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

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. 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)
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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
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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
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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
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6. Motivation Model Data Results Conclusion
Research questions:
Which industries and companies are behind the aggregated numbers?
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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?
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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
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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)
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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)
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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)
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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
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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)
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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. 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. 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
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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
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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
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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
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