1. ICT and Intangible Capital:
Complementary Relations and Industry Productivity Growth
C. Corrado, (The Conference Board), New York
J. Haskel, (Imperial College, CEPR and IZA), London
C. Jona-Lasinio, (ISTAT and LUISS Lab), Rome
World Conference on Intellectual Capital for Communities - 11th Edition
28-29 May 2015, Paris
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 1 / 22

2. Introduction
• Background literature
• ICT and new sources of growth in the EU countries:
descriptive evidence
• ICT, R&D and Non-R&D Intangible Capital:
complementary relations and growth
• New policy challenges
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 2 / 22

3. Defining intangible assets
Computerized
Information
Innovative
Property
Economic
Competencies
• Software
• Databases
• R&D
• Mineral exploration
• Entertainment and artistic originals
• Design and other new product development costs
• Branding (mkt. research and long-lived advertising)
• Firm-specific human capital (training)
• Organizational capital (business process investment)
Broad&category&&&&&Type&of&Investment&&&&&&&&&&&&&&&&&
The$CHS$framework$
Non-R&D intangibles: what are they?
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4. Motivation
• Empirical evidence shows that once intangible capital is included
in a sources-of-growth analysis it accounts for 20-33% of labour
productivity growth in the market sector of the US and EU
economies.
• As a consequence, the measurement of intangible investment is a
potentially important addition to both sources-of-growth analysis
and national accounting practice.
• The inclusion of different types of capital (and labour) inputs into
a sources-of-growth model is a relevant improvement in the
understanding of the drivers of productivity changes due to
composition shifts to higher-performing types.
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5. Aim
• Investigate the productivity effects of unmeasured intangible
capital and its possible synergies with ICT.
• Our main assumption is that the interaction between ICT and
co-investment in intangible capital is a relevant omitted effect in a
production function.
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 5 / 22

6. Background literature I
• ICT is traditionally described as a general purpose technology
(Bresnahan and Trajtenberg,1995) economically beneficial
because it facilitates complementary innovations that in turn lead
to productivity increases (Brynjolfsson and Hitt (2000)).
• But the causal relationship between ICT adoption and
productivity growth is complex and not yet fully explored.
• Microeconomic evidence demonstrates that the link from
firm-level ICT adoption to productivity growth is composite,
emphasysing for example the relevance of co-investments in
training and organizational change, to generate competitive
advantage (e.g., Bresnahan, Brynjolfsson, and Hitt, 2002;
Brynjolfsson, Hitt, and Yang, 2002)).
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 6 / 22

7. Background literature II
• Macroeconomic evidence on the joint/disjoint contribution of ICT
and intangibles to industry productivity growth is still scant,
mainly because of lack of industry data on intangibles.
• Some macroeconomic productivity studies have nonetheless
suggested that the returns to ICT and productivity growth are
higher once proxies for intangibles are included, (e.g., Basu,
Fernald, Oulton, Srinivasan (2004) and Acraya and Basu, (2010)).
• Other papers have explored directly the linkage between ICT and
R&D but with differing results (Cerquera and Klein, (2008);
Polder et al., (2009); Hall et al (2012)).
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8. Background literature III
• Intangible capital has emerged as a new relevant source of
productivity growth in the U.S. (Corrado, Hulten and Sichel,
2005, 2009) and in the European economies (Corrado, Haskel,
Jona-Lasinio, Iommi, 2012 and 2014a).
• Corrado, Haskel, Jona-Lasinio (2014), found that in a production
function the estimated output elasticities of ICT capital are
reduced when intangibles are introduced, suggesting that, as
conjectured in much of the pre-intangible data literature, returns
to ICT depend crucially on the presence of unmeasurable
intangibles.
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9. The data
• INTAN-Invest harmonized cross country industry estimates of
intangible investments (www.INTAN-Invest.net)
• Country coverage: Austria, Belgium, Germany, Denmark,
Finland, France, Greece, Netherlands, Italy, Ireland, Portugal,
Spain, Sweden and UK
• Industry coverage: NACE Rev2 sectors A through N
(excluding real estate) plus sectors R and S.
• Time coverage: 1995-2010
• EUKLEMS (2012 release)
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10. Intangible investment and growth in the European
countries and industries
Intangible investments (chain linked volumes):
compounded average rates of growth 1995-2010
0.00#
1.00#
2.00#
3.00#
4.00#
5.00#
6.00#
7.00#
8.00#
9.00#
10.00# Manufacturing+ Services+
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11. ICT, R&D and Non-R&D investment intensity in the EU:
manufacturing vs services
0.0$
1.0$
2.0$
3.0$
4.0$
5.0$
6.0$
7.0$
8.0$
at$ be$ de$ dk$ es$ fi$ fr$ it$ nl$ pt$ se$ uk$
ICT$intensity:$manufacturing$and$services$
$(average'shares'of'value'added)'
manC serC
0.0$
2.0$
4.0$
6.0$
8.0$
10.0$
12.0$
14.0$
at$ be$ de$ dk$ es$ fi$ fr$ it$ nl$ pt$ se$ uk$
Intangible$(no$R&D)$intensity:$manufacturing$and$services$
(average'shares'of'value'added)'
manC serC
0.0$
2.0$
4.0$
6.0$
8.0$
10.0$
12.0$
at$ be$ de$ dk$ es$ fi$ fr$ it$ nl$ pt$ se$ uk$
R&D$intensity:$manufacturing$and$services$
$(average'shares'of'value'added)'
manC serC
Non-R&D intangibles include: Design, Advertising and Market research, Organizational capital, Training and artistic originals
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 11 / 22

