library(tidyverse)
library(plotly)
library(scales)
# The Conversation-inspired palette
# Core style: mostly black, white and neutral grey, with tomato used sparingly for emphasis.
tc_black <- "#000000"
tc_charcoal <- "#383838"
tc_casper <- "#f1f1f2"
tc_tomato <- "#d8352a"
tc_white <- "#ffffff"
tc_indigo <- "#29339b"
tc_emerald <- "#1f7a5c"
theme_conversation <- function() {
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", colour = tc_black, size = 15),
plot.subtitle = element_text(colour = tc_charcoal, size = 10.5),
axis.title = element_text(colour = tc_charcoal),
axis.text = element_text(colour = tc_charcoal),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_line(colour = "#e6e6e6"),
panel.grid.major.y = element_blank(),
legend.position = "bottom",
legend.title = element_blank(),
plot.caption = element_text(colour = tc_charcoal, size = 8.5, hjust = 0),
plot.background = element_rect(fill = tc_white, colour = NA),
panel.background = element_rect(fill = tc_white, colour = NA)
)
}In Australia we still tell young people that education is the road to opportunity. In many ways, that promise is still true: qualifications are still associated with better employment outcomes. But for students and recent graduates the road from study to stability is no longer a clear one.
The visual story looks at three pressures that sit on top of each other - education, employment and housing. The point is not that Australia is no longer a land of opportunity. The tougher question is whether opportunity is still becoming security for young people who study, work and try to build an independent life.
Hover over each chart to explore the values behind the story.
Education still benefits employment outcomes The Australian Bureau of Statistics said in May 2025 that 80% of people aged 15-74 who had a non-school qualification were employed, compared with 58% of those without a non-school qualification. Of those who completed a non-school qualification in 2024, 84% were employed in 2025.
qualification_employment <- tibble(
group = c(
"With non-school qualification",
"Without non-school qualification",
"Completed qualification in 2024"
),
employment_rate = c(80, 58, 84),
note = c(
"People aged 15-74 with a non-school qualification",
"People aged 15-74 without a non-school qualification",
"People who finished a non-school qualification in 2024 and were employed in 2025"
)
)
p1 <- ggplot(
qualification_employment,
aes(
x = reorder(group, employment_rate),
y = employment_rate,
text = paste0(note, "<br>Employment rate: ", employment_rate, "%")
)
) +
geom_col(fill = tc_charcoal, width = 0.6) +
geom_text(aes(label = paste0(employment_rate, "%")), hjust = -0.15, size = 4) +
coord_flip() +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(0, 95)) +
labs(
title = "Qualifications still matter",
subtitle = "Employment rates are higher for Australians with non-school qualifications.",
x = NULL,
y = "Employment rate",
caption = "Source: Australian Bureau of Statistics, Education and Work, Australia, May 2025."
) +
theme_conversation()
ggplotly(p1, tooltip = "text", width = 600, height = 420) %>%
config(displayModeBar = FALSE)What to notice: There is a big employment gap between people with and without non-school qualifications. Education holds real promise, but it is just the beginning.
The charts below show why the promise is now accompanied with more strings attached.
The education to work pathway is not the same for all of Australia. In 2025, people aged 15-24 in major cities were more likely to be fully engaged in work or study than people in inner regional, outer regional and remote areas.
youth_engagement <- tibble(
area = rep(c("Major Cities", "Inner Regional", "Outer Regional and Remote"), each = 3),
engagement = rep(c("Fully engaged", "Partially engaged", "Not engaged"), times = 3),
percent = c(83.6, 9.7, 6.8, 75.2, 14.5, 10.6, 74.7, 11.7, 13.0)
)
youth_engagement$engagement <- factor(
youth_engagement$engagement,
levels = c("Fully engaged", "Partially engaged", "Not engaged")
)
p2 <- ggplot(
youth_engagement,
aes(
x = area,
y = percent,
fill = engagement,
text = paste0(area, "<br>", engagement, ": ", percent, "%")
)
) +
geom_col(position = position_dodge(width = 0.75), width = 0.68) +
scale_fill_manual(values = c(tc_charcoal, tc_indigo, tc_tomato)) +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(0, 90)) +
labs(
title = "Young people outside major cities face a wider engagement gap",
subtitle = "The share not engaged in work or study is highest in outer regional and remote areas.",
x = NULL,
y = "Share of people aged 15-24",
caption = "Source: Australian Bureau of Statistics, Education and Work, Australia, 2025, Table 15."
