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Intangible Assets and Spanish Economic
Growth
Juan Fernández de Guevara & Matilde Mas
Universidad de Valencia & Ivie
Fourth World KLEMS Conference
May 23-24, 2016
SPINTAN Project: Smart Public intangibles. This project has received funding from the European Union’s Seventh Framework
Programme for research, technological development and demonstration under grant agreement no: 612774.
The views expressed in this website are the sole responsibility of the author and do not necessarily reflect the views of the
European Commission.
22
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.
33
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.
44
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).
55
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:
• 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)
66
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).
77
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.
88
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 and (private and public) intangible capital.
• 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)
 Public capital is not available at industry level.
99
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.
1010
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.
1111
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.
1212
Data and methodological approach
code
AGRICULTURE, FORESTRY AND FISHING A
MINING AND QUARRYING B
ELECTRICITY, GAS AND WATER SUPPLY D-E
Food products, beverages and tobacco 10-12
Textiles, wearing apparel, leather and related prodcuts 13-15
Wood and paper products; printing and reproduction of recorded media 16-18
Coke and refined petroleum products 19
Chemicals and chemical products 20-21
Rubber and plastics products, and other non-metallic mineral products 22-23
Basic metals and fabricated metal products, except machinery and equipment 24-25
Electrical and optical equipment 26-27
Machinery and equipment n.e.c. 28
Transport equipment 29-30
Other manufacturing; repair and installation of machinery and equipment 31-33
CONSTRUCTION F
WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES G
TRANSPORTATION AND STORAGE H
ACCOMMODATION AND FOOD SERVICE ACTIVITIES I
Publishing, audiovisual and broadcasting activities 58-60
Telecommunications 61
IT and other information services 62-63
FINANCIAL AND INSURANCE ACTIVITIES K
PROFESSIONAL, SCIENTIFIC, TECHNICAL, ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES M-N
ARTS, ENTERTAINMENT, RECREATION AND OTHER SERVICE ACTIVITIES R-S
Industrial classification and correspondence with CNAE
2009/NACE Rev. 2. Ivie’s estimation
We include all industries of the market non-farm economy. We also exclude
mining and quarrying (B) and the financial and insurance sector (K)
1313
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 log-
differences 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.
• Therefore we estimate the following panel data production function:
• 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.
       *
ln / / / /TIC NTIC INTANGIBLE
it it it it it it it it i itY L K L K L K L u        
1414
10-12
10-12
10-1210-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
13-15
13-15
13-15
13-1513-15
13-15
13-15
13-15
13-1513-15
13-15
13-15
16-1816-18 16-18
16-18
16-18
16-18
16-1816-1816-18
16-18
16-18
16-18
20-21
20-21
20-21 20-21
20-21
20-21
20-21
20-21
20-21
20-2120-21
20-21
22-23
22-2322-23
22-23
22-23
22-23
22-2322-23
22-23
22-23
22-23
22-23
24-25
24-25 24-25
24-25
24-2524-25
24-25
24-25
24-25
24-25
24-25
24-25
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
28 28
28
2828
28
28
28
28
28
28
28
29-30
29-30
29-30
29-30
29-30
29-30
29-3029-30
29-3029-30
29-30
29-30
31-33
31-33
31-33
31-33
31-33
31-33
31-3331-33
31-33
31-33
31-33
31-33
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-6061
61
61 61
61
61
6161
61
61
61
61
62-6362-6362-63
62-63
62-63
62-63
62-6362-6362-6362-63
62-63
62-63
D-E
D-E
D-E
D-E
D-E
D-E D-E
D-E
D-ED-E
