Intangible Assets and Spanish Economic
Growth
Juan Fernández de Guevara & Matilde Mas
Universidad de Valencia & Ivie
Fourth World KLEMS Conference
May 23-24, 2016
Introduction
• This paper focus on the role of accumulation of capital as a source of
economic growth in the Spanish economy.
• We distinguish the effect of different types of capital:
• Tangible non-ICT capital
• ICT capital
• Public (tangible) capital (infrastructures)
• Intangible capital (public and private)
• The aim is to:
• 1) look for the direct effect of capital on economic growth
• 2) look for complementarities particularly in the case of intangibles and ICTs.
• We adopt a cross-industry econometric approach using a database
that comprise 20 market sectors of the Spanish economy.
• Although data is available from 1995 to 2011 we primarily focus on the
pre-crisis period (1995-2007)
• The crisis has implied a break in the growth pattern and in the pace of capital
accumulation.
2
Introduction
• We seek preliminary evidence of the following broad
hypothesis:
• Is intangible capital relevant to explain differences in
productivity across industries?
• Do all intangible assets have the same effect on
economic growth?
• Are intangible assets complementary to ICT assets?
• Although public intangible and public capital data
are aggregated for the whole economy, we explore
different channels for their influence on each
industry.
• This document is a work in progress. Some results
are very preliminary.
3
Background
• Capital deepening is recognized as a source of economic
growth.
• Since the mid-90s the literature has shown the positive role of
ICT assets in explaining economic growth (Oliner and Sichel, 1994,
Jorgenson and Stiroh, 1995; and many others since then). See the
extensive survey by Biagi (2013).
• There are also studies that focus on the indirect (spillover)
effects of ICTs. The evidence is not conclusive:
• Some papers do not find evidence of spillovers (Stiroh, 1998; Inklaar et
al, 2008; Acharya and Basu 2010, for example).
• In other cases, weak evidence is found (O’Mahony and Vecchi, 2005).
• Firm level data has also shown that the relationship between ICT
capital and growth is complex:
• It requires that the economy, industries and firms change their structures human capital, management, business models, and so on- to reap the
benefits of this new disruptive types of capital (Bresnahan, Brynjolfsson,
Hitt, 2001; Brynjolfsson, Hit 1995 and 2000).
4
Background
• Recently, the attention has also shift to the role of
intangible assets. Corrado, Hulten and Sichel (2005,
2009).
• CHS framework has been applied to develop different
databases of intangible assets:
• Comparative perspective: Innodrive, Coinvest, INTAN-Invest,
KBC (OECD), TCB & SPINTAN.
• Individual countries:
•
•
•
•
•
•
•
•
•
•
•
5
Australia: Barnes & McClure (2009) and Barnes (2010)
Canada: Baldwin, Gu & Mcdonald (2011)
Finland: Julava, Aulin-Ahmavaara & Alanen (2007)
Japan: Fukao et al. (2009)
Netherlands: van Rooijen-Horsten, van den Bergen & Tanriseven
(2008)
Sweden: Edquist (2011)
UK: Marrano, Haskel & Wallis (2009)
Spain: Mas & Quesada (2013)
China: Hulten & Hao (2012)
India: Hulten, Hao & Jaeger (2012)
Brazil: World Bank (Dutz 2012)
Background
• The study of intangibles and economic growth have
focused in:
• The direct impact of intangibles. This approach follows the
seminal paper of Griliches (1979) in which R&D are treated
as an additional production factor.
• Intangible assets account ¼ of growth in the US and in UK, and a lower
percentage in the EU and in Japan.
• Complementarities: test whether intangibles and other types
of capital reinforce their effects, particularly with ICTs.
• To reap the most from intangibles, they have to be combined with ICT
assets.
• Spillovers: test the existence of externalities of intangibles
that goes beyond the direct use of intangibles in the
production function.
Corrado, Hulten and Sichel (2009), Marrano, Haskel and Wallis (2009), Fukao, Miyagawa,
Mukai, Shinoda and Tonogi (2009), Van Ark, Hao, Corrado and Hulten (2009); Corrado, Haskel,
Jona-Lasinio and Iommi (2013), Corrado, Haskel and Jona Lasinio (2014), Venturini (2015).