) +
theme_conversation() +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
ggplotly(p2, tooltip = "text", width = 600, height = 450) %>%
config(displayModeBar = FALSE)What to notice: In outer regional and remote areas there is almost double the level of non-engagement compared to major cities.
This gets more complex here. It’s not just about whether young people study. It’s also about the places they live and the opportunities they have.
The level of engagement in work or study among young people also varies across states and territories. In 2025, the Australian Capital Territory recorded the highest full engagement rate for persons aged 15-24 years, whereas Tasmania had the lowest.
state_engagement <- tibble(
state = c("NSW", "Vic.", "Qld", "SA", "WA", "Tas.", "NT", "ACT", "Aust."),
fully_engaged = c(83.5, 82.7, 78.9, 79.5, 80.4, 75.4, 79.6, 84.3, 81.5)
) %>%
mutate(
highlight = if_else(state %in% c("ACT", "Tas."), "Key point", "Other"),
label = paste0(state, "<br>Fully engaged: ", fully_engaged, "%")
)
p3 <- ggplot(
state_engagement,
aes(
x = reorder(state, fully_engaged),
y = fully_engaged,
fill = highlight,
text = label
)
) +
geom_col(width = 0.65) +
geom_text(aes(label = paste0(fully_engaged, "%")), hjust = -0.1, size = 3.4) +
coord_flip() +
scale_fill_manual(values = c("Key point" = tc_tomato, "Other" = tc_charcoal)) +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(0, 90)) +
labs(
title = "The youth pathway is uneven across states and territories",
subtitle = "Full engagement in work or study ranges from 75.4% in Tasmania to 84.3% in the ACT.",
x = NULL,
y = "Fully engaged in work and/or study",
caption = "Source: Australian Bureau of Statistics, Education and Work, Australia, 2025, Table 16."
) +
theme_conversation() +
guides(fill = "none")
ggplotly(p3, tooltip = "text", width = 600, height = 430) %>%
config(displayModeBar = FALSE)What to notice: The gap between Tasmania and the ACT isn’t huge, but it matters because it compounds with labour market access and housing costs.
For a graduate, location can shape the early-career experience. A qualification may open doors, but those doors are not placed evenly across the country.
In February 2026, there were 337,900 job vacancies in Australia. Vacancies in the private sector increased from November 2025, while those in the public sector fell slightly. The labour market is not closed, but it is not a simple story.
job_vacancies <- tibble(
sector = c("Private sector", "Public sector", "Private and public sectors"),
vacancies_thousand = c(299.0, 39.0, 337.9),
quarterly_change = c(3.2, -0.7, 2.7),
yearly_change = c(3.6, 4.7, 3.7)
)
p4 <- ggplot(
job_vacancies,
aes(
x = reorder(sector, vacancies_thousand),
y = vacancies_thousand,
fill = sector,
text = paste0(
sector,
"<br>Vacancies: ", vacancies_thousand, " thousand",
"<br>Quarterly change: ", quarterly_change, "%",
"<br>Yearly change: ", yearly_change, "%"
)
)
) +
geom_col(width = 0.6) +
geom_text(aes(label = paste0(vacancies_thousand, "k")), hjust = -0.15, size = 4) +
coord_flip() +
scale_fill_manual(values = c(tc_charcoal, tc_indigo, tc_tomato)) +
scale_y_continuous(limits = c(0, 370)) +
labs(
title = "Australia still has vacancies",
subtitle = "But the public and private sectors are moving differently.",
x = NULL,
y = "Job vacancies, February 2026 ('000)",
caption = "Source: Australian Bureau of Statistics, Job Vacancies, Australia, February 2026."
) +
theme_conversation() +
guides(fill = "none")
ggplotly(p4, tooltip = "text", width = 600, height = 420) %>%
config(displayModeBar = FALSE)What to notice: private-sector vacancies are doing most of the heavy lifting. That means graduate opportunity depends on where private demand is actually appearing.
