A Practical Framework for Closing the Productivity Data Gap: From Firm-Level Intangible Measurement to Better National Statistics

Over the previous four posts in this series, I have set out the case that productivity statistics in advanced economies are systematically incomplete. The metrics we rely on — GDP per hour worked, total factor productivity, capital deepening — were designed for an economy dominated by tangible assets and physical output. They are increasingly unreliable guides to an economy where the majority of value is created by intangible capital: organisational systems, data, human expertise, brands, customer relationships, and intellectual property beyond the boundaries of what national accounts currently capture.

The diagnosis is clear. The question is what to do about it.

In this final post, I want to lay out a practical framework — not a theoretical ideal but a set of concrete actions that different actors can take, starting now, to begin closing the gap. The framework operates at five levels, from the individual firm to the international statistical system.

The Five-Level Framework

Level 1: Firm-Level Intangible Asset Identification

The foundation of better productivity data is better firm-level data. And better firm-level data starts with firms themselves understanding what intangible assets they possess.

Most companies have never conducted a systematic inventory of their intangible assets. They know they have valuable software, important customer relationships, a strong brand, and experienced employees. But they have not categorised these assets using a consistent taxonomy, assessed their condition and quality, identified interdependencies between them, or evaluated which intangible assets are being developed, maintained, or depleted.

The first practical step is an intangible asset audit: a structured process that identifies, categorises, and assesses the intangible assets within a business. At Opagio, we use a taxonomy that covers seven primary categories — organisational capital, proprietary data, human capital, brand equity, customer relationships, intellectual property, and ecosystem position — each broken down into specific asset types.

This audit does not require complex valuation methodologies at the initial stage. It requires a rigorous process for identifying what exists, understanding how each asset contributes to business performance, and establishing a baseline against which change can be measured.

The immediate beneficiaries are the firms themselves. An intangible asset audit typically reveals assets that are underleveraged, risks that are unmanaged (key-person dependencies, undocumented processes, customer concentration), and investment opportunities that are invisible without a structured view.

Level 2: Intangible Asset Valuation and Reporting

Once intangible assets have been identified, the next step is developing consistent approaches to valuing them and incorporating them into management reporting.

Valuation of intangible assets is inherently more challenging than valuation of tangible assets. There are no active secondary markets for most intangibles, useful life is uncertain, and the relationship between investment and returns is less direct. But difficulty is not impossibility. The valuation profession has developed robust methodologies for intangible assets, including:

Cost-based approaches that estimate the investment required to recreate the asset. These are most applicable to organisational capital, data assets, and human capital, where the accumulated investment is identifiable even if the asset does not trade.

Income-based approaches that estimate the future economic benefits attributable to the asset and discount them to present value. These are most applicable to customer relationships, brand equity, and intellectual property, where revenue or profit streams can be attributed to specific assets.

Market-based approaches that use transaction data from acquisitions and licensing agreements to calibrate values. These are useful for benchmarking but require sufficient comparable transactions.

Valuation Approaches by Asset Category

The critical advance is not methodological perfection — it is consistency. If firms adopt a common taxonomy and apply consistent valuation principles, the resulting data becomes aggregable and comparable. This is the raw material that statistical agencies need.

For management purposes, intangible asset reporting should sit alongside financial reporting, providing decision-makers with a view of the full asset base rather than just the tangible fraction. For PE firms, this means intangible asset dashboards for portfolio companies. For public companies, it means supplementary intangible asset disclosures that give investors information the financial statements alone cannot provide.

See this framework in action. Opagio's Intangible Asset Valuator applies exactly these principles — consistent taxonomy, standardised valuation, and aggregable output — to produce firm-level intangible asset assessments from basic financial data. Investors can import entire portfolios from CSV or connect Xero accounting software to eliminate manual data entry entirely. Try it free

Level 3: Industry-Level Benchmarking and Standards

Individual firm-level data becomes powerful when it can be compared across firms, sectors, and geographies. This requires standardisation.

Several initiatives are moving in this direction. The International Sustainability Standards Board (ISSB) has expanded the scope of corporate disclosure beyond traditional financial reporting. The EU's Corporate Sustainability Reporting Directive (CSRD) requires large companies to report on a range of non-financial topics. Industry bodies in sectors from technology to pharmaceuticals are developing intangible asset disclosure frameworks.

But these initiatives are fragmented and often focused on sustainability rather than productivity. What is needed is a dedicated effort to create intangible asset reporting standards that are designed specifically to capture the assets that drive productivity and value creation.

