34407. Cambridge, MA: National Bureau of Economic Research.
Deutscher N (2020) 'Place, peers, and the teenage years: long‑run neighborhood effects in Australia'. American Economic Journal: Applied Economics, 12(2):220-249.
Deutscher N and Mazumder B (2020) 'Intergenerational mobility across Australia and the stability of regional estimates', Labour Economics, 66:101861.
Deutscher N and Siminski P (2026) 'Intergenerational welfare persistence: Causal evidence from Australia's Vietnam‑era draft lotteries', unpublished manuscript, 30 March.
Fischer M, Halliday T, Karlsson M, Lleras‑Muney A and Mazumder B (24 April 2026) 'The production of health from cradle to grave: Evidence from Sweden' [conference paper], Inequality and Intergenerational Mobility Workshop, Australian National University, Canberra.
Franklin M (1901) My Brilliant Career, Edinburgh: William Blackwood and Sons.
Haider S and Solon G (2006) 'Life‑cycle variation in the association between current and lifetime earnings', American Economic Review, 96(4):1308-1320.
Hoy C, Page L, Eckel C, Grossman P and Goldstein D (2025) Political polarization, wage inequality and preferences for redistribution, Melbourne Institute Working Paper No. 08/25. Melbourne: Melbourne Institute of Applied Economic and Social Research.
Hurps M (2026) Startup Muster 2025 Report, UTS Sydney.
Karmel C (24 April 2026) 'Intergenerational income mobility of migrants in Australia' [conference paper], Inequality and Intergenerational Mobility Workshop, Australian National University, Canberra.
Kennedy T and Siminski P (2022) 'Are we richer than our parents were? Absolute income mobility in Australia', Economic Record, 98(320):22-41.
Krueger AB (12 January 2012), 'The Rise and Consequences of Inequality in the United States' [speech].
Leigh A (2007) 'Intergenerational mobility in Australia', The B.E. Journal of Economic Analysis & Policy, 7(2).
Mazumder B (2005) 'Fortunate sons: New estimates of intergenerational mobility in the United States using Social Security earnings data', Review of Economics and Statistics, 87(2):235-255.
Mendolia S and Siminski P (2016) 'New Estimates of Intergenerational Mobility in Australia', Economic Record, 92(298):361-373.
Productivity Commission (2024) Fairly equal? Economic mobility in Australia. Research paper. Canberra: Productivity Commission.
Siminski P and Wilkins R (2025) Housing, income inequality and progressivity of taxes and transfers. IZA Discussion Paper No. 18303. Bonn: IZA Institute of Labor Economics.
Solon G (1992) 'Intergenerational income mobility in the United States', American Economic Review, 82(3):393-408.
Winton T (1991) Cloudstreet. Melbourne: McPhee Gribble.
