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Driving Productivity Growth: The Importance of Firm-Specific Knowledge Assets

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Driving Productivity Growth: The Importance of Firm-Specific Knowledge Assets

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Rebecca Riley National Institute of Economic and Social Research & LLAKES, OECD Global Forum on Productivity UK Workshop, HM Treasury, London 14 October 2016

Rebecca Riley National Institute of Economic and Social Research & LLAKES, OECD Global Forum on Productivity UK Workshop, HM Treasury, London 14 October 2016

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Driving Productivity Growth: The Importance of Firm-Specific Knowledge Assets

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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