6
Background
• The last piece of our puzzle is public capital.
• Aschauer (1989) adopted the production function approach in
which public capital is included as an additional factor of
production.
• It is also argued that economies of scale exist due to network
externalities (World Bank (1994)).
• Barro (1990) points that public capital will have a positive
effect on growth if the expected increase in private
investment returns exceed the cost of the associated
increased fiscal costs.
• Aschauer, Bom y Ligthart (2014) and Bom y Ligthart (2014)
survey the recent literature dealing with the effect of public
capital on growth based on the production function
framework, finding large variation in the results.
• Furthermore, despite the fact that public capital generally has
a positive effect on growth, results with negative
contributions are relatively frequent.
7
Data and methodological approach
• We will follow an econometric production function approach.
• The analysis is carried out for the market non-farm sector of the
Spanish economy at industry level.
• To this end, we need data on value added, employment, (private and
public) tangible capital, (private and public intangible capital) and TFP.
•
We use several datasets to tests the hypothesis:
• Capital services of tangible non-residential capital (Pubic and private).
FBBVA-Ivie. 1995-2013
• ICT capital (KICT)

Ksoftware

Kcommunications

Khardware
• Non-ICT capital (Knon-ICT): motor vehicles, other transport material, metal products,
machinery and mechanical equipment, other machinery and equipment nec
• Public capital: Infrastructures: (roads, water infrastructure, railways, airports, ports,
urban infrastructures and other non-residential infrastructures)

8
Public capital is not available at industry level.
Data and methodological approach
• Intangible assets: Mas and Quesada (2014). 1995-2011
• 24 market sectors of the economy
• The database follows the CHS taxonomy. Similar methodology
than INTAN-Invest
• Only departs from INTAN-Invest methodology to make data compatible
with the statistics of the Spanish tangible capital, and because of the
different data sources.
• We use the following assets:
• Total intangible capital: total CHS intangibles except for Software, and
mineral exploration already included in the tangible capital
• R&D
• Training
• Organizational structure
• Public Intangibles: SPINTAN project. 1995-2011.
• Aggregate data of intangibles in the non-market sector: Public
sector + Health + Education.
9
Data and methodological approach
• In the case of public tangible and intangible capital
there is no information at industry level but at the
economy wide level.
• Hence, we need to test different channels by which
these variables affect economic growth at industry
level.
• We explore the following hypothesis:
• Public infrastructures will affect each industry depending on
the share of Transport tangible capital on total capital.
• Public intangible capital may affect industry performance
depending on:
• Industry’s human capital (% share of tertiary education employees).
• Industry’s ICT use.
10
Data and methodological approach
• Value added (Y and Y*): EU KLEMS. 1995-2011.
• Standard NA industry VA is used.
• An additional VA indicator is also considered for
accounting for intangibles.
• Each industry value added is extended (Y*) to account for
capital compensation of intangible assets:
• Total economy intangible investment (the increase in value added
associated to intangibles) for each year is broken down by industries
• To this end, the capital compensation of intangible assets
(aggregated capital services) by industry is used.
• To calculate the intangible capital compensation, it is necessary to
calculate the capital intangibles user costs (depreciation rates
similar to INTAN-Invest; 4% of rate of return) and, its prices (GVA
deflator, as in INTAN-Invest) and the intangible capital (PIM).
• Employment (L): Total hours worked. EU KLEMS.
1995-2011.
11
Data and methodological approach
Industrial classification and correspondence with CNAE
2009/NACE Rev. 2. Ivie’s estimation
code
AGRICULTURE, FORESTRY AND FISHING
MINING AND QUARRYING
ELECTRICITY, GAS AND WATER SUPPLY
Food products, beverages and tobacco
Textiles, wearing apparel, leather and related prodcuts
Wood and paper products; printing and reproduction of recorded media
Coke and refined petroleum products
Chemicals and chemical products
Rubber and plastics products, and other non-metallic mineral products
Basic metals and fabricated metal products, except machinery and equipment
Electrical and optical equipment
Machinery and equipment n.e.c.