D-E
D-E
F F
F F
F
F
FF F
FF
F
G
GG
G
G
GGGG
GG
G
H
HH
H
H
H
H
H
H
H
H
H
I
I I
I
II
I
II
II
I
M-N
M-N
M-N
M-N
M-N
M-N
M-N
M-NM-N
M-N
M-N
M-N
R-S
R-S
R-S
R-S
R-S
R-S
R-SR-S
R-SR-S
R-S
R-S
-10
-5
05
1015
dlva_e
-10 0 10 20 30
dlktic_e
10-12
10-12
10-1210-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
13-15
13-15
13-15
13-1513-15
13-15
13-15
13-15
13-1513-15
13-15
13-15
16-1816-18 16-18
16-18
16-18
16-18
16-1816-1816-18
16-18
16-18
16-18
20-21
20-21
20-2120-21
20-21
20-21
20-21
20-21
20-21
20-2120-21
20-21
22-23
22-2322-23
22-23
22-23
22-23
22-2322-23
22-23
22-23
22-23
22-23
24-25
24-2524-25
24-25
24-2524-25
24-25
24-25
24-25
24-25
24-25
24-25
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
28 28
28
2828
28
28
28
28
28
28
28
29-30
29-30
29-30
29-30
29-30
29-30
29-3029-30
29-3029-30
29-30
29-30
31-33
31-33
31-33
31-33
31-33
31-33
31-3331-33
31-33
31-33
31-33
31-33
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-6061
61
61 61
61
61
6161
61
61
61
61
62-6362-6362-63
62-63
62-63
62-63
62-6362-6362-63 62-63
62-63
62-63
D-E
D-E
D-E
D-E
D-E
D-ED-E
D-E
D-ED-E
D-E
D-E
FF
FF
F
F
FFF
FF
F
G
GG
G
G
GGGG
GG
G
H
HH
H
H
H
H
H
H
H
H
H
I
I I
I
I I
I
II
II
I
M-N
M-N
M-N
M-N
M-N
M-N
M-N
M-NM-N
M-N
M-N
M-N
R-S
R-S
R-S
R-S
R-S
R-S
R-SR-S
R-SR-S
R-S
R-S
-10
-5
05
1015
dlva_e
-10 0 10 20 30
dlkntic_e
10-12
10-12
10-1210-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
13-15
13-15
13-15
13-1513-15
13-15
13-15
13-15
13-1513-15
13-15
13-15
16-1816-1816-18
16-18
16-18
16-18
16-1816-1816-18
16-18
16-18
16-18
20-21
20-21
20-2120-21
20-21
20-21
20-21
20-21
20-21
20-2120-21
20-21
22-23
22-2322-23
22-23
22-23
22-23
22-2322-23
22-23
22-23
22-23
22-23
24-25
24-2524-25
24-25
24-2524-25
24-25
24-25
24-25
24-25
24-25
24-25
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
28 28
28
2828
28
28
28
28
28
28
28
29-30
29-30
29-30
29-30
29-30
29-30
29-3029-30
29-3029-30
29-30
29-30
31-33
31-33
31-33
31-33
31-33
31-33
31-3331-33
31-33
31-33
31-33
31-33
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-6061
61
61 61
61
61
6161
61
61
61
61
62-63
62-6362-63
62-63
62-63
62-63
62-6362-6362-6362-63
62-63
62-63
D-E
D-E
D-E
D-E
D-E
D-ED-E
D-E
D-ED-E
D-E
D-E
FF
FF
F
F
F FF
F F
F
G
GG
G
G
GGGG
GG
G
H
HH
H
H
H
H
H
H
H
H
H
I
I I
I
I I
I
I I
I I
I
M-N
M-N
M-N
M-N
M-N
M-N
M-N
M-NM-N
M-N
M-N
M-N
R-S
R-S
R-S
R-S
R-S
R-S
R-SR-S
R-S R-S
R-S
R-S
-10
-5
05
1015
dlva_e
-10 0 10 20 30
dlkint_e
10-12
10-12
10-1210-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
10-12
13-15
13-15
13-15
13-1513-15
13-15
13-15
13-15
13-1513-15
13-15
13-15
16-1816-1816-18
16-18
16-18
16-18
16-1816-1816-18
16-18
16-18
16-18
20-21
20-21
20-2120-21
20-21
20-21
20-21
20-21
20-21
20-2120-21
20-21
22-23
22-2322-23
22-23
22-23
22-23
22-2322-23
22-23
22-23
22-23
22-23
24-25
24-2524-25
24-25
24-2524-25
24-25
24-25
24-25
24-25
24-25
24-25
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
26-27
28 28
28
2828
28
28
28
28
28
28
28
29-30
29-30
29-30
29-30
29-30
29-30
29-3029-30
29-3029-30
29-30
29-30
31-33
31-33
31-33
31-33
31-33
31-33
31-3331-33
31-33
31-33
31-33
31-33
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-60
58-6061
61
61 61
61
61
6161
61
61
61
61
62-63
62-6362-63
62-63
62-63
62-63
62-6362-6362-6362-63
62-63
62-63
D-E
D-E
D-E
D-E
D-E
D-ED-E
D-E
D-ED-E
D-E
D-E
FF
FF
F
F
FFF
F F
F
G
GG
G
G
GGGG
GG
G
H
HH
H
H
H
H
H
H
H
H
H
I
II
I
I I
I
II
II
I
M-N
M-N
M-N
M-N
M-N
M-N
M-N
M-NM-N
M-N
M-N
M-N
R-S
R-S
R-S
R-S
R-S
R-S
R-SR-S
R-SR-S
R-S
R-S
-10
-5
05
1015
dlva_e
-10 0 10 20 30
dlkpub_e
Descriptives
• Correlations. 1995-2007
ln(Y/L)ln(Y/L)
ln(Y/L)ln(Y/L)
 ln(KICT/L)  ln(KNICT/L)
 ln(KINTANGIBLES/L)  ln(KKPUB/L)
1515
Descriptives
 ln (Y/L)  ln (KTIC/L)  ln (KNTIC/L)  ln (KINTA/L)  ln (KPUB/L)
 ln (Y/L) 1
 ln (KTIC/L) 0.42 * 1.00
 ln (KNTIC/L) 0.53 * 0.58 * 1.00
 ln (KINTANGIBLE/L) 0.64 * 0.54 * 0.64 * 1.00
 ln (KPUB/L) 0.60 * 0.52 * 0.54 * 0.71* 1.00
Mean p25 p75
1996 2007 1996 2007 1996 2007
 ln (Y/L) -1.42 2.42 -3.58 0.35 0.99 3.55
 ln (KTIC/L) 6.07 9.08 2.96 6.29 8.15 10.89
 ln (KNTIC/L) 0.05 4.41 -4.88 1.36 3.68 7.13
