In 1957, Robert Solow published one of the most influential papers in economic history. Using data from the US economy between 1909 and 1949, he demonstrated that only 12.5% of output growth could be explained by increases in labour and physical capital. The remaining 87.5% was attributed to a residual — commonly interpreted as "technological progress" — that the model could not explain.
This "Solow Residual" has haunted economists for nearly seven decades. It represents our collective ignorance: the portion of economic growth we can measure but cannot explain.
The CHS framework offers a compelling resolution. The residual is not technological progress in some abstract sense — it is the measurable result of investment in intangible capital: software, R&D, brand, training, and organisational design. When these investments are properly measured and included in growth accounting, the unexplained residual shrinks dramatically.
This is not an academic curiosity. It has direct implications for how companies measure performance, how investors evaluate businesses, and how governments set economic policy.
Robert Solow's growth model decomposed economic output into three factors: labour (hours worked), capital (machines, buildings, equipment), and a residual. The residual was everything the model could not explain — and it was enormous.
The Solow Residual was initially labelled "technological progress" or "the measure of our ignorance," depending on the economist. It captured everything from education improvements to management innovations to government policy to pure luck. It was a single number that conflated dozens of distinct growth drivers.
Subsequent economists — Edward Denison, Dale Jorgenson, Zvi Griliches — refined the Solow framework by decomposing the residual into identified factors. Education was separated out. Capital quality adjustments were made. R&D was studied as an input.
But even with these refinements, a large residual persisted. Something was driving growth that standard measurement frameworks could not capture.
The Solow Residual is not a mystery — it is a measurement failure. When intangible capital is properly measured and included in growth accounting, a substantial portion of the "unexplained" residual is explained. What looked like magic was actually investment in software, R&D, brand, training, and organisational capital.
Carol Corrado, Charles Hulten, and Daniel Sichel's landmark 2005 paper demonstrated that when intangible investment was treated as capital formation — rather than expensed as intermediate consumption — the measured capital stock of the US economy increased substantially, and total factor productivity growth was correspondingly reduced.
The logic is straightforward. If a company invests £1M in a new software platform, and that investment is expensed rather than capitalised, two distortions occur. First, the capital stock is understated (the software exists and is productive, but it is not counted). Second, TFP is overstated (the output produced by the software is attributed to "technological progress" rather than to a measured capital input).
When CHS reclassified intangible spending as investment, approximately 40% of the Solow Residual for the US non-farm business sector (1973-2003) was explained by intangible capital deepening.
Jonathan Haskel (Imperial College London) and collaborators applied the CHS framework to the UK economy, producing similar results. UK intangible investment — estimated at over £185 billion annually — represents capital formation that is largely excluded from official growth accounting.
When included, the UK's measured capital stock increases by approximately 30%, and labour productivity growth attributable to capital deepening increases correspondingly. The TFP residual shrinks.
The productivity paradox — first identified by Robert Solow himself in 1987 ("You can see the computer age everywhere but in the productivity statistics") — takes on new meaning in the context of intangible capital.
In the 1980s and 1990s, businesses invested heavily in computers and information technology, yet measured productivity growth remained sluggish. Solow's quip captured the frustration: the technology was visibly transforming how businesses operated, but the statistics showed no improvement.
The resolution, proposed by Erik Brynjolfsson and others, was partly about measurement lags (IT takes time to generate productivity gains) and partly about complementary investments (IT requires organisational change to be productive). But it was also about measurement itself: IT investment generates intangible outputs (better processes, better decisions, better customer experiences) that standard productivity measures miss.
The paradox has returned with greater force since the 2008 financial crisis. The UK and other advanced economies have experienced near-zero measured TFP growth despite rapid technological advancement — particularly in AI, cloud computing, and digital platforms.
The intangible capital explanation is now more compelling than ever. AI systems, for example, are productive capital — they generate measurable economic returns. But they are classified as operating expenses, not investments. The productivity gains they create are real but statistically invisible.
A professional services firm deploys an AI system that reduces research time by 40%, allowing each consultant to handle 25% more client engagements. Measured productivity (revenue per employee) increases. But the AI investment does not appear in the capital stock. In national accounts, the productivity gain is attributed to the TFP residual — "technological progress" — rather than to a specific, measurable intangible capital investment.
The macro-level research has direct implications for how individual companies measure and manage productivity.
Standard productivity metrics — revenue per employee, output per hour — miss the capital story. A company that doubles revenue per employee by investing £5M in software and AI has not become twice as productive in the economic sense. It has deepened its capital base. The productivity gain is real, but it is attributable to capital investment, not to some mystical efficiency improvement.