Transport equipment
Other manufacturing; repair and installation of machinery and equipment
CONSTRUCTION
WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES
TRANSPORTATION AND STORAGE
ACCOMMODATION AND FOOD SERVICE ACTIVITIES
Publishing, audiovisual and broadcasting activities
Telecommunications
IT and other information services
FINANCIAL AND INSURANCE ACTIVITIES
PROFESSIONAL, SCIENTIFIC, TECHNICAL, ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES
ARTS, ENTERTAINMENT, RECREATION AND OTHER SERVICE ACTIVITIES
A
B
D-E
10-12
13-15
16-18
19
20-21
22-23
24-25
26-27
28
29-30
31-33
F
G
H
I
58-60
61
62-63
K
M-N
R-S
We include all industries of the market non-farm economy. We also exclude
mining and quarrying (B) and the financial and insurance sector (K)
12
Data and methodological approach
• We use a production function framework (Corrado, Haskel and
Jona-Lasinio, 2014) to test the direct effect of each type of capital
and to measure the complementarities among them.
•
We have implemented Pesaran (2007) panel unit root tests for the
different variables and in different specifications (levels, logs and logdifferences of absolute values and of per-hour-worked terms).
•
In general the null of nonstationarity is rejected and no evidence of
cointegration (panel data cointegration test by Westerlund, 2007) is found among
GVA and the dependent variables is found.
•
The null that ICT and non-ICT capital are cointegrated cannot be rejected.
•
However, the log difference of per hour-worked variables with a trend is
stationary for all the variables previously described.
•
13
Therefore we estimate the following panel data production function:
 ln Yit* / Lit     K itTIC / Lit     K itNTIC / Lit     K itINTANGIBLE / Lit   i  uit
•
Fixed or random -Hausman test- panel data models are used. Additionally,
instrumental variables estimation is used to control for endogeneity. Instruments
are the first and second difference of the productive factors.
•
We impose constant returns to scale.
Descriptives
-10
0
10
dlktic_e
TIC
20
15
61
10
5
0
 ln(Y/L)
-10
0
0

14
10
dlkint_e
ln(KINTANGIBLES/L)
20
-10
5
0
-5
 ln(Y/L)
10
15
61 61
62-63
26-27 61 13-15 61
29-30
62-63
28
29-30
10-12
10-12
16-18
20-21 13-15
28
61
28
D-E
26-27
D-E
29-30
61
31-33
61
31-33
24-25
29-30
29-30
31-33
26-27
58-60
22-23
22-23
H
D-E62-63
R-S
26-27 29-30
20-21
29-30
16-18
G
26-27
R-S
22-23
13-15
M-N
26-27
28
D-E
G
13-15
22-23
22-23
31-33
62-63
10-12
22-23
31-33
20-21
20-21
G
31-33
HI 24-25
24-25
HG
28
31-33
28
61M-N
22-23
16-18
20-21
10-12
D-E
16-18
22-23
G
16-18
26-27
61
D-E
GG
10-12
20-21
28
31-33
31-33
28
24-25
24-25
62-63
20-21
R-S
13-15
29-30
58-60
26-27
61
24-25
F10-12
31-33
28
G
R-S
10-12
H
58-60
G
H
13-15
22-23
58-60
13-15
16-18
H
24-25
M-N
62-63
10-12
29-30
D-E
F
16-18
D-E
58-60
D-E
D-E
R-S
16-18
F
62-63
10-12
29-30
H
G
M-N
R-S
13-15
