- Email: [email protected]
ICT’s effect on trade: Perspective of comparative advantage
Accepted Manuscript ICT’s effect on trade: Perspective of comparative advantage Yao Wang, Jie Li PII: DOI: Reference:
S0165-1765(17)30124-6 http://dx.doi.org/10.1016/j.econlet.2017.03.022 ECOLET 7557
To appear in:
Economics Letters
Received date: 8 February 2017 Revised date: 11 March 2017 Accepted date: 19 March 2017 Please cite this article as: Wang, Y., Li, J., ICT’s effect on trade: Perspective of comparative advantage. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.03.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights: 1.
This paper demonstrates that information and communication technology (ICT) can be a source of comparative advantage in international trade.
2.
Country-level ICT data and bilateral trade industry-level data in year 2013 are used.
3.
Gravity model and Interactions between industrial characteristics and exporting country characteristics are used for identification.
4.
Empirical results show that ICT promotes export more in R&D-intensive industries and higher task complexity industries.
*Title Page
ICT's effect on trade: perspective of comparative advantage Authors: Yao Wang School of Management, Zhejiang University, China. E-mail: [email protected].
Jie Li (Corresponding Author) Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore, Singapore. Email: [email protected].
Address correspondence to：Jie Li, Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore, 1 Arts Link, Singapore 117570, Singapore. Email: [email protected].
Statement: This manuscript has not been published elsewhere and it has not been submitted simultaneously for publication elsewhere.
Total words: 1982. *********** Abstract: This paper uses country-level ICT data and bilateral trade data in 2013 to test whether cross-country differences in ICT can be a source of comparative advantage in international trade. Empirical results show that a country’s export in one industry increases 10 percent if the country’s ICT development index increases 1 standard deviation (SD) and industry’s R&D intensity increases 1 SD. The export increase is 25 percent in the case of task complexity. Keywords: Information and communication technology (ICT) development; R&D intensity; task complexity; comparative advantage
JEL code: D2, F1, O3
*Manuscript Click here to view linked References
1
ICT's effect on trade: perspective of comparative advantage
2
Abstract: This paper uses country-level ICT data and bilateral trade data in 2013 to test
3
whether cross-country differences in ICT can be a source of comparative advantage in
4
international trade. Empirical results show that a country’s export in one industry increases 10
5
percent if the country’s ICT development index increases 1 standard deviation (SD) and industry’s
6
R&D intensity increases 1 SD. The export increase is 25 percent in the case of task complexity.
7 8 9
Keywords: Information and communication technology (ICT) development; R&D intensity; task complexity; comparative advantage 1. Introduction
10
The rapid development of information and communication technology (ICT) has taken the
11
world to the era of digital economy, bringing significant digital dividends (World Bank, 2016). ICT,
12
usually regarded as one type of General Purpose Technology, can help improve productivity and
13
resource allocation efficiency (Bresnahan, 2010). Countries with higher ICT development level
14
can provide easier access to ICT to firms. Industries, on the other hand, differs in their demand
15
for ICT in the production process. Consequently, industries using ICT intensively and located in
16
ICT developed countries are abler to improve their productivity and output, thus generating the
17
ICT-induced comparative advantage in international trade.
18
According to endogenous growth theory as well as network externalities theory, ICT
19
positively affect productivity through ICT-leveraged innovations and ICT-induced externalities
20
(Chou et al, 2014). The improved productivity mainly benefits R&D intensive industries and task
21
complexity industries. Firstly, innovation is key to R&D intensive industries. Previous research
22
showed that ICT investment can boost innovation by promoting knowledge sharing and
23
distribution directly (Czernich et al., 2011) and serve as complements to R&D investment (Hall et
24
al., 2012). Thus, ICT is more intensively used in R&D intensive industries. In countries with high
25
ICT development level, R&D intensive industries can benefit more from the ICT’s capacity as well
26
as people’s ICT skills such as workers’ internet-savvy and information acquisition. Secondly,
27
Industries with a high level of specialization face more severe organizational inefficiencies due to
28
contract enforcement problems and higher transaction cost. Task complexity, which is defined as
29
the number of tasks that must be performed before getting one final unit, is commonly used to
30
measure industry’s specialization levels (Costinot, 2009). ICT can improve organizational
31
efficiencies by IT-enabled organizational change (Bresnahan et al, 2002), tougher “people
32
management” practices (Bloom et al, 2012) and transaction cost reduction. Thus, we expect
33
countries with better ICT development to specialize in high task complexity industries. Figure 1 1/7
34
shed light on our hypothesis. The figure shows that countries with higher ICT development index
35
are more specialized in R&D intensive and more complex industries in international trade.
