Telecommunications Infrastructure and Regional ...

Telecommunications Infrastructure and Regional Income Convergence in China: Panel Data Approaches Lei Ding University of North Carolina

Kingsley E. Haynes George Mason University

Yanchun Liu George Mason University

Content • • • • • •

Background Introduction Literature Methods & Data Results & Discussion Conclusion -

George Mason University School of Public Policy

Preface: Merging Two Research Themes 1. Technology, Innovation and Economic

Policy 2. Infrastructure Investments and Regional Development: Transportation and Telecommunications-


Theme 1: Technology, Innovation and Economic Policy • Economic Transition – China’s Dualist and Leading Sector Policies (Jin and Haynes, 1997) • Dynamics of Knowledge Regimes: Technology, Culture and Competitiveness (Jin, 2001) • Regional Development Policy: Output, Productivity & Verdoorn;s Law or Why Technology & Innovation Matters (Haynes, 2005) • Modeling Knowledge Networks and Learning: (Shibusawa and Haynes, 2004)

• Tech & Innovation in Late-comer Strategies: Telecom in China (Ding and Haynes, 2006) George Mason University School of Public Policy

Theme 2: Infrastructure and Regional Development • Telecommunications/Transportation Substitution vs. Complementarity (Dinc and Haynes, 1998) • Impact of Telecommunications Investments Across US States – 1970-1997 (Yilmaz, Haynes and Dinc, 1999)

• Sectoral Impacts of Telecommunication Investments (Yilmaz, Haynes and Dinc, 2000) • Spatial Spillover of Telecommunication Investments (Yilmaz, Haynes and Dinc, 2001) • Efficient Use of Telecommunication Investments (Dinc, Haynes and Yilmaz, 2002)George Mason University School of Public Policy

Background • In 1949, only 263,000 telephones for the 500 million people in China. • No telephones in rural area and more than 90% of the counties in 1949. • 1950s-1980s, slow development of telecommunications sector due to its underemphasized role in national policy framework. • In 1980, 0.43 telephone terminals per 100 residents and 4.1 million subscribers in total.-


• The post and telecommunication sector share in national total fixed investment from 0.73% in 1985 to more than 7% in 2001; • China installed more than 159 million fixed telephone lines and got more than 84 million mobile subscribers from 1990 to 2000; • Total number of telephone subscribers increased from 6.26 million in 1985 to 421.04 million in 2002; • Switchboard capacity leaped from four million lines prior to 1985 to 287 million in 2002. George Mason University School of Public Policy

China’s Telecommunications Sector Development – 1985-2003 600 500

300 200 100

Year

20 03

20 01

19 99

19 97

19 95

19 93

19 91

19 89

19 87

0

19 85

Millions

400

Mobile Total Telephones


Telecommunications Events 300 Number of mobile subscribers exceeds fixed-line users

250

Subscriber size reached 120.6 millions in July 2001, largest mobile user base

Million

200

Subscriber size reached 10 million, more than half are GSM users

150

GSM network in business operation in China

100 50

Mobile phone communications Introduced Into China

0 1985

1987

1989

1991

1993

1995 Year

1997

1999 Fixed-line

2001

2003

Moblie


Mobile Subscribers in Large Developing Countries (per 100)


8 7 6 5 4 3 2 1

20 01

19 99

19 97

19 95

19 93

19 91

19 89

19 87

0

19 85

Percent of national investment

Share of Fixed Investment in P&T Sector – National Total

Year


Sources of Regional Disparity in Sector Growth 1986-2002 • Rural – Urban Differences • Central government imbalanced policy in telecom infrastructure investment allocation (e.g. favorable to coastal vs interior regions) • Regional governments gained more power in telecom infrastructure investment policy making • Posts and telecom enterprises obtained more autonomy in operations and service provision.George Mason University School of Public Policy

