The Role of ICT in Economic Growth of Korea - cerge-ei

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The Role of ICT
in Economic Growth of Korea:
Productivity Changes across Industries since 1985
January, 29, 2009
Chang-Ho Yoon*,
Kyoung-Youn Na** and Hyun-Joon Jung***
* Professor, Department of Economics, Korea University
** Ph.D. student, Department of Economics, Korea University
*** Researcher, Korea Information Society Development Institute
The Role of ICT in Economic Growth of Korea
I. Introduction
II. Empirical Model and Estimation
III. Concluding Comments
I. Introduction
• Information Technology and Productivity Growth in
OECD countries (2003): Diverging patterns of ICT
diffusion across countries and industries, and differential
impact over time.
• Need to examine a case of ICT leading country where a
sustained increase of productivity has been traced in ICT
intensive industries.
• Recovery of Korea after financial turmoil in the late 1990s.
• Since the financial turmoil, capital deepening in Korea has
been relatively modest compared to the previous rapidly
growing period. The annual rate of growth of net capital
stock of the Korean economy over the period of 1970- 2005
was 10.8%, while 6.0% for 1998 – 2005. In the case of ICT
sector, the rate was 41.2% for the period of 1984-1998,
and then reduced to 3.4% for 1998 – 2005.
• The trend growth rate of Korea has been lowered after the
crises with much less volatility until 2007.
- Source : Bank of Korea
• Sources of GDP growth using best estimate and harmonized prices
- Source : Takahito Kanamori and Kazuyuki Motohashi (2007)
• Decomposition of TFP Growth
- Source : Takahito Kanamori and Kazuyuki Motohashi (2007)
• Literature on growth enhancing effects of ICT: Jorgenson,
D and K. Stiroh (1999, 2000), Stiroh, K. (2002), Jorgenson,
D and Motohashi, K (2005), Kanamori, T and Motohashi,
K (2008)
• Suspicious perspective on the New Economy: Gordon
(2000)
• Roller and Waverman (2001) on the positive role of ICT
infrastructure in economic growth.
II. Empirical Model and Estimation
Methodological comments on previous research
(1) Technology specification
– Suppose the economy is divided into ICT producing
sector and using sector. Then ICT capital stock of ICT
producing sector often becomes the ICT infrastructure
of the whole economy and complementary to ICT
capital of ICT using sector (e.g. fixed and wireless
networks, R&D facilities)
– The effects of ICT on productivity of ICT using
industry can be underestimated if focusing on ICT
capital of that industry alone.
0
.1
.2
.3
.4
• The Ratio of ICT capital stock (OECD 2002 classification)
1985
1990
1995
year
ICT_MAN
ICT
- Source : EU KLEMS
2000
ICT_SER
2005
(2) OECD ICT sector definition- capital measurement
– OECD ICT sector definition(2007): “The production
(goods and services) of a candidate industry must
primarily be intended to fulfill or enable the function of
information processing and communication by
electronic means, including transmission and display.”
– New ICT definition exclude the sentence that
“Industries producing products (technologies) that use
electronic processing to detect, measure, record or
control a physical process.”
• ICT Convergence sectors use more and more ICT
intermediate inputs or ICT capital which are not classified
as ICT.
– Motor vehicle is a good example.
– The components of Motor vehicle such as ECU, sensors
and embedded SW are not classified as ICT.
– ECU or sensors uses electronic processing to detect,
measure, record or control a physical process (sensing,
computing and communicating).
(3) Inter-industry spill-over effects
– In many competitive or oligopoly industries, an
investment game in ICT often results in cost and price
reduction, and wealth redistribution from firms to
consumers. Gain in consumer surplus boosts
consumption in other industries.
– Due to heavy development cost and low marginal cost of
ICT equipment and software, strong network effects
prevail among the users across industries (e.g. software
for internet security).
Industrial Classifications
- Source: EU KLEMS
Estimation of spill-over effects
• We consider aggregate production function
Yit = f ( Lit , K it , ICK t , t )
• ICK t is aggregate ICT capital stock at t.