36 37
Figure 1. Correlation between country's ICT development and exporting shares of industries
38
Notes: 1. High R&D intensive industries are industries with R&D intensity above 75th percentiles while low-R&D
39
intensive industries are industries with R&D intensity below 25th percentiles. Task complex industries are similar. 2.
40
X-axis is country’s ICT development index. Y-axis is the export share of industries.
41
Previous researches show that labor force, human capital, physical capital, demographic
42
structure (Cai, 2016) and financial institution (Manova, 2013) all can be sources of comparative
43
advantage. To our best knowledge, research on the ICT’s potential to be a source of comparative
44
advantage is rare. Choi (2010) tested the effect of the internet on trade but not on comparative
45
advantage. Freund and Weinhold (2004) tested the effect of the internet but not ICT. The
46
contribution of this paper to previous literature is that we make use of bilateral trade data to
47
study whether ICT can be a source of comparative advantage.
48
2. Regression Specification and Data
49
We adopt the empirical strategy of Chor (2010), which identifies comparative advantage by
50
the interactions between country characteristics and industrial characteristics. The interactions
51
give us insights on how one industry’s trade flow is affected by exporting country’s characteristics.
52
The regression specification is shown in equation (1).
53
ln exportijk 1Complexity ICTi 2 RDintensity ICTi + n n I kn Fi n ij jk ijk
(1)
54
The dependent variable is the log of trade volume from country i to country j in industry k.
55
The variable of interest here is the interaction of task complexity with ICT development and the
2/7
56
interaction of R&D intensity with ICT development. We control for importer-exporter fixed effect
57
μij to take into account bilateral trade cost factors such as bilateral distance, cultural differences,
58
and historical colonial relationships. Another FE is importer-industry fixed effect γjk, which
59
controls for industry size and productivity in the importer country. However, there are potential
60
endogeneity concerns because exporting countries might adjust its investment in ICT according
61
to certain industries’ performance on the global market. To relieve such concern, we use ICT
62
development data in 2000 as instruments for IV regressions. The reason for using year 2000’s ICT
63
data is that they are predictive of year 2013’s ICT development level but not affected by trade in
64
2013.
65
The bilateral trade data is from CEPII’s BACI dataset. We aggregate trade at HS6 level to
66
NAICS 2002 four-digit level. The data for regressions consist of 152 countries and 86 industries in
67
2013. R&D intensity is calculated as R&D investment over total sales for each company in 2005
68
using Orbis dataset then averaged for each industry. Task complexity data is from Costinot (2009)
69
which uses PSI survey from 1985 to 1993 that asks workers the number of months needed to be
70
fully trained for the job in the industry.
71
Three proxies for ICT development are from International Telecommunication Union. The
72
first is ICT development index, which comprises the access, the use and the skill level of ICT. The
73
second is ICT subscription index, which is measured by broadband subscribers per 100 persons.
74
The third is ICT usage index, which is measured by internet users per 100 people. In addition, we
75
control for another two standard Heckscher-Ohlin model comparative advantage factors, which
76
are cross-country differences in physical capital and human capital. The country level physical
77
capital and human capital data are from Penn World tables and the industry level skill intensity
78
and capital intensity data are calculated from NBER-CES manufacturing dataset. Summary
79
statistics are listed in table 1.
