With Darcy W.E. Allen, Aaron M. Lane, and Jason Potts1
Dear expert panel,
We are a group of academic economists and legal scholars with a specialisation in innovation and the development and adoption of frontier technology. The Strategic Examination of Research and Development discussion paper underlines that Australia’s innovation system is one of underperformance and malinvestment.
This is not the first inquiry to investigate and underline Australia’s innovation system underperformance. Each has found the same deficiencies in the system that the Expert Panel seeks to understand:
- In 1995 the Industry Commission found a need to “raise the social and economic payoff from public sector R\&D by achieving a wider external influence over what research gets done” and increase cost effectiveness (Industry Commission 1995)
- In 2007 the Productivity Commission found that there were “major improvements” needed across the board and that research commercialisation was fraught with misaligned incentives and bureaucratic barriers (Productivity Commission 2007)
- The Cutler Review found “shortcomings in the institutional framework that underpins the innovation system” (Cutler 2008).
- In 2015 the Commonwealth Senate Economics References Committee inquiry into Australia’s Innovation System found that “Australia performs well in research”, but “such innovation is not developed into tangible wealth creation” (Senate Economics References Committee 2015).
- In 2016 Joint Select Committee on Trade and Investment Growth Inquiry into Australia’s Future in Research and Innovation also underlined Australia’s underperformance in converting research into market ready innovation (Joint Select Committee on Trade and Investment Growth 2016)
It will be tempting for the expert panel to travel down the same paths as its predecessors. Australia’s innovation system consists of a large network of publicly funded organisations that rely on grants and subsidies as their base revenue model.
As the discussion paper notes, Australia has 40 universities, 55 medical research institutes, and a dozen publicly funded research agencies. The Australian Government distributes funds through 151 programs and across 14 portfolios. Even if this was a story of great success, this represents an enormous network of vested interests in defending the status quo approach to innovation. It is indicative of the Australian conversation about innovation that it is dominated by organisations that directly benefit from the funding structures established by government, with only limited contributions from the firms and individuals that these inquiries recognise have to drive innovation to deliver the growth benefits we seek.
Unfortunately the discussion paper shares the same foundational mistakes that have driven the malinvestment and failure in innovation and R&D in Australia.
The discussion paper bases its analysis on the idea that research is a critical factor of production for the economy. It conceptualises an Australian research industry producing high-quality research, but in insufficient quantity. The discussion paper expresses a desire for more resources to be allocated to produce more research and they want it to have a bigger effect. But the discussion paper is asking the wrong questions, and as a result implies policy solutions which will continue Australia on our disappointing and wasteful innovation trajectory.
In this submission we argue that research is not innovation. Innovation happens in the economy. New ideas require new firms. There is not enough creative destruction that is needed to get new ideas into the economy because of economy-wide regulatory and taxation settings that hinder creative destruction. Australia needs better market institutions to allow for more experimentation. New digital technologies facilitate the development of new economic infrastructure and more commons-led innovation.
The discussion paper is motivated by an outdated model of innovation
The discussion paper seeks a strategic examination of Australia’s R\&D system. It seeks ways to improve and strengthen it, for the benefit of Australian industry and economy. The report suggests that other countries have successfully adopted new strategies for leveraging R\&D for social and economic gain. The implication is that Australia is not achieving this, and risks being left behind. It is noted that this is odd, as Australia is, proportionally considered, a research powerhouse, producing many high-quality academic publications. We have some of the best universities. The implicit diagnosis, however, is that none of this great research is being used by industry. The discussion paper complains that this research is not being transformed into economic and social value. It is also noted that the complexity of the Australian economy, as measured, is inconsistent with such truly amazing research and such high and growing research output.
The cause of this disconnect according to the paper is that the R\&D system is underfunded, not fit for purpose, being “barely engaged with national need” and with “too little planning” and delivering to a business community that is “largely indifferent”.
Our research suggests that there is indeed an opportunity to strategically reset Australia’s innovation system, but not in the direction the discussion paper suggests. The main beneficiaries of an expanded and better funded research industry are those that supply inputs to that research industry (a point that is true of industry policy in general), and that the spillover benefits are unlikely to be much changed, i.e. will not drive far reaching innovation and productivity dynamics in the real economy. Indeed, it is the spillover model itself that is flawed.