58-60
16-18
M-N
13-15
10-12
20-21
13-15
G
M-N
R-S
F
13-15
62-63
10-12
62-63
F
26-27
31-33
26-27
I 20-21
F24-25
24-25
16-18
24-25
28
I FM-N
D-E62-63
58-60
I62-63
R-S
IR-S
24-25
22-23
M-N
H
16-18
28
II16-18
29-30
I 20-21
26-27
20-21
ID-E
31-33
F
R-S
H
28
IFM-N
R-S
58-60
58-60
H
I
10-12
F
13-15
26-27
M-N
I
F
58-60 F HR-S
62-6322-2329-30
M-N
24-25
58-60
M-N
-10
10
dlkntic_e
NTIC
 ln(K
dlva_e
5
0
 ln(Y/L)
-10
-5
dlva_e
10
15
 ln(K /L)
58-6061
61
30
61
26-27
13-15
29-30 61 29-30
62-63
28
10-12
10-12
16-18
20-21
6128
28
D-E
13-15
D-E
29-30
61
31-33
61 26-27
24-25
31-33
29-30
29-30
31-33
26-27
58-60
22-23
62-6320-21
22-23
H
R-S
D-E
26-27
20-21
29-30
29-30
16-18
G
26-27
R-S
13-15
22-23
M-N
26-27
28
D-E
G
22-23
13-15
22-23
31-33
62-63
22-23
10-12
24-25
31-33
20-2126-27
20-21
G
I28
31-33
H
24-25
H16-18
31-33
22-23
28
61
M-N
22-23
10-12
20-21
22-23
G
16-18
D-E
16-18
61
GG
20-21
10-12
D-E
28
31-33
28
31-33
24-25
24-25
G
20-21
13-15
29-30
R-S 62-63
58-60
26-27
61
24-25
FFFG
31-33
28
G
10-12
10-12
R-S
58-60
H
13-15
22-23
13-15
16-18
58-60
24-25
HH
10-12
62-63
M-N
29-30
D-E
D-E
D-E
16-18
58-60
D-E
16-18
R-S
10-12
62-63
29-30
R-S
M-N
G
H
H
13-15
58-60
16-18
M-N
10-12
13-15
20-21
13-15
G
M-N
R-S
13-15
10-12
62-63
31-33
26-27
FF
I28
M-N
26-27
24-25
F
16-18
20-21
62-63
24-25
24-25
I16-18
D-E
58-60
IR-S
IR-S
D-E
F
22-23
M-N
H
16-18
62-63
I
29-30
I
26-27
I 24-25
20-21
31-33
H
F
R-S
FI28
20-21
I R-S
28
M-N
58-60
H
F26-27 58-60
M-N
I I13-15
F10-12
HF 58-60
R-S
22-23
62-6329-30
M-N
24-25
58-60
M-N
-5
30
61
61
58-60
62-63
-10
61 61
61
58-60
61
62-63
26-27
61
13-15 61
62-63
28 29-30
29-30
10-12
10-12
16-18
20-21
61
28
28
D-E
26-27
13-15
D-E
29-30
61
31-33
61 24-25
31-33
29-30
29-30
31-33
26-27
58-60
22-23
62-63 26-27
22-23
H 13-15
R-S
D-E
20-21
222-23
0-2116-18
29-30
29-30
G
26-27
R-S
22-23
M-N
26-27
28
D-E
G
13-15
31-33
22-23
62-63
22-23
10-12
24-25
31-33
20-21
20-21
G24-25
IH
H31-33
31-33
28
22-23
28
61
10-12
M-N
16-18
22-23
20-21
D-E
22-23
16-18
16-18
G
26-27
61
GG
20-21
D-E
10-12
28F
31-33
28 24-25
31-33
24-25
G
24-25
G
20-21
62-63
13-15
R-S
29-30
58-60
26-27
61
31-33
G
28
10-12
10-12
58-60
H
R-S
H
G
22-23
13-15
58-60
13-15
16-18
62-63
H
10-12
24-25
M-N
29-30
D-E
D-E
F M-N
D-E
16-18
58-60
F
D-E
16-18
R-S
62-63
10-12
29-30
G R-S
H
M-N
R-S
H
13-15
58-60
16-18
10-12
13-15
20-21
13-15
G
M-N
F
13-15
10-12
62-63
62-63
31-33
F
26-27
I
26-27
R-S
M-N
24-25
F
16-18
20-21
62-63
24-25
24-25
28
I
D-E
58-60
16-18
I
R-S
D-E
I
F
24-25
22-23
M-N
28
62-63
16-18
29-30
26-27
I HIH
20-21
31-33
F
R-S M-N
F20-21
III58-60
28
58-60
10-12
F HR-S
13-15
26-27
M-N
IFIF
58-60
H
22-23R-S
62-63
29-30
M-N
24-25
58-60
M-N
dlva_e
5
0
 ln(Y/L)
-10
-5
dlva_e
10
15
• Correlations. 1995-2007
20
61
30
/L)
61
58-60
61
61
61
62-63
26-27
61 13-15
61
29-30
62-63
2829-30
10-12
10-12
16-18
20-21
28
61
28
D-E
26-27
D-E
61 31-33
29-30
31-33
61
24-25
29-30