This matters because graduates do not enter a single national labour market. They enter specific industries, occupations and locations, and those parts of the labour market do not all move in lockstep.
The last chart brings the story together by comparing two conditions that matter to young people at the same time: connection to work or study, and rental pressure. The chart links state/territory youth engagement with the nearest capital-city rent measure where public figures use different geographies. It’s a visual pressure measure, not an all-encompassing affordability index.
graduate_pressure <- tibble(
place = c("Australia", "Western Australia / Perth", "ACT / Canberra", "Tasmania / Hobart"),
youth_fully_engaged = c(81.5, 80.4, 84.3, 75.4),
rent_change = c(5.5, 8.9, 0.6, -1.3),
interpretation = c(
"National reference point",
"High rental pressure despite solid youth engagement",
"Strong youth engagement and low rent growth",
"Lower youth engagement, but rents paid fell over the period"
)
)
p5 <- ggplot(
graduate_pressure,
aes(
x = rent_change,
y = youth_fully_engaged,
label = place,
text = paste0(
"<b>", place, "</b>",
"<br>Youth fully engaged in work/study: ", youth_fully_engaged, "%",
"<br>Change in rents paid: ", rent_change, "%",
"<br>", interpretation
)
)
) +
geom_hline(yintercept = 81.5, colour = "#d6d6da", linewidth = 0.5) +
geom_vline(xintercept = 5.5, colour = "#d6d6da", linewidth = 0.5) +
geom_point(aes(size = abs(rent_change), colour = place), alpha = 0.9) +
geom_text(nudge_y = 0.8, size = 3.4, colour = tc_charcoal) +
scale_colour_manual(values = c(
"Australia" = tc_charcoal,
"Western Australia / Perth" = tc_tomato,
"ACT / Canberra" = tc_indigo,
"Tasmania / Hobart" = tc_emerald
)) +
scale_size_continuous(range = c(4, 10)) +
scale_x_continuous(labels = function(x) paste0(x, "%"), limits = c(-2, 10)) +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(72, 87)) +
labs(
title = "Where opportunity and housing pressure collide",
subtitle = "Youth engagement is not enough if rent growth absorbs the benefit of early-career work.",
x = "Change in rents paid, Q1 2024 to Q1 2025",
y = "People aged 15-24 fully engaged in work and/or study",
caption = "Sources: ABS Education and Work, Australia, 2025, Table 16; AIHW Housing affordability, 2025. State youth engagement is paired with capital-city rent change where available."
) +
theme_conversation() +
guides(colour = "none", size = "none")
ggplotly(p5, tooltip = "text", width = 600, height = 450) %>%
config(displayModeBar = FALSE)That’s why this final comparison is about more than just education or jobs. A young person could be doing all the right things - studying, working, staying engaged - but still find the housing market making independence feel like it’s on hold. Perth is the most obvious example of this pressure: the employment pathway might still be there but rent growth can squeeze the space young people need to convert work into security.
There’s still real opportunity for young graduates in Australia. Education still leads to better employment outcomes and vacancies still exist in the labour market. But the data suggest that the road from study to stability has become more conditional.
“Now the question is not if young people study or if jobs are available. The harder question is whether education, employment and housing still fit together well enough to allow young people to build stable lives.
That’s why the “lucky country” might not feel so lucky at the moment, depending on where a graduate lives, what they studied, and how much of their income is eaten up before they can even think about saving.
Australian Bureau of Statistics. (2025). Education and Work, Australia, May 2025. https://www.abs.gov.au/statistics/people/education/education-and-work-australia/latest-release
Australian Bureau of Statistics. (2026). Job Vacancies, Australia, February 2026. https://www.abs.gov.au/statistics/labour/jobs/job-vacancies-australia/latest-release
Australian Institute of Health and Welfare. (2025). Housing affordability. https://www.aihw.gov.au/reports/australias-welfare/housing-affordability
Jobs and Skills Australia. (2026). Internet Vacancy Index. https://www.jobsandskills.gov.au/data/internet-vacancy-index
The Conversation. (n.d.). Brand colours style guide.
I acknowledge the use of ChatGPT to help brainstorm the story structure, refine wording, and plan the sequence of visualisations. All data sources were checked against official public sources. The final story direction, chart selection, interpretation, R code review and submission decisions were completed by me.