The economy has evolved considerably in the past 30-40 years, and the innovation system and innovation policy has not caught up. We need new institutions for innovation. These institutions need to be adapted to a new type of open system digital economy. Our response will outline what the new institutions need to be and the sorts of policy frameworks that can achieve them.
Industrial innovation policy frameworks – rooted in neoclassical market failure and Schumpeterian techno-nationalism – fail to adequately account for the distributed, open, and platform-based innovation dynamics of the digital economy. A new framework is needed, one that recognises commons-based knowledge production, institutional diversity in innovation ecosystems.
Mainstream innovation policy has traditionally been shaped by neoclassical economic theory, which treats innovation as a public good that suffers from underinvestment due to market failure. This framework justifies policy interventions such as R\&D tax incentives, subsidies for university research, and patent protections with a focus on optimal allocation of resources to innovation inputs. The main critique of the neoclassical model is that it overlooks the dynamic institutional and social processes through which knowledge is generated, shared, and applied. As a result, innovation policy becomes an exercise in filling gaps in funding or coordination, rather than enabling the emergence of new forms of value creation.
The Schumpeterian and evolutionary economics approach is however focused on the growth of knowledge and its effect on economic dynamics, placing innovation at the center of economic development, emphasising entrepreneurship, disruption, and the role of large firms in driving technological change. While these approaches offer a richer account of innovation than neoclassical models, they still tend to rely on hierarchical, top-down institutional structuresgovernment agencies, national labs, and large incumbents. This has reinforced the researchindustrial complex, where public funds are funneled into a narrow band of elite institutions, often optimizing for bureaucratic performance metrics rather than exploratory or open-ended inquiry. The discussion paper is right to observe that this is not working well, and that change is needed.
A major policy framework that followed from the evolutionary approach was National Innovation Systems (NIS, developed by Freeman, Nelson and Lundvall, among others, see Dodgson et al 2011) which viewed innovation as a systemic process embedded in institutions and linkagesparticularly between universities, firms, and government. However, it remains tied to a territorial, state-centric model of innovation that struggles to account for transnational digital networks and open-source knowledge production. Moreover, NIS frameworks often assume the institutional forms involved in innovation are fixed and stable, rather than being themselves subject to innovation and reconfiguration. Furthermore, the tight coupling of academic research, industrial commercialization, and state subsidy has shaped innovation policy toward a model of linear technology transfer and IP capture. This kind of works in fields like defence and biomedicine, it marginalizes open innovation (Potts 2025). It privileges institutions that are good at absorbing grants and publishing papers, rather than those that foster novel forms of coordination or learning.
Schumpeter’s seminal work, describing the innovation dynamics of market capitalism, is over 110 years old (Schumpeter 1911, 1926, 1928). Richard Nelson’s work on market failure in science is from the 1950s (Ken Arrow formalised this in 1962). Chris Freeman’s work on National Innovation Systems was developed in the late 1960s and 1970s (Lundvall and Nelson systematised it in the 1990s). Even the modern theory of endogenous economic growth (Romer 1986, Lucas 1988), which is the basis of the two sector model of the research industry and which formalises the idea of spillovers as the mechanism to translate new ideas to productivity dynamics, is over 40 years old.
The point is not that old ideas are stale, but that the economy itself has evolved a lot over the past 40 years and definitely over the past 100 years. The most obvious thing to say is that we do not really have an ‘industrial economy’ anymore, in the way we did last century, or of the sort that Schumpeter and Nelson alike were describing and developing policy for.
What we now have can be described as a digital economy in the sense that the main economic infrastructure are digital communications systems (internet) and digital marketplaces, digital platforms, and compute is increasingly in everything, so that anything digital can interact with anything else digital, and that data is a primary resource. A digital economy is digital in its institutions, which is where a lot of innovation (i.e. in institutional technologies, see Berg et al 2019) has occurred over the past few decades. This is where a new approach to innovation policy should focus.
The theory of industrial innovation, which is the basis of modern innovation policy, as set out in the discussion paper, is a theory and policy framework that describes an industrial economy that no longer exists. Schumpeter’s deepest insight is surely that capitalism itself evolves. Innovation policy also needs to evolve to keep up with it. That is the opportunity that we have right now.
The failure of innovation policy is not primarily a matter of insufficient funding or weak uptake. The failure is in the underlying theory. We are operating with the wrong model of innovation.
