This document discusses the importance of firm-specific knowledge assets, or intangible assets, in driving productivity growth. It finds that investments in intangible assets like digitized information, intellectual property, brands, and organizational capital can be as important as or even more important than investments in tangible physical capital for determining firms' productivity performance. Intangible assets appear to play a particularly important role in knowledge-intensive sectors. The document also finds heterogeneity in the links between different types of intangible assets and productivity across sectors and firms.

1. Driving Productivity Growth:
The Importance of Firm-Specific Knowledge Assets
Rebecca Riley
National Instituteof Economic and Social Research & LLAKES
OECD GlobalForum on Productivity
UK Workshop, HMTreasury,London
14 October 2016
Disclaimer:
This work contains statistical data which is Crown Copyright; it has been made available by the Office for National Statistics (ONS)
through the Secure Data Service (SDS) and has been used by permission. Neither the ONS nor the SDS bear any responsibility for the
analysis or interpretation of the data reported here. This work uses research datasets which may not exactly reproduce National
Statistics aggregates.
The financial support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The work was part of the
programme of the Centre for Learning and Life Chances in Knowledge Economies and Societies (LLAKES), an ESRC-funded Research
Centre – grant reference ES/J019135/1.

2. Growth in the knowledge economy
• Sustained increases in the demand and supply of skilled labour
– Technology
• New technology - skill complementarity (Goldin & Katz, 1998)
• Displacement of routine tasks (Manning & Goos, 2003; Autor, Levy, Murnane, 2003)
– Globalisation
• Tradewith labour intensive markets (Autor, Dorn, Hanson, 2013)
– Rapid expansionof higher education
• Skills matter for productivity
– Labourquality directly influences productivity
– As well as via spillovers/knowledgediffusion
• And the output of skilled labour has “investment” properties
– Intangiblesseen as the “missing input” in the knowledge economy
• Stems fromthe ICT-growth literature(O’Mahony & van Ark, 2003)
• Growing recognition of the importance of management and organisation (Black and Lynch,
2001; Basu et al, 2003; Bloomet al, 2007 )
• R&D literature

3. How much do intangibles matter?
• Macro studies have looked at intangibles in the national accounting
framework (Corrado et al, 2005; Giorgio Marrano et al, 2009; Haskel
et al, 2011)
– Economic competencies, Innovative property, Computerized information
– Treated as intermediates
– Need to be capitalised
• Magnitudes
– Evidence for the US: Corrado et al (2009) estimate around $800 billion are missing from
US GDP
– Evidence for Europe: Taking a relatively broad definition, estimates range from 7-12 per
cent of GDP over the 1990s and early 2000s (Roth and Thum, 2010)
– Evidence for the UK:
• Giorgio Marrano et al (2009) find GVA in market sectors is understated to the tune of around 6
per cent in 1970, increasing to 13 per cent by 2004.
• Haskel et al (2011) find investments in intangibles to be as important as tangibles in the 1990s
and MORE importantfrom2000 onwards.
• Dal Borgo et al (2013) analysing input-outtables suggestorganisationalinvestmentaccounts
for morethan half of UK intangible investment(mostly management & training).

4. More to learn from looking at firms’
use of intangibles
• Develop new data on firms’ knowledge assets
– Based on similar methods to those used in the recent macroeconomic
literatureon intangibles
– Providing a bottom-up approachto evaluatingpotentialmagnitudes and
patterns in intangibleinvestmentand capital
– Facilitatingnew analysis
• Study the role of knowledge assets in driving growth
– Within the unifying framework of the macroeconomic intangiblesliterature
– But at a more disaggregate level
– Within regression analysisframeworks

5. Measuring firms’ investments in intangibles
• Evaluate firms’ expenditures on intangibles:
– Using information on firms’ purchases of intangibles
– And costs of workers undertaking “intangible” tasks
– Evaluate investment share
• using common assumptions in the literature
– Capitalise investment flow (PIM)
• using depreciation rates in the literature
• Data sources:
– Annual Respondents Database
– Annual Survey of Hours and Earnings

6. Occupations involved in the
production of knowledge assets
• Digitised Information
– ICT professionals& managers
• Intellectual Property
– Natural& SocialScience professionals & managers
– Architects, Engineering professionals, Business research professionals
– Highly skilled artistic workers, designers
• Organisational Capital (Economic Competencies)
– HRM: human resources managers and directors, vocational
and industrialtrainers
– BRAND: sales, marketing, advertising & public relationsmanagers
– MANAGEMENT: chief executive and senior officials, production&
operationsdepartment managers
For related , but broader, occupational classifications of occupations involved in the production of intangibles see FP7 INNODRIVE and
Riley and Robinson (2011) Skills and Economic Performance: The Impact of Intangible Assets on UK Productivity Growth, UK Commission
for Employment and Skills.