Regional Disparity of Telephones Penetration

Teledensity in 2002 (per 100) 0 - 15 15 - 30 30 - 50 50 - 75 >75

Teledensity in 1986 (per 100) 4

1986

2002 George Mason University School of Public Policy

Regional Economic Growth in China • Rapid economic growth from 1980s to 2002 • GDP growth rate 7+% each year during the same period • Inter-province inequality in economic growth performance • A concentration of coastal regions with over 9+% growth rate per year • Western and central regions with much lower growth rates.George Mason University School of Public Policy

Growth Rate of GDP and Teledensity – 1975-2002 60 50 Teledensity grow th

40 30 20 10 0 19 75 19 77 19 79 19 81 19 83 19 85 19 87 19 89 19 91 19 93 19 95 19 97 19 99 20 01

Growth rate (%)

GDP grow th

-10

Year


Growth Performance: Average Annual Growth Rate 1986-2002

Average Growth, 86-02 10.4


Introduction • Neoclassical growth models: – Solow aggregate production function approach – Conditional convergence framework

• Conditional convergence model (Barro, 1991): – Adds to Solow equation a set of variables reflecting differences in the steady-state equilibrium (for China, see Gundlach, 1997) – Assumes identical aggregate production functions for all target countries or regions George Mason University School of Public Policy

MRW Modification by Islam (1995) • • • •

Develop a dynamic framework Propose a panel data approach Include unobservable country effects Estimate those effects over time and space • Compare Islam results to Mankiw, Romer, and Weil (MRW) (1992) results-


Focus of Study • Note theoretical differences among different LSDV models or estimation methods • Examine telecommunication infrastructure role in regional economic dynamics in China • Application of different dynamic approaches George Mason University School of Public Policy

Literature: Contributions to Regional Economy • Lower transaction costs for businesses (Saunders et al., 1994)

• Facilitate information flows and generate market opportunities (Saunders et al., 1994) • Expand markets and improve organizational efficiency of firms (Madden and Savage, 1998) • Serve as direct input in production process (e.g. Yilmaz et al., 2001)

• Increase productivity of other sectors (Saunders et al., 1994)

• Attract economic resources from other regions (Mody, 1997; Sun, Tong, & Yu, 2002)George Mason University School of Public Policy

Literature: Relationship to Economic Growth • Empirical studies of telecommunications infrastructure: – Positive impacts on regional economic growth in United States (Yilmaz, Haynes & Dinc, 2001, 2002)

– Positive role for economic development in China context (Ding & Haynes, 2006)George Mason University School of Public Policy

Literature: Income Convergence in China • Extended Solow growth model using 1978-1993 panel dataset (Chen & Fleisher, 1996) • Dynamic panel data used to investigate income convergence – pre-reform and reform periods (Weeks & Yao, 2003) • Panel data used on output of 23 industrial sectors for seven coastal regions for late 1980s (Mody & Wang 1997)

• Rural area telecommunications development helped reduce burden of isolation with positive impact on economic growth (Demurger 2001)George Mason University School of Public Policy

Methods and Data • Models – Barro Cross Section (Barro, 1991) – Barro Extended – Estimation – • OLS (Ordinary Least Squares) • LSDV (Least Squares Dummy Variable) • GMM (Generalized Methods of Moments)

– Regression analysis George Mason University School of Public Policy

Barro Cross-Section Approach GRTH i = α 0 + β1Ln(GDP )i ,0 + ∑ β j X i ,0 + β i ,tTELi ,0 + ε i j =2

i – indexes provinces in China GRTH – annual growth rate of real GDP per capita Ln(GDP)I,0 – initial level of real GDP per capita - logarithm TEL – measure of telecommunications infrastructure X – production factors variables and other conditional variables at beginning of study


Model A: Barro Model Extended GRTH it = α 0 + β1 Ln (GDP ) i ,t −1 + ∑ β j X i ,t + β i ,tTELi ,t + α i + η t + ν it j =2

t – indexes year αi – province- and time- specific parameters for unmeasured regional characteristics ηt – Province- and time- specific parameters for temporary shocks or policy changes


Model B: Islam Model Transformed GRTH it = α 0 + γGRTH i ,t −1 + β1Ln(GDP )i ,t −1 +