The dominant view treats innovation as the output of something like a “research industry,” a sector that produces knowledge as a factor of production, an input into other sectors of the industrial economy.2 The two-sector logic is clear, if research is an input into every other sector, and economic analysis can find the optimal size of the research industry that maximises a steady state growth path, then the purpose of innovation policy is to direct factor inputs into the research industry to get the economy onto that path. Now it’s not quite that simple, because the research industry is actually a complex system – a national innovation system – in which firms, universities and governments must be connected in particular ways to translate research into commercial productivity. The suggestion is that if we could just get the right amount of R\&D spending, and the right connections between all the institutions, that publicly supported innovation would drive economic prosperity.
The discussion paper points to economic complexity and productivity growth as indicators of innovation failure. Their claim is that the economy is not adopting research outputs at scale, and that research is either too small or poorly diffused. This misdiagnoses the problem. It assumes that research is an input to innovation, and so if you want more innovation you must do more research.
This of course sounds sensible, but it is wrong. That is a story that describes how to benefit the research industry. What we actually need to care about is how to get new ideas into the economy. There is some overlap, but mostly these are not the same thing at all.
Innovation is what happens when new ideas enter and reshape the economy. Research might contribute to this process, but the critical factor is institutions that incentivise and coordinate this process. What matters is powerful and effective institutions for innovation in the economy which produces new sources of economic value as an output.
New ideas generally require new firms. We should not be trying to figure out how to get existing firms to consume inputs they do not want and aren’t built for. It doesn’t work with horses and water and it doesn’t work with Australian firms and university research either. What we need to figure out is how to make it much easier to start and grow firms. That is a different problem.
Innovation happens through institutional processes of creative destruction – an idea that Schumpeter taught! – not through better research-commercialization pipelines. Australia’s problem is not a lack of research, but a lack of institutional diversity and dynamism. Australia’s innovation bottlenecks are due to high regulatory barriers, high costs of entry, and an economy structured to protect incumbents rather than foster experimentation. This is particularly evident in areas that the authors have been studying intently recently – like crypto, robotics, space and Al – where Australian innovators often take their ideas offshore due to hostile or uncertain regulatory regimes.
A digital economy does not mean that the economy produces digital products (the digital sector or the digital industries, like the failed attempt to shoehorn the information economy inside the creative industries so that it could be appropriately policy managed!). Rather, a digital economy means that the economic infrastructure and economic institutions and forms of organisation and capital is natively digital (Potts 2022, Allen et al 2025).
Why is digital different? One, because it is computational – information processing can be embedded in part of the economy. And two, because it is fully connected – anything digital can talk to anything else digital – a digital economy is a hyperstructure. This massively increases the operating efficiency and powers of coordination that a digital economy can run. Now this also has structural implications. Platforms and networks are a far more important infrastructure. Data is a critically important resource. Industries don’t really make sense anymore. Composability and modularity matters much more. Private infrastructure is the norm. The economy goes wherever the internet goes. A digital economy (platforms and protocols) really is different to an industrial economy (factories and governments), in the same way an industrial economy really was different to an agrarian economy (farms and kings).
The right model of innovation: the innovation commons
So, what is the right model of innovation? Let us propose that it is centred around the commons, as the central institution of innovation (that supports the market). This is in contrast to the industrial model of innovation, where the research organisation was the central institution of innovation (that is supported by the market and by government). Industrial innovation policy supports innovating firms (that is the single unifying lesson of Schumpeter, Arrow, Nelson, Freeman, Romer, Mazzucato et al.) But the digital model of innovation, which is focused on getting the institutions of innovation right (see Davidson and Potts 2016, 2022), is built on the innovation commons (Allen and Potts 2016, Potts 2018, 2019, 2025 Potts et al 2023).
The theory of the innovation commons – developed by Potts and Allen during 2014-2018 (Australian Research Council funded research) – presents a new institutional framework for understanding the origins of innovation. Traditional economic models explain innovation as an output of entrepreneurial firms responding to market signals, supplemented by public investment to correct market failures. These models treat innovation as an investment allocation problem, in the context of market failure in the production of new ideas, and are focused on allocating resources to R\&D, creating incentives through IPR, and correcting underinvestment in knowledge production through subsidy and public investment.
