The New Political Economy of Innovation: Why Australian Policymakers Need Better Tools

Rajesh Gopalakrishnan Nair and John H Howard, 14 October 2025.

When the Commonwealth Government reorganised its innovation responsibilities for the fourth time in a decade, public servants made jokes about updating their email signatures again. The humour masked a deeper problem: innovation remains everybody's concern and nobody's clear responsibility (Howard, 2025). This institutional restlessness extends beyond Australia. The UK's Bartlett Review (2017) noted that its responsible department had changed names four times in recent memory, undermining confidence in state leadership on innovation.

Such turbulence is not administrative tidying gone wrong. It reflects something more fundamental: policy machinery lacks the analytical tools to handle what innovation actually is—a deeply political process that determines who prospers, who loses, and how societies reorganise themselves around new technologies.

Innovation policy rhetoric sends messages about technological visions, when it is really about power, interests, and institutional design (Howard, 2025).

As artificial intelligence reshapes labour markets, Australian policymakers find themselves reactive rather than strategic, caught by automation narratives pushed by technology companies and rising public anxiety about displacement. Recent IMF analysis warns that up to 40 per cent of global employment faces exposure to AI, with developed economies potentially seeing 60 per cent of jobs affected (Cazzaniga et al. 2024). Geographic isolation from major innovation centres intensifies the risk: by the time policy responses crystallise, the terms of technological change may already be set elsewhere.

Such pessimistic predictions should not, however, panic policy and politics. Human progress has always been influenced by cycles of change and what some term as creative destruction. These disruptions need to be harnessed, which requires proactive institutional changes in anticipation of longer-term impacts. Through this lens we can think about augmentation rather than automation. This is precisely the situation that political economy thinking was designed to address.

Unfortunately, political economy as an intellectual tradition has been marginalised in Australian policy circles for decades, with its presence in the machinery of government effectively eliminated during the neo-classical and new public management consolidation of the 1980s and 1990s. That elimination amounted to a political act that foreclosed certain ways of thinking about economic change and narrowed the range of policy options considered legitimate.

An understanding of innovation and its importance to growth and productivity cannot be derived from neoclassical static equilibrium models where technological change is a residual.

This can only be done from Schumpeterian models, based on classical and Marxian political economy, which are capable of analysing the dynamic process of capital accumulation, particularly in the context of national innovation systems. Many other research fields have rights of admission into this debate, reflected in, for example, Acemoglu & Johnson’s influential book Power and Progress: Our Thousand-year Struggle Over Technology and Prosperity (2023).

Why Standard Economic Tools Fail

The Department of Urban and Regional Development, under Tom Uren (1973-75), represented an earlier attempt at multidisciplinary policy work in place-based innovation, bringing together economists, urban planners, civil engineers, landscape architects, and sociologists within the same institutional space. The experiment proved difficult and short-lived, but the attempt acknowledged something important: complex socio-technical transitions cannot be understood through a single disciplinary lens.

Contemporary innovation policy faces similar complexities but typically relies on frameworks borrowed from neoclassical economics. These treat technological change as an unexplained "residual" in growth equations, somehow accounting for 90 per cent of economic expansion yet remaining analytically opaque. When pressed on deployment questions, the standard response defaults to market mechanisms and corrective interventions after problems have emerged.

This market-solution approach produces short-term reactive decision-making rather than strategic shaping of technological transitions. The loss of Australian manufacturing capability after the removal of protectionism is a case in point. The same mistake is not being made with the transition to renewable energy.

The political dimensions of technological change are hardly new. David Ricardo recognised in 1821 that the deployment of machinery could be "frequently detrimental to workers' interests", a view he considered compatible with sound political economy. French legislation in 1623 restricted printed calicoes, triggering political unrest that resulted in 16,000 deaths. The nineteenth-century Luddites destroyed textile machinery that threatened their livelihoods. These were not irrational responses to progress; they were political contests over how technological change would be governed and whose interests would prevail.

Technological transitions create winners and losers. The real issue is whether policy actively shapes these transitions or manages their aftermath.

Standard economic frameworks tend to favour the latter. They lack the conceptual apparatus to address questions of directionality: innovation towards what purposes, benefiting whom, and governed by what mechanisms?

What Political Economy Offers

Political economy thinking recognises that markets are political constructs shaped by legal frameworks, power relationships, and institutional designs that favour certain interests over others (Kay, 2023). Technology does not arrive as an external force acting on society; it emerges from specific institutional contexts and embeds particular assumptions about how work, exchange, and social relationships should be organised.

