Rich Country, Simple Economy

Five charts on why Australia’s luck is getting riskier

Faheemahmed Dehalvi (s4197364)

07 June 2026

Rich Country, Simple Economy

Australia has one of the world’s highest incomes. It also has the export profile of a developing economy: a handful of rocks, dug up and shipped, mostly to one buyer. These five charts trace the bet behind the phrase “the lucky country”, and why the odds are shifting.


1. We sell a handful of things

Iron ore, coal and gas by themselves are more than half of what Australia sells abroad. It’s a short list, and nearly all of it comes out of the ground.


2. Rich but simple: the paradox

Rich countries usually make complicated things, like machines, chemicals and instruments. Australia doesn’t. It’s wealthy but scores near the bottom third for complexity, sitting next to oil and mining economies.


3. And the gap keeps widening

Norway and Canada are resource economies too, and they’ve held their ground or moved up. Australia has gone the other way. What it sells is getting simpler, not more advanced.


4. Minerals swallowed the basket

Back in the late 1990s exports were a mix of farm goods, metals and manufactured products. Now mineral products on their own are over 60%. One sector carries the whole thing.


5. One basket, mostly one buyer

While prices stay high and China keeps buying, this looks like a smart play. The catch is that the same chart showing how lucky Australia is also shows how exposed it is.


The bet

Australia’s wealth comes down to a few rocks, sold mostly to one part of the world. That has worked for decades. But a narrow export base built on mining, a complexity score that keeps slipping, and one buyer doing most of the purchasing is closer to a gamble than a plan. The luck has held so far. Staying rich might mean Australia has to start making more complex things.


Acknowledgements

I used Claude (Anthropic, Opus 4.8) as a generative AI assistant on this assignment. It proposed the initial story angle and a first draft of the narrative; it also wrote first versions of the Python data-retrieval code and the R visualisation code, including styling and accessibility choices. I chose the final framing, rewrote the pitch and captions in my own words, read and ran all code, and verified every figure against the original datasets. I am responsible for the accuracy of the data, figures and narrative.

References

Data retrieved programmatically from the World Bank and OEC public APIs (June 2026). Retrieval and pre-processing code is in the submission. All visualisations built in R with ggplot2 and plotly.