Where the weights are asset-level nominal incomes as shares of total operating surplus. The applied weights should be Tornqvist weights for reasons already given. Intangible capital services can be aggregated in the exact same way, with user costs for tangible and intangible capital summing to total adjusted operating surplus.
1.7. Implementation of the framework
The first step in implementation of the framework set out in this chapter is to identify intangibles that meet asset criteria and estimate investment in those assets. In practice, a large proportion of these investments are undertaken in the form of in-house production, so a method for measuring this activity is also required.
1.7.1. Which intangibles should be counted as capital goods?
The role of knowledge as a factor of production has long been a feature of the economic literature. On the practical implications for measuring investment in knowledge, Machlup (1962) correctly identified the standard investment definition as the appropriate criteria for capitalisation, but backed away from its full implications and only finally proposed the capitalisation of education and R&D. In addition to these two activities, Abramovitz (1956) also identified expenditure on health and training as among those designed to enhance productivity. Due to his stance on education, it might be expected that Machlup (1962) would take a similar view of firm-training, but instead he argued it ought to be counted as an intermediate good, due to high labour turnover. Whilst labour turnover does almost certainly reduce the service life of firm-provided training, it seems unlikely that benefits to firms last less than one year. Awano, Franklin et al. (2010a) find that on average, firms expect to benefit from training for 2 to 3.5 years, and staff turnover will have been considered in forming those expectations. The appropriate way to account for labour turnover in estimation is in the application of service lives and the rates of deterioration, depreciation and discard.
Regardless of which form of knowledge is being considered, if the decision is taken that the good in question meets capital criteria, it is important that all investment is recorded, not just that which is successful. The correct way to account for failure is in the estimation of the rates of discard and deterioration. Consider mineral exploration, the full costs of a discovery also include the cost of past failed exploration.
Measurement of only successful investments would result in over-estimation of their returns. As an example
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of this, Machlup (1962) cited a study by Ewell (1955) which estimated rates of return to R&D of 100-200%
p.a., due to the exclusion of failed or discarded investments.
The case for capitalising R&D has been made by many authors, culminating in the incorporation of R&D as an asset in the 2008 SNA. The 1993 revision had also incorporated software (purchased and own-account), artistic originals and mineral exploration as assets, following which, Chamberlin, Clayton et al. (2007) identified under recording of UK own-account software investment and Soloveichik ((2010c); (2010b);
(2010a)) worked to rectify the exclusion of artistic originals from the US National Accounts.16 Other authors have made the case for capitalising a wider range of intangibles, for instance Nakamura ((1999);(2001)), identified business process improvement, reputation, product development and design, as productive assets invested in by firms. The first comprehensive evaluation of intangibles that meet asset criteria was made by CHS ((2005); (2006)) who presented the following three broad categories to use in identification:
Table 1.4: Intangible asset categories, CHS (2006)
Computerised information Innovative Property Economic Competencies Computer software Scientific R&D Firm-specific training
Computerised databases Non-scientific R&D Reputation (Advertising and Market Research)
Design Organisational Capital
New financial product development Artistic Originals
Mineral Exploration
Investments in innovative property can be regarded as the investment in innovation itself, and those in economic competencies as the co-investments necessary to successfully undertake and commercialise the innovation, and appropriate revenue. CHS noted that knowledge assets can be purchased or produced in-house, and both need to be measured, although in-house creation of assets to be sold should not be counted twice. Due to the literatures focus on R&D, predominantly undertaken in-house, purchases of intangible assets had received less consideration up to this point, although Machlup (1962) observed the growing role of knowledge purchased from business service industries such as management consultancy and market research. Machlup also noted that it may be difficult to accurately identify such knowledge purchases if they are bundled in with other goods and services in market transactions.
1.7.2. Methods for estimating investment in intangible goods
In general, purchased investments in knowledge are easier to identify since they are recorded in the official data for intermediate consumption, although distinguishing between asset purchases and short-lived services is more challenging, as is recognising genuine purchases from licence payments. Therefore, much of the
16Work to produce new and improved estimates of UK investment in artistic originals is presented in Chapter 2 of this thesis. At the time of writing these estimates have recently been incorporated in a revision to the UK National Accounts.