80
TABLE 1
81
Summary Variables
Definition
Export
Export
value
Observation at
industry
level
(in 477,456
Mean
SD
Min
Max
13,153
196,119
1.02*10^-3
6.98*10^7
16.71
6.597
2.380
31.84
0.011
0.013
0
0.049
thousand dollars) Task_complexity
Task complexity of industry at industry 442,959 level
RD_int
R&D-intensity of industry at industry level
386,987
3/7
Idi
ICT development index at country level
477,456
6.067
1.913
0.960
8.860
Internet
Internet user per 100 persons at country 476,437
59.80
24.61
0.900
96.55
18.42
12.20
0
40.53
level Bband
Broad band user per 100 persons at 477,268 country level
Internet2000
internet in year 2000
597,596
16.41
16.52
0.020
52
Bband2000
bband in year 2000
306,552
1.237
1.960
0
8.420
Fixedtel2000
fixed telephone at country level in year 605,250 32.70
22.47
0.120
73.07
2000 Skill_int
skill intensity at industry level
610,906
0.300
0.104
0.099
0.642
Cap_int
Log capital intensity at industry level
610,906
5.049
0.666
3.808
6.689
Skill_abund
human capital at country level
582,244
2.948
0.573
1.186
3.726
Capital_abund
Physical capital stock at country level
606,507
11.19
1.076
7.377
12.90
82
3. Results
83
Table 2 shows baseline regression results. Column (1) includes the interaction between task
84
complexity and ICT development indicator. Column (2) includes the interaction of R&D intensity
85
and ICT development indicator. Column (3) includes both interactions. Column (4) uses log of
86
internet users per 100 people to interact with R&D intensity and task complexity and Column (5)
87
uses the log of broadband subscribers per 100 people. As shown in table 2, the interaction terms
88
of ICT index with R&D intensity and task complexity are all significantly positive in all columns,
89
showing that countries with wider ICT usage export more in industries that are R&D intensive
90
and of high task complexity. From column (3), we can calculate that a country’s export will
91
increase 10 percent if the country’s ICT development index increases 1 standard deviation (SD)
92
and industry’s R&D intensity increases 1 SD. The export increase is 25 percent in the case of task
93
complexity.
1
1
One standard deviation of complexity is 6.597 and one standard deviation of ICT development index is 1.913. Thus the difference in export would be exp (6.597*1.913*0.0174)-1=0.245. For R&D intensity, the export difference is exp(0.0125*1.913*3.929)-1=0.098 4/7
Table 2 Baseline Regression ICT development index
Depend Variable
(Task_complexity)k×(idi)i
ICT subscription index
ICT usage indexer
ln_export
ln_export
ln_export
ln_export
ln_export
(1)
(2)
(3)
(4)
(5)
0.0199***
0.0174***
(0.00144)
(0.00172)
(RD_int)k×(idi)i
5.684***
3.929***
(0.734)
(0.759)
(Task_complexity)k×(ln_internet)i
0.0456*** (0.00472)
(RD_ int)k×(ln_internet)i
9.254*** (1.948)
(Task_complexity)k×(ln_bband)i
0.0152*** (0.00206)
(RD_int)k×(ln_bband)i
4.663*** (0.848)
(Skill_int)k×(Skill_ abund)i
(Capital_int)k×(Capital_ abund)i
2.544***
3.121***
1.703***
2.179***
2.535***
(0.291)
(0.318)
(0.350)
(0.349)
(0.356)
-0.158***
-0.152***
-0.126***
-0.136*** -0.178***
(0.0228)
(0.0229)
(0.0263)
(0.0261)
(0.0262)
538,182
470,289
431,438
430,337
431,233
0.630
0.629
0.634
0.633
0.632
Imp-Exp FE
Yes
Yes
Yes
Yes
Yes
Imp-Industry FE
Yes
Yes
Yes
Yes
Yes
Observations Adjusted R
2
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
94
Table 3 shows Instrument Variable (IV) results. Year 2000’s internet user per 100 people is
95
used as the IV for its 2013 data and year 2000’s broadband data as IV for 2013 data. Fixed
96
telephone subscription per 100 people in 2000 is used as IV for 2000’s ICT development index.