This lens changes which questions are asked. Rather than treating automation as an inevitable technological trajectory, political economy analysis asks who benefits from framing AI deployment as automation rather than augmentation. What institutional arrangements would favour augmentation approaches? How do procurement rules, funding criteria, and regulatory frameworks channel innovation in particular directions?

These questions reveal decision points where policy choices matter, rather than presenting technological change as a force that merely happens to societies.

The Science Policy Research Unit at Sussex University has worked on this kind of analysis for decades, moving beyond treating technological change as a mysterious residual. Freeman and Soete's (1994) work on information and communication technologies showed how the organisation of work might be transformed, projecting technology as offering greater autonomy, responsibility, and skill development within workforces, but only if appropriate institutional frameworks are designed to harness these possibilities.

Their analysis demonstrated a middle path between techno-optimism and pessimism, acknowledging disruption while illustrating how societies could adapt. Later, Luc Soete (2025) argues that the net employment impacts of technologies like AI depend on macroeconomic compensation mechanisms, diffusion timing, and institutional factors. The potential exists for positive outcomes but realising them requires proactive policy that maintains social cohesion rather than reactive crisis management.

South Korea provides evidence that this thinking can translate into effective policy. Its economic transition drew heavily on innovation systems analysis that understood technology as something to be actively governed rather than passively received. But, even this success story leaves a gap: while offering analytical frameworks, the approach has not fully solved the translation problem. Convincing policymakers to turn their attention from a single-minded focus on economic growth to societal challenges (sustainability, equity, social cohesion) remains difficult.

The Directionality Challenge

This is where innovation policy becomes clearly political. Choosing to orient innovation around societal challenges rather than merely economic expansion requires sustained political commitment. It means redirecting resources, establishing new priorities, and building coalitions around goals that do not receive the automatic political support that economic growth commands.

Mission-oriented innovation policy attempts to address this choice by providing clearer direction. Politics shapes who gets to innovate, for what purposes, and on what terms. A political vision becomes a prerequisite for orchestrating innovation ecosystems around shared goals (Gopalakrishnan Nair, 2025).

Implementing political visions, however, faces substantial obstacles. Public service capabilities need strengthening to comprehend the complexity of innovation and its interlocking systemic nature to be able to provide internally consistent and coherent driven policy advice and direction. Institutional structures may need redesigning, and even inventing, to support sustained focus rather than the churn of reorganisations and bureau shuffling we have seen over the last decade.

Political leaders need frameworks for making decisions with conviction when facing uncertainty and competing interests.

Eminent scholar Carlota Perez argues (2025) we are approaching a situation where directional choices become unavoidable: "When you are at the juncture where we have to give directionality to the new wave, conflict arises. As the old system tries to hold on and the new system uses its powers to replace it, politics becomes absolutely central." The ICT revolution may be approaching the plateau of its S-curve, creating potential for major disruptions with significant political consequences—social instability, uncertainty, and populist responses that are already visible globally.

This pattern has recurred through previous techno-economic transitions. Each cycle passes through stages, starting with increasing inequality, navigating a turning point, and then potentially entering a period of broader prosperity. How harsh the turning point proves to be depends on how well public policies manage the transition through proactive structural changes: managing shifts in sectors, firms, and employment; addressing skill needs and displacement; and building institutions capable of governing new technological capabilities.

What This Means for Practice

Australian policymakers face the current challenges from an exposed position. Geographic isolation limits direct participation in global innovation networks. Institutional diffusion of innovation responsibility prevents concentrated capability building. The tendency in some quarters to treat substantial research evidence as mere academic output delays responses to emerging challenges.

Recovering from this requires understanding how policy shifts actually occur. Recent CSIRO research indicates that meaningful change in policy direction requires sustained boundary-spanning collaboration between researchers, political actors, and policy implementers (Gopalakrishnan-Nair and Hall 2025). Without this, transformative change becomes impossible, regardless of how compelling the evidence is or how urgent the challenge may be.

The approach is much more than better communication between separate domains. It is about building institutions that can think politically about technology while operating within political constraints. That requires different skills and incentives than those typically rewarded by either universities or the current structures of public administration.