29-30 13-15
31-33
26-27
58-60
22-23
62-63 29-30
22-23
HG
D-E
R-S
26-27
20-21
29-30
16-18
26-27
R-S
22-23
M-N
13-15
28
26-27
D-E
G
13-15
22-23
22-23
31-33
62-63
22-23
10-12
24-25
31-33
20-21
20-21
G
IG
H
31-33
24-25
H
28
31-33
22-23
28 29-30 26-27
61
M-N
16-18
20-21
10-12
22-23
22-23
D-E
16-18
G
61
G
D-E
10-12
20-21
28
31-33
31-33
28
24-25
G
G
24-25
62-63
20-21
13-15
R-S
26-27
61
24-25
FR-S
31-33
28
G
H
10-12
10-12
58-60
R-S
H
G
13-15
58-60
22-23
13-15
H
16-18
24-25
62-63
M-N
10-12
29-30
F
16-18
D-E
58-60
16-18
D-E
F
62-63
29-30
10-12
M-N
H
G
R-S
H
13-15
M-N
58-60
16-18
13-15
10-12
20-21
13-15
G
M-N
R-S
F
13-15
10-12
62-63
F
62-63
31-33
26-27
M-N
R-S
I
26-27
24-25
F
16-18
20-21
62-63
24-25
24-25
28
I
D-E
58-60
16-18
I
R-S
I
D-E
F
M-N
22-23
24-25
HH
16-18
IR-S
62-63
29-30
I28
26-27
F
F 28
20-21
I31-33
I I H26-27
R-S
58-60
58-60
IM-N
10-12
F F58-60
13-15
I M-N
F
H
R-S 22-23
62-63
M-N29-30
24-25
58-60
M-N
-10
0
10
dlkpub_e
KPUB
 ln(K
/L)
20
30
Descriptives
Average values across industries and dispersion (%)
 ln (Y/L)
 ln (KTIC/L)
 ln (KNTIC/L)
 ln (KINTANGIBLE/L)
 ln (KPUB/L)
Mean
1995
-1.42
6.07
0.05
-0.54
-0.48
2007
2.42
9.08
4.41
6.20
4.58
p25
1995
-3.58
2.96
-4.88
-2.82
-2.76
p75
2007
0.35
6.29
1.36
4.45
2.66
1995
0.99
8.15
3.68
1.83
2.19
2007
3.55
10.89
7.13
7.66
6.03
Correlations
 ln (Y/L)
 ln (Y/L)
 ln (KTIC/L)
 ln (KNTIC/L)
 ln (KINTANGIBLE/L)
 ln (KPUB/L)
* Significant at 5% level
15
1
0.42 *
0.53 *
0.64 *
0.60 *
 ln (KTIC/L)
1.00
0.58 *
0.54 *
0.52 *
 ln (KNTIC/L)
1.00
0.64 *
0.54 *
 ln (KINTA/L)
1.00
0.71 *
 ln (KPUB/L)
1.00
Descriptives
Gross value added
Non-ICT capital (private)
Intangible capital (private)
Public capital
Public intangible capital
TFP (1996=100)
2010
2008
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
2006
50
2004
100
2002
150
2000
200
1998
250
200
150
100
50
0
-50
-100
-150
1996
250
2011
2009
2007
2005
2003
ICT capital (private)
Hours worked
Private capital
16
2001
0
1999
0
1995
100
2011
50
2009
200
2007
100
2005
300
2003
150
2001
400
1999
200
1997
500
1995
250
1997
Evolution of GVA, ICT, non-ICT tangible, intangible and public capital and TFP
(1995=100)
Results. Production function. Basline estimation
• We impose constant returns to scale, and use as a first
approach total capital.
• Capital elasticity is higher than expected: 0.44 - 0.47
Dependent variable:  ln (Y / L)
 ln (K / L)
Trend
Constant
Obs.
R2 for overall model
OLS (RE)
(3)
0.439 ***
(0.071)
0.002 **
(0.001)
-0.018 ***
(0.005)
240
0.339
IV
(4)
0.474 ***
(0.089)
0.002 ***
(0.001)
-0.026 ***
(0.009)
200
0.343
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours
worked. All variables in logarithmic differences and weighted by employed person. Specifications include sector fixed effects. Heteroskedasticity
robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
Source: Author’s calculations.