∑β j =2

j

X i ,t + β i ,tTELi ,t + α i + ηt + ν it

Model B adds variable lagged growth rate Estimations performed on both Model A (Barro Extended) and Model B (Islam Model Transformed)George Mason University School of Public Policy

Estimation Methods • OLS estimation – Does not consider unobserved time/regional effects – Suffers from positive correlation between lagged dependent variable and error term (Roodman, 2007)

• LSDV estimation – Based on fixed-effects assumption – Does not eliminate dynamic panel bias as lagged dependent variable negatively correlates w/ error term – Biasing coefficient estimate downward (Roodman, 2007)

• GMM estimation (system GMM estimator) – Eliminates individual-effect term through first differencing (Caselli et al., 1996) – Designed for small T and large N panelsGeorge Mason University School of Public Policy

Variables Follow Literature Guidelines GRTH it = α 0 + β1Ln(GDP )i ,t −1 + β 2 INVit + β 3 FDI it + β 4 POPit + β 5 EMPit + β 6 HCit + β 7URBAN it + β8 SOEit + β 9TRANSit + β10TELit + ν it GRTH – Ln(GDP) – INV – FDI – POP – EMP – HC – URBAN – SOE – TRANS – TEL –

annual growth rate of real GDP per capita log value of real GDP per capita in 1995 RMB share of fixed investment in GDP share of foreign direct investment divided by total investment annual population growth rate percentage of total employment to total population human capital measured by average years of schooling share of urban population to total population share of state-owned enterprises in total industrial output transportation density number of telephones per 100 inhabitants George Mason University School of Public Policy

Data • 29 regions of Mainland China between 1986 and 2002 – except Tibet Province and Chongqing Municipality

• Data Sources – Comprehensive Statistical Data and materials from 50 Years of New China (NSB, 1999) – Statistics on Investment in Fixed Assets of China (NSB, 2002) – China Statistics Yearbooks (NSB, 1999-2003)

• Data measured in Ren Min Bi adjusted to 1995 valuesGeorge Mason University School of Public Policy

Methods and Data Variable GROWTH GROWTHt-1 LGDP t-1 INV FDI POPGROW EMPLOY TELE HC URBAN SOE TRANS

Definition annual growth rate of real GDP per capita. one-year lag of growth rate. log value of real GDP per capita in 1995 RMB. fixed investment in GDP. foreign direct investment to total fixed investment. annual population growth rate. total employment to total population. number of telephones per capita. human capital measured by the average years of schooling for the population aged 6 and above. urban population to total population. output by state-owned enterprises to total industrial output transportation density; length of rail, highway, and waterway networks per square kilometer.

Expected sign

1986 0.061 0.128 7.688 0.307 0.012 0.016 0.481 0.008

Mean 1995 0.104 0.124 8.441 0.335 0.126 0.012 0.504 0.054

2001 0.085 0.079 8.933 0.371 0.080 0.008 0.481 0.277

+ + + + +

4.648 0.291

5.356 0.337

6.089 0.417

+ +

0.686

0.448

0.292

-

0.257

0.309

0.418

+


Random vs. Fixed Effects • Hausman test indicates fixed effect model is preferred over random effect model • Estimation based on these two models should be a two-way fixed effect approach -

Coefficients (b) fixed 0.27 -7.76 19.76 0.14 -1.33 0.03 2.70 0.23 0.07 -1.91 0.01

GRTH t-1 Ln(GDP)t-1 INV FDI POP EMP HC URBAN SOE TRANS TEL Chi Square = 68.48 Probability>Chi Square = 0.0000

(B) . 0.34 0.69 9.98 0.08 -1.00 0.12 -0.94 0.00 0.61 -0.34 0.00

(b-B) Difference Standard Error -0.06 0.01 -8.45 1.45 9.78 1.82 0.06 0.03 -0.32 0.06 -0.09 0.01 3.64 1.07 0.23 0.10 -0.54 0.19 -1.57 3.96 0.01 0.005


