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)
)
}Australia continues to persuade its youth that education is the key to opportunity. In many ways, qualifications continue to be associated with better work prospects, fulfilling that promise. However, the route from education to stability is no longer simple for students or recent graduates.
Three pressures - education, jobs, and housing that now coexist are examined in this visual narrative. The idea is not that opportunities in Australia have ceased to exist. The more difficult question is whether opportunity continues to translate into security for young people who work, study, and want to live independent lives.
Hover over each chart to explore the values behind the story.
Education continues to lead to better job results. Based on a May 2025 report from the Australian Bureau of Statistics, 80% of individuals between the ages of 15 and 74 who had a non-school certificate were employed, compared to 58% of those who did not. 84% of those who earned a non-school qualification in 2024 had a job 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)The first graph illustrates the reasons why youth continue to make educational investments. The promise is true. However, the following charts demonstrate why the promise now has additional requirements.
Not everyone in Australia experiences the education-to-work pathway in the same manner. Individuals between the ages of 15 and 24 who lived in major cities in 2025 were more likely than those who lived in inner, outer, and remote regions to be completely involved in their work or studies.
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)At this point, the narrative gets more complicated. It goes beyond whether or not young people attend school. It also has to do with where they reside and the opportunities in the area.
Each state and territory has a different percentage of young people who are completely employed or enrolled in school. In 2025, Tasmania had the lowest full participation rate among individuals aged 15 to 24, while the Australian Capital Territory had the highest.
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)The early work experience of a graduate might be influenced by their location. Although a qualification could lead to opportunities, these opportunities are not distributed equally throughout the nation.
Australia had 337,900 open positions as of February 2026. Since November 2025, the number of open positions in the private sector has grown, while the number of open positions in the public sector has somewhat declined. The labor market is not closed, but it is also not an easy narrative.
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)This is important because there isn’t a single national job market for graduates. They enter particular sectors, professions, and geographical areas, and those segments of the labor market do not move in unison.
The transition to adulthood involves more than just finding employment. The cost of housing has an impact on whether employment leads to independence. Australia saw a 5.5% increase in rent between the first quarters of 2024 and 2025. Perth saw the biggest capital city gain over that time, at 8.9%, while Canberra saw the least, at 0.6%. The exception was Hobart, which had a 1.3% decrease.
rent_paid_change <- tibble(
location = c("Australia", "Perth", "Canberra", "Hobart"),
rent_change = c(5.5, 8.9, 0.6, -1.3),
group = c("National", "Largest rise", "Smallest rise", "Decline")
)
p5 <- ggplot(
rent_paid_change,
aes(
x = reorder(location, rent_change),
y = rent_change,
fill = group,
text = paste0(location, "<br>Rent paid change: ", rent_change, "%")
)
) +
geom_col(width = 0.6) +
geom_hline(yintercept = 0, colour = tc_black, linewidth = 0.3) +
geom_text(
aes(label = paste0(rent_change, "%")),
hjust = if_else(rent_paid_change$rent_change >= 0, -0.15, 1.15),
size = 4
) +
coord_flip() +
scale_fill_manual(values = c(
"National" = tc_charcoal,
"Largest rise" = tc_tomato,
"Smallest rise" = tc_indigo,
"Decline" = tc_emerald
)) +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(-2, 10)) +
labs(
title = "Housing is where the pathway gets squeezed",
subtitle = "Rents paid rose nationally, with Perth recording the largest capital city increase.",
x = NULL,
y = "Change in rents paid, Q1 2024 to Q1 2025",
caption = "Source: Australian Institute of Health and Welfare, Housing affordability, 2025."
) +
theme_conversation() +
guides(fill = "none")
ggplotly(p5, tooltip = "text", width = 600, height = 420) %>%
config(displayModeBar = FALSE)This is the pressure point for recent graduates. If rent takes up the space required to save, relocate, or take early-career risks, a person may study and get employment but still feel trapped.
Australia continues to provide young graduates with genuine opportunities. The labor market still has openings, and education keeps improving employment results. However, the evidence indicates that the route from study to stability is now more conditional.
These days, the question is more than just whether or not young people attend school or have jobs. The more difficult question is whether housing, work, and education still align sufficiently to allow young people to lead secure lives.
Because of this, a graduate’s perception of the “lucky country” may vary based on where they reside, what they studied, and how much of their income is spent before they can even consider 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.