These standards should specify a common taxonomy of intangible asset categories, consistent measurement principles for each category, minimum disclosure requirements that balance information value against compliance cost, and clear guidance on how intangible asset data should be presented to enable comparability.

The accounting profession, in collaboration with the research community and standard-setting bodies, is the natural home for this work. But it will require a conceptual shift: from treating intangible assets as too difficult to measure to treating their measurement as essential infrastructure for a functioning knowledge economy.

Level 4: Statistical Agency Innovation

National statistical agencies face a genuine challenge. Their frameworks are built on international standards (the SNA) that evolve slowly and by consensus. They cannot unilaterally redefine what counts as capital or how productivity is calculated. But they can — and should — do more within existing constraints.

Expanded experimental statistics. The ONS already publishes experimental estimates of intangible investment that go beyond the SNA boundary. These need to be expanded, updated more frequently, and given more visibility. Users of productivity statistics should routinely see both the SNA-based figure and the broader estimate that includes unmeasured intangibles.

Business survey redesign. Current business surveys were designed to capture tangible investment and conventional expenditure categories. They need to be updated to capture intangible investment in categories like organisational development, data infrastructure, and workforce training with sufficient granularity to be useful.

Administrative data exploitation. Tax records, company filings, and regulatory submissions contain information about intangible investments that is currently underexploited for statistical purposes. Linking these administrative sources with survey data could significantly improve coverage without increasing respondent burden.

Collaboration with the private sector. Firms like Opagio that are developing practical intangible asset measurement methodologies at the firm level are generating data and insights that could inform statistical methodology. A more structured relationship between official statistics and private-sector analytics would benefit both.

Sensitivity analysis and alternative scenarios. When publishing productivity statistics, agencies should routinely provide sensitivity analysis showing how different assumptions about unmeasured intangible capital would affect the results. This would make the measurement uncertainty transparent rather than hidden.

Level 5: International Coordination and Policy Integration

The ultimate goal is a productivity measurement framework that reflects the reality of intangible-intensive economies. This requires action at the international level.

The OECD, which coordinates much of the work on productivity measurement and comparison across advanced economies, has a critical role. Its Compendium of Productivity Indicators, its Global Forum on Productivity, and its statistical working parties are the venues where measurement standards are debated and agreed. Intangible asset measurement needs to be elevated from a specialist research topic to a central priority within these programmes.

The 2025 SNA revision is a step forward, but the next revision — whenever it comes — should aim to bring the major categories of currently unmeasured intangible capital within the asset boundary. This requires starting the conceptual and practical groundwork now, not waiting until the next revision cycle.

For policymakers, the practical implication is that productivity strategies should be designed with explicit recognition of what the data does and does not capture. Policy evaluation should include assessment of impacts on intangible capital formation, not just measured capital investment. And policy design should avoid inadvertently penalising intangible investment through tax treatment, accounting rules, or regulatory requirements that favour tangible assets.

Why This Matters Now

The productivity challenge facing advanced economies is real and urgent. The UK's stagnation since 2008, the EU's growing gap with the US, the disappointing aggregate impact of AI on measured productivity — these are problems that demand accurate diagnosis and effective policy responses.

But accurate diagnosis requires accurate data. And accurate data requires measurement frameworks that reflect economic reality. As long as the majority of intangible capital remains outside those frameworks, our diagnosis will be incomplete and our policy responses will be imprecise.

Closing the productivity data gap is not an academic exercise. It is a prerequisite for evidence-based economic policy, for informed investment decision-making, and for an honest assessment of whether advanced economies are genuinely stagnating or simply not measuring their own performance correctly.

The framework I have outlined here is ambitious but practical. Each level can be acted on independently — a firm can conduct an intangible asset audit without waiting for international statistical reform, and statistical agencies can expand their experimental programmes without waiting for SNA revisions. But the greatest impact comes from action at all levels simultaneously, creating a virtuous cycle where better firm-level data enables better statistics, which enables better policy, which incentivises better firm-level measurement.

At Opagio, we are starting at Level 1 — helping firms see, measure, and manage the intangible assets that drive their productivity. We believe this is where the most immediate practical value lies, and where the data foundation for everything else must be built.

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).

Series Navigation

1. The Productivity Measurement Gap: Why GDP Per Hour Worked No Longer Tells the Full Story
2. Seven Categories of Intangible Assets That Productivity Statistics Ignore
3. The Intangible Blind Spot in Private Equity
4. From SNA 2008 to SNA 2025: Why National Statistics Still Undercount
5. A Practical Framework for Closing the Productivity Data Gap (this post)