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Are Intangibles More Productive in ICT-Intensive
Industries?
Evidence from EU Countries
by Wen Chen, Thomas Niebel, Marianne Saam
Discussed by
Cecilia Jona-Lasinio
(ISTAT and LUISS Lab)
IARIW 33rd General Conference
Rotterdam, the Netherlands, August 24-30, 2014
C. Jona-Lasinio
Outline
โ€ข Motivation and goal of the paper
โ€ข Empirical ๏ฌndings
โ€ข Major remarks
โ€ข Theoretical model
โ€ข Econometric strategy
โ€ข Suggestions
C. Jona-Lasinio
Motivation I
โ€ข ICT is a fundamental driver of economic growth
(Jorgenson and Stiroh, (1995) and (1999); Oliner, Sichel
and Stiroh (2007)), but ICT alone does not explain the
large country-industry productivity gap between Europe
and the U.S.
โ€ข 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).
โ€ข Microeconomic evidence emphasyses the relevance of
co-investments in training and organisational change to
generate ICT productivity bene๏ฌts (e.g., Bresnahan,
Brynjolfsson, and Hitt, 2002; Brynjolfsson, Hitt, and Yang,
2002).
C. Jona-Lasinio
Motivation II
โ€ข Macroeconomic productivity studies suggest that the
returns to ICT and productivity growth are higher once
proxies for intangibles are explicitly accounted for, e.g.
Basu, Fernald, Oulton, and Srinivasan (2004).
โ€ข Based on an econometric analysis of a 10 country, 10 year
KLEMS sample of productivity growth in the EU, Corrado,
Haskel and Jona-Lasinio (2014a)1
found that productivity
in ICT-intensive industries is stronger in countries with
relatively fast-growing intangible capital, suggesting
complementarity between ICT and intangible capital.
1Revised, working paper version of paper presented at 2nd World KLEMS
conference, August, 2012, and 3rd SEEK Conference (ZEW, Manheim, April 2013)
C. Jona-Lasinio
Aim and main contribution of the paper
โ€ข The authors explore to what extent the contribution of
intangible capital to industry productivity growth is related
to the degree of ICT intensity at the industry level.
โ€ข Main contribution of the paper:
โ€ข Extend the analysis developed in Corrado, Haskel,
Jona-Lasino (2014) including industry measures of
intangible capital.
โ€ข Test di๏ฌ€erent measures of ICT intensity
C. Jona-Lasinio
Empirical strategy
โ€ข They adopt a di๏ฌ€erence-in-di๏ฌ€erence approach a la Rajan
and Zingales (1998) to test the productivity impact of
intangible assets in ICT intensive industries (Corrado,
Haskel, Jona-Lasinio(2014a)).
โ€ข Data on intangibles are obtained breaking down the
INTAN-Invest Harmonized business investment by industry
(Niebel, Oโ€™Mahony and Saam, 2013).
โ€ข Sample: 10 European countries, 11 NACE Rev1.1 industries
(A to O) over the period 1995-2007.
C. Jona-Lasinio
Empirical model
Vc,i,t = Ac,i,tLฮฑ
c,i,t(KNICT
c,i,t )ฮฒ1
(KICT
c,i,t )ฮฒ2
(KINT
c,i,t )ฮฒ3
Taking log, ๏ฌrst di๏ฌ€erencing and assuming CRS:
โˆ†(v โˆ’ l)c,i,t = ฮฒ1โˆ†(kNICT
โˆ’ l)c,i,t
+ ฮฒ2โˆ†(kICT
โˆ’ l)c,i,t
+ ฮฒ3โˆ†(kINT
โˆ’ l)c,i,t + ยตc,i,t
C. Jona-Lasinio
Empirical model
โˆ†(v โˆ’ l)c,i,t = ฮณ1โˆ†(kNICT
โˆ’ l)c,i,t โˆ— DICT
c,i
+ ฮณ2โˆ†(kICT
โˆ’ l)c,i,t โˆ— DICT
c,i
+ ฮณ3โˆ†(kINT
โˆ’ l)c,i,t โˆ— DICT
c,i
+ ฮฒX + ฯ‰c,i + ฯ„t + c,i,t
The main assumption is that ฮณ3 > 0 while ฮณ1 and ฮณ2 < 0
C. Jona-Lasinio
ICT intensity measures
observe that transport (I), financial intermediation (J), and business services (K) are ICT-intensive
