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Productivity effects of knowledge-based capital – New evidence from German firm-level data

OECD Global Forum on Productivity Workshop - Berlin - 15 September 2017,Alexander Schiersch, DIW Berlin

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Productivity effects of knowledge-based capital – New evidence from German firm-level data

  1. 1. Productivity effects of knowledge-based capital – New evidence from German firm-level data Alexander Schiersch DIW Berlin OECD Global Forum on Productivity, Berlin, September 15, 2017
  2. 2. Motivation Model Data Results Conclusion What is KBC? Knowledge-based capital (KBC) is an umbrella term for a number of intangible assets. These create future benefits but, unlike machines, equipment, vehicles and structures, they do not have a physical or financial embodiment. They are generally grouped into three categories: innovative property (e.g. R&D, patents, design, trademarks) economic competencies (e.g. organisational capital, training, advertising) computerised information (software and databases) 2 of 21
  3. 3. Motivation Model Data Results Conclusion Importance of knowledge-based capital Figure: Investment shares, German business economy ex. housing, 2015 Source: DIW, VGR, INTAN-Invest 3 of 21
  4. 4. Motivation Model Data Results Conclusion Previous literature Main findings: Strong growth of investments in the various KBC elements in all advanced economies (cf. OECD, 2012; OECD, 2013) Increase in KBC investment/capital stock enhances labour productivity 4 of 21
  5. 5. Motivation Model Data Results Conclusion Previous literature Main findings: Strong growth of investments in the various KBC elements in all advanced economies (cf. OECD, 2012; OECD, 2013) Increase in KBC investment/capital stock enhances labour productivity Open issues: Very little information at firm level No analyses at detailed industry (two-digit) level Hardly any knowledge about substitution elasticities Hardly any knowledge about differences between SMEs and large firms KBC elements are usually treated as inputs 4 of 21
  6. 6. Motivation Model Data Results Conclusion Research questions: Which industries and companies are behind the aggregated numbers? 5 of 21
  7. 7. Motivation Model Data Results Conclusion Research questions: Which industries and companies are behind the aggregated numbers? How does KBC affect the total factor productivity of firms? 5 of 21
  8. 8. Motivation Model Data Results Conclusion Model Standard approach: Cobb-Douglas - KBC elements as inputs : Yit = L βl it · Cβc it · K βk1 1,it · ... · K βkn n,it · e εit ωit + it (1) with Yit being value added, Lit as labour, Cit as tangible capital stock, Kn,it as capital stock of n-th KBC element, εit as observed error term that contains ωit as TFP and it that captures measurement error etc. in year t and company i 6 of 21
  9. 9. Motivation Model Data Results Conclusion Model Standard approach: Cobb-Douglas - KBC elements as inputs : Yit = L βl it · Cβc it · K βk1 1,it · ... · K βkn n,it · e εit ωit + it (1) with Yit being value added, Lit as labour, Cit as tangible capital stock, Kn,it as capital stock of n-th KBC element, εit as observed error term that contains ωit as TFP and it that captures measurement error etc. in year t and company i Alternative approach: Production function with KBC directly affecting TFP Yit = L βl it Cβc it e εit G(K1,it, ..., Kn,it, ωit−1; γ) + it (2) 6 of 21
  10. 10. Motivation Model Data Results Conclusion Model Standard approach: Cobb-Douglas - KBC elements as inputs : Yit = L βl it · Cβc it · K βk1 1,it · ... · K βkn n,it · e εit ωit + it (1) with Yit being value added, Lit as labour, Cit as tangible capital stock, Kn,it as capital stock of n-th KBC element, εit as observed error term that contains ωit as TFP and it that captures measurement error etc. in year t and company i Alternative approach: Production function with KBC directly affecting TFP Yit = L βl it Cβc it e εit G(K1,it, ..., Kn,it, ωit−1; γ) + it (2) Econometric method: Structural approach along the lines of Ackerberg et al. (2015) 6 of 21