Historical precedent demonstrates that such boundary-spanning coalitions can succeed. The OECD's Directorate of STI (DSTI) facilitated self-referent learning between academics and policy makers during the 1980s, when neoliberalism was at its height. Innovation scholars and OECD policy insiders engaged in continuous interaction and in joint publications.  The DSTI-led efforts culminated in National Innovation Systems becoming the favoured policy framework of OECD by 1991. Detailed accounts are available in Mytelka and Smith (2002), Sharif (2006), and Eklund (2017). 

The current situation differs markedly. Academics and policymakers operate from very different epistemic silos[1], producing a vicious cycle: academic work becomes disconnected from policy needs, while policy lacks the analytical depth that engaged and integrated scholarship could provide (for example, Mills 1959, Schön 1983, Boyer 1990, Nowotny, et al 2001, and Martin 2009).

Mission-Oriented Innovation Policy became appealing to politicians precisely because it synthesised political economy arguments in clear language accessible to decision-makers.

What made the OECD experience work was not academics working in isolation, but structured institutional spaces where researchers and policy insiders engaged in sustained, reflexive learning. Australia now faces a useful opportunity to create similar conditions. This is both desirable and possible but in practice will need political will with some bureaucratic activism. Concretely, this means several interconnected changes.

• Creating institutional anchors for innovation policy to ensure stability, provide resources, and establish clear ministerial responsibility. The pattern of reorganisation every few years destroys institutional memory and prevents capability accumulation.

• Developing public service expertise in innovation systems thinking going beyond standard economic frameworks to understand how technological transitions actually unfold and can be governed.

• Establishing arrangements for sustained boundary-spanning engagement between researchers, policymakers, and political actors that go beyond episodic consultations or one-way evidence provision.

• Making explicit political choices about innovation priorities, rather than maintaining the fiction that market mechanisms alone should determine technological directions.

• Building coalitions around mission-oriented approaches that align innovation activity with societal challenges to maintain commitment across electoral and ministerial cycles.

• Examining how AI and other technologies are actually deployed in Australian contexts, rather than accepting automation narratives imported from elsewhere.

The choice between automation and augmentation approaches has very different implications for workforce development, productivity distribution, and social cohesion.

Beyond the Valley of Policy Death

The gap between academic research and policy implementation, the "valley of death" in innovation policy, exists partly because researchers and policymakers operate under different logics and face different constraints. But it also reflects a deeper problem: the analytical frameworks dominating policy discourse cannot adequately grasp what innovation is or how it can be governed strategically.

Political economy thinking offers better tools, but these remain outside mainstream policy practice. Bringing them back requires more than publishing papers or holding conferences. It requires political action: building institutions, allocating resources, establishing new priorities, and challenging existing distributions of influence.

Treating innovation as political means recognising these dynamics and working within them. It means understanding that choices regarding research funding, regulatory design, procurement rules, and institutional structures are never purely technical; they inevitably prioritise some interests over others and either open or foreclose particular futures.

Australian policymakers could continue to treat innovation as a technical optimisation problem, deferring to market mechanisms and reacting to disruptions as they emerge. Or they could recognise innovation as the political process it actually is and develop the institutional capacity to govern it strategically. The first path is familiar but increasingly inadequate. The second is challenging, but necessary.

Now is the right time for Australian politicians, policymakers and academic theorists to come together, learn and set new directions to policy. Many examples of such successful processes exist.

The emergence of National Innovation Systems as a major OECD policy framework, at the height of neoliberalism, is a major case in point. Facilitated by the OECD's Directorate of STI, enterprising academics and policymakers worked together to revolutionise the way the world looked at science, technology and innovation. Circumstances are now such that a new wave of policies to address societal challenges is urgently required.

The choice itself is political. Making it wisely requires better analytical tools than those provided by mainstream economics. Political economy thinking offers those tools, but only if policymakers will use them.

The authors appreciate comments by Professor Roy Green on an earlier draft of this Innovation Insight. 

Note

[1] In this context, “epistemic silos” means that academics and policymakers operate with fundamentally different standards for what constitutes valid knowledge. Academics value peer-reviewed theoretical contributions that advance understanding, while policymakers need actionable answers under time pressure. The questions each group asks diverge: academics seek to explain phenomena, while policymakers need to know “what should be done by Tuesday”. Their validation processes differ fundamentally. Academic knowledge gains legitimacy through peer reviewed journal publication and citation counts, while policy knowledge gains legitimacy through political feasibility and ministerial approval. Each group has developed its own terminology, making mutual comprehension difficult even when they attempt dialogue.

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