 ln (KINTANGIBLE/L) -0.54 6.20 -2.82 4.45 1.83 7.66
 ln (KPUB/L) -0.48 4.58 -2.76 2.66 2.19 6.03
Average values across industries and dispersion (%)
Correlations
* Significant at 5% level
1616
Descriptives
Evolution of GVA, ICT, non-ICT tangible, intangible and public capital and TFP
(1995=100)
0
100
200
300
400
500
1995
1997
1999
2001
2003
2005
2007
2009
2011
ICT capital (private) Non-ICT capital (private)
Intangible capital (private)
0
50
100
150
200
250
1995
1997
1999
2001
2003
2005
2007
2009
2011
Gross value added Hours worked
Private capital
0
50
100
150
200
250
1995
1997
1999
2001
2003
2005
2007
2009
2011
Public capital Public intangible capital
-150
-100
-50
0
50
100
150
200
250
1996
1998
2000
2002
2004
2006
2008
2010
TFP (1996=100)
1717
Results. Production function. Basline estimation
Dependent variable:  ln (Y / L)
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.
OLS (RE) IV
(3) (4)
 ln (K / L) 0.439*** 0.474***
(0.071) (0.089)
Trend 0.002** 0.002***
(0.001) (0.001)
Constant -0.018*** -0.026***
(0.005) (0.009)
Obs. 240 200
R2 for overall model 0.339 0.343
• We impose constant returns to scale, and use as a first
approach total capital.
• Capital elasticity is higher than expected: 0.44 - 0.47
1818
Results. Production function. Basline estimation
Dependent variable:  ln (Y / L)
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.
OLS (RE) IV OLS (FE) IV
(1) (2) (3) (4)
 ln (KNonICT
/ L) 0.288*** 0.161* 0.271*** 0.274***
(0.078) (0.089) (0.083) (0.101)
 ln (KICT
/ L) 0.102 0.190**
(0.092) (0.076)
Trend 0.002* 0.002** 0.002* 0.002
(0.001) (0.001) (0.001) (0.001)
 ln (KICT: Hardware
/ L) 0.018 -0.012
(0.028) (0.042)
 ln (KICT: Communic.
/ L) 0.045 0.034
(0.049) (0.082)
 ln (KICT: Software
/ L) 0.015 0.017
(0.042) (0.039)
Constant -0.019*** -0.025** -0.021** -0.014
(0.006) (0.010) (0.010) (0.014)
Obs. 240 200 240 200
R2 for overall model 0.317 0.277 0.302 0.331
• 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.
1919
Results. Production function. Intangibles & ICT complementarities
Dependent variable:  ln (Y* / L)
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.
OLS (FE) IV OLS (FE) IV
(1) (2) (3) (4)
 ln (KNonICT
/ L) 0.089 0.015 0.097 0.067
(0.080) (0.087) (0.081) (0.088)
 ln (KICT
/ L) 0.048 0.117* 0.060 0.183**
(0.072) (0.069) (0.076) (0.078)
 ln (KINT
/ L) 0.388*** 0.319*** 0.431*** 0.438***
(0.074) (0.087) (0.089) (0.109)
Trend 0.001 0.001 0.001 0.001**
(0.001) (0.001) (0.001) (0.001)
 ln (KICT
/ L) *  ln (KINT
/ L) -0.617 -1.871**
(0.597) (0.929)
Constant -0.011** -0.015 -0.011** -0.021**
(0.005) (0.009) (0.005) (0.010)
Obs. 240 200 240 200
R2 for overall model 0.498 0.464 0.495 0.430
• 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?
The net effect of
intangibles is positive (at
the average values and in
the P25 and P75)
Hours worked 0.60
ICT K 0.05
Non ICT capital 0.29
Intangibles 0.06
Factor shares
2020
Results. Production function. Different types of intangibles
Dependent variable:  ln (Y* / L)
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.
OLS (RE) IV OLS (RE) IV OLS (FE) IV
(1) (2) (3) (4) (5) (6)
 ln (KNonICT
/ L) 0.350*** 0.346*** 0.194** 0.107 0.104 -0.006
(0.061) (0.086) (0.086) (0.092) (0.074) (0.086)
 ln (KICT
/ L) 0.048 0.121 0.059 0.175** 0.048 0.115*
(0.084) (0.076) (0.076) (0.072) (0.088) (0.069)
Trend 0.002** 0.003*** 0.001** 0.002*** 0.001 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
 ln (KINT: R&D
/ L) -0.013 0.007
(0.013) (0.028)
 ln (KINT: Training
/ L) 0.205*** 0.162**
(0.074) (0.070)
 ln (KINT: Org.K
/ L) 0.448*** 0.380***
(0.070) (0.095)
Constant -0.018*** -0.037*** -0.007 -0.019* -0.008** -0.009
(0.006) (0.009) (0.007) (0.010) (0.004) (0.009)
Obs. 228 190 240 200 240 200
R2 for overall model 0.379 0.402 0.350 0.312 0.512 0.487
• 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)
2121
Results. Production function. Different types of intangibles & ICT complementarities
Dependent variable: ln (Y* / L)
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.