The theory of the innovation commons argues that this approach misses a crucial prior phase of innovation: the moment before firms or markets emerge, when new ideas are still uncertain, incomplete, and poorly understood. The innovation commons is a distinct institutional solution to this early-stage innovation problem. In this germinal phase of the innovation trajectory, the central challenge is not investment, but opportunity discovery under high uncertainty and distributed knowledge. An innovation commons is:
an institution to facilitate cooperation and supply governance among a group of technology enthusiasts in order to create, under high uncertainty, a pooled resource from which the individual members of the community might seek to discover and develop entrepreneurial opportunities for innovation. The innovation commons is not the peer production of new technology per se: it is, rather, the peer production of the information necessary to discover the opportunities from which to develop markets, firms and industries. It is a marketmaking, firm-making (and entrepreneurship-making) institution. The institutional origin of innovation is not, as in the Schumpeterian canon, entrepreneurial action in firms and markets. Rather, innovation originates in a prior state of non-market coordination among proto-entrepreneurs and technology enthusiasts who develop governance rules to facilitate cooperation under uncertainty. (Potts 2018: 1026)
The theory of innovation commons builds on the work of three Nobel prize winning economists. It develops Elinor Ostrom’s discovery that commons institutions can be efficient and effective for the governance and production of natural resources, and extends this to innovation resources. It builds on FA Hayek’s insight into the importance of distributed knowledge and the need for mechanisms to pool and interpret fragmented, non-price information and connects this to the entrepreneurial problem in early stage innovation. And it extends Oliver Williamson’s insights into how idiosyncratic investments and transaction costs inhibit market formation to identify key governance problems in assembling information for innovation.
The true innovation problem in this new theory is not the undersupply of the outputs of research scientists – i.e. Ken Arrow’s formulation of the problem, as operationalised by Romer and Nelson into public sector supporting innovation policy. Rather, the actual innovation problem according to the theory of innovation commons – is in building the resources required for innovation – and those critical resources are information and knowledge from which to discover entrepreneurial opportunities. The critical resource is that which enables the discovery of opportunities, and much of that information is market information that can only be obtained by those involved in trying to actually do new things. Moreover, this information is not just how-to recipes. It can involve contextual and often sticky knowledge of costs and constraints (e.g. regulatory barriers) and where to find resources and aspects of market discovery. Universities and public research organisations produce very little of these critical resources that are inputs into innovation.
Innovation commons are not produced by government – they are institutions of civil society (they are private collectives, von Hippel and von Krogh 2003, and they are contribution goods, Kealey and Ricketts 2014) and are enhanced by new digital technologies of coordination and communication (Benkler 2005). Innovation commons are the entrepreneurial front end of open knowledge systems (Neylon et al 2018, Hartley et al 2019, Allen et al 2021). But they can be supported by governments (Potts 2019, Potts et al 2023).
The critical point to understand is that new digital technologies – themselves the product of last century’s epochal industrial innovations in ICT and computing – have created the conditions for a new type of economy – a digital economy – that has very different transaction and coordination costs (e.g. Goldfarb and Tucker 2019, Potts 2023) because of digital technology affordances, i.e. ubiquitous cheap computation. A digital economy in this sense is evolving into a computable economy, which is an economic system in which all core components – transactions, agents, capital, institutions, and governance – are represented as digital, programmable processes. Unlike traditional economies, where many functions rely on analog, paper-based, or humanmediated coordination, a computable economy operates through algorithmic rule systems that are machine-executable and interoperable. A computable economy has the capacity for end-toend automation, verification, and coordination across all layers of economic activity. It is “computable all the way down,” meaning that economic actions (e.g. payments, trades, contracts) can be initiated, validated, and executed entirely within a digital environment without the need for external intermediaries. A digital economy and its increasingly advanced computational capacities is enabling institutional acceleration (Allen et al 2025). This has many diverse consequences, but an important one for our purposes is that commons work better.
We can see this already clearly in open digital innovation ecosystem contexts such as open source software, crypto and Al. These sectors are the bellwethers of the new digital economy (Allen et al 2025). They are where we observe the shape of the new innovation system emerging and evolving.