industries according to all four measures; while agriculture (AtB), manufacturing (D), and
construction (F) are always ICT non-intensive. Mining and quarrying (C) remain below the median
for three measures.8
Figure 4.1: FOUR MEASURES OF E.U. INDUSTRY ICT INTENSITY
Since the ICT intensity might be endogenous, we follow Michaels, Natraj and Van Reenen (2014)
.02 .027 .042
.1
.13 .14
.25 .25
.29 .31
.47
0
.1.2.3.4.5.6.7.8.9
1
AtB F H D O G K I E C J
Ratio of ICT capital to labour services
.0057 .011 .014 .019 .028 .035 .038 .038 .061
.085
.11
0
.1.2.3.4.5.6.7.8.9
1
AtB F H C D G E O K I J
ICT capital share of value added
.043
.073 .085 .11 .12
.16
.21 .21
.26
.32
.37
0
.1.2.3.4.5.6.7.8.9
1
AtB C E F D H G O I K J
ICT capital share of total capital services
.025
.051 .055 .059 .073 .087
.11 .13
.2 .21
.24
0
.1.2.3.4.5.6.7.8.9
1
AtB C E F D H G O K I J
ICT capital share of total capital compensation
C. Jona-Lasinio
Empirical results
Table 5.1: COBB-DOUGLAS PRODUCT FUNCTION ESTIMATION
DV: โˆ†๐ฅ๐ง ย (๐‘ฝ/๐‘ณ) ๐’„,๐’Š,๐’•
(1)
Two-capital inputs
(2)
INT augmented
(3)
Full Model
(4)
Full Model
OLS OLS OLS IV
NICT	
 ย (ฮฒ1) 0.372***
(0.050)
0.313***
(0.048)
0.312***
(0.049)
0.312***
(0.056)
ICT	
 ย (ฮฒ2) 0.087***
(0.026)
0.080***
(0.025)
0.066***
(0.024)
0.034
(0.024)
INT	
 ย (ฮฒ3) 0.130***
(0.031)
0.161***
(0.034)
0.126***
(0.029)
๐‘๐ผ๐ถ๐‘‡ร— ๐ท , (ฮณ1) -0.060
(0.284)
-0.251
(0.271)
๐ผ๐ถ๐‘‡ร— ๐ท , (ฮณ2) -0.207**
(0.088)
0.086
(0.354)
๐ผ๐‘๐‘‡ร— ๐ท , (ฮณ3) 0.340*
(0.193)
0.752***
(0.208)
Year dummies Yes Yes Yes Yes
N 1320 1320 1320 1320
Adjusted R2 0.187 0.209 0.214
Note: The output V (i.e. value-added) in Column (1) is not adjusted for the inclusion of intangible capital; whereas for column (2)-
(4), intangibles are added both as an input and an output. Hence, output V is adjusted for intangibles in these columns. All
specifications in column (1)-(4) include the country-industry-specific fixed effects. Standard errors shown in parentheses are
heteroscedastic-robust to country-industry clustering. Column (3) and (4) calculate the country-industry ICT intensity as the ratio
C. Jona-Lasinio
Empirical results
Figure 5.1: MARGINAL EFFECT OF INTANGIBLE CAPITAL
-.2-.1
0
.1.2.3.4.5.6.7
-20 -10 0 10 20 30 40
Demeaned_ICT_Intensity*100
Marginal Effect 95% lower band C.I.
95% upper band C.I.
C. Jona-Lasinio
Main contribution of the paper
โ€ข The empirical ๏ฌndings support the assumption that ICT
and intangible capital are complementary in the
production process (Corrado et al 2014)
โ€ข Results are robust across di๏ฌ€erent measures of ICT
intensity and to alternative industry grouping criteria
C. Jona-Lasinio
Major remarks: research question
The research question should be better focused
โ€ข The interaction model, as it is speci๏ฌed, is based on the
assumption that the productivity impact of all capital
inputs depends on the degree of ICT intensity.
โ€ข But what is the theoretical motivation supporting this
assumption?
โ€ข What are the omitted e๏ฌ€ects that should be captured by
the interaction between ICT intensity and ICT capital
accumulation? How this is related to the complementary
relations between ICT and intangible capital?
โ€ข ... and what about the interaction with NON-ICT capital
(including Residential buildings)?