OLS (RE) IV OLS (RE) IV OLS (FE) IV
(1) (2) (3) (4) (5) (6)
 ln (KNonICT
/ L) 0.346 *** 0.345 *** 0.194 ** 0.133 0.106 0.014
(0.060) (0.086) (0.083) (0.093) (0.076) (0.088)
 ln (KICT
/ L) -0.006 0.106 0.056 0.173 ** 0.051 0.142 *
(0.093) (0.096) (0.074) (0.072) (0.090) (0.080)
Trend 0.002 ** 0.003 *** 0.001 ** 0.002 *** 0.001 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
 ln (KR&D
/L) -0.035 0.003
(0.021) (0.034)
 ln (KI CT
/ L)  ln (KINT: R&D
/ L) 0.501 ** 0.086
(0.252) (0.395)
 ln (KINT: Training
/L) 0.217 ** 0.226 **
(0.099) (0.094)
 ln (KI CT
/ L)  ln (KINT: Training
/ L) -0.180 -0.995
(0.621) (0.927)
 ln (KINT: Org.K
/ L) 0.465 *** 0.453 ***
(0.092) (0.144)
 ln (KI CT
/ L)  ln (KINT: Training
/ L) -0.240 -0.995
(0.717) (1.398)
Constant -0.016 ** -0.035 *** -0.007 -0.021 ** -0.008 ** -0.012
(0.007) (0.011) (0.007) (0.010) (0.004) (0.010)
Obs. 228 190 240 200 240 200
R2 for overall model 0.385 0.406 0.349 0.309 0.510 0.476
• 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.
2222
Results. Production function. Public intangibles
Dependent variable:  ln (Y* / L)
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.
OLS (FE) IV OLS (FE) IV
(1) (2) (3) (4)
 ln (KNonICT
/ L) 0.083 0.009 0.091 0.061
(0.087) (0.089) (0.087) (0.090)
 ln (KICT
/ L) 0.044 0.115* 0.055 0.181**
(0.066) (0.069) (0.070) (0.078)
 ln (KINT
/ L) 0.397*** 0.330*** 0.440*** 0.449***
(0.082) (0.094) (0.094) (0.114)
Trend 0.001 0.001 0.001 0.002
(0.001) (0.001) (0.001) (0.001)
Human Capital* ln (KINT PUBLIC
/ L) -0.004 -0.002 -0.004 -0.002
(0.009) (0.007) (0.009) (0.007)
 ln (KICT
/ L) *  ln (KINT
/ L) -0.622 -1.877**
(0.604) (0.931)
Constant -0.012** -0.015* -0.012** -0.022**
(0.004) (0.009) (0.005) (0.009)
Obs. 240 200 240 200
R2 for overall model 0.492 0.460 0.490 0.427
• 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.
2323
Results. Production function. Public intangibles & ICT complementarities
Dependent variable:  ln (Y* / L)
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.
OLS (FE) IV OLS (FE) IV
(1) (2) (3) (4)
 ln (KNonICT
/ L) 0.087 0.013 0.096 0.054
(0.081) (0.091) (0.081) (0.091)
 ln (KNonICT
/ L) 0.045 0.118* 0.057 0.188**
(0.069) (0.069) (0.073) (0.078)
 ln (KINT
/ L) 0.389*** 0.328*** 0.438*** 0.467***
(0.075) (0.086) (0.091) (0.112)
Trend 0.001* 0.001 0.001* 0.002**
(0.001) (0.001) (0.001) (0.001)
 ln (KICT
/ L) *  ln (KINT PUBLIC
/ L) 0.000 0.000 -0.001 -0.001
(0.001) (0.001) (0.001) (0.001)
 ln (KICT
/ L) *  ln (KINT
/ L) -0.694 -1.995**
(0.627) (0.968)
Constant -0.012** -0.015* -0.012** -0.022**
(0.004) (0.009) (0.005) (0.010)
Obs. 240 200 240 200
R2 for overall model 0.486 0.456 0.477 0.387
• 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.
2424
Results. Production function. Public capital
Dependent variable:  ln (Y* / L)
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.
OLS (RE) IV
(7) (8)
 ln (KNonICT
/ L) 0.111 0.016
(0.072) (0.087)
 ln (KICT
/ L) 0.033 0.114
(0.067) (0.070)
 ln (KINT
/ L) 0.411*** 0.320***
(0.052) (0.088)
Trend 0.001 0.001
(0.001) (0.001)
%K trasport
 ln (Kpublic
) -0.564** -0.312
(0.226) (1.415)
Constant -0.008 -0.013
(0.006) (0.012)
Obs. 240 200
R2 for overall model 0.515 0.485
• 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?