7. Key assumptions
Intangibles Data description Source Investment Depreciation
share rate
Digitised information
Own account Labour costs of IToccupations ASHE/ARD 0.50 0.33
Purchased Investment in Software ARD 1.00 0.33
Intellectual property (OA) Labour costs of R&D occupations ASHE/ARD 1.00 0.20
Organisational
Brand
Own account Labour costs of sales occupations ASHE/ARD 0.40 0.55
Purchased Purchases of Advertising Services ARD 0.60 0.55
Management (OA) Labour costs of manager occupations ASHE/ARD 0.20 0.40
HRM (OA) Labour costs of HR occupations ASHE/ARD 0.20 0.40
Depreciation rates and investment shares based on assumptions in Corrado, Hulten & Sichel (2005, 2006), Giorgio
Marrano, Haskel & Wallis (2009), Görzig,Piekkola& Riley (2011), Corrado, Haskel, Jona-Lasinio & Iommi (2012).

8. Occupations, qualifications &
experience
Potential
Experience % with Highest Educational Qualification
IntangibleOccupations (years) Higher
Degree Education GCE, A-level Other
Digitised information 19 62% 10% 15% 13%
Intellectual property 21 63% 14% 13% 10%
Organisational
Brand 22 53% 10% 17% 20%
Management 26 35% 12% 22% 31%
Other Occupations 24 26% 10% 24% 40%
Source: Labour Force Survey,Apr-Jun 2012; Authors' calculations
Note: Potential experience=Age-Age leftcontinuous full-time education.

9. Broad sectors
• High tech manufacturing
– chemicals, computers, electrical machinery & communicationsequipment,
non-electricalmachinery, precision instruments, motor vehicles & other
transport equipment
• Low (medium) tech manufacturing
– petroleum refinery, rubber & plastic products, non-metalmineral products,
non-ferrous metals, fabricatedmetals, manufacturingnec, recycling
• Knowledge intensive services
– Post & telecommunications;computers & related activities;R&D; water & air
transport; renting of machinery & equipment;other business activities;
recreational,cultural& sporting activities
• Other services
– wholesale & retailtrade, hotels& restaurants, landtransport, supporting
transport activities,sewage & refuse, activities of membership organisations,
other services
Based on Eurostatdefinitions

10. Intangible investment by sector
(share of GVA, average 2000-2012)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
MF High Tech MF Other SERV Knowledge
Intensive
SERV Other
Tangible
Intangible
Source: ARD and ASHE; NIESR LLAKES research – preliminary results

11. Intangible investment by sector
(change in share of GVA between 2001-2005 and 2006-2010)
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
0.005
0.01
MF High Tech MF Other SERV Knowledge
Intensive
SERV Other
Tangible
Intangible
Source: ARD and ASHE; NIESR LLAKES research – preliminary results

12. Intangible investment by sector and type
(share of GVA, average 2000-2012)
Source: ARD and ASHE; NIESR LLAKES research – preliminary results
Note: No account of purchased intellectual property or management here or elsewhere in this presentation.
Manufacturing Services
High Tech Other KnowledgeIntensive Other
Digitised Information 0.015 0.008 0.037 0.012
Intellectual Property (OA) 0.065 0.027 0.046 0.010
Brand 0.035 0.038 0.043 0.045
Management (OA) 0.016 0.016 0.017 0.020
All Intangibles 0.130 0.089 0.142 0.088

13. Production function coefficients
(very large firms)
Manufacturing Services
High-tech Other Knowledge intensive Other
Employment 0.625 *** 0.431 *** 0.637 *** 0.795 ***
Physical capital 0.056 0.248 ** 0.022 0.146 ***
Digitised information 0.076 *** -0.032 0.071 ** 0.03
Intellectual property 0.044 * 0.027 -0.025 0.005
Brand -0.018 0.129 ** 0.006 0.061 *
Management 0.073 *** 0.019 0.041 ** -0.025
HRM 0.015 0.048 ** 0.002 0.002
Observations 394 435 1078 2007
Source:Annual Respondents Database and Annual Surveyof Hours andEarnings;Machinery&Equipment capital stocks made available byRichardHarris; NIESR LLAKES
research – preliminaryresults.
Notes:Large firm sample; 1998-2012;manufacturing & business services excl. finance; tangibles include machinery& equipment;firms witha minimumof 4 observations;
GMM systemestimation;DPV is log GVA.

14. Firms, intangible assets and productivity
• Clear role for intangibleassets in explainingfirms’ productivityperformance
• Potentiallyat least as importantas physicalcapitalin determining growth
– particularlyin knowledge intensivesectors
• Organisationalcapitalis importantin all sectors
– possiblymore importantin low-skill sectors,depending on asset type
– difficult to disentangle individual components
– and matters fordecisions to invest in innovation
• Digitised information more important in the skill-intensive/high-techsectors
– IT/Skill complementarity
• Intellectualproperty (own account) mainlyimportant in high-tech manufacturing
• Heterogeneity in links to productivity(across sectors/types of firm)
• Complexlinkages