97
The results show that all the key interaction terms are still positive and significant. 5/7
98 99 100
Table 3 Instrument Variable Regression
Depend Variable
ICT development index
ICT subscription index
ICT usage index
ln_export ln_export ln_export
ln_export
ln_export
(4)
(5)
(1)
(Task_complexity)k×(idi)i
(2)
(3)
0.0196***
0.0164***
(0.00156)
(0.00176)
(RD_int)k×(idi)i
5.285*** 3.704*** (0.772)
(0.790)
(Task_complexity)k×(ln_internet)i
0.0570*** (0.00600)
(RD_int)k×(ln_internet)i
19.77*** (3.032)
(Task_complexity)k×(ln_bband)i
0.0414** (0.0183)
(RD_int)k×(ln_bband)i
18.19** (8.808)
Observations Adjusted R2 Kleibergen-Paap F stats. (First stage)
533,920
466,603
428,022
422,011
200,995
0.630
0.630
0.634
0.636
0.699
832.592
168.738
8920.863 4822.772 2684.556
Imp-Exp FE
Yes
Yes
Yes
Yes
Yes
Imp-Industry FE
Yes
Yes
Yes
Yes
Yes
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 (Skill_int)k × (Skill_abund)i and (Capital_int)k × (Capital_abund)i is not reported here because of limited space.
101
4. Conclusion
102
The idea of comparative advantage has been studied over time. In the age of digital
103
economy, a revisit to this old concept can shed light on new source of comparative advantage.
104
We use industry level trade data and country level ICT development data to show that ICT
105
developed countries have comparative advantage in industries that are R&D intensive or task
106
complex. The theory implication of this paper is to extend ICT’s GPT attribute by its effect on
6/7
107
international trade.
108 109 110 111 112 113 114
References Bloom, N., Sadun, R., Reenen, J., 2012. Americans Do IT Better: US Multinationals. American Economic Review 102(1), 167–201 Bresnahan, T., 2010. General Purpose Technologies, Handbook of Economics of Innovation, Vol. 2, Ch. 18.
115
Bresnahan, T., Brynjolfsson, E., Hitt, L., 2002. Information technology, workplace
116
organization and the demand for skilled labor: firm-level evidence. Quarterly Journal of
117
Economics, 117, 339–76.
118 119 120 121 122 123
Cai, J., Stoyanovb, A., 2016. Population aging and comparative advantage. Journal of International Economics 102, 1–21. Czernich, N., Falck, O., Kretschmer, T., Woessmann, L., 2011. Broadband Infrastructure and Economic Growth. The Economic Journal, 121(552), 505-532 Chor, D., 2010. Unpacking sources of comparative advantage: A quantitative approach. Journal of International Economics 82, 152–167.
124
Choi, C., 2010. The effect of the Internet on service trade. Economics Letters 109,102–104
125
Chou, Y., Chuang, H., Shao, B., 2014. The impacts of information technology on total factor
126 127 128 129 130 131 132 133 134 135 136
productivity: A look at externalities and innovations. Int. J. Production Economics 158, 290–299 Costinot, A., 2009. On the origins of comparative advantage. Journal of International Economics 77, 255–264. Freund, C., Weinhold, D., 2004. The effect of the Internet on international trade. Journal of International Economics 62, 171–189. Hall, B., Lotti, F., Mairesse, J.,2012. Evidence on the impact of R&D and ICT investments on innovation and productivity in Italian firms. Economics of Innovation and New Technology,1-29. Manova, K., 2013. Credit Constraints, Heterogeneous Firms, and International Trade. Review of Economic Studies 80, 711–744. World Bank, 2016. World Development Report 2016: Digital Dividends. Washington, DC: World Bank.
7/7
S0165-1765(17)30124-6 http://dx.doi.org/10.1016/j.econlet.2017.03.022 ECOLET 7557
To appear in:
Economics Letters
Received date: 8 February 2017 Revised date: 11 March 2017 Accepted date: 19 March 2017 Please cite this article as: Wang, Y., Li, J., ICT’s effect on trade: Perspective of comparative advantage. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.03.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights: 1.
This paper demonstrates that information and communication technology (ICT) can be a source of comparative advantage in international trade.
2.
Country-level ICT data and bilateral trade industry-level data in year 2013 are used.
3.
Gravity model and Interactions between industrial characteristics and exporting country characteristics are used for identification.
4.
Empirical results show that ICT promotes export more in R&D-intensive industries and higher task complexity industries.
*Title Page
ICT's effect on trade: perspective of comparative advantage Authors: Yao Wang School of Management, Zhejiang University, China. E-mail: [email protected].
Jie Li (Corresponding Author) Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore, Singapore. Email: [email protected].