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literature has focused on developing methods for estimating own-account investment, where no asset sale is observed. OECD (2010) provides a useful survey of NSI methods of estimating own-account investments in particular intangible assets. Machlup (1962) noted that the task of estimating knowledge production is made much easier if separate departments or occupations can be identified as knowledge producers, and this is exploited in the methodologies used by NSIs and in the wider literature
The two predominant approaches to estimation can be explained using the framework already discussed.
The first uses data on upstream input costs, as set out in (19), and applied by NSIs in estimating R&D investment (see for example Galindo-Rueda (2007)), using data on labour, capital and material inputs as reported in R&D surveys. Chamberlin, Clayton et al. (2007) use the same principles to measure own-account software investment, identifying the cost of upstream labour input using firm-level microdata, and adjusting those costs to: a) exclude maintenance of software and other short-lived activity; and b) account for the additional input of capital and materials in own-account software production.
The second method exploits the asset value equation in (27), but estimation requires data on the revenues that assets earn through explicit rental. Therefore the most common application of this method is to artistic originals as in Soloveichik (2010a). For instance, films earn revenue from payments by cinemas, DVD producers, TV broadcasters etc. Likewise for books and music, where royalties are paid for sales of copies, audio-visual rights, performance etc.. Chapter 2 of this thesis concerns the estimation of UK investment in artistic originals, which includes applications of both methods described here.
1.7.3. The prices of intangibles
The adjustment of data for real output and the construction of intangible capital stocks, requires data on real intangible investment meaning that we need some estimates of the prices of intangibles. This clearly presents a problem in the case of own-account investment where no asset sale occurs. Therefore the standard method of estimation is again based on a model of the upstream, with output prices estimated as share weighted averages of input prices, as in (51). This method been applied to estimating the price of R&D by researchers (e.g. Cameron (1996)) and NSIs (e.g. Galindo-Rueda (2007) and Copeland, Medeiros et al.
(2007)). A similar approach is taken to estimate the price of own-account software in the UK (see Chamberlin, Clayton et al. (2007)). Note that as written (51) does not allow for productivity change in the upstream. Improvements in upstream productivity increase the volume of upstream output and reduce its implicit price. The true model therefore ought to incorporate a term (with a negative sign) for upstream total factor productivity. On some occasions a productivity adjustment is applied. For example, in work to estimate the price of R&D in the US, Fixler (2009) subtracts an estimate of TFP based on observed data for the R&D services industry. In the UK, the official method for estimating the price of own-account software is to subtract a labour productivity growth (LPG) term for the service sector, as a proxy for upstream TFP (Chamberlin, Clayton et al. (2007)). However, if it is thought that the upstream is an innovative, productive
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sector, a LPG figure for services, inherently under-estimated for reasons discussed previously, is unlikely to be appropriate. Chapter 3 of this thesis presents new estimates for the price of UK own-account software that incorporate estimates of upstream TFP.
ln ln ln ln
; ;
N L L K K M M
N N N
L N K N M N
L K M
N N N N N N
P s P s P s P
P L P K P M
s s s
P N P N P N
(51)
A related method to this upstream approach is used in Copeland and Fixler (2012), who model in-house R&D using data for the R&D services industry. Proxying real quantities using volume indicators for output (patents) and input (scientists), and combining them with nominal data on sales and costs, they back out an implied estimate of R&D prices, and assume those prices also reflect those for in-house R&D. The main limitation of the method is that numbers of patents and scientists are imperfect proxies of real output and input.
A second approach to estimation is to use data on final output prices. The most common application of this is the use of the GDP deflator, but a more specific application in Copeland, Medeiros et al. (2007) estimates R&D prices as a weighted average of output prices in R&D intensive industries, where the weights are industry shares of total R&D investment. It is therefore assumed that the predominant input in these industries is R&D, so that the output price is primarily driven by the implicit price of R&D:
lnPN
j lnPjY
(52)A novel approach to estimating the price of knowledge, based on a decomposition of final output prices, can be found in Corrado, Goodridge and Haskel (2011). Those authors exploit data on measured outputs and inputs in the downstream (final goods) sector, and back out the implicit input price for R&D which incorporates estimated upstream TFP (ΔlnTFPN). That paper is submitted as an appendix to this thesis.