The development of national GDP measurement and productivity reporting accelerated dramatically in WW2 and became standardised in the early 1950s by a sub-committee of the United Nations.

Bureau of Labor Statistics

The brief review of the Marshall Plan (Article 2) summarised the contribution of the US Bureau of Labor Statistics during WW II and the important role it played in the US Productivity Program through its firm level measurements. As Gottwald (1991) explained:

The Bureau of Labor Statistics, the American organization whose primary purpose was to enable government and private officials to assess US economic performance relative to other countries and to evaluate the competitive position of the United States in international trade. The BLS started making productivity studies in 1894 and by the mid-1940s it had become a 200-man organization pioneering in studying the impact of technological change on productivity. (Gottwald, p. 110)

During WW II, as Wasser and Dolfman (2005) outline, Silberman’s broad understanding of both the details and the issues in productivity derived from his responsibilities as Director of BLS’s Division of Industrial Productivity and Technological Development. His experience was honed during 1,900 personal visits to American manufacturing plants, his knowledge expanded by the 3,000 plant visits made annually by the division’s staff, and his access to information collected each year from 20,000 manufacturers.

The differences between attitudes in the US and UK to productivity were not cultural; they were differences about the importance of efficiency measurement at the shop floor within the firm, exactly as Taylor had documented. Both the BLS and leading US Manufacturers had learned from Taylor that you cannot manage what you cannot measure. Their own production experience had confirmed that the details hide the greatest challenges.
By 1948, BLS had had many years of experience in the systematic collection and appraisal of productivity measures covering almost every type of industry in the United States. Each year more than 3,000 American factories were visited, and BLS representatives conferred with plant managers, engineers, comptrollers, and cost accountants, among others. Detailed company output per person hour and production statistics were obtained and factual information obtained regarding the numerous factors affecting operational efficiency. With this experience in the analysis of productivity data, BLS maintained a body of specialised knowledge relating to productivity measurement which could be found nowhere else in the country. Additionally, the BLS technical abstract service, initiated in 1942, had served through the war as the official source of for abstract information on factory equipment and methods.

Development of the System of National Accounts (SNA)

Driven by the need to maximise the production of armaments and the need to redeploy predominantly male factory workers to the Army, Navy, and Air Force both the US and the UK implemented national economic planning institutions to manage resource allocation. This had led to the development of input-output tables which were needed to net out intermediate inputs from final industry outputs. After WW II, this work continued and was taken up by the newly formed United Nations as the System of National Accounts (SNA), it being of being of benefit to all countries in the UN. The latest version of SNA was edited by Sanjiv Mahajan (2019) of ONS which is the national statistical agency in the UK responsible for GDP and productivity reporting.

What Determines Productivity?

Understanding what determines productivity is one of the major concerns of macro-economics. Syverson (2011) looks at the state of understanding of the key drivers, which include innovation, Wright’s Law, labour quality, management practices, tangible and intangible assets. He points out that the main difficulties in understanding the relative importance of these drivers at both industry and firm level is that the relative weights or importance of each of these factors cannot be determined using aggregate data. One would need firm-level management production system data, which does not exist. Total factor productivity (TFP) therefore weights each factor and holds these weights constant. If productivity could be measured within the firm at employee, department, and work activity system levels, there would no longer be a need to weight inputs according to a pre-determined index. The actual values of labour and capital could be used to calculate both labour and capital productivity.

The Rise of Intangible Assets

Syverson identified intangible assets as one of the drivers of productivity growth. However, it was only in 2001 when Lev put these firmly on the table as the most important drivers of productivity growth and firm value.

Starting in the 1990s, a number of authors including Edvinsson and Malone (1997), Stewart (1997) and Sveiby (1997) discussed what they all called ‘Intellectual Capital’ and made predictions about its impact and potential importance. Lev (2001) was the first to quantify the impact of this phenomenon. He illustrated the growth in innovation by referring to the number of professional creative workers (knowledge workers) in the US economy. During the first seventy years of the twentieth century, that number increased by 2.4 million. In the last 30 years, it has increased by an additional 5 million. Lev calls this ‘the ascendancy of the intangibles’, which are characterised by fundamental corporate change with an ‘emphasis on innovation, deverticalisation, and intensive use of information technology to create innovation-based intangibles, human resource intangibles, and organizational intangibles.’