Address correspondence to：Jie Li, Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore, 1 Arts Link, Singapore 117570, Singapore. Email: [email protected].
Statement: This manuscript has not been published elsewhere and it has not been submitted simultaneously for publication elsewhere.
Total words: 1982. *********** Abstract: This paper uses country-level ICT data and bilateral trade data in 2013 to test whether cross-country differences in ICT can be a source of comparative advantage in international trade. Empirical results show that a country’s export in one industry increases 10 percent if the country’s ICT development index increases 1 standard deviation (SD) and industry’s R&D intensity increases 1 SD. The export increase is 25 percent in the case of task complexity. Keywords: Information and communication technology (ICT) development; R&D intensity; task complexity; comparative advantage
JEL code: D2, F1, O3
*Manuscript Click here to view linked References
1
ICT's effect on trade: perspective of comparative advantage
2
Abstract: This paper uses country-level ICT data and bilateral trade data in 2013 to test
3
whether cross-country differences in ICT can be a source of comparative advantage in
4
international trade. Empirical results show that a country’s export in one industry increases 10
5
percent if the country’s ICT development index increases 1 standard deviation (SD) and industry’s
6
R&D intensity increases 1 SD. The export increase is 25 percent in the case of task complexity.
7 8 9
Keywords: Information and communication technology (ICT) development; R&D intensity; task complexity; comparative advantage 1. Introduction
10
The rapid development of information and communication technology (ICT) has taken the
11
world to the era of digital economy, bringing significant digital dividends (World Bank, 2016). ICT,
12
usually regarded as one type of General Purpose Technology, can help improve productivity and
13
resource allocation efficiency (Bresnahan, 2010). Countries with higher ICT development level
14
can provide easier access to ICT to firms. Industries, on the other hand, differs in their demand
15
for ICT in the production process. Consequently, industries using ICT intensively and located in
16
ICT developed countries are abler to improve their productivity and output, thus generating the
17
ICT-induced comparative advantage in international trade.
18
According to endogenous growth theory as well as network externalities theory, ICT
19
positively affect productivity through ICT-leveraged innovations and ICT-induced externalities
20
(Chou et al, 2014). The improved productivity mainly benefits R&D intensive industries and task
21
complexity industries. Firstly, innovation is key to R&D intensive industries. Previous research
22
showed that ICT investment can boost innovation by promoting knowledge sharing and
23
distribution directly (Czernich et al., 2011) and serve as complements to R&D investment (Hall et
24
al., 2012). Thus, ICT is more intensively used in R&D intensive industries. In countries with high
25
ICT development level, R&D intensive industries can benefit more from the ICT’s capacity as well
26
as people’s ICT skills such as workers’ internet-savvy and information acquisition. Secondly,
27
Industries with a high level of specialization face more severe organizational inefficiencies due to
28
contract enforcement problems and higher transaction cost. Task complexity, which is defined as
29
the number of tasks that must be performed before getting one final unit, is commonly used to
30
measure industry’s specialization levels (Costinot, 2009). ICT can improve organizational
31
efficiencies by IT-enabled organizational change (Bresnahan et al, 2002), tougher “people
32
management” practices (Bloom et al, 2012) and transaction cost reduction. Thus, we expect
33
countries with better ICT development to specialize in high task complexity industries. Figure 1 1/7
34
shed light on our hypothesis. The figure shows that countries with higher ICT development index
35
are more specialized in R&D intensive and more complex industries in international trade.
36 37
Figure 1. Correlation between country's ICT development and exporting shares of industries
38
Notes: 1. High R&D intensive industries are industries with R&D intensity above 75th percentiles while low-R&D
39
intensive industries are industries with R&D intensity below 25th percentiles. Task complex industries are similar. 2.
40
X-axis is country’s ICT development index. Y-axis is the export share of industries.
41
Previous researches show that labor force, human capital, physical capital, demographic
42
structure (Cai, 2016) and financial institution (Manova, 2013) all can be sources of comparative
43
advantage. To our best knowledge, research on the ICT’s potential to be a source of comparative
44
advantage is rare. Choi (2010) tested the effect of the internet on trade but not on comparative
45
advantage. Freund and Weinhold (2004) tested the effect of the internet but not ICT. The
46
contribution of this paper to previous literature is that we make use of bilateral trade data to
47
study whether ICT can be a source of comparative advantage.