17
Results. Production function. Basline estimation
•
Is the distinction of capital between ICT and non-ICT relevant? Is the distinction
of the different types of ICT capital relevant?
• Yes, it is relevant: coefs. of ICT and non-ICT capital are statistically significant. The
elasticity is now below 0.4
•
Different types of ICT capital are not relevant: very close relationship among them
(multicollinearity?)
Dependent variable:  ln (Y / L)
 ln (KNonICT/ L)
 ln (KICT/ L)
Trend
OLS (RE)
(1)
0.288 ***
(0.078)
0.102
(0.092)
0.002 *
(0.001)
IV
(2)
0.161 *
(0.089)
0.190 **
(0.076)
0.002 **
(0.001)
-0.019 ***
(0.006)
240
0.317
-0.025 **
(0.010)
200
0.277
 ln (KICT: Hardware/ L)
 ln (KICT: Communic./ L)
 ln (KICT: Software/ L)
Constant
Obs.
R2 for overall model
OLS (FE)
(3)
0.271 ***
(0.083)
IV
(4)
0.274 ***
(0.101)
Factor shares
0.002 *
(0.001)
0.018
(0.028)
0.045
(0.049)
0.015
(0.042)
-0.021 **
(0.010)
240
0.302
0.002
(0.001)
-0.012
(0.042)
0.034
(0.082)
0.017
(0.039)
-0.014
(0.014)
200
0.331
Hours worked
ICT K
Non ICT capital
Intangibles
0.60
0.05
0.29
0.06
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and
weighted by employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
18
Source: Author’s calculations.
Results. Production function. Intangibles & ICT complementarities
•
•
Are private intangible assets relevant to explain economic growth?
• Yes, they are.
• The coefficient of Intangibles reap all the significativity of the coefficients of
the three types of capital.
Are private intangible capital and ICT capital complementary?
• No, they do not seem to be complementary. Just the opposite.
• Why? Is it a matter of the types of intangibles?
Dependent variable:  ln (Y* / L)
 ln (KNonICT/ L)
 ln (KICT/ L)
 ln (KINT/ L)
Trend
OLS (FE)
(1)
0.089
(0.080)
0.048
(0.072)
0.388 ***
(0.074)
0.001
(0.001)
IV
(2)
0.015
(0.087)
0.117 *
(0.069)
0.319 ***
(0.087)
0.001
(0.001)
-0.011 **
(0.005)
240
0.498
-0.015
(0.009)
200
0.464
 ln (KICT/ L) *  ln (KINT/ L)
Constant
Obs.
R2 for overall model
OLS (FE)
(3)
0.097
(0.081)
0.060
(0.076)
0.431 ***
(0.089)
0.001
(0.001)
-0.617
(0.597)
-0.011 **
(0.005)
240
0.495
IV
(4)
0.067
(0.088)
0.183 **
(0.078)
0.438 ***
(0.109)
0.001 **
(0.001)
-1.871 **
(0.929)
-0.021 **
(0.010)
200
0.430
The net effect of
intangibles is positive (at
the average values and in
the P25 and P75)
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and
weighted by employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
19
Source: Author’s calculations.
Results. Production function. Different types of intangibles
•
Are all private intangible assets equally relevant?
• No, they are not.
• Coefficients of Training and organizational capital are statistically significant,
whereas R&D is not.
• R&D is the result of a complex process with uncertain results (Hall,
Mairesse and Mohen, 2010)
Dependent variable:  ln (Y* / L)
 ln (KNonICT/ L)
 ln (KICT/ L)
Trend
 ln (KINT: R&D/ L)
OLS (RE)
(1)
0.350 ***
(0.061)
0.048
(0.084)
0.002 **
(0.001)
-0.013
(0.013)
IV
(2)
0.346 ***
(0.086)
0.121
(0.076)
0.003 ***
(0.001)
0.007
(0.028)
 ln (KINT: Training/ L)
OLS (RE)
(3)
0.194 **
(0.086)
0.059
(0.076)
0.001 **
(0.001)
IV
(4)
0.107
(0.092)
0.175 **
(0.072)
0.002 ***
(0.001)
0.205 ***
(0.074)
0.162 **
(0.070)
 ln (KINT: Org.K/ L)
Constant
Obs.