C. Jona-Lasinio
Major remarks: econometric approach
The econometric model is not robustly speci๏ฌed
โ€ข Interaction models should include all constitutive terms
otherwise they produce biased and inconsistent estimates
(Brambor, Clark and Golder (2005)).
โˆ†(v โˆ’ l)c,i,t = ฮฑ1DICT
c,i + ฮณ1โˆ†(kNICT
โˆ’ l)c,i,t โˆ— DICT
c,i
+ ฮณ2โˆ†(kICT
โˆ’ l)c,i,t โˆ— DICT
c,i + ฮณ3โˆ†(kINT
โˆ’ l)c,i,t โˆ— DICT
c,i
+ ฮฒX + ฯ‰c,i + ฯ„t + c,i,t
โ€ข Omitting ฮฑ1DICT
c,i it is as assuming that ฮฑ1 is zero. That is
based on the expectation that DICT
c,i has no e๏ฌ€ect on
โˆ†(v โˆ’ l)c,i,t when โˆ†(kj
โˆ’ l)c,i,t is zero, where
(j= ICT, NON-ICT, INT).
C. Jona-Lasinio
Minor remarks
โ€ข The econometrics has to be improved
โ€ข The output elasticities of ICT and intangible in cols 1 and 2 (Table
5.1) might be severely biased because of the well known endogeneity
of capital inputs. Instrumental variable estimation (including
statistical tests) has to be reported.
โ€ข The labour quality control might have a relevant e๏ฌ€ect on the size of
the interaction coe๏ฌƒcients and has to be included in all the
speci๏ฌcations.
โ€ข The background literature has to be extended. Recently,
other studies have developed estimates of intangible
investment at the industry level: (Chun, Fukao, Hisa, Miyagawa
(2012), Miyagawa and Hisa (2013) and Dal Borgo, Goodridge, Haskel,
and Pesole (2013), Corrado, Haskel, Jona-Lasinio, Iommi(2014b)).
C. Jona-Lasinio
Suggestions
โ€ข Better focus the research question
โ€ข Check how/if the inclusion of all the interacted terms
a๏ฌ€ect the empirical ๏ฌndings
โ€ข Improve the econometrics
โ€ข Provide information about industry estimates of intangible
investment
C. Jona-Lasinio

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Session 4 a chen et al discussion

  • 1. Are Intangibles More Productive in ICT-Intensive Industries? Evidence from EU Countries by Wen Chen, Thomas Niebel, Marianne Saam Discussed by Cecilia Jona-Lasinio (ISTAT and LUISS Lab) IARIW 33rd General Conference Rotterdam, the Netherlands, August 24-30, 2014 C. Jona-Lasinio
  • 2. Outline โ€ข Motivation and goal of the paper โ€ข Empirical ๏ฌndings โ€ข Major remarks โ€ข Theoretical model โ€ข Econometric strategy โ€ข Suggestions C. Jona-Lasinio
  • 3. Motivation I โ€ข ICT is a fundamental driver of economic growth (Jorgenson and Stiroh, (1995) and (1999); Oliner, Sichel and Stiroh (2007)), but ICT alone does not explain the large country-industry productivity gap between Europe and the U.S. โ€ข 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). โ€ข Microeconomic evidence emphasyses the relevance of co-investments in training and organisational change to generate ICT productivity bene๏ฌts (e.g., Bresnahan, Brynjolfsson, and Hitt, 2002; Brynjolfsson, Hitt, and Yang, 2002). C. Jona-Lasinio
  • 4. Motivation II โ€ข Macroeconomic productivity studies suggest that the returns to ICT and productivity growth are higher once proxies for intangibles are explicitly accounted for, e.g. Basu, Fernald, Oulton, and Srinivasan (2004). โ€ข Based on an econometric analysis of a 10 country, 10 year KLEMS sample of productivity growth in the EU, Corrado, Haskel and Jona-Lasinio (2014a)1 found that productivity in ICT-intensive industries is stronger in countries with relatively fast-growing intangible capital, suggesting complementarity between ICT and intangible capital. 1Revised, working paper version of paper presented at 2nd World KLEMS conference, August, 2012, and 3rd SEEK Conference (ZEW, Manheim, April 2013) C. Jona-Lasinio
