The stock beneath the flow: Why innovation measurement counts activity and misses the capacity that makes it pay off.

John H Howard, 8 September 2026.

An extract from the Handbook of Innovation Ecosystems, Volume II, to be published in the first half of October.

Flows counted, stocks missed, and the infrastructure foundation that decides whether activity adds value.

The overlooked condition

A study of innovation ecosystems can integrate placemaking, economic drivers, business networks and governance and still leave the central question open. Some places achieve that integration and underperform; others succeed despite visible gaps in their arrangements.

The pattern points to a condition sitting beneath the four dimensions rather than among them. That condition is infrastructure, the sustained investment in physical, knowledge, financial and social systems on which activity depends.

Infrastructure runs well beyond, and beneath, the research laboratories and incubators usually associated with innovation.

It takes in metros and rail, data centres and undersea cables, and the squares and pedestrian streets between them. Without that substrate, ecosystem design stays potential rather than actual.

Flows counted, stocks missed

How innovation is conceived shapes how it is measured, and measurement shapes where investment goes. The standard indicators, research and development expenditure, patents, publications and firm formation, capture the visible flows of activity. They miss the accumulated stock of infrastructure that makes those flows productive.

The distinction is one of flow against stock. Expenditure measures an annual flow. Infrastructure is the accumulated stock, the foundational capacity that decides how productively a place turns those flows to account. A city with reliable metros, high-speed networks and well-designed public space holds innovation capital that standard statistics do not record, although it governs what becomes possible.

It is the one every corporate analyst applies without a second thought. An income statement records “flows” over a fixed period, usually a year: revenue, expenses and the profit between them. A balance sheet records “stocks” at a specified date: the assets held, the liabilities against them and the net position. Neither statement is complete without the other, because a firm can report strong earnings while running down the assets that produced them, or hold a fine balance sheet that earns nothing.

Innovation statistics publish the income statement and omit the balance sheet. Research spending, patents and publications are the period’s activity; the infrastructure, institutions and relationships that made the activity productive are the assets, and no national series records them.

The point carries directly into the current debate about AI and productivity. AI raises output where the complements are in place: data, skills, management capability, and the computing and connectivity infrastructure on which it runs. Those complements form a stock, so a firm may spend heavily on AI and still see little return while that stock stays thin.

Measurement that tracks AI spending or model adoption without the state of the complementary stock reads activity as capacity, which is the innovation statistics error in a new setting.

The technology arrives quickly while the stock that lets it perform accumulates slowly. The interval between the two is where measured productivity disappoints and premature conclusions form.

The dividend nobody claims

Much infrastructure carries an innovation dividend that appears in other accounts. A metro line connecting universities, institutes and commercial districts compresses the cost of interaction between researchers, entrepreneurs and investors. Spectrum licences and cable landings decide whether firms reach cloud capacity and collaborate across borders. Squares and mixed-use streets create the spaces where useful conversations begin.

Governments justify, fund and evaluate these investments through other lenses: transport efficiency, telecommunications capacity and urban livability. The innovation return therefore arrives as a positive externality that no portfolio claims and few attempt to record. A process that cannot see the investment through an innovation lens will undervalue it, and will design it without innovation outcomes in view.

Responsibility compounds the problem, since infrastructure and innovation policy sit in separate institutions that rarely meet. The people who commission a metro line, a cable landing or a data centre seldom count the innovation capacity it creates, and the people charged with innovation seldom shape the infrastructure on which it depends.

Why districts concentrate the four infrastructures

Districts, precincts and hubs succeed because they bring the four infrastructures into proximity.

The physical systems enable movement and interaction, the knowledge systems support research and learning, the financial systems channel capital towards opportunity, and the social systems build the trust collaboration needs.

Agglomeration then produces its documented effects (Marshall, 1890; Duranton & Puga, 2004).

Density accelerates the exchange of ideas through face-to-face contact, lowers transaction costs, and lets smaller firms reach capabilities they could not fund alone (Storper & Venables, 2004).

These environments follow deliberate investment rather than market forces alone. Kendall Square needed MIT as an anchor, and decades of transport, zoning and urban design (Katz & Wagner, 2014). The same sequence appears in Barcelona’s 22@ district, Berlin’s Adlershof and Sydney’s Tech Central, where public investment in transport, digital networks and public space preceded the private development that followed. Places with strong research but a weak foundation tend to lose their talent, and the potential dissipates (Glaeser, 2011).

Bringing the foundation into view

If infrastructure decides innovation capacity, measurement should record it.

Major infrastructure projects could carry an innovation impact assessment alongside environmental and social assessment, quantifying gains in talent attraction, firm density, network effects and knowledge spillovers. National statistics could report infrastructure quality beside research and development, giving a fuller account of capacity.

Assessment at the level of the individual indicator would serve better than a single composite score. Ecosystem scoreboards aggregate components that behave differently, so a headline number invites ranking and the conclusion that a low-scoring place has failed (OECD & Eurostat, 2018). A profile that reports each element on its own terms points instead to the binding constraint (Howard, 2025g).

Implications for Policy and Practice

The argument reframes the task facing regions that want to build innovation capacity, and the firms and governments now weighing AI investment. Direct programs and technology purchases remain necessary and are insufficient on their own. Without the infrastructure foundation the inputs may sit underused, and the returns that justified the spending do not arrive on the timetable expected. Two conclusions follow for policymakers and advisers.

The diagnosis is that measurement and funding have captured only part of what shapes innovation. The response is to treat infrastructure as innovation investment, to design it with innovation outcomes in view, and to build the coordinating capacity that spans the traditional silos, in the knowledge that capacity of this kind accumulates slowly and returns over decades.

References

Duranton, G., & Puga, D. (2004). Micro-foundations of urban agglomeration economies. In J. V. Henderson & J.-F. Thisse (Eds.), Handbook of regional and urban economics (Vol. 4, pp. 2063–2117). Elsevier.

Glaeser, E. L. (2011). Triumph of the city: How our greatest invention makes us richer, smarter, greener, healthier, and happier. Penguin Press.

Howard, J. H. (2025). The handbook of innovation ecosystems: Placemaking, economics, business, governance. Acton Institute for Policy Research and Innovation.

Katz, B., & Wagner, J. (2014). The rise of innovation districts: A new geography of innovation in America. Brookings Institution.

Marshall, A. (1890). Principles of economics. Macmillan.

OECD, & Eurostat. (2018). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD Publishing.

Storper, M., & Venables, A. J. (2004). Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 4(4), 351–370.

About the author

Dr John H Howard is Executive Director of the Acton Institute for Policy Research and Innovation, Sydney, and author of the Handbook of Innovation Ecosystems (2025) and Making Sense of AI in 2026. The Handbook of Innovation Ecosystems, Volume II will be published in October. John's work is focused on how innovation ecosystems function and why some generate sustained economic and social value while others stall.