• Let θ it denote the proportion of ICT capital stock of
industry i at t.
• Then
ICKt = ∑θit K it
i
1. Standard Cobb-Douglas Function
Yit = Fi ( Ai (t ), Lit , K it ) = Bi (t ) Lαit K itβ ICK tγ
ICK tγ = (∑θ it K it )γ
α , β ,γ > 0
i
2. Other specification to emphasize the dependency of
industry spill-over on θ it
α
β
γ ⋅θ it
Yit = Bi (t ) Lit K it ICK t
• Specification for industrial value added. All the variables
are in natural logarithms.
yit = β n nit + β k kit + β ick ickt + (ηi +ν it + mit )
ν it = αvi ,t −1 + eit
| α |< 1
eit , mit ~ MA(0)
• Both n and k are determined endogenously within the
system (depend on current and lagged output levels as well
as technology shocks)
• Dynamic representation
(1)
yit = π 11nit + π 12 ni ,t −1 + π 13kit + π 14 ki ,t −1 + π 15ickt + π 16ickt −1 + π 17 yi ,t −1 + (η1*i + w1it )
(2)
yit = π 21nit + π 22 ni ,t −1 + π 23kit + π 24 ki ,t −1 + π 25θit ⋅ ickt + π 26θit ⋅ ickt −1 + π 27 yi , t −1 + (η2*i + w2it )
• In either case we can obtain consistent estimates of the
unrestricted parameter vector π = (π 1 , π 2 , π 3 , π 4 , π 5 , π 6 , π 7 )
using the GMM methods.
Data
Variables
Definition
GO
Gross output at current basic prices (million won)
VA
Gross value added at current basic prices (million won)
GO_P
Gross output, price indices, 1995 = 100
VA_P
Gross value added, price indices, 1995 = 100
L
Total hours worked by employees (millions)
KICT
ICT assets, Real fixed capital stock, 1995 prices
K
All assets, Real fixed capital stock, 1995 prices
LAB
Labor compensation (million won)
CAP
Capital compensation (million won)
TFP
TFP (value added based) growth, 1995=100
- Source : EU KLEMS
• Descriptive Statistics
Variable
Obs
Mean
Std. Dev. Min
Max
VA
630
15.862
1.011
13.098
18.347
n
630
6.449
1.106
3.341
8.271
k
630
16.655
1.366
13.359
20.150
ick
630
17.015
0.772
15.658
18.060
Learning 630
18.996
1.064
15.676
21.406
rTFP
600
0.033
0.074
-0.243
0.372
rGO
600
0.073
0.097
-0.459
0.942
rick
600
0.120
0.043
0.039
0.185
θ
630
0.039
0.055
0.001
0.339
(ln level)
Estimation of production function
• Production Function Estimation Result (conti…)
*, ** and *** denote significance of the test statistic at the 10, 5 and 1% level of significance, respectively.
• rTFP Regressions
d ln Ait = α + β d ln GOit + γ ln Learning it + δd ln ICK t + ε it
• Ait is TFP of industry i in year t.
• GOit is Gross Output of industry i in year t.
• Learning it is cumulative Gross Output of industry from year
t-5 to year t-1.
• ICK t is ICT capital stock of all industries in year t.
• rTFP Regressions Result
• Dummy Variable rTFP Regressions
(1)
d ln Ait = α + β D + γC + δd ln GOit + φd ln ICK t + ε it
• D = 1 if t ≤ 1998 , D = 0 otherwise.
• C = 1 if ICT using industry,
C = 0 if ICT producing industry.