Lev illustrates the financial consequences of this change by reference to the changes in the price-to-book ratio for S&P 500 Companies from 1977 to 2001. This ratio measures the book value of tangible assets which can be freely bought and sold to the stock market value of the firm. From 1977 to 2001 the ratio went from 1 to a peak of 7 at the height of the boom before falling back to 5. This analysis suggests that intangibles accounted for four out of every five dollars of market value (author’s bolding).

In 2005, three macro-economists (Corrado, Hulten and Sichel), published a paper describing three categories of Intangible Assets:

  1. Computerized information: Knowledge embedded in computer programs and computerized databases.
  2. Innovative property: Knowledge acquired through scientific R&D and non-scientific inventive and creative activities.
  3. Economic competencies: Knowledge embedded in firm specific human and structural resources, including brand names.

Each of these three categories they then expanded to include the detailed definitions as listed below:

  1. Computerized information
    – Computerised software
    – Computerised databases
  2. Innovative property
    – Science and Engineering R&D (costs of new products and new production processes, usually leading to a patent or licence
    – Mineral exploration (spending for the acquisition of new reserves)
    – Copyright and license costs (spending for the development of entertainment and artistic originals, usually leading to a copyright or license)
    – Other product development, design, and research expenses (not necessarily leading to a patent or copyright)
  3. Economic competencies
    – Brand equity (advertising expenditures and market research for the development of brands and trademarks)
    – Firm-specific human capital (costs of developing workforce skills i.e., on-the-job training and tuition payments for job-related education)
    – Organisational structure (costs of organisational change and development; company formation expenses)

This came to be known as the CHS intangibles classification, and in 2012 Peter Goodridge and Jonathan Haskel applied this classification to an analysis of the UK economy. They followed this up in 2014 and 2016 and then in 2018 they handed over this work to ONS, who published their first estimates of intangible assets the following year (Martin, 2019). The most recent version of the UK Intangibles estimates was published by ONS (Lewis, 2021).

There are two important conclusions from these data sources. The first is that the value of intangible assets is now greater than the value of tangible assets in the UK economy. The second is that only one third of these intangible assets can be reliably measured by ONS. These are the intangible assets that are already recognised by International Accounting Standards bodies and reliably recorded in firm balance sheets. Other intangible assets are not routinely accounted for in most statutory accounting systems.

Martin (2021) presented the reasons for the difficulty in collecting accurate data at the ESCoE Conference on Intangible Assets held in London in November 2021. His presentation was titled The F words: Why Surveying Businesses About Intangibles is So Hard, and he then went on to describe the four F words. They are ‘Forgotten, Framing, Fuzzy, and Frequency’. His main argument was that the survey method used by ONS may be too infrequent and that firms may not themselves have a very good understanding of these assets or what they spent creating them. It may also be that they do not really understanding the CHS classification.

At this same conference, I presented a paper (Stroll 2021b) setting out an empirical method for collecting data on intangible assets directly from the firm, to which the audience and Martin responded positively. The latest data from ONS (Fotopolou, 2022) for 2020 was somewhat affected by the COVID pandemic, but shows that investments in intangible investments (including training) are still greater than those in tangible assets and highly concentrated in three industry sectors: Manufacturing, Information and Communication, and Financial Services, with strong contributions from Wholesale and Retail and Professional, Scientific, and Technical Services.


Syverson also notes that, like intangible assets, innovation is one of the drivers of productivity growth. However, the measurement of innovation takes place outside the System of National Accounts. The OECD has long focused on the need for better measures of innovation which resulted in the publication of the first Oslo manual in 1992 which focused on manufacturing. Large scale surveys, such as the European Community Innovation Survey followed, and these contributed to further editions of the Oslo manual, the most recent of which was published in 2005 (OECD, 2005) and which extended the range of sectors and types of innovation.

