"We're not going to do it like Victoria"- Policy Learning from Other Places when Things Don't Go to Plan

Heather Lovell

Associate Professor Heather Lovell is an Australian Research Council Future Fellow in the School of Social Sciences, at University of Tasmania, Australia.

In the UK there is something that became known as ‘doing a Ratner’. The head of UK jewellery chain famously wiped £500 million off the value of his firm in one short speech to the UK Institute of Directors that was unusually honest about the shortcomings of his company. He said:

"We…do cut-glass sherry decanters complete with six glasses on a silver-plated tray that your butler can serve you drinks on, all for £4.95. People say, 'How can you sell this for such a low price?' I say, because it's total crap."

Such a clear admission of poor quality is rare, for obvious reasons.  This reluctance to talk about mistakes and failure extends, however, to many areas of everyday life, including the world of policy.  There is extensive academic research on policy learning and lesson drawing from other places, both within countries and internationally.  Policies are variously described as diffusing, converging, transferred and emulated.  As these terms suggest, scholars have typically focused on best practices examples: policies that have worked well and are judged to be successful. Less well covered are examples of negative policies; the ones that didn’t work. Do these ‘travel’ in the same way? And are the types of learning and lesson drawing that occur different?

In this short blog I briefly explore these questions in relation to a current example of just such a negative case in Australia, namely the Victorian smart or ‘advanced’ electricity metering infrastructure programme (hereafter the ‘AMI’).

The AMI was approved by the Victorian state government in 2006, at the time with an expectation that a similar policy would be applied across the whole of Australia’s National Electricity Market. In other words Victoria was acting early, anticipating a ‘first mover’ advantage. Some 2.8 million new digital and communications-enabled electricity meters were installed in homes and businesses in Victoria between 2009 and 2013.  It was a mandatory programme, centrally-managed by the electricity distribution companies and the state government, as this was judged to be the best way of minimising costs whilst achieving programme objectives. But problems emerged, particularly with regard to increasing costs for consumers, and there were significant protests. A consumer protest group formed - Stop Smart Meters Australia - and also in 2014 a political party emerged – People Power Victoria – with the core mandate of halting the implementation of smart meters.

Subsequently other Australian States and the organisations governing the Australian National Electricity Market have said they will not implement the same policy as Victoria. Instead customers will have a choice in the type of electricity meter they have, with the market in electricity meters being opened up to competition under a new rule currently being finalised by the Australian Energy Market Commission, and due to come into force in 2017. Perhaps a little unusually, other States have been very explicit about the negative lessons drawn from Victoria. For example, the Queensland Government explained:

“Based on the Victorian experience, the Queensland Government has ruled out a mandated rollout of advanced meters in Queensland and will support the customer-driven approach.”[1]

and similarly the Tasmanian Government’s Energy Strategy states how:

“The [Tasmanian] Government will not initiate a mandated roll-out of smart meters (such as occurred in Victoria). The government’s position is that any take up of smart meters must be consumer-led, where consumers may choose to have a smart meter in order to enable their preferred retail product.”[2]

What does this tell us? I’m still in the process of working through the empirical material, but I think a few initial things stand out.  First, that it is important to recognise that flows of policy can be negative as well as positive, as we otherwise risk missing some of what is going on in relation to policy transfer. Second, and relatedly, negative policy flows might not necessarily occupy the same channels, sites and spaces as best practice policy examples. So there is a methodological issue at stake. For it would be a brave government minister or civil servant that wrote a press release or gave a conference paper on an unexpectedly poor policy outcome.  In my research for instance, empirical material about the Victorian AMI has mostly been derived from confidential one-to-one research interviews. Third, are issues of longevity and institutional memory: how long do cases of negative policy learning remain active for?  Interviewees have talked about the effect of the Victorian AMI programme lasting “… for a generation”.  Will this indeed be the case, or might it be forgotten about sooner than this? I would be interested to hear about research on the institutional memory of such cases.  Fourthly, and finally, one finding that seems pretty clear as shared across both positive and negative policy learning and policy transfer cases is the integral role of politics.  In a similar way to there being a clear political advantage to emulating successful policies implemented overseas (particularly in borrowing from political allies), there is politics behind the decision to clearly label the Victorian AMI programme as a failure. With an unexpected fall in demand for electricity in Australia the period 2010 to 2013, there was no longer such a clear policy problem for smart meters to solve, and for some key players in this policy sector there was a sense that the Victorian AMI provided “… a good excuse” for justifying a shift in policy.  



[1] Queensland Department of Energy and Water Supply (2013) The 30-year electricity strategy Discussion paper: Powering Queensland’s future; pp12.

[2] Department of State Growth (2015), Tasmanian Energy Strategy: Restoring Tasmania’s Energy Advantage;  pp20.


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