• Dummy Variable rTFP Regressions Result
• Continuous ICT Variable rTFP Regressions
(2)
d ln Ait = α1 + β1D ⋅ d ln ICK t + δ1d ln GOit + φ1d ln ICK t + ε1it
(3)
d ln Ait = α 2 + β 2 D ⋅ d ln ICK t + γ 1C ⋅ θit + δ 2 d ln GOit + φ2 d ln ICK t + λ1θit + ε 2it
• Continuous ICT Variable rTFP Regressions Result
-.04
-.02
total_r_TFP
0
.02
.04
.06
• Aggregate TFP growth rate
1985
–
Source : EU KLEMS
1990
1995
year
2000
2005
• ICT producing industries
13
ELECTRICAL AND OPTICAL EQUIPMENT
23
POST AND TELECOMMUNICATIONS
id = 13
Variable
Obs
indus_rTFP
Mean
20 0.1076
Std. Dev.
0.0847
id = 23
Variable
indus_rTFP
Obs
Mean
20 0.1190
Std. Dev.
0.0576
• ICT producing industries
23
-.4
-.2
0
.2
.4
13
1985
1990
1995
2000
2 0 0 51 9 8 5
1990
1995
2000
2005
year
i ndus_r_T F P
total_r_TF P
G r ap h s b y id
– ICT producing industries (both manufacturing and service) show
consistently higher productivity growth. During the financial crisis, still
positive growth.
• ICT intensive industries
– The proportion of ICT capital(θ ) is greater than the
average proportion of all the industries. The status can
change over time.
• Among the industries which use ICT intensively, select
industries with TFP growth rate persistently higher than
economy wide growth rate since 2002.
8
Chemicals and chemical
12
MACHINERY, NEC
14
TRANSPORT EQUIPMENT
• ICT intensive industries (code 8, 12, 14)
id = 8
Variable
Obs
indus_rTFP
Mean
20 0.0524
Std. Dev.
0.0453
id = 12
Variable
Obs
indus_rTFP
Mean
20 0.0745
Std. Dev.
0.1079
id = 14
Variable
indus_rTFP
Obs
Mean
20 0.0648
Std. Dev.
0.0870
• ICT intensive industries
14
-.4
-.2
0
.2
.4
12
1985
1990
1995
2000
2 0 0 51 9 8 5
1990
1995
2000
2005
ye a r
i n d u s_ r _T F P
to ta l_ r _ TF P
G r ap h s b y id
– Among ICT intensive industries, industries 12 and 14 show high
volatility.
• ICT intensive industries (conti…)
-.4
-.2
0
.2
.4
8
1985
1990
1995
year
indus_r_TFP
2000
2005
total_r_TFP
G raphs by id
– Industry 8 shows consistently higher productivity growth with
small volatility.
• ICT intensive service industry
24
FINANCIAL INTERMEDIATION
id = 24
Variable
Obs
Mean
Std. Dev.
indus_rTFP
20
0.0447
0.0995
• 23 Post and Telecommunications
• 24 Financial Intermediation
24
-.4
-.2
0
.2
.4
23
1985
1990
1995
2000
20051985
1990
1995
year
indus_r_T FP
G rap h s b y id
total_r_TFP
2000
2005
• Service industries with increasing ICT intensity since mid
1990s
18
19
20
Sale, maintenance and repair of motor vehicles
and motorcycles; retail sale of fuel
Wholesale trade and commission trade, except of
motor vehicles and motorcycles
Retail trade, except of motor vehicles and
motorcycles; repair of household goods
Right axis
θ =share
.2
.1
0
-.1
-.05 0 .05 .1
.015 .02 .025 .03
19
.015 .02 .025 .03
18
1998
2000
2002
2004
2006
0
.1
.015 .02 .025 .03
20
-.1
•
1998
2000
2002
2004
2006
year
indus_rTFP
share
total_r_TFP
Graphs by id
– ICT intensity rises and then declines recently. Better performance recently.
.2
.25
.3
.35
.4
• Export ratio of ICT using industry and producing industry
1990
1995
2000
year
ex_ratio_ICT_high
–
Source : Bank of Korea
2005
ex_ratio_ICT_pro
2010
50
100
150
200
• Export volume indices (1995=100)
1990
1995
2000
year
ex_vol_ICT_high
ex_vol_sum
- Source : Bank of Korea
2005
ex_vol_ICT_pro
2010
III. Concluding Comments
• In contrast to previous research, ICT has strong effects on
the productivity of ICT using export industries
(notwithstanding ICT producing industries).