  • 5. Aim and main contribution of the paper โ€ข The authors explore to what extent the contribution of intangible capital to industry productivity growth is related to the degree of ICT intensity at the industry level. โ€ข Main contribution of the paper: โ€ข Extend the analysis developed in Corrado, Haskel, Jona-Lasino (2014) including industry measures of intangible capital. โ€ข Test di๏ฌ€erent measures of ICT intensity C. Jona-Lasinio
  • 6. Empirical strategy โ€ข They adopt a di๏ฌ€erence-in-di๏ฌ€erence approach a la Rajan and Zingales (1998) to test the productivity impact of intangible assets in ICT intensive industries (Corrado, Haskel, Jona-Lasinio(2014a)). โ€ข Data on intangibles are obtained breaking down the INTAN-Invest Harmonized business investment by industry (Niebel, Oโ€™Mahony and Saam, 2013). โ€ข Sample: 10 European countries, 11 NACE Rev1.1 industries (A to O) over the period 1995-2007. C. Jona-Lasinio
  • 7. Empirical model Vc,i,t = Ac,i,tLฮฑ c,i,t(KNICT c,i,t )ฮฒ1 (KICT c,i,t )ฮฒ2 (KINT c,i,t )ฮฒ3 Taking log, ๏ฌrst di๏ฌ€erencing and assuming CRS: โˆ†(v โˆ’ l)c,i,t = ฮฒ1โˆ†(kNICT โˆ’ l)c,i,t + ฮฒ2โˆ†(kICT โˆ’ l)c,i,t + ฮฒ3โˆ†(kINT โˆ’ l)c,i,t + ยตc,i,t C. Jona-Lasinio
  • 8. Empirical model โˆ†(v โˆ’ l)c,i,t = ฮณ1โˆ†(kNICT โˆ’ l)c,i,t โˆ— DICT c,i + ฮณ2โˆ†(kICT โˆ’ l)c,i,t โˆ— DICT c,i + ฮณ3โˆ†(kINT โˆ’ l)c,i,t โˆ— DICT c,i + ฮฒX + ฯ‰c,i + ฯ„t + c,i,t The main assumption is that ฮณ3 > 0 while ฮณ1 and ฮณ2 < 0 C. Jona-Lasinio
  • 9. ICT intensity measures observe that transport (I), financial intermediation (J), and business services (K) are ICT-intensive industries according to all four measures; while agriculture (AtB), manufacturing (D), and construction (F) are always ICT non-intensive. Mining and quarrying (C) remain below the median for three measures.8 Figure 4.1: FOUR MEASURES OF E.U. INDUSTRY ICT INTENSITY Since the ICT intensity might be endogenous, we follow Michaels, Natraj and Van Reenen (2014) .02 .027 .042 .1 .13 .14 .25 .25 .29 .31 .47 0 .1.2.3.4.5.6.7.8.9 1 AtB F H D O G K I E C J Ratio of ICT capital to labour services .0057 .011 .014 .019 .028 .035 .038 .038 .061 .085 .11 0 .1.2.3.4.5.6.7.8.9 1 AtB F H C D G E O K I J ICT capital share of value added .043 .073 .085 .11 .12 .16 .21 .21 .26 .32 .37 0 .1.2.3.4.5.6.7.8.9 1 AtB C E F D H G O I K J ICT capital share of total capital services .025 .051 .055 .059 .073 .087 .11 .13 .2 .21 .24 0 .1.2.3.4.5.6.7.8.9 1 AtB C E F D H G O K I J ICT capital share of total capital compensation C. Jona-Lasinio
  • 10. Empirical results Table 5.1: COBB-DOUGLAS PRODUCT FUNCTION ESTIMATION DV: โˆ†๐ฅ๐ง ย (๐‘ฝ/๐‘ณ) ๐’„,๐’Š,๐’• (1) Two-capital inputs (2) INT augmented (3) Full Model (4) Full Model OLS OLS OLS IV NICT ย (ฮฒ1) 0.372*** (0.050) 0.313*** (0.048) 0.312*** (0.049) 0.312*** (0.056) ICT ย (ฮฒ2) 0.087*** (0.026) 0.080*** (0.025) 0.066*** (0.024) 0.034 (0.024) INT ย (ฮฒ3) 0.130*** (0.031) 0.161*** (0.034) 0.126*** (0.029) ๐‘๐ผ๐ถ๐‘‡ร— ๐ท , (ฮณ1) -0.060 (0.284) -0.251 (0.271) ๐ผ๐ถ๐‘‡ร— ๐ท , (ฮณ2) -0.207** (0.088) 0.086 (0.354) ๐ผ๐‘๐‘‡ร— ๐ท , (ฮณ3) 0.340* (0.193) 0.752*** (0.208) Year dummies Yes Yes Yes Yes N 1320 1320 1320 1320 Adjusted R2 0.187 0.209 0.214 Note: The output V (i.e. value-added) in Column (1) is not adjusted for the inclusion of intangible capital; whereas for column (2)- (4), intangibles are added both as an input and an output. Hence, output V is adjusted for intangibles in these columns. All specifications in column (1)-(4) include the country-industry-specific fixed effects. Standard errors shown in parentheses are heteroscedastic-robust to country-industry clustering. Column (3) and (4) calculate the country-industry ICT intensity as the ratio C. Jona-Lasinio