2525
Main findings
• In this paper we show preliminary results of the role of different types of intangibles on
economic growth.
• Our approach:
• 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 additionally 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.
2626
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.
• Identify the precise effect of each type of capital.

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Intangible Assets and Spanish Economic Growth

  • 1. Intangible Assets and Spanish Economic Growth Juan Fernández de Guevara & Matilde Mas Universidad de Valencia & Ivie Fourth World KLEMS Conference May 23-24, 2016 SPINTAN Project: Smart Public intangibles. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no: 612774. The views expressed in this website are the sole responsibility of the author and do not necessarily reflect the views of the European Commission.
  • 2. 22 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.
  • 3. 33 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.
  • 4. 44 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).
  • 5. 55 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: • 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)
  • 6. 66 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).
  • 7. 77 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.
  • 8. 88 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 and (private and public) intangible capital. • 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)  Public capital is not available at industry level.
  • 9. 99 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.
  • 10. 1010 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.
  • 11. 1111 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.
  • 12. 1212 Data and methodological approach code AGRICULTURE, FORESTRY AND FISHING A MINING AND QUARRYING B ELECTRICITY, GAS AND WATER SUPPLY D-E Food products, beverages and tobacco 10-12 Textiles, wearing apparel, leather and related prodcuts 13-15 Wood and paper products; printing and reproduction of recorded media 16-18 Coke and refined petroleum products 19 Chemicals and chemical products 20-21 Rubber and plastics products, and other non-metallic mineral products 22-23 Basic metals and fabricated metal products, except machinery and equipment 24-25 Electrical and optical equipment 26-27 Machinery and equipment n.e.c. 28 Transport equipment 29-30 Other manufacturing; repair and installation of machinery and equipment 31-33 CONSTRUCTION F WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES G TRANSPORTATION AND STORAGE H ACCOMMODATION AND FOOD SERVICE ACTIVITIES I Publishing, audiovisual and broadcasting activities 58-60 Telecommunications 61 IT and other information services 62-63 FINANCIAL AND INSURANCE ACTIVITIES K PROFESSIONAL, SCIENTIFIC, TECHNICAL, ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES M-N ARTS, ENTERTAINMENT, RECREATION AND OTHER SERVICE ACTIVITIES R-S Industrial classification and correspondence with CNAE 2009/NACE Rev. 2. Ivie’s estimation We include all industries of the market non-farm economy. We also exclude mining and quarrying (B) and the financial and insurance sector (K)
  • 13. 1313 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 log- differences 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. • Therefore we estimate the following panel data production function: • 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.        * ln / / / /TIC NTIC INTANGIBLE it it it it it it it it i itY L K L K L K L u        