The theory of the innovation commons therefore has two parts. First, all innovation begins in the commons because that is the most efficient and effective instruction to create the critical resource for innovation, which is to pool information for entrepreneurial discovery of opportunities. Everything else is downstream of the creation of that critical resource. Second, digital technology massively accelerates and amplifies that capability. The more resources can
be pushed and developed in the commons, the better a digital economy works. Harnessing that feedback process should be the governing principle of new innovation policy.
Pro-innovation policy is economy-wide policy
As we have laid out, subsidising the ‘translation’ of academic research into commercial outputs misdiagnoses the fundamental bottleneck of innovation in Australia. While invention can and does occur within universities, innovation happens in markets and in the commons. It happens through piecing together bits of local market information about where value can be created. As Joseph Schumpeter taught us, we can distinguish between invention (e.g. creating a new material) and innovation (the commercially successful application of the new material for consumers). Innovation is an emergent process of discovery through application, and that application happens out there in the wild – in markets and commons and firms. Entrepreneurs innovate by identifying problems, experimenting with solutions and testing business models. Then they adapt based on market feedback. It is a messy and evolutionary market process. But it is the most effective translation mechanism we have for turning ideas (embodied in inventions), whether from labs or elsewhere, into economic value and prosperity. In this section we look at how
One tempting path to power-up innovation in markets is to subsidise the costs of entrepreneurs – or researchers who want to become entrepreneurs – doing the innovation. We could directly fund them, giving them grants or buying equity. Or we could coach them through Australia’s regulatory barriers. Directly subsidising commercialisation efforts – such as funding specific incubators, accelerators, or university translation programs – is a flawed attempt to centrally manage a process that is inherently decentralised and market-driven. Such top-down approaches inevitably lead to public resources being used to pick winners, often based on bureaucratic criteria or perceived potential rather than genuine market validation. This distorts incentives, potentially diverting effort towards activities that satisfy funding metrics instead of creating sustainable economic value.
This approach fundamentally misunderstands translation not as a discrete, fundable stage, but as the complex outcome of successful market experimentation.
Government policy aimed at fostering innovation – that is, to create economic value, rather than just research and invent things – should focus on creating a fertile institutional environment for economic experimentation.
This approach focuses on systematically reducing barriers that prevent entrepreneurs from testing, adapting, and scaling their ideas within the Australian market. The core challenge for innovators is not necessarily a lack of specific funding, but the high underlying cost and difficulty of experimenting within the existing economic environment. Governments should direct attention towards reducing barriers that impede innovation, rather than channeling funding into specific intermediaries. An added bonus of this approach is that all Australian businesses will benefit. So, what are some of the major barriers, and what should we do about them?
Uncompetitive taxes reduce capital for experimentation
Australian companies are burdened with one of the highest corporate tax rates in the world. Reducing the company tax rate would encourage experimentation by directly increasing the financial capacity and incentive for businesses across the economy to invest in turning new ideas into economic value. The corporate tax rate directly reduces the capital available for private sector innovation, and makes it comparatively more expensive to ‘translate’ research here than elsewhere. This tax burden diminishes the retained earnings that businesses could otherwise use to fund research and development, invest in new technologies, or undertake the risky experimental ventures inherent in bringing new ideas to market. High tax rates also make Australia a less attractive destination for both domestic and international investment seeking innovative opportunities.
Excessive red tape stifles business creation and technology application
Australia is excessively burdened by red tape (Allen and Berg 2018). These regulatory burdens impose significant costs on existing and potential Australian innovators. The high administrative complexity involved in doing business acts as a direct barrier to entry, deterring potential innovators and slowing down the launch of new experimental ventures. Ongoing compliance burdens also make it difficult for businesses to adapt their models quickly in response to market signals, hindering the iterative learning process.
There are two main paths to tackle Australia’s deepening red tape problem, with the view to increase the capacity for entrepreneurship and innovation to happen in the market.
First, major economic reforms driven by strong political will for a better, more innovative, Australia. For instance, Australia has complex national environmental laws (the EPBC Act) that can prevent or delay building. Legislation such as the EPBC Act prevents real world experimentation both directly through added costs (years in delays), and indirectly through the flow-on effects of more expensive inputs into innovation. While we transition from an industrial to a digital economy we will also need to build significant physical infrastructure to support the extra compute, including large data centres and new energy sources. While comprehensive regulatory reform to reduce these burdens across the board should be the primary goal, the inherent difficulty and slowness of such reform necessitates pragmatic tools in the interim.