Companies that track intangible capital investment alongside productivity metrics get a more accurate picture. The Opagio Calculator enables this comparison by computing intangible-adjusted productivity ratios.
Intangible capital creates a distinctive productivity cycle.
Phase 1: Investment. Heavy spending on software, R&D, training, and organisational design. Measured productivity may decline as costs increase before returns materialise.
Phase 2: Absorption. The investments begin producing returns. New software is adopted. Trained employees become more effective. Organisational processes mature. Measured productivity rises.
Phase 3: Compounding. Intangible assets interact and reinforce each other. Better software enables better training. Better-trained employees improve organisational processes. Better processes accelerate R&D. Productivity growth accelerates.
Phase 4: Depreciation. Without continued investment, intangible capital depreciates. Software becomes obsolete. Key employees leave. Organisational knowledge erodes. Productivity gains reverse.
The J-curve pattern — where productivity initially dips during heavy intangible investment before rising sharply — is well documented in the research. See the J-Curve Effect (Productivity) glossary entry for details.
This cycle explains why companies that cut intangible investment during downturns often experience prolonged productivity declines. They are depleting their capital stock, but because that stock is unmeasured, the damage is invisible until output falls.
Intangible capital has economic properties that distinguish it from physical capital — properties that amplify its productivity effects.
Intangible capital generates spillovers — benefits that accrue to parties beyond the investor. R&D conducted by one firm creates knowledge that benefits competitors, customers, and the broader economy. Training provided by one employer develops skills that employees carry to subsequent employers. These spillovers mean that the social return on intangible investment exceeds the private return, which in turn means that market-based investment in intangibles is likely below the socially optimal level.
Intangible assets are complementary. Software capital is more productive when combined with trained employees who know how to use it. Brand equity is more valuable when combined with customer relationships that reinforce it. Organisational capital amplifies the returns on every other intangible category. These synergies create a non-linear relationship between intangible investment and productivity — firms that invest broadly across multiple CHS categories achieve disproportionate productivity gains compared to those that invest in a single category.
Unlike physical capital, intangible capital can be used simultaneously in multiple locations and applications without being depleted. A software platform serves its millionth user at the same marginal cost as its first. A brand's reputation extends to new products without additional investment. This scalability means that intangible-intensive firms can grow output much faster than their investment rate would suggest — creating measured productivity gains that appear "unexplained" in standard frameworks.
A UK professional services firm invests in three intangible categories simultaneously: a knowledge management system (software capital), a structured training programme (human capital), and a consulting methodology (organisational capital). Each investment individually improves productivity by perhaps 5-8%. But the combination — trained consultants using purpose-built software to deliver a codified methodology — improves productivity by 25-30%. The synergy effect is larger than the sum of individual effects.
Understanding the intangible-productivity link changes how you invest. It means that R&D, training, software development, and organisational improvement are not "discretionary costs" — they are capital investments in productive capacity. Cutting them reduces your capital stock and, eventually, your output.
The Opagio Intangibles Questionnaire assesses your intangible capital across all six CHS categories and benchmarks it against peers. The Valuator provides tools for estimating the monetary value of specific intangible assets.
The firms with the highest multi-factor productivity growth are almost always those with the highest intangible investment intensity — measured properly. Due diligence that examines intangible capital alongside financial metrics produces a more accurate assessment of a company's productive capacity and growth trajectory.
National productivity statistics that exclude most intangible capital are misleading. They understate investment, overstate TFP, and misdiagnose the productivity slowdown. The OECD Productivity Framework is evolving to incorporate intangible capital, but progress is slow.
For the full picture of how national measurement frameworks are adapting, see What is IAS 38 and how does it affect intangible asset reporting?, How much of a company's value is intangible?, and the glossary entry on Unmeasured Intangibles.
The link between intangible capital and productivity is not a hypothesis — it is established economics. Companies that measure their intangible capital, invest in it strategically, and understand the productivity cycle it creates will outperform those that treat it as an unmeasurable cost. The Productivity 250 programme in the Opagio Academy explores this link in depth, with historical case studies and practical frameworks.
About the Author
David Stroll is Co-Founder and Chief Scientist at Opagio, where he leads research into intangible capital measurement and productivity frameworks. A former DEC engineer and co-founder of PayMode, David brings decades of experience at the intersection of technology, economics, and organisational productivity. Meet the team →
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