  • 14. 1414 10-12 10-12 10-1210-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 13-15 13-15 13-15 13-1513-15 13-15 13-15 13-15 13-1513-15 13-15 13-15 16-1816-18 16-18 16-18 16-18 16-18 16-1816-1816-18 16-18 16-18 16-18 20-21 20-21 20-21 20-21 20-21 20-21 20-21 20-21 20-21 20-2120-21 20-21 22-23 22-2322-23 22-23 22-23 22-23 22-2322-23 22-23 22-23 22-23 22-23 24-25 24-25 24-25 24-25 24-2524-25 24-25 24-25 24-25 24-25 24-25 24-25 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 28 28 28 2828 28 28 28 28 28 28 28 29-30 29-30 29-30 29-30 29-30 29-30 29-3029-30 29-3029-30 29-30 29-30 31-33 31-33 31-33 31-33 31-33 31-33 31-3331-33 31-33 31-33 31-33 31-33 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-6061 61 61 61 61 61 6161 61 61 61 61 62-6362-6362-63 62-63 62-63 62-63 62-6362-6362-6362-63 62-63 62-63 D-E D-E D-E D-E D-E D-E D-E D-E D-ED-E D-E D-E F F F F F F FF F FF F G GG G G GGGG GG G H HH H H H H H H H H H I I I I II I II II I M-N M-N M-N M-N M-N M-N M-N M-NM-N M-N M-N M-N R-S R-S R-S R-S R-S R-S R-SR-S R-SR-S R-S R-S -10 -5 05 1015 dlva_e -10 0 10 20 30 dlktic_e 10-12 10-12 10-1210-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 13-15 13-15 13-15 13-1513-15 13-15 13-15 13-15 13-1513-15 13-15 13-15 16-1816-18 16-18 16-18 16-18 16-18 16-1816-1816-18 16-18 16-18 16-18 20-21 20-21 20-2120-21 20-21 20-21 20-21 20-21 20-21 20-2120-21 20-21 22-23 22-2322-23 22-23 22-23 22-23 22-2322-23 22-23 22-23 22-23 22-23 24-25 24-2524-25 24-25 24-2524-25 24-25 24-25 24-25 24-25 24-25 24-25 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 28 28 28 2828 28 28 28 28 28 28 28 29-30 29-30 29-30 29-30 29-30 29-30 29-3029-30 29-3029-30 29-30 29-30 31-33 31-33 31-33 31-33 31-33 31-33 31-3331-33 31-33 31-33 31-33 31-33 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-6061 61 61 61 61 61 6161 61 61 61 61 62-6362-6362-63 62-63 62-63 62-63 62-6362-6362-63 62-63 62-63 62-63 D-E D-E D-E D-E D-E D-ED-E D-E D-ED-E D-E D-E FF FF F F FFF FF F G GG G G GGGG GG G H HH H H H H H H H H H I I I I I I I II II I M-N M-N M-N M-N M-N M-N M-N M-NM-N M-N M-N M-N R-S R-S R-S R-S R-S R-S R-SR-S R-SR-S R-S R-S -10 -5 05 1015 dlva_e -10 0 10 20 30 dlkntic_e 10-12 10-12 10-1210-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 13-15 13-15 13-15 13-1513-15 13-15 13-15 13-15 13-1513-15 13-15 13-15 16-1816-1816-18 16-18 16-18 16-18 16-1816-1816-18 16-18 16-18 16-18 20-21 20-21 20-2120-21 20-21 20-21 20-21 20-21 20-21 20-2120-21 20-21 22-23 22-2322-23 22-23 22-23 22-23 22-2322-23 22-23 22-23 22-23 22-23 24-25 24-2524-25 24-25 24-2524-25 24-25 24-25 24-25 24-25 24-25 24-25 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 28 28 28 2828 28 28 28 28 28 28 28 29-30 29-30 29-30 29-30 29-30 29-30 29-3029-30 29-3029-30 29-30 29-30 31-33 31-33 31-33 31-33 31-33 31-33 31-3331-33 31-33 31-33 31-33 31-33 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-6061 61 61 61 61 61 6161 61 61 61 61 62-63 62-6362-63 62-63 62-63 62-63 62-6362-6362-6362-63 62-63 62-63 D-E D-E D-E D-E D-E D-ED-E D-E D-ED-E D-E D-E FF FF F F F FF F F F G GG G G GGGG GG G H HH H H H H H H H H H I I I I I I I I I I I I M-N M-N M-N M-N M-N M-N M-N M-NM-N M-N M-N M-N R-S R-S R-S R-S R-S R-S R-SR-S R-S R-S R-S R-S -10 -5 05 1015 dlva_e -10 0 10 20 30 dlkint_e 10-12 10-12 10-1210-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 13-15 13-15 13-15 13-1513-15 13-15 13-15 13-15 13-1513-15 13-15 13-15 16-1816-1816-18 16-18 16-18 16-18 16-1816-1816-18 16-18 16-18 16-18 20-21 20-21 20-2120-21 20-21 20-21 20-21 20-21 20-21 20-2120-21 20-21 22-23 22-2322-23 22-23 22-23 22-23 22-2322-23 22-23 22-23 22-23 22-23 24-25 24-2524-25 24-25 24-2524-25 24-25 24-25 24-25 24-25 24-25 24-25 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 26-27 28 28 28 2828 28 28 28 28 28 28 28 29-30 29-30 29-30 29-30 29-30 29-30 29-3029-30 29-3029-30 29-30 29-30 31-33 31-33 31-33 31-33 31-33 31-33 31-3331-33 31-33 31-33 31-33 31-33 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-60 58-6061 61 61 61 61 61 6161 61 61 61 61 62-63 62-6362-63 62-63 62-63 62-63 62-6362-6362-6362-63 62-63 62-63 D-E D-E D-E D-E D-E D-ED-E D-E D-ED-E D-E D-E FF FF F F FFF F F F G GG G G GGGG GG G H HH H H H H H H H H H I II I I I I II II I M-N M-N M-N M-N M-N M-N M-N M-NM-N M-N M-N M-N R-S R-S R-S R-S R-S R-S R-SR-S R-SR-S R-S R-S -10 -5 05 1015 dlva_e -10 0 10 20 30 dlkpub_e Descriptives • Correlations. 1995-2007 ln(Y/L)ln(Y/L) ln(Y/L)ln(Y/L)  ln(KICT/L)  ln(KNICT/L)  ln(KINTANGIBLES/L)  ln(KKPUB/L)