  11. 11. Motivation Model Data Results Conclusion Data Dataset created from administrative data sets of the German Statistical Offices (AFiD Panel) and Federal Employment Agency (IAB linked employer-employee Data) 7 of 21
  12. 12. Motivation Model Data Results Conclusion Data Dataset created from administrative data sets of the German Statistical Offices (AFiD Panel) and Federal Employment Agency (IAB linked employer-employee Data) Dataset contains 1.9 million observations covering the period 2003-2014 It contains 54 of the 71 two-digit industries of the business economy, including all R&D- and knowledge-intensive industries and services 7 of 21
  13. 13. Motivation Model Data Results Conclusion Data Dataset created from administrative data sets of the German Statistical Offices (AFiD Panel) and Federal Employment Agency (IAB linked employer-employee Data) Dataset contains 1.9 million observations covering the period 2003-2014 It contains 54 of the 71 two-digit industries of the business economy, including all R&D- and knowledge-intensive industries and services KBC elements covered: R&D (variable name Rit) Software (variable name Sit) Organizational capital (variable name Oit) Concessions, patents, licenses, trademarks and similar rights (variable name Zit) 7 of 21
  14. 14. Motivation Model Data Results Conclusion Some descriptive results Figure: Average annual investment in software; 2009-2013 Source: AFiD-Panel Industrieunternehmen, AFiD-Panel Dienstleistungen, LIAB; Calculations DIW Berlin.8 of 21
  15. 15. Motivation Model Data Results Conclusion Some descriptive results Figure: Contribution of large firms to value added and software investment; 2013 Source: AFiD-Panel Industrieunternehmen, AFiD-Panel Dienstleistungen, LIAB; Calculations DIW Berlin.9 of 21
  16. 16. Motivation Model Data Results Conclusion Estimation results Table: Elasticities per one-digit industry Manufact.(C)‡ Transport(H) Communication(J) Realestate(L) Profes.services(M) Admin.services(N) PCrepairetc.(S95) Variable Elasticities regarding output Labour (L) 0.779*** 0.488*** 0.612*** 0.203*** 0.670*** 0.567*** 0.774*** (0.007) (0.004) (0.008) (0.009) (0.003) (0.004) (0.011) Capital (C) 0.237*** 0.406*** 0.237*** 0.39*** 0.240*** 0.215*** 0.198*** (0.006) (0.005) (0.009) (0.014) (0.004) (0.003) (0.008) Variable Elasticities regarding TFP R&D (R) 0.013*** 0.005*** 0.012*** 0.026*** 0.027*** 0.022*** (0.0001) (0.0004) (0.0002) (0.0001) (0.0003) (0.0005) Software (S) 0.012*** 0.035*** 0.041*** 0.097*** 0.038*** 0.049*** 0.021*** (0.0002) (0.0003) (0.0004) (0.0011) (0.0002) (0.0003) (0.0008) Licencies (Z) 0.005*** 0.006*** 0.019*** 0.047*** 0.016*** 0.023*** 0.005*** (0.0002) (0.0004) (0.0003) (0.0012) (0.0002) (0.0004) (0.0011) Organis.cap (O) 0.01*** 0.03*** 0.053*** 0.024*** 0.01*** 0.025*** 0.014*** (0.0002) (0.0002) (0.0003) (0.0007) (0.0002) (0.0002) (0.0005) N 67,936 144,258 97,812 100,367 314,117 156,489 12,365 Annual, sectoral, legal and regional dummies are considered in the first stage of the method; ‡ period 2010-2014 10 of 21
  17. 17. Motivation Model Data Results Conclusion Box plot of significant elasticities, two-digit industries Figure: Elasticities regarding TFP −.10.1.2.3 MagnitudederKoeffizienten R&D Software Licenses Org. Capital 11 of 21
  18. 18. Motivation Model Data Results Conclusion Conclusion Main results: Bulk of the investments in different KBC elements are made by few industries Increase in Software, Organisational capital and R&D capital stocks significantly increases TFP of firms Concessions, licenses etc. are less relevant No dominance of a single KBC element across industries 12 of 21
  19. 19. Motivation Model Data Results Conclusion Wissensbasiertes Kapital in Deutschland: Analyse zu Produktivitäts- und Wachstumseffekten und Erstellung eines Indikatorsystems Studie im Auftrag Bundesministeriums für Wirtschaft und Energie Heike Belitz, Alexander Eickelpasch, Marie Le Mouel, Alexander Schiersch Berlin, Juni 2017 Kontakt: DIW Berlin Dr. Alexander Schiersch E-Mail: aschiersch@diw.de Tel.: 030 89789-262 Thank you for your attention 13 of 21

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