The System of National Accounts is the statistical framework that underpins virtually every macroeconomic indicator governments, central banks, and international organisations use to assess economic performance. When we quote GDP, measure capital formation, calculate productivity growth, or compare economic performance across countries, we are relying on the SNA.
The framework is revised approximately every 15-20 years. The 2008 revision (SNA 2008) was significant for productivity measurement because it brought research and development within the "asset boundary" — the line that determines what counts as capital investment rather than current expenditure. Overnight, billions of pounds of spending that had been treated as an intermediate cost became investment in a productive asset.
The asset boundary is the line in national accounts that determines what counts as capital investment (creating a durable asset) versus current expenditure (consumed in the period). Everything outside this boundary is invisible to productivity statistics as a capital input.
The 2025 revision (SNA 2025) promises to be equally significant for intangible assets. But understanding both what it achieves and what it leaves unresolved is essential for anyone who cares about the quality of productivity data.
The most consequential change is the formal recognition of data as a productive asset. SNA 2025 creates a new asset category — "data and databases" — that sits alongside software, R&D, and mineral exploration in the intellectual property products classification. This is a landmark moment. For the first time, national accounts will formally treat data as something that is produced, accumulated, and generates returns over time rather than something that is simply consumed when acquired.
SNA 2025 also provides statistical definitions for cloud computing services, artificial intelligence, and digital platforms. These definitions matter because they determine how transactions involving these technologies are classified — as investment, intermediate consumption, or final output — and therefore how they affect measured productivity.
On multinational enterprises, SNA 2025 clarifies the treatment of intellectual property products held by affiliates in different jurisdictions. This addresses a significant measurement distortion: when a pharmaceutical company locates its patent portfolio in Ireland for tax purposes, the current framework can attribute the associated output and productivity to Ireland rather than to the countries where the actual research and production occur. SNA 2025 provides clearer rules for allocating output and income where production actually takes place.
| Area | What Changes | Impact |
|---|---|---|
| Data assets | New "data and databases" asset category | Data formally treated as productive capital |
| Cloud & AI | Statistical definitions for cloud, AI, platforms | Clearer classification of digital transactions |
| Multinationals | IP allocation rules clarified | Output attributed where production occurs |
| Organisational capital | No change | Remains outside the asset boundary |
| Human capital | No change | Employer training still expensed |
| Brand equity | No change | Internally developed brands still expensed |
Despite these advances, the fundamental architecture of the asset boundary remains restrictive. SNA 2025 does not bring the following within the capital measurement framework:
Organisational capital. The management systems, process innovations, and institutional knowledge that research consistently identifies as among the most important drivers of firm-level productivity remain outside the asset boundary. A company that invests heavily in redesigning its operating model, implementing new governance structures, or building decision-support systems is making an investment that will generate returns for years. SNA 2025 continues to treat this as current expenditure.
Human capital formation by firms. Employer-funded training, professional development, mentoring programmes, and knowledge-transfer systems remain expensed rather than capitalised. The satellite accounts that some agencies produce for human capital are not integrated into the core SNA framework and therefore do not affect headline productivity statistics.
Brand equity developed internally. Marketing, advertising, and brand-building expenditure continues to be treated as intermediate consumption. Only brands acquired through business combinations appear on balance sheets, and even then, only in the acquiring firm's accounts.
Customer relationships and network effects. The investment that firms make in building, maintaining, and deepening customer relationships — and the durable economic value that those relationships represent — has no place in national accounts.
Non-R&D intellectual property. Design capabilities, proprietary methodologies, trade secrets, and process innovations that emerge from operational learning rather than formal research programmes remain unmeasured.
SNA 2025 adds data assets to the measurement framework — a genuine landmark. But organisational capital, human capital, brand equity, customer relationships, and non-R&D intellectual property all remain outside the asset boundary. These are the categories that research consistently identifies as the largest drivers of firm-level productivity.
The consequence of this incomplete measurement is not just imprecise statistics. It is distorted policy.
The UK's productivity strategy may be targeting the wrong constraints. The Productivity Institute has documented the UK's underinvestment problem across multiple dimensions. But if the measurement framework systematically undercounts intangible investment, then the investment gap may be smaller than it appears in some categories and larger in others. Policy interventions designed around measured investment gaps may miss the intangible categories where underinvestment is most acute.
Cross-country productivity comparisons are unreliable for intangible-intensive economies. The OECD's productivity comparisons are only as good as the underlying national accounts data. If different countries measure intangibles differently — or if some countries are more intangible-intensive than their statistics reflect — then the rankings and gaps that policymakers use to set priorities may be misleading. The EU-US productivity gap, for example, may partly reflect a measurement gap rather than a genuine performance gap.
Monetary and fiscal policy operates with an incomplete picture. Central banks and finance ministries rely on productivity statistics to assess potential output, the output gap, and the sustainable rate of economic growth. If productivity is systematically undermeasured because intangible capital formation is undercounted, then estimates of potential output may be too low, with consequences for interest rate decisions and fiscal planning.
Industrial strategy misallocates resources. Governments that design industrial strategy around measured productivity — identifying "high-productivity" and "low-productivity" sectors, targeting support at measured capital formation — may be inadvertently favouring tangible-intensive sectors over intangible-intensive ones. A services sector that appears to have low capital intensity may actually be highly capital-intensive once intangible assets are properly accounted for.
The EU-US productivity gap may partly reflect a measurement gap rather than a genuine performance gap — US firms invest significantly more in intangibles that fall outside the SNA boundary, making their measured productivity appear relatively stronger.
The path from SNA 2025 to comprehensive intangible measurement is long, but several priorities are clear.
Accelerate development of experimental intangible statistics. The ONS, Eurostat, and other agencies already produce experimental estimates of intangible investment beyond the SNA boundary. These need more resources, more methodological development, and — critically — more prominence. Experimental statistics should be published alongside headline productivity figures so that users understand the sensitivity of conclusions to measurement choices.
Invest in firm-level intangible asset data collection. The aggregate statistics will only improve when the underlying micro-data improves. Business surveys need to be redesigned to capture intangible investment in categories that the SNA does not yet cover. This requires collaboration with the business community to develop questions that firms can meaningfully answer.
Develop bridging methodologies. The gap between the SNA asset boundary and a comprehensive intangible asset framework is too large to close in a single revision. Statistical agencies should develop and publish bridging estimates that show how productivity figures would change under alternative, more inclusive measurement approaches.
Collaborate with the private sector on measurement innovation. Firms that specialise in intangible asset identification and valuation — including Opagio — are developing practical methodologies that could inform statistical measurement. The traditional separation between official statistics and private-sector analytics is less productive than collaboration would be.
The SNA framework has evolved considerably since its origins in the mid-20th century. Each revision has brought the measurement of economic activity closer to economic reality. SNA 2025 continues this trajectory, and the statisticians and economists responsible for it deserve recognition for tackling genuinely difficult conceptual and practical problems.
But the pace of economic transformation is outrunning the pace of statistical adaptation. The economy of 2026 is dominated by intangible-intensive firms, platform business models, and value creation mechanisms that the SNA framework captures only partially. The productivity statistics derived from this framework — the statistics that policymakers use to diagnose problems and design solutions — are increasingly incomplete descriptions of economic reality.
SNA 2025 is genuine progress, but the pace of economic transformation is outrunning the pace of statistical adaptation. Every user of productivity data — whether in government, academia, or the investment community — must understand the limitations of what they are working with.
David Stroll is CTO of Opagio, which specialises in the identification and valuation of intangible business assets. He brings 40 years of experience in strategy, technical systems delivery, and macro-economic theory (FTSE 250).
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