  • 15. 1515 Descriptives  ln (Y/L)  ln (KTIC/L)  ln (KNTIC/L)  ln (KINTA/L)  ln (KPUB/L)  ln (Y/L) 1  ln (KTIC/L) 0.42 * 1.00  ln (KNTIC/L) 0.53 * 0.58 * 1.00  ln (KINTANGIBLE/L) 0.64 * 0.54 * 0.64 * 1.00  ln (KPUB/L) 0.60 * 0.52 * 0.54 * 0.71* 1.00 Mean p25 p75 1996 2007 1996 2007 1996 2007  ln (Y/L) -1.42 2.42 -3.58 0.35 0.99 3.55  ln (KTIC/L) 6.07 9.08 2.96 6.29 8.15 10.89  ln (KNTIC/L) 0.05 4.41 -4.88 1.36 3.68 7.13  ln (KINTANGIBLE/L) -0.54 6.20 -2.82 4.45 1.83 7.66  ln (KPUB/L) -0.48 4.58 -2.76 2.66 2.19 6.03 Average values across industries and dispersion (%) Correlations * Significant at 5% level
  • 16. 1616 Descriptives Evolution of GVA, ICT, non-ICT tangible, intangible and public capital and TFP (1995=100) 0 100 200 300 400 500 1995 1997 1999 2001 2003 2005 2007 2009 2011 ICT capital (private) Non-ICT capital (private) Intangible capital (private) 0 50 100 150 200 250 1995 1997 1999 2001 2003 2005 2007 2009 2011 Gross value added Hours worked Private capital 0 50 100 150 200 250 1995 1997 1999 2001 2003 2005 2007 2009 2011 Public capital Public intangible capital -150 -100 -50 0 50 100 150 200 250 1996 1998 2000 2002 2004 2006 2008 2010 TFP (1996=100)
  • 17. 1717 Results. Production function. Basline estimation Dependent variable:  ln (Y / L) 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. OLS (RE) IV (3) (4)  ln (K / L) 0.439*** 0.474*** (0.071) (0.089) Trend 0.002** 0.002*** (0.001) (0.001) Constant -0.018*** -0.026*** (0.005) (0.009) Obs. 240 200 R2 for overall model 0.339 0.343 • We impose constant returns to scale, and use as a first approach total capital. • Capital elasticity is higher than expected: 0.44 - 0.47
  • 18. 1818 Results. Production function. Basline estimation Dependent variable:  ln (Y / L) 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. OLS (RE) IV OLS (FE) IV (1) (2) (3) (4)  ln (KNonICT / L) 0.288*** 0.161* 0.271*** 0.274*** (0.078) (0.089) (0.083) (0.101)  ln (KICT / L) 0.102 0.190** (0.092) (0.076) Trend 0.002* 0.002** 0.002* 0.002 (0.001) (0.001) (0.001) (0.001)  ln (KICT: Hardware / L) 0.018 -0.012 (0.028) (0.042)  ln (KICT: Communic. / L) 0.045 0.034 (0.049) (0.082)  ln (KICT: Software / L) 0.015 0.017 (0.042) (0.039) Constant -0.019*** -0.025** -0.021** -0.014 (0.006) (0.010) (0.010) (0.014) Obs. 240 200 240 200 R2 for overall model 0.317 0.277 0.302 0.331 • 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.
  • 19. 1919 Results. Production function. Intangibles & ICT complementarities Dependent variable:  ln (Y* / L) 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. OLS (FE) IV OLS (FE) IV (1) (2) (3) (4)  ln (KNonICT / L) 0.089 0.015 0.097 0.067 (0.080) (0.087) (0.081) (0.088)  ln (KICT / L) 0.048 0.117* 0.060 0.183** (0.072) (0.069) (0.076) (0.078)  ln (KINT / L) 0.388*** 0.319*** 0.431*** 0.438*** (0.074) (0.087) (0.089) (0.109) Trend 0.001 0.001 0.001 0.001** (0.001) (0.001) (0.001) (0.001)  ln (KICT / L) *  ln (KINT / L) -0.617 -1.871** (0.597) (0.929) Constant -0.011** -0.015 -0.011** -0.021** (0.005) (0.009) (0.005) (0.010) Obs. 240 200 240 200 R2 for overall model 0.498 0.464 0.495 0.430 • 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? The net effect of intangibles is positive (at the average values and in the P25 and P75) Hours worked 0.60 ICT K 0.05 Non ICT capital 0.29 Intangibles 0.06 Factor shares
  • 20. 2020 Results. Production function. Different types of intangibles Dependent variable:  ln (Y* / L) 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. OLS (RE) IV OLS (RE) IV OLS (FE) IV (1) (2) (3) (4) (5) (6)  ln (KNonICT / L) 0.350*** 0.346*** 0.194** 0.107 0.104 -0.006 (0.061) (0.086) (0.086) (0.092) (0.074) (0.086)  ln (KICT / L) 0.048 0.121 0.059 0.175** 0.048 0.115* (0.084) (0.076) (0.076) (0.072) (0.088) (0.069) Trend 0.002** 0.003*** 0.001** 0.002*** 0.001 0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)  ln (KINT: R&D / L) -0.013 0.007 (0.013) (0.028)  ln (KINT: Training / L) 0.205*** 0.162** (0.074) (0.070)  ln (KINT: Org.K / L) 0.448*** 0.380*** (0.070) (0.095) Constant -0.018*** -0.037*** -0.007 -0.019* -0.008** -0.009 (0.006) (0.009) (0.007) (0.010) (0.004) (0.009) Obs. 228 190 240 200 240 200 R2 for overall model 0.379 0.402 0.350 0.312 0.512 0.487 • 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)
