I acknowledge the Ngunnawal people, the traditional owners of the lands we are meeting on today. In so doing, I recognise that the issues we are discussing today have special resonance for First Nations communities. Governments that continually learn and improve will make faster progress at Closing the Gap.
1. A learning machine, not a guessing game
When a German bakery chain wanted to improve sales, it didn't bring in consultants or introduce a sweeping new business model. Instead, it tried something much simpler: it ran a randomised trial (Friebel et al. 2017).
Some of its 193 stores were offered a modest group bonus for staff. Others weren't. After a few months, the results were in. The bonus group had increased sales by 3 per cent. For every dollar spent on bonuses, the company gained $3.80 in revenue and $2.10 in operational profit. Encouraged by these findings, the company rolled the program out more broadly. Profit margins rose by more than 60 per cent, which might be the best thing to come out of a bakery since pretzels.
It's a reminder that in both business and policy, good ideas are important - but better still is knowing whether they work. And that's what randomised trials offer: the ability to learn what works, what doesn't, and where public resources will do the most good.
We've seen this thinking increasingly embraced in government, too. Across Australia's public service, we're embedding a culture of testing and learning - through small‑scale trials, behavioural insights, and rigorous evaluation. From tax compliance nudges to SMS reminders that improve service delivery, we're building an evidence base for better decisions.
Because being willing to learn isn't a sign of weakness; it's a sign of seriousness.
Almost a century ago, the philosopher John Dewey wrote that 'a problem well put is half‑solved'. Randomised trials help us frame problems clearly. They allow us to compare options fairly. And they help ensure that taxpayer dollars are used not just efficiently, but wisely.
In a world of tight budgets and rising expectations, that kind of disciplined curiosity matters more than ever. As a government, our job isn't just to deliver services - it's to keep making them better. And that begins with learning.
Over the next few minutes, I want to share how randomised trials are helping us do exactly that - from small changes that improve service delivery, to better policy design, to the infrastructure we're building to make learning part of how government does business.
2. What is government productivity - and how do we learn to improve it?
In the private sector, productivity is relatively straightforward: output per unit of input. A delivery company that reduces the cost per parcel is improving its productivity. A call centre that shortens the average handling time without compromising service is doing the same.
In government, the outputs are more complex, and arguably more important. They're things like higher school completion rates, shorter surgery wait times, fewer people stuck in long‑term unemployment. What we care about is not profit margins, but public value.
So when we talk about government productivity, we're talking about better outcomes for citizens - achieved with the same, or fewer, public resources.
And just like in the private sector, we improve productivity in government by understanding what works. Not just what sounds plausible, or what's been done before, but what actually improves results.
That's where randomised trials come in.
By comparing 2 versions of a program - one that includes a new intervention, and one that doesn't - we can isolate the effect of that change. It might be an SMS reminder. A redesigned letter. A new digital prompt. Or a pilot coaching service for jobseekers. Some of these interventions work remarkably well. Others don't. But each trial helps us learn, and over time, build a more effective, more responsive and more productive public sector.
Crucially, these aren't abstract exercises. They're grounded in real‑world decisions. Should we send this letter or that one? Should we roll out this new program nationally, or trial it first in 2 regions? Should we allocate resources toward one approach, or a better‑tested alternative?
Every trial is a chance to find out.
And as we accumulate this evidence, we're not just improving individual programs. We're improving the system's ability to learn. The learning machine gets stronger with each iteration. That's the difference between policy and guesswork. It saves us from reinventing the wheel, only to discover it's square.
3. Learning from micro‑experiments: the text message trials
Some of the most valuable lessons in government have come not from major reforms, but from small experiments: short messages, subtle nudges, and modest changes that quietly improved the way services are delivered.
Take Services Australia. In one randomised trial, a simple text message was sent to people who had submitted a form to the department. It was a confirmation message, only 3 sentences, letting them know their submission had been received (Department of Prime Minister and Cabinet 2017a).
The intervention was simple: a brief text message confirming that a submission had been received. The kind of small gesture most people might ignore, but quietly appreciate. It was the public service version of a thumbs‑up emoji: understated, reassuring and surprisingly effective. The result? A reduction of 11 percentage points in follow‑up calls to the department.
On average, those who did call waited nearly 2 weeks longer to do so. If rolled out to everyone who sent in a form, this intervention would result in thousands of fewer calls to call centres each year. That means freeing up time for staff and reducing wait times for other callers. Sometimes, the most productive thing a system can say is: 'We've got it'.
Another trial tested whether a text message reminder could prompt more income support recipients to report their earnings on time (Department of Prime Minister and Cabinet 2017b). It worked. On‑time reporting increased by 13.5 percentage points. Using the most effective text message reminder nearly halved the rate of payment suspensions.
The study authors estimated that the most effective text message reminder would save 6,000 hours of staff time per year - time that could be directed towards helping those in need. And it happened without changing the underlying policy, just the way the message was delivered.
These are examples of what behavioural economists call low‑friction interventions: changes that are easy to implement, low‑cost to run and quick to test. They're not about changing people's values or rewriting legislation. They're about helping people do the right thing more easily.
They also illustrate the power of randomised trials. Without them, we wouldn't know whether the message made a difference, or whether the change would have happened anyway. But by comparing outcomes between those who received the message and those who didn't, we can say with confidence: it worked.
Other trials have followed a similar pattern. The Australian Charities and Not‑for‑profits Commission has recently completed a trial to see whether improved messaging could increase on‑time submission of annual information to the regulator. The results of that trial will be released soon, and will add to the growing body of knowledge on how small changes can improve regulatory compliance.
The Australian Taxation Office, in partnership with the Behavioural Economics Team of the Australian Government, also ran a trial involving letters to tax agents (Department of Prime Minister and Cabinet 2018). These letters gently pointed out potential over‑claiming of work‑related deductions. The result: average claims fell by $191 per taxpayer. Across the sample, that translated into more than $2 million in reduced deductions.
These aren't just examples of operational efficiency. They're case studies in how government learns. Each trial helps us discover what works, and then improve what we do. Not in theory, but in practice.
Importantly, these trials also respect the scale and complexity of government. Not every challenge needs a parliamentary inquiry. Some can start with a pilot and a control group. That kind of experimentation allows us to fail safely, adapt quickly and succeed at scale.