Initial Regression Results Variables

Model A LSDV

System GMM

Model B LSDV

System GMM ***

0.561(6.42) ***

GRTH t-1

-

-

0.296(6.84)

LGDP t-1

-0.095(-5.56) ***

-0.019(-1.14)

-0.099(-6.12) ***

-0.018(-1.99) *

INV

0.128(5.1) ***

0.150(5.56) ***

0.087(3.53) **

0.063(3.4) **

FDI

0.111(3.47) **

0.121(2.05) *

0.077(2.48) **

0.073(2.9) **

POP

-0.791(-4.94) ***

-0.740(-4.54) ***

-0.812(-5.32) ***

-0.832(-6.38) ***

EMP

0.105(1.94) *

0.058(0.87)

0.076(1.47)

-0.008(-0.25)

TEL

0.069(2.66) ***

0.026(0.77)

0.055(2.21) *

0.031(1.06)

HC

0.001(0.12)

0.003(0.26)

0.001(0.11)

-0.008(-1.27)

URBAN

0.027(0.27)

0.001(0.02)

0.109(1.11)

0.069(1.7)

SOE

-0.001(0.22)

-0.015(-1.06)

0.000(0.05)

0.006(0.87)

TRANS

-0.0272(0.71)

-0.008(-0.25)

0.005(0.14)

Intercept

0.698(5.50)

R Square

0.65

***

0.132 (1.24)

0.689(5.71)

-0.008(-0.42) ***

0.136(2.28) *

0.68

Note: Results based on data for 29 regions over 17-year period George Mason University School of Public Policy

Spatial Dependency Issue M o ra n 's I Te s ts o n M o d e l B fo r S p a tia l E rro r D e p e n d e n c y Ye a r

OLS

LSD V

S y s te m G M M

M o ra n ’s I

P -v a lu e

M o ra n ’s I

P -v a lu e

M o ra n ’s I

P -v a lu e

1986

-0 .1 5

0 .5 6

0 .0 5

0 .2

-0 .2 0

0 .2 9

1987

-0 .0 7

0 .8 7

-0 .0 8

0 .9 4

-0 .0 3

0 .5 8

1988

-0 .0 9

0 .9 9

-0 .0 5

0 .7 2

-0 .0 4

0 .6 2

1989

-0 .1 2

0 .7 5

-0 .2

0 .3

-0 .1 6

0 .4 9

1990

-0 .0 1

0 .4 5

-0 .1 5

0 .5 7

-0 .0 0 0 4

0 .4 1

1991

-0 .1 4

0 .6 0

-0 .11

0 .8 7

-0 .1 6

0 .4 8

1992

-0 .1 9

0 .3 0

-0 .0 8

0 .9 5

-0 .1 8

0 .2 7

1993

-0 .0 7

0 .8 6

-0 .1

0 .9

-0 .0 3

0 .6 0

0 .2 3

***

1995

0 .1 1

*

0 .0 6

-0 .11

1996

0 .0 7 *

0 .0 9

-0 .2 5

1997

-0 .11

0 .8 5

-0 .1 6

1994

**

0 .0 0 2

-0 .0 3

0 .0 3

-0 .2 8

-0 .1 7

0 .4 5

2000

-0 .0 9

2001 2002

0 .2 5

***

0 .8 5

0 .1 3

**

0 .0 4

0 .1 5

-0 .0 4

0 .6 8

0 .5 2

-0 .1 4

0 .5 8

*

0 .0 0 1

0 .5 9 **

0 .0 8

-0 .3 1

0 .0 2

0 .3 2

-0 .1 3

0 .6 5

0 .9 8

-0 .1 7

0 .4 5

-0 .0 2

0 .5 4

-0 .2 2

0 .2 1

-0 .0 0 2

0 .4 2

-0 .1 9

0 .3 0

-0 .1 6

0 .5 0

-0 .1 7

0 .4 5

-0 .1 8

0 .3 9

1998

-0 .3 1

1999

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

0 .0 3

Moran’s I Statistics for Model A are similar. George Mason University School of Public Policy