R2 for overall model
-0.018 ***
(0.006)
228
0.379
-0.037 ***
(0.009)
190
0.402
-0.007
(0.007)
240
0.350
-0.019 *
(0.010)
200
0.312
OLS (FE)
(5)
0.104
(0.074)
0.048
(0.088)
0.001
(0.001)
IV
(6)
-0.006
(0.086)
0.115 *
(0.069)
0.001
(0.001)
0.448 ***
(0.070)
-0.008 **
(0.004)
240
0.512
0.380 ***
(0.095)
-0.009
(0.009)
200
0.487
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and
weighted by employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
Source: Author’s calculations.
20
Results. Production function. Different types of intangibles & ICT complementarities
•
Are all types of intangible capital equally relevant to explain growth?
• Again, training and organizational capital are, wheras R&D no evidence.
•
Are all types of capital really not complementary with ICTs?
• No evidence, except partially for R&D.
Dependent variable: ln (Y* / L)
OLS (RE)
(1)
 ln (KNonICT/ L)
 ln (KICT/ L)
Trend
 ln (KR&D/L)
 ln (KI CT/ L)  ln (KINT: R&D/ L)
0.346 ***
(0.060)
-0.006
(0.093)
0.002 **
(0.001)
-0.035
(0.021)
0.501 **
(0.252)
IV
(2)
0.345 ***
(0.086)
0.106
(0.096)
0.003 ***
(0.001)
0.003
(0.034)
0.086
(0.395)
 ln (KINT: Training/L)
 ln (KI CT/ L)  ln (KINT: Training/ L)
OLS (RE)
(3)
IV
(4)
0.194 **
(0.083)
0.056
(0.074)
0.001 **
(0.001)
0.133
(0.093)
0.173 **
(0.072)
0.002 ***
(0.001)
0.217 **
(0.099)
-0.180
(0.621)
0.226 **
(0.094)
-0.995
(0.927)
 ln (KINT: Org.K/ L)
 ln (KI CT/ L)  ln (KINT: Training/ L)
Constant
Obs.
R2 for overall model
21
-0.016 **
(0.007)
228
0.385
-0.035 ***
(0.011)
190
0.406
-0.007
(0.007)
240
0.349
-0.021 **
(0.010)
200
0.309
OLS (FE)
(5)
IV
(6)
0.106
(0.076)
0.051
(0.090)
0.001
(0.001)
0.014
(0.088)
0.142 *
(0.080)
0.001
(0.001)
0.465 ***
(0.092)
-0.240
(0.717)
-0.008 **
(0.004)
240
0.510
0.453 ***
(0.144)
-0.995
(1.398)
-0.012
(0.010)
200
0.476
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and weighted by
employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses..
***, **, *: significant at 1%, 5% and 10% levels, respectively.
Source: Author’s calculations.
Results. Production function. Public intangibles
•
What about public intangibles?
• Public intangibles are only available at the economy-wide aggregate. We
interact them with the human capital of the industry (% of highly educated
employees).
•
No evidence.
Dependent variable:  ln (Y* / L)
 ln (KNonICT/ L)
 ln (KICT/ L)
 ln (KINT/ L)
Trend
Human Capital* ln (KINT PUBLIC/ L)
OLS (FE)
(1)
0.083
(0.087)
0.044
(0.066)
0.397 ***
(0.082)
0.001
(0.001)
-0.004
(0.009)
IV
(2)
0.009
(0.089)
0.115 *
(0.069)
0.330 ***
(0.094)
0.001
(0.001)
-0.002
(0.007)
-0.012 **
(0.004)
240
0.492
-0.015 *
(0.009)
200
0.460
 ln (KICT/ L) *  ln (KINT/ L)
Constant
Obs.
R2 for overall model
OLS (FE)
(3)
0.091
(0.087)
0.055
(0.070)
0.440 ***
(0.094)
0.001
(0.001)
-0.004
(0.009)
-0.622
(0.604)
-0.012 **
(0.005)
240
0.490
IV
(4)
0.061
(0.090)
0.181 **
(0.078)
0.449 ***
(0.114)
0.002
(0.001)
-0.002
(0.007)
-1.877 **
(0.931)
-0.022 **
(0.009)
200
0.427
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and weighted by
employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
22
Source: Author’s calculations.