  • 11. Empirical results Figure 5.1: MARGINAL EFFECT OF INTANGIBLE CAPITAL -.2-.1 0 .1.2.3.4.5.6.7 -20 -10 0 10 20 30 40 Demeaned_ICT_Intensity*100 Marginal Effect 95% lower band C.I. 95% upper band C.I. C. Jona-Lasinio
  • 12. Main contribution of the paper โ€ข The empirical ๏ฌndings support the assumption that ICT and intangible capital are complementary in the production process (Corrado et al 2014) โ€ข Results are robust across di๏ฌ€erent measures of ICT intensity and to alternative industry grouping criteria C. Jona-Lasinio
  • 13. Major remarks: research question The research question should be better focused โ€ข The interaction model, as it is speci๏ฌed, is based on the assumption that the productivity impact of all capital inputs depends on the degree of ICT intensity. โ€ข But what is the theoretical motivation supporting this assumption? โ€ข What are the omitted e๏ฌ€ects that should be captured by the interaction between ICT intensity and ICT capital accumulation? How this is related to the complementary relations between ICT and intangible capital? โ€ข ... and what about the interaction with NON-ICT capital (including Residential buildings)? C. Jona-Lasinio
  • 14. Major remarks: econometric approach The econometric model is not robustly speci๏ฌed โ€ข Interaction models should include all constitutive terms otherwise they produce biased and inconsistent estimates (Brambor, Clark and Golder (2005)). โˆ†(v โˆ’ l)c,i,t = ฮฑ1DICT c,i + ฮณ1โˆ†(kNICT โˆ’ l)c,i,t โˆ— DICT c,i + ฮณ2โˆ†(kICT โˆ’ l)c,i,t โˆ— DICT c,i + ฮณ3โˆ†(kINT โˆ’ l)c,i,t โˆ— DICT c,i + ฮฒX + ฯ‰c,i + ฯ„t + c,i,t โ€ข Omitting ฮฑ1DICT c,i it is as assuming that ฮฑ1 is zero. That is based on the expectation that DICT c,i has no e๏ฌ€ect on โˆ†(v โˆ’ l)c,i,t when โˆ†(kj โˆ’ l)c,i,t is zero, where (j= ICT, NON-ICT, INT). C. Jona-Lasinio
  • 15. Minor remarks โ€ข The econometrics has to be improved โ€ข The output elasticities of ICT and intangible in cols 1 and 2 (Table 5.1) might be severely biased because of the well known endogeneity of capital inputs. Instrumental variable estimation (including statistical tests) has to be reported. โ€ข The labour quality control might have a relevant e๏ฌ€ect on the size of the interaction coe๏ฌƒcients and has to be included in all the speci๏ฌcations. โ€ข The background literature has to be extended. Recently, other studies have developed estimates of intangible investment at the industry level: (Chun, Fukao, Hisa, Miyagawa (2012), Miyagawa and Hisa (2013) and Dal Borgo, Goodridge, Haskel, and Pesole (2013), Corrado, Haskel, Jona-Lasinio, Iommi(2014b)). C. Jona-Lasinio
  • 16. Suggestions โ€ข Better focus the research question โ€ข Check how/if the inclusion of all the interacted terms a๏ฌ€ect the empirical ๏ฌndings โ€ข Improve the econometrics โ€ข Provide information about industry estimates of intangible investment C. Jona-Lasinio