The 2005 Oslo manual classifies four types of innovation: product; process; market and organisational, which can be: new to the firm; new to the market; or new to the world. In order to deliver these innovations, firms engage in sets of ‘innovation activities’ which may be successful; ongoing; or abandoned. Innovation is measured through community innovation surveys of private sector firms, which are recommended to be conducted every two years by all OECD members. These surveys measure innovation activities, and the results of these in terms of revenue, growth, and profit. In the UK, the community innovation survey is conducted every two years by ONS on behalf of BEIS (2019) and these survey questions are fully documented.

GDP and Productivity Measurement in the UK

There is much discussion in government circles about ‘outputs’ and ‘outcomes’. Indeed, the two terms are often used interchangeably. In order to clarify this point, the definitions developed by the UK Office of National Statistics are helpful here. In the ONS Productivity Handbook (Camus, 2007), productivity is defined as follows:

The first definition of productivity can be thought of as being about the ability to produce outputs, such as goods or services, taking into consideration the number of inputs, such as raw materials, capital, and labour, used to produce them. High productivity means producing as much output as possible using as little input as possible. The second, economic, definition is a formal quantification of the first:

Productivity = Output/Input

Productivity is defined as the ratio between output and input. Therefore, increasing productivity means greater efficiency in producing output of goods and services from labour, capital, materials, and any other necessary inputs.

Labour and Total Factor Productivity

The ONS actually provides two productivity measures of as part of their quarterly GDP reports: labour productivity and multi-factor productivity (MFP). It is worth noting that many other organisations and authors call MFP total factor productivity (TFP). These different names all refer to the same concept, so this thesis will refer to the two measures of productivity as labour productivity and total factor productivity (TFP) for consistency’s sake throughout.

Labour productivity reviews the total hours spent by the firm’s employees on delivering outputs of products and services at market price. Data is collected from surveys and administrative data, and is reported quarterly (Taylor, 2018).

TFP is more complicated. It involves three sets of inputs: labour (for which the preferred measure is hours worked), capital (which is measured as a service charge based on an imputed rental cost), and purchases (energy, materials, and services) (Franklin, 2018). There are also potentially different output measures. These are usually measured by market prices, but in Franklin’s example (a small bakery) TFP output is measured in units (i.e., the quantity of loaves sold). As Franklin says, ‘Productivity is simply the rate of conversion of inputs to outputs. The tricky part is measuring the inputs and outputs properly’ (Franklin, Op Cit.).

Labour Quality and Capital Deepening

There are two further concepts which are used by ONS, these are labour quality and capital deepening. Labour quality refers to the fact that not all labour is equal. There are important differences in the level of education, the level of experience, and wages. ONS weights labour inputs by labour quality, assuming that more experienced, better education, and higher paid workers are higher quality than others. In order to increase their labour quality, firms might ask for additional educational qualification or more years of experience. In return, they offer higher wages.

In order to understand capital deepening, we need to start with a clear definition of capital, such as the one provided by Franklin (2018):

Capital input includes anything that provides an ongoing use to output without being used up in the production process. In our bakery, capital inputs would include things such as the oven and the building of the bakery; as they can be used in the production process more than once, they are not simply ‘used up’ in each production cycle.

In order to simplify data collection for TFP calculations ONS use an imputed rental input cost rather than accounting depreciation, which is used in financial accounts. Capital deepening is measured by an increase in imputed rental payments to pay for rental of more tangible and intangible assets.

In order to calculate TFP, statisticians need to remove purchases from outputs (measured in market prices) to provide what economists call gross value added (GVA). This is necessary because there are many intermediate inputs which need to be reflected in the final output prices of the goods of services provided and which are computed for all economies using the input-output tables reviewed earlier.

The next step is to calculate the ‘weights’ of quality-adjusted labour services and capital services of GVA. This is done by calculating labour costs and deducting them from GVA. The result is expressed as a percentage and, by definition, the remaining percentage (which includes gross operating surplus) is allocated to capital services. It is important to note that this is a statistical convention used to simplify the calculations.