48
2. Regression Specification and Data
49
We adopt the empirical strategy of Chor (2010), which identifies comparative advantage by
50
the interactions between country characteristics and industrial characteristics. The interactions
51
give us insights on how one industry’s trade flow is affected by exporting country’s characteristics.
52
The regression specification is shown in equation (1).
53
ln exportijk 1Complexity ICTi 2 RDintensity ICTi + n n I kn Fi n ij jk ijk
(1)
54
The dependent variable is the log of trade volume from country i to country j in industry k.
55
The variable of interest here is the interaction of task complexity with ICT development and the
2/7
56
interaction of R&D intensity with ICT development. We control for importer-exporter fixed effect
57
μij to take into account bilateral trade cost factors such as bilateral distance, cultural differences,
58
and historical colonial relationships. Another FE is importer-industry fixed effect γjk, which
59
controls for industry size and productivity in the importer country. However, there are potential
60
endogeneity concerns because exporting countries might adjust its investment in ICT according
61
to certain industries’ performance on the global market. To relieve such concern, we use ICT
62
development data in 2000 as instruments for IV regressions. The reason for using year 2000’s ICT
63
data is that they are predictive of year 2013’s ICT development level but not affected by trade in
64
2013.
65
The bilateral trade data is from CEPII’s BACI dataset. We aggregate trade at HS6 level to
66
NAICS 2002 four-digit level. The data for regressions consist of 152 countries and 86 industries in
67
2013. R&D intensity is calculated as R&D investment over total sales for each company in 2005
68
using Orbis dataset then averaged for each industry. Task complexity data is from Costinot (2009)
69
which uses PSI survey from 1985 to 1993 that asks workers the number of months needed to be
70
fully trained for the job in the industry.
71
Three proxies for ICT development are from International Telecommunication Union. The
72
first is ICT development index, which comprises the access, the use and the skill level of ICT. The
73
second is ICT subscription index, which is measured by broadband subscribers per 100 persons.
74
The third is ICT usage index, which is measured by internet users per 100 people. In addition, we
75
control for another two standard Heckscher-Ohlin model comparative advantage factors, which
76
are cross-country differences in physical capital and human capital. The country level physical
77
capital and human capital data are from Penn World tables and the industry level skill intensity
78
and capital intensity data are calculated from NBER-CES manufacturing dataset. Summary
79
statistics are listed in table 1.
80
TABLE 1
81
Summary Variables
Definition
Export
Export
value
Observation at
industry
level
(in 477,456
Mean
SD
Min
Max
13,153
196,119
1.02*10^-3
6.98*10^7
16.71
6.597
2.380
31.84
0.011
0.013
0
0.049
thousand dollars) Task_complexity
Task complexity of industry at industry 442,959 level
RD_int
R&D-intensity of industry at industry level
386,987
3/7
Idi
ICT development index at country level
477,456
6.067
1.913
0.960
8.860
Internet
Internet user per 100 persons at country 476,437
59.80
24.61
0.900
96.55
18.42
12.20
0
40.53
level Bband
Broad band user per 100 persons at 477,268 country level
Internet2000
internet in year 2000
597,596
16.41
16.52
0.020
52
Bband2000
bband in year 2000
306,552
1.237
1.960
0
8.420
Fixedtel2000
fixed telephone at country level in year 605,250 32.70
22.47
0.120
73.07
2000 Skill_int
skill intensity at industry level
610,906
0.300
0.104
0.099
0.642
Cap_int
Log capital intensity at industry level
610,906
5.049
0.666
3.808
6.689
Skill_abund
human capital at country level
582,244
2.948
0.573
1.186
3.726
Capital_abund
Physical capital stock at country level
606,507
11.19
1.076
7.377
12.90
82
3. Results
83
Table 2 shows baseline regression results. Column (1) includes the interaction between task
84
complexity and ICT development indicator. Column (2) includes the interaction of R&D intensity
85
and ICT development indicator. Column (3) includes both interactions. Column (4) uses log of
86
internet users per 100 people to interact with R&D intensity and task complexity and Column (5)
87
uses the log of broadband subscribers per 100 people. As shown in table 2, the interaction terms
88
of ICT index with R&D intensity and task complexity are all significantly positive in all columns,
89
showing that countries with wider ICT usage export more in industries that are R&D intensive
90
and of high task complexity. From column (3), we can calculate that a country’s export will
91
increase 10 percent if the country’s ICT development index increases 1 standard deviation (SD)
92
and industry’s R&D intensity increases 1 SD. The export increase is 25 percent in the case of task
93
complexity.