Results. Production function. Public intangibles & ICT complementarities
•
•
What about public intangibles? Are they complementary with industry ICT
assets?
• No evidence.
We need to look for additional channels for measuring the contribution of
public capital.
• Types of intangible public assets
• Types of industries: education, health and public administration.
Dependent variable:  ln (Y* / L)
 ln (KNonICT/ L)
 ln (KNonICT/ L)
 ln (KINT/ L)
Trend
 ln (KICT/ L) *  ln (KINT PUBLIC/ L)
OLS (FE)
(1)
0.087
(0.081)
0.045
(0.069)
0.389 ***
(0.075)
0.001 *
(0.001)
0.000
(0.001)
IV
(2)
0.013
(0.091)
0.118 *
(0.069)
0.328 ***
(0.086)
0.001
(0.001)
0.000
(0.001)
-0.012 **
(0.004)
240
0.486
-0.015 *
(0.009)
200
0.456
 ln (KICT/ L) *  ln (KINT/ L)
Constant
Obs.
R2 for overall model
OLS (FE)
(3)
0.096
(0.081)
0.057
(0.073)
0.438 ***
(0.091)
0.001 *
(0.001)
-0.001
(0.001)
-0.694
(0.627)
-0.012 **
(0.005)
240
0.477
IV
(4)
0.054
(0.091)
0.188 **
(0.078)
0.467 ***
(0.112)
0.002 **
(0.001)
-0.001
(0.001)
-1.995 **
(0.968)
-0.022 **
(0.010)
200
0.387
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and weighted by
employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
Source: Author’s calculations.
23
Results. Production function. Public capital
•
What about public capital (infrastructures)?
• Mixed evidence –even negative- is found in the interaction of public capital
with transport equipment in total capital (% Ktransport).
•
Too many infrastructures in Spain?
Dependent variable:  ln (Y* / L)
 ln (KNonICT/ L)
 ln (KICT/ L)
 ln (KINT/ L)
Trend
%K trasport ln (Kpublic)
Constant
Obs.
R2 for overall model
OLS (RE)
(7)
0.111
(0.072)
0.033
(0.067)
0.411 ***
(0.052)
0.001
(0.001)
-0.564 **
(0.226)
-0.008
(0.006)
240
0.515
IV
(8)
0.016
(0.087)
0.114
(0.070)
0.320 ***
(0.088)
0.001
(0.001)
-0.312
(1.415)
-0.013
(0.012)
200
0.485
Note: Labour productivity has been calculated using extended GVA and employment corrected by the composition of human capital and hours worked. All variables in logarithmic differences and weighted by
employed person. Specifications include sector fixed effects. Heteroskedasticity robust standard errors in parentheses.. ***, **, *: significant at 1%, 5% and 10% levels, respectively.
24
Source: Author’s calculations.
Main findings
•
In this paper we show preliminary results of the role of different types of intangibles on
economic growth.
•
Our approach:
•
•
25
•
Seek to assess the contribution of the different types of capital assets: tangible ICT, tangible
non-ICT, intangibles (public and private) and public capital (infrastructures).
•
Based on industry data (20 market non-farm industries) for the period 1995-2007.
Our results suggest that:
•
Intangible capital has a positive and significant role in explaining industry differences in labour
productivity.
•
We do not find complementarities between the intensity of use of ICT assets and of intangible
assets.
•
Different intangible assets have a different effect on labour productivity: we do not find evidence
on the effect of R&D, whereas organizational capital and training are relevant.
We aditionally consider public capital (intangible and infrastructures).
•
Given the nature of our data (industries) we need to explicit the mechanism by which this
aggregated capital influence industries.
•
We postulate that their influence will be channeled through human capital and ICT capital
(public intangible) and through the share of transport equipment within the industry (public capital).
•
However, no evidence is found that this are the channel for their influence.
Main findings
• To complement our results we need to progress in
the analysis. We are considering several
directions:
• Look for additional channels by which public capital
and intangibles affects growth.
• Test the existence of spillovers.
26