Determinants of Regional Economic Growth Variables

Model A

Model B

OLS

LSDV

System GMM

OLS

LSDV

System GMM

GRTHt-1

-

-

-

0.406(10.04) ***

0.291(6.83) ***

0.565(7.71) ***

LGDPt-1

-0.005(-1.12)

-0.096(-5.98) ***

-0.016(-2.08) *

-0.005(-1.32)

-0.096(-6.24) ***

-0.011(-2.92) **

INV

0.043(2.36) **

0.129(5.38) ***

0.107(3.29) **

0.0267(1.59)

0.082(3.42) **

0.054(2.73) **

FDI

0.130(6.95) ***

0.109(3.49) **

0.135(2.72) **

0.079(4.47) ***

0.081(2.67) **

0.068(2.83) **

POP

-0.791(-4.82) ***

-0.799(-5.02) ***

-0.577(-4.21) ***

-0.776(-5.21) ***

-0.815(-5.38) ***

-0.681(-6.01) ***

EMP

0.061(2.17) *

0.108(2.06) *

0.138(2.42) **

0.027(1.03)

0.071(1.41)

0.037(1.34)

TEL

0.030(1.42)

0.061(2.61) **

0.029(0.98)

0.036(1.85) *

0.053(2.39) **

0.041(1.65) *

Intercept

0.065(1.97) *

0.713(5.94) ***

0.082(1.45)

0.036(1.22)

0.703(6.15) ***

0.045(1.94) *

Implied λ

0.5%

10.1%

1.6%

0.5%

10.1%

1.1%

(years)

138

6.9

43.0

138

6.9

62.7

R Square

0.53

0.65

0.60

0.68

Half-life

*** significant at 1% level; t-statistics in parentheses; ** significant at 5% level; *significant at 10% level. George Mason University School of Public Policy

Comparative Analysis with Other Studies Comparison of Convergence Speed of Recent Studies Study

Chen and Fleisher,

Weeks and Yao,

Ding, Haynes, and

1996

Gundlach, 1997

2003

Liu, 2007

Sample

25 provinces

29 provinces

28 provinces

29 provinces

Dependent variable

GDP

output per worker

GDP per capita

GDP

Study period

h 1978-1993

1979-1989

1978-1997

h 1986-2002

Implied λ

1.6%

2.2%

2.23%

1.1%-1.6%

Half-life (years)

44.4

31.5

30.1

43-63

Methods

Panel data, OLS

Cross-sectional

System GMM

System GMM

per

capita

per

capita


Discussion • System GMM estimator – Advantage in controlling individual-specific heteroscedasticity and autocorrelation problems – Omitted variable bias

• Credible range for the coefficient of lagged GDP per capita variable (LGDPt-1) should be -0.096 (from LSDV) to -0.005 (from OLS) for both Model A and Model B. • Convergence speeds (implied λ) – 1.6% for Model A – 1.1% for Model B which are significantly slower than convergence speed estimated based on LSDV (about 10%).

• Half-life time needed – 43 years for Model A – 63 years for Model B George Mason University School of Public Policy

Comparative Analysis Findings • Most models suggest telecom infrastructure has a positive impact on regional economic growth – Exceptions: OLS estimation and the system GMM estimation for Model A – but the sign of the coefficient is also positive

• Both models indicate China’s regional growth rates are – positively related to fixed investments, employment, foreign direct investment – negatively related to population growth

• Model B also shows a positive relationship between growth rates and lagged growth ratesGeorge Mason University School of Public Policy

Conclusion • Results confirm early studies that the system GMM estimation is more likely to produce consistent and efficient estimates • GMM is preferred – Over OLS for control of unobserved characteristics – Over LSDV for more accurate estimates of convergence speed

• Telecommunication infrastructure contributes positively and significantly to economic performance and growth • Evidence validates conditional convergenceGeorge Mason University School of Public Policy

Telecommunications Infrastructure and Regional Income Convergence in China: Panel Data Approaches Lei Ding University of North Carolina

Kingsley E. Haynes George Mason University

Yanchun Liu George Mason University