Second, redouble efforts to implement red tape reform tools. We have written widely about mechanisms to reduce red tape (Allen et al 2021). Tools can include red tape repeal days, 1-in-2-out rules, or regulatory budgets. These tools might seem crude. And they are indeed imperfect. But as the red tape crisis worsens, political will for reform wanes, and opportunity costs of stagnation rise, these tools become more important bounds on an expanding regulatory state. Another related mechanism is the use of regulatory sandboxes to provide temporary, supervised relief from specific existing rules for approved participants testing innovative products, services, or business models. They function as designated spaces where the high regulatory barriers normally faced by radical experiments are deliberately lowered. Sandboxes help accelerate the learning process for potentially transformative innovations, although they must be sufficiently broad, well-defined and incentivised.
Australia’s typical response to new technologies – like blockchain, artificial intelligence, reusable rockets – is hostile. We don’t let new technologies go through the experimental process that propels growth.
But where does our anti-technology mentality come from? Scholars in the humanities, business and law departments use government-funded grants to problematise new technologies, rouse hypothetical fears, and call for overbearing regulatory straitjackets. Not only are academics failing to ‘translate’, they’re actively harming others from doing so.
We see rapid calls for bans, moratoriums, or heavy-handed regulation of emerging technologies before their potential benefits, risks, and viable applications can be understood through realworld use and market experimentation. Alternatively we undertake inquiry after inquiry, year on year, with no legislative clarity (Allen and Lane 2025).
Our highly precautionary approach towards technology acts as a powerful barrier to innovation through two mechanisms. First, in its influence on popular and political culture perceptions of technology and innovation – shifting us away from a culture of growth and progress, towards one of stagnation and fear. Second, in the emergence of formal rules, regulators, guidelines, proposals, and guardrails that stop entrepreneurs from experimenting. Restrictive rules prevent the market exploration and discovery – the very essence of ‘translation’ – from occurring.
Shifting towards a culture of permissionless innovation, where new technologies and business models are generally allowed unless and until clear harm is demonstrated, is crucial for enabling the experimentation needed to drive progress (Thierer 2016). Aside from pushing back on antitechnology mentality, we could create new zones or “technology safe spaces” with radically reduced permissions and rules (Allen 2025).
To boost innovation, the strategic focus must shift from targeted interventions towards making a more dynamic and enabling institutional environment. This requires systematically dismantling the key barriers that currently stifle market experimentation and adaptation: uncompetitive taxes that drain capital, excessive red tape that increases costs and delays (while utilising tools like sandboxes where necessary), and overly precautionary attitudes that prevent exploration.
Better, Faster, Cheaper Innovation
We need a new framework for innovation in order to guide innovation policy. The basis of this new framework should be centred on good institutions to create high-powered incentives to foster the sorts of resources that support innovation (this is the theory of the innovation commons), and also the sort of institutions that incentivise the development of new ideas into new organisations that can create and discover value (i.e. the unfettered and technologically enhanced economic institutions of capitalism, e.g. Allen et al 2020, Davidson and Potts 2016, McCloskey 2016). This is the new institutional approach to modern innovation policy.
The argument for why it works, and why it is superior to the old industrial model of innovation policy (which is really a species of industry policy, to support the research industry) is that it
locates the problem in the right place, namely that innovation takes place in the economy (not in universities), and that the main actors are entrepreneurs (not scientists). The key insight as to the opportunity that we have now, however, rests on a further point still, namely that new digital technologies (from internet to Al) have massively increased and improved the design space of institutions. Digitally enhanced institutional technologies can power and support next generation innovation systems.
The problem is not that our research institutions, which are great, are nevertheless too small and ineffective, but would be much better with more resources and greater planning. But that path only benefits the research industry itself (so of course it can be expected to argue for it). Our claim is that if the goal is truly to find better ways for innovation to drive economic prosperity, then we need to focus our attention in the right place, which is in the economy and the institutional incentives that govern it. That is what we urge the expert panel to focus on.
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- The authors are with RMIT University. They provide this submission in their personal capacity. ↩︎
- The classic Romer (1990) model of innovation has precisely this two-sector formulation, in which technological progress is the outcome of intentional investment in knowledge production, rather than an exogenous driver of growth (as in the Solow model). ↩︎