• Dynamic comparative advantage has been revealed toward
the ICT using export industries which have managed to
adopt convergent technologies. However from the 4th
quarter of 2008, exports began to decline rapidly.
• Does technology convergence extend to other promising
industries?
References
•
Bailey, Martin N. and Robert Lawrence (2001), “Do We Have a New E-Conomy?”, NBER
Working Paper 8243.
•
Basu, Susanto, Fernald, John G., Oulton , Nicholas and Srinivasan , Sylaja (2003), “The Case
of the Missing Productivity Growth: Or, Does Information Technology Explain why
Productivity Accelerated in the US but not the UK?”, NBER Working Paper, No. W10010.
•
Blundell R.W. and S.R. Bond (2000), “GMM Estimation with Persistent Panel Data: An
Application to Production Functions”, Econometric Reviews, 19, pp.321-340.
•
Daveri, Francesco (2003), “Information Technology and Productivity Growth Across
Countries and Sectors”, IGIER Working Paper, No. 227
•
Dewan, S., Kraemer, K. L. (2000), “Information technology and productivity: evidence from
country-level data.”, Management Science, 46, pp.548-562.
•
Gordon, Robert J. (2000), "Does the “New Economy” Measure up to the Great Inventions of
the Past?”, Journal of Economic Perspectives, Vol. 14, pp.49-74.
•
Greene, W.H. (2002), Econometric Analysis, 5th ed. Upper Saddle River, NJ: Prentice-Hall.
•
Hausman J. A, (1978), “Specification tests in econometrics”, Econometrica, 46, pp.12511271.
References
•
Il Houng Lee and Yougesh Khatri (2003), “IT and Productivity Growth in Asia”, IMF
working paper WP/03/15.
•
Jorgenson, Dale W. and Kevin J. Stiroh(2000), “Raising the Speed Limit: U. S. Economic
Growth in the Information Age”, Brookings Papers on Economic Activity.
•
Jorgenson , Dale W. (2001), “Information Technology and the U.S. Economy”, Harvard
Institute of Economic Research Paper, No. 1911.
•
Kevin J. Stiroh (2001), “Information Technology and the U.S. Productivity Revival: What Do
the Industry Data Say?”, Federal Reserve Bank of New York.
•
_____________ (2002), “Information Technology and the U.S. Productivity Revival: What
Do the Industry Data Say?”, The American Economic Review, Vol. 92, No. 5, pp.1559-1576.
•
Ki-Dong Lee (2001), “An Analysis on the Productivity Effects of the Information and
Communication Technology Investment in Korean Industries”, Kukje Kyungje Yongu, Vol.7,
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•
KwanHo Shin, YoungSoo Lee and JongWha Lee (2004), “Productivity Growth in Information
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Kyungje Yongu, Vol.10, No.2. (in Korean)
References
•
Lars-Hendrik Roller and Leonard Waverman (2001), "Telecommunications Infrastructure and
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91, No. 4, pp. 909-923.
•
Marcel Timmer, Ton van Moergastel and Edwin Stuivenwold et al. (2007) “EU KLEMS
Growth and Productivity Account”, Version 1.0, PART I Methodology.
•
Oliner, Stephen D. and Sichel, Daniel E. (1994), “Computers and Output Growth Revisited:
How Big Is the Puzzle?,” Brookings Papers on Economic Activity, Economic Studies Program,
The Brookings Institution, vol. 25, pp.273-334.
•
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Technology the Story?”, FEDS Working Paper, No. 2000-20.
•
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where are we going?”, Journal of Policy Modeling, Vol. 25, Issue 5, Special Silver
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•
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•
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Thank You

The Role of ICT in Economic Growth of Korea - cerge-ei

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