12. Stylized facts
• Average annual rate of growth of intangible investment is
relatively higher in the service sectors (5.4 %) than in
manufacturing (3.0%) in all sample countries (besides FI).
• ICT intensity is higher in services, R&D intensity is highly skewed
towards manufacturing, and Non-R&D intangible investment, by
contrast, looks much more like ICT, most strong in services.
• For ICT, Portugal and Belgium are obvious laggards, but there is
no very clear North/South divide.
• For R&D, the Southern countries such as Spain, Italy and
Portugal are rather lagging with intensity particularly high in the
Nordic countries.
• For Non-R&D intangibles, Spain is somewhat of a laggard, but
the Nordic countries are by no means leaders.
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13. Sources of labour productivity growth: 1995-2009
!1.50&
!1.00&
!0.50&
0.00&
0.50&
1.00&
1.50&
2.00&
2.50&
3.00&
Austria& Germany& Spain& Finland& France& Italy& Netherlands& UK&
TFP& 0.99& 0.34& !1.05& 1.64& 0.17& !0.79& 0.76& 0.58&
Non!R&D& 0.22& 0.18& 0.11& 0.30& 0.32& 0.11& 0.32& 0.42&
R&D& 0.21& 0.13& 0.08& 0.42& 0.11& 0.03& 0.10& 0.09&
NonICT& 0.10& 0.39& 0.74& !0.27& 0.33& 0.35& 0.25& 0.48&
ICT& 0.40& 0.35& 0.41& 0.37& 0.32& 0.23& 0.49& 0.80&
Labor&composiNon& 0.26& 0.11& 0.38& 0.17& 0.46& 0.28& 0.32& 0.41&
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14. Broaden our perspective for productivity analysis:
spillovers and complementarities
PRODUCTIVITY
ICT
Non
R&D
INTG
R&D
INNOVATION
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15. Extended framework
• Besides the direct growth contribution of each single capital asset
(solid blue arrows) there are relevant synergies between them
(dashed green arrows) possibly affecting productivity growth:
• Complementarities
• Spillover effects
• Are the spillovers (CHJ, 2014) the product of knowledge diffusion
or complementarity with ICT (or both)?
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16. ICT versus Intangible, Non-R&D and R&D capital stocks
-.050.05.1
DlnKH_Intang
-.1 0 .1 .2 .3
DlnKH_ICT
“Fr” “De” “Fi” “Uk” “It” “Es” “Nl” “At” Fitted values
(a) ICT vs Intangibles
-.050.05.1
DlnKH_NonR&DIntangibles
-.1 0 .1 .2 .3
DlnKH_ICT
“Fr” “De” “Fi” “Uk” “It” “Es” “Nl” “At” Fitted values
(b) ICT vs Non-R&D Intangibles
-.050.05.1.15
DlnKH_R&D
-.1 0 .1 .2 .3
DlnKH_ICT
“Fr” “De” “Fi” “Uk” “It” “Es” “Nl” “At” Fitted values
(c) ICT vs R&D
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17. Exploring complementarities between ICT and Intangibles:
Econometric strategy
∆ln(Qi,c,t /Hi,c,t ) = α1∆ln(KICT
i,c,t /Hi,c,t ) + α2∆ln(KNonICT
i,c,t /Hi,c,t )
+ α3∆ln(Ri,c,t /Hi,c,t ) + α4∆ln(Ri,c,t /Hi,c,t ) ∗ ln(KICT /H)i,c
+ α5ln(KICT /H)i,c + α6∆ln(L/H)i,c,t + λi + λc + λt + ηi,c,t .
where all variables refer to sector i, country c at time t:
• (Q)i,c,t is value added;
• (H)i,c,t is total hours worked;
• (KNonICT
)i,c,t and (KICT
)i,c,t are Non ICT and ICT capital;
• (R)i,c,t is intangible capital;
• (L)i,c,t is labour quality;
• KICT /H)i,c denotes country-industry’s average (log) ICT intensity;
• λi , λc , λt are a set of industry, country and time dummies.
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18. Estimation results: augmented production function
Table 3—: Augmented production function (dependent variable: ln(Q/H)i,c,t
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
VARIABLES
ln(L/H)i,c,t 1 0.16* 0.20** 0.34*** 0.32*** 0.28*** 0.34*** 0.28*** 0.40*** 0.29*** 0.26***
(1.92) (2.40) (4.77) (3.90) (3.45) (4.72) (3.35) (5.06) (3.59) (3.13)
ln(KNon ICT /H)i,c,t 0.18*** 0.12** 0.28*** 0.12** 0.11** 0.27*** 0.14*** 0.06 0.22*** 0.15***
(3.49) (2.46) (6.62) (2.24) (2.31) (6.09) (2.74) (1.14) (4.00) (2.87)
ln(KICT /H)i,c,t 0.12*** 0.08*** 0.11*** 0.18*** 0.12*** 0.12*** 0.15*** 0.13*** 0.13*** 0.15***
(4.76) (3.14) (5.24) (6.84) (4.73) (5.03) (5.84) (5.28) (5.14) (5.77)
ln(R/H)i,c,t 0.07 0.04
(1.37) (0.76)
ln(RExcl R&D/H)i,c,t 0.27*** 0.20***
(5.94) (4.05)
ln(RR&D/H)i,c,t -0.01
(-0.50)
ln(KICT /H)i,c 0.01** 0.01* -0.00 0.01*** 0.01*** 0.00 0.01***
(2.47) (1.83) (-0.17) (2.68) (3.91) (0.72) (2.76)
ln(R/H)i,c,t ⇤ ln(KICT /H)i,c 0.06**
(2.49)
ln(RExcl R&D/H)i,c,t ⇤ ln(KICT /H)i,c 0.04*
(1.78)
ln(RR&D/H)i,c,t ⇤ ln(KICT /H)i,c 0.00
(0.44)
ln(RORG/H)i,c,t ⇤ ln(KICT /H)i,c 0.04*
(1.93)
ln(RTRAIN /H)i,c,t ⇤ ln(KICT /H)i,c 0.05**
(2.35)
ln(RDES/H)i,c,t ⇤ ln(KICT /H)i,c 0.09***
(4.14)
ln(RMKT /H)i,c,t ⇤ ln(KICT /H)i,c 0.05***
(2.79)
ln(RORG/H)i,c,t 0.09*
(1.74)
ln(RTRAIN /H)i,c,t 0.35***
(6.09)
ln(RDES/H)i,c,t 0.08
(1.37)
ln(RMKT /H)i,c,t 0.09*
(1.79)
Observations 528 528 440 528 528 440 528 528 517 528
R-squared 0.34 0.38 0.46 0.40 0.42 0.46 0.41 0.48 0.43 0.43
Notes to table. T-statistics in brackets. *** p¡0.01, ** p¡0.05, * p¡0.1. Final four columns also include ln(R
Ra/H)i,c,t where R(a) is the column asset.
C. Jona-Lasinio IC11 - "ICT Intangibles and Complementarities" May 28-29, 2015 18 / 22