Franklin defines TFP as follows: ‘For any given change in output, [total] factor productivity [TFP] measures the amount that cannot be accounted for by changes in inputs of quality-adjusted labour and capital’ (Franklin, 2018). It represents a change in efficiency over time between transformations of capital and labour inputs to outputs. These transformations cannot be captured by the variables used for TFP calculation because they exclude the transformation process defined by the Division of Labour to which innovations in product, process, market, and organisation apply.

None of this should be taken as a criticism of either macro-economists or statisticians. They must work with the data that they have and are constrained by the internationally adopted System of National Accounts and the need to apply double deflation techniques to all input and output prices to provide inflation-adjusted reports. These adjustments are needed to ensure that historical records are not unduly affected by different rates of inflation at different times. An understanding of TFP at the firm level is critical for managing productivity at the firm level, but insights about TFP at the firm level can only come from managers with access to firm-level data, not macro-economists.

Why is TFP Needed at Firm Level?

TFP is the internationally agreed measure of productivity. It combines labour productivity reporting with the addition of capital services charges, which represent the depreciation of tangible and intangible assets. We discussed the weakness of firm-level recording of intangible assets earlier, and the same is true of innovation expenditures. There is however no reason why a complete TFP report cannot be completed at firm level. This would have the great merit of providing much richer and more frequent data for ONS.

From the firm perspective it is clear that additional variables could be provided which would provide richer estimates of nominal TFP. These variables could include additional labour quality measures, department and work activity system level production functions, richer information on growth investments and the allocation of these investments to intangible assets and capital services charges as well as to innovation activities. These additional variables could then be used to complement and enrich TFP accounting by ONS in due course.

However, there is an even more important reason for reporting on TFP at the firm level. Most SMEs are unfamiliar with productivity’s exponential growth impact on their gross operating surplus, which is roughly equivalent to EBITDA. Many owners and investors are quite happy to grow EBITDA at the same rate as revenue, provided their customers are happy to support them. If TFP could be measured the production function level within the firm, it would be possible to assign investment and innovation spending to specific production functions and to test the effectiveness of these investments and innovation expenditures at increasing revenue and productivity.

That, ability to measure TFP at the production function level, though, is a critical missing piece. When discussing production functions, the main focus of productivity statisticians is on the inputs and the outputs of the production function. As the Figure 13 suggests, the economists’ definition of the production function is mathematical (a suggestion reinforced by one of the most comprehensive publications on the topic, a 600-page textbook on productivity functions [Sickles and Zelenyuk, 2019] that is entirely mathematical). While such mathematical descriptions of a production function may be useful for economists and statisticians, they will be of little or no use to those managers and employees who have to make a specific production function work in practice.

Figure 1: The Production Function

In a later article (Unexpected Discovery of the Management Production Function) three management production functions are described, for Marketing, for Sales and for Manufacturing Operations. This makes it possible, for the first time, to assign investment and innovation spending to a specific production function and to test the effectiveness of future investments and innovation expenditures for increasing revenue and productivity.

It is often claimed that UK firms do not invest at the same level as their competitors in other countries. This might actually be for the very sound reason that firms find it hard to evaluate the impact on revenue growth and profit growth which these investments will deliver. If that specific problem can be solved, the data would enable managers to present investors with more data about which investments work, boosting investor confidence in the firm and the decisions being taken.

There is one remaining point to be made about the capital productivity component of TFP. Because it is a ‘residual’ in national income accounting, it cannot be tied back to the actual detailed investments made by the firm which are later classified as tangible or intangible assets. But when these investments are classified at the firm level — and, more importantly, within a single management production function — they can an appear as a separate capital services line in the input costs. What managers want to know is which intangible investments deliver the greatest growth in both revenue and profit, and what are the best recipes for combining labour and capital inputs to maximise both the quantity of outputs and, where appropriate, their market value. At the level of the management production function within the firm a measurement of capital productivity now becomes possible.

A Brief History of the Division of Labour

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