  • 21. 2121 Results. Production function. Different types of intangibles & ICT complementarities Dependent variable: ln (Y* / L) 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. OLS (RE) IV OLS (RE) IV OLS (FE) IV (1) (2) (3) (4) (5) (6)  ln (KNonICT / L) 0.346 *** 0.345 *** 0.194 ** 0.133 0.106 0.014 (0.060) (0.086) (0.083) (0.093) (0.076) (0.088)  ln (KICT / L) -0.006 0.106 0.056 0.173 ** 0.051 0.142 * (0.093) (0.096) (0.074) (0.072) (0.090) (0.080) Trend 0.002 ** 0.003 *** 0.001 ** 0.002 *** 0.001 0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)  ln (KR&D /L) -0.035 0.003 (0.021) (0.034)  ln (KI CT / L)  ln (KINT: R&D / L) 0.501 ** 0.086 (0.252) (0.395)  ln (KINT: Training /L) 0.217 ** 0.226 ** (0.099) (0.094)  ln (KI CT / L)  ln (KINT: Training / L) -0.180 -0.995 (0.621) (0.927)  ln (KINT: Org.K / L) 0.465 *** 0.453 *** (0.092) (0.144)  ln (KI CT / L)  ln (KINT: Training / L) -0.240 -0.995 (0.717) (1.398) Constant -0.016 ** -0.035 *** -0.007 -0.021 ** -0.008 ** -0.012 (0.007) (0.011) (0.007) (0.010) (0.004) (0.010) Obs. 228 190 240 200 240 200 R2 for overall model 0.385 0.406 0.349 0.309 0.510 0.476 • 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.
  • 22. 2222 Results. Production function. Public intangibles Dependent variable:  ln (Y* / L) 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. OLS (FE) IV OLS (FE) IV (1) (2) (3) (4)  ln (KNonICT / L) 0.083 0.009 0.091 0.061 (0.087) (0.089) (0.087) (0.090)  ln (KICT / L) 0.044 0.115* 0.055 0.181** (0.066) (0.069) (0.070) (0.078)  ln (KINT / L) 0.397*** 0.330*** 0.440*** 0.449*** (0.082) (0.094) (0.094) (0.114) Trend 0.001 0.001 0.001 0.002 (0.001) (0.001) (0.001) (0.001) Human Capital* ln (KINT PUBLIC / L) -0.004 -0.002 -0.004 -0.002 (0.009) (0.007) (0.009) (0.007)  ln (KICT / L) *  ln (KINT / L) -0.622 -1.877** (0.604) (0.931) Constant -0.012** -0.015* -0.012** -0.022** (0.004) (0.009) (0.005) (0.009) Obs. 240 200 240 200 R2 for overall model 0.492 0.460 0.490 0.427 • 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.
  • 23. 2323 Results. Production function. Public intangibles & ICT complementarities Dependent variable:  ln (Y* / L) 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. OLS (FE) IV OLS (FE) IV (1) (2) (3) (4)  ln (KNonICT / L) 0.087 0.013 0.096 0.054 (0.081) (0.091) (0.081) (0.091)  ln (KNonICT / L) 0.045 0.118* 0.057 0.188** (0.069) (0.069) (0.073) (0.078)  ln (KINT / L) 0.389*** 0.328*** 0.438*** 0.467*** (0.075) (0.086) (0.091) (0.112) Trend 0.001* 0.001 0.001* 0.002** (0.001) (0.001) (0.001) (0.001)  ln (KICT / L) *  ln (KINT PUBLIC / L) 0.000 0.000 -0.001 -0.001 (0.001) (0.001) (0.001) (0.001)  ln (KICT / L) *  ln (KINT / L) -0.694 -1.995** (0.627) (0.968) Constant -0.012** -0.015* -0.012** -0.022** (0.004) (0.009) (0.005) (0.010) Obs. 240 200 240 200 R2 for overall model 0.486 0.456 0.477 0.387 • 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.
  • 24. 2424 Results. Production function. Public capital Dependent variable:  ln (Y* / L) 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. OLS (RE) IV (7) (8)  ln (KNonICT / L) 0.111 0.016 (0.072) (0.087)  ln (KICT / L) 0.033 0.114 (0.067) (0.070)  ln (KINT / L) 0.411*** 0.320*** (0.052) (0.088) Trend 0.001 0.001 (0.001) (0.001) %K trasport  ln (Kpublic ) -0.564** -0.312 (0.226) (1.415) Constant -0.008 -0.013 (0.006) (0.012) Obs. 240 200 R2 for overall model 0.515 0.485 • 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?
  • 25. 2525 Main findings • In this paper we show preliminary results of the role of different types of intangibles on economic growth. • Our approach: • 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 additionally 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.
  • 26. 2626 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. • Identify the precise effect of each type of capital.