19. Estimation results:
marginal effects of intangibles on productivity growth
-.4-.20.2.4
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
All intan
-.10.1.2.3.4
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
Non-R&D intan
-.1-.050.05
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
R&D
-.2-.10.1.2.3
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
Org
0.2.4.6
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
Train
-.4-.20.2.4
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
Design
-.4-.20.2.4
EffectsonFittedValues
-3 -2 -1 0 1 2
ICT/H
Mkting
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20. Estimation results
• The positive interaction effects between average ICT and
intangibles suggests that labour productivity growth in
above-average ICT intensive countries-industries was faster in
countries-industries experiencing higher increases in intangible
capital accumulation, or that ICT capital and intangible capital
are complements in production.
• The marginal effect is increasing as the degree of ICT intensity
increases.
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21. Conclusions I
• Intangible capital accumulation more dynamic in services than
manufacturing (excluding Finland)
• R&D, Non-R&D and ICT average intensities:
• Broad differences between Northern and Southern Europe
• Northern countries relatively more intangible and ICT intensive than Southern
countries
• Services are more intensive in Non-R&D Intangibles and ICT than
manufacturing
• The contribution of intangibles to labour productivity growth is
similar in both manufacturing and services (about 25% of labour
productivity growth).
• Non-R&D intangible capital accounts for a relatively higher share of labour
productivity growth compared to R&D.
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22. Conclusions II
• ICT and intangibles are complement in the production process (a
relation holding for sub-classes of intangible assets).
• Intangible assets may have a stronger effect on productivity
growth when interacted with other variables, particularly with ICT.
• The strong correlation between Non-R&D intangibles and ICT
suggests they are strategic variables in the innovation policy
agenda.
• Policymakers need more accurate data to identify sound policies
to foster investment in innovation and the diffusion of innovative
activity.
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