1
1
One standard deviation of complexity is 6.597 and one standard deviation of ICT development index is 1.913. Thus the difference in export would be exp (6.597*1.913*0.0174)-1=0.245. For R&D intensity, the export difference is exp(0.0125*1.913*3.929)-1=0.098 4/7
Table 2 Baseline Regression ICT development index
Depend Variable
(Task_complexity)k×(idi)i
ICT subscription index
ICT usage indexer
ln_export
ln_export
ln_export
ln_export
ln_export
(1)
(2)
(3)
(4)
(5)
0.0199***
0.0174***
(0.00144)
(0.00172)
(RD_int)k×(idi)i
5.684***
3.929***
(0.734)
(0.759)
(Task_complexity)k×(ln_internet)i
0.0456*** (0.00472)
(RD_ int)k×(ln_internet)i
9.254*** (1.948)
(Task_complexity)k×(ln_bband)i
0.0152*** (0.00206)
(RD_int)k×(ln_bband)i
4.663*** (0.848)
(Skill_int)k×(Skill_ abund)i
(Capital_int)k×(Capital_ abund)i
2.544***
3.121***
1.703***
2.179***
2.535***
(0.291)
(0.318)
(0.350)
(0.349)
(0.356)
-0.158***
-0.152***
-0.126***
-0.136*** -0.178***
(0.0228)
(0.0229)
(0.0263)
(0.0261)
(0.0262)
538,182
470,289
431,438
430,337
431,233
0.630
0.629
0.634
0.633
0.632
Imp-Exp FE
Yes
Yes
Yes
Yes
Yes
Imp-Industry FE
Yes
Yes
Yes
Yes
Yes
Observations Adjusted R
2
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
94
Table 3 shows Instrument Variable (IV) results. Year 2000’s internet user per 100 people is
95
used as the IV for its 2013 data and year 2000’s broadband data as IV for 2013 data. Fixed
96
telephone subscription per 100 people in 2000 is used as IV for 2000’s ICT development index.
97
The results show that all the key interaction terms are still positive and significant. 5/7
98 99 100
Table 3 Instrument Variable Regression
Depend Variable
ICT development index
ICT subscription index
ICT usage index
ln_export ln_export ln_export
ln_export
ln_export
(4)
(5)
(1)
(Task_complexity)k×(idi)i
(2)
(3)
0.0196***
0.0164***
(0.00156)
(0.00176)
(RD_int)k×(idi)i
5.285*** 3.704*** (0.772)
(0.790)
(Task_complexity)k×(ln_internet)i
0.0570*** (0.00600)
(RD_int)k×(ln_internet)i
19.77*** (3.032)
(Task_complexity)k×(ln_bband)i
0.0414** (0.0183)
(RD_int)k×(ln_bband)i
18.19** (8.808)
Observations Adjusted R2 Kleibergen-Paap F stats. (First stage)
533,920
466,603
428,022
422,011
200,995
0.630
0.630
0.634
0.636
0.699
832.592
168.738
8920.863 4822.772 2684.556
Imp-Exp FE
Yes
Yes
Yes
Yes
Yes
Imp-Industry FE
Yes
Yes
Yes
Yes
Yes
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 (Skill_int)k × (Skill_abund)i and (Capital_int)k × (Capital_abund)i is not reported here because of limited space.
101
4. Conclusion
102
The idea of comparative advantage has been studied over time. In the age of digital
103
economy, a revisit to this old concept can shed light on new source of comparative advantage.
104
We use industry level trade data and country level ICT development data to show that ICT
105
developed countries have comparative advantage in industries that are R&D intensive or task
106
complex. The theory implication of this paper is to extend ICT’s GPT attribute by its effect on
6/7
107
international trade.
108 109 110 111 112 113 114
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