CALL ME MAYBE: THE IMPACT
OF TELECOMMUNICATIONS
ON ECONOMIC GROWTH IN
THE ASEAN REGION
DLSU - SOE
MCREY BANDERLIPE II, MSc (CPA)
Introduction



Communication plays a very important role for
everyone. We cannot live without communicating
with one another.
The advent of new modes and technologies in
communications resulted to real-time transfer of
information to make relevant decision.
Economic activities are also affected by the ingress
of telecommunications.
Introduction

Increased realization of benefits of new
technologies in information and communications is
one of the targets towards attaining the UN
Millennium Development Goals due on 2015.
 Increased
telendensity of fixed line telephones
 Increased teledensity of mobile cellular phone
subscriptions
 Increased Internet users.
Introduction
Goals and Targets
(from the Millennium Declaration)
Indicators for Monitoring
Progress
Goal 8: Develop a global partnership for development
Target 8.F: In cooperation with the private sector,
make available the benefits of new technologies,
especially information and communications
1. Fixed telephone lines per 100
inhabitants
2. Mobile cellular subscriptions
per 100 inhabitants
3. Internet users per 100
inhabitants
Introduction


We focus our attention on ASEAN region.
Go Chok Tong (2003) iterated the need to redirect
the future of ICT
 To
promote economic recovery
 Job creation
 Sustained economic growth
 Bring more economies closer
Statement of the Problem
The need to embrace technology would support
continuous improvement in terms of productivity,
efficiency, competitiveness, and the quality of the lives
of people, for these are the true benefits of a
connected ASEAN Region. Thus, in this study, we seek
to answer this question:
Does teledensity/penetration rate of mobile and
fixed telecommunications affect economic growth
among countries in the ASEAN region?
Literature Review



Origin: Solow (1956) paper on neoclassical growth
theory, explained through labor, capital, and
knowledge (technological progress)
Convergence Theory and the Possible Sources of
Growth (Barro and Sala-I-Martin, 1992; Datta and
Agarwal, 2004; Chakraborty and Nandi, 2011)
Liberalization of trade and services towards
export-led growth, allocative efficiency and
technology transfer (Snow, 1989)
Literature Review



Landmark Paper: Roller and Waverman (2001)
Micro modelling with Macro Production Function
approach
Succeeding studies found association of
telecommunications and econ growth. (e.g.,
Waverman, Meschi, and Fuss (2005), Negash and
Patala (2006), Melamed (2007), Shiu and Lam
(2008), Sridhar and Sridhar (2009), Lam and Shiu
(2010), Biancini (2011), and Grouber and
Koutrompis (2011), among others).
Literature Review

Development initiatives in the ASEAN region
 1997:
ASEAN Vision 2020
 1998: Hanoi Plan of Action

ASEAN’s thrust
 Provision
of reliable ICT infrastructure
 Literacy and comfort in using ICT services
 Harmonize regulations in the telecom industry
 Linkages outside the region
 Embrace technology for improvement
 Protection from intentional harm and degradation
Note. Data obtained from the World Bank database (data.worldbank.org)
Note. Data obtained from the World Bank database (data.worldbank.org)
Framework of the Study

Growth (Solow, 1956)
 Knowledge
comes in many forms
 Knowledge accumulation is understood to contribute to
economic growth (Romer, 2006).

Network Externalities and Spillover Effects
 Telecommunications
create information superhighway
 Growth in the number of users increases the derived
utility from such use of infrastructure
 Low costs of doing business, benefiting businesses,
increasing productivity and growth.
Framework of the Study

Transaction Costs and Spillover Effects
 Reduced
transaction costs of acquiring and transmitting
information
 More efficient production mechanism that supports
growth.

Social Overhead Capital
 Expenditures
on economic and social services.
 Establishment of the New Economy with better
competition and enhanced production processes.
Working Model
ln GDPPCit  1  2 ln GNEPCit  3 LFPRit  4 PENRit  5ti   it
Where:
ln GDPPCit = ln of real GDP per capita (constant 2000 US$) of country i
at year t;
ln GNEPCit = proxy for expenditure (ln of gross national expenditure per
capita at constant 2000 US$) for country i at year t;
LFPRit
= labor force participation rate of country i at year t;
PENRit
= teledensity or penetration rate for country i at year t, and
= a variable that captures the essence of time trend for
ti
country i (1 = 1992, 2 = 1993,…, 19 = 2010).
Methodology

Balanced Panel Data Analysis of 7 ASEAN Countries
 (Brunei
Darussalam, Indonesia, Malaysia, Philippines,
Singapore, Thailand, Vietnam; indexed in order)
 Relevant data were obtained from 1992 – 2010.

Data Source
 World
Bank database (data.worldbank.org)
 Teledensity (No. of subscribers for every 100 inhabitants)
 Penetration Rate (Teledensity / 100) for fixed line
(FLPENR), mobile (CPPENR) and total penetration rate
(TPENR)
Results

Naïve Model
 lnGNEPC
and LFPR are identified to be significant at α
= 0.001
 High R-squared for all regressions
 Failure to account for unobserved heterogeneity makes
this model not suitable.
 This model failed the tests for plausibility and
robustness of econometric models.
. reg lnGDPPC lnGNEPC LFPR TPENR t
Source
SS
df
MS
Model
Residual
272.837262
1.07131962
4
128
68.2093154
.008369685
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_cons
1.07074
-.2934206
-.0085078
.0037928
-.2935006
Std. Err.
.0108887
.1430431
.041167
.0028799
.1587204
t
98.34
-2.05
-0.21
1.32
-1.85
Number of obs
F( 4,
128)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.042
0.837
0.190
0.067
=
133
= 8149.57
= 0.0000
= 0.9961
= 0.9960
= .09149
[95% Conf. Interval]
1.049195
-.5764559
-.0899638
-.0019056
-.6075561
1.092286
-.0103854
.0729481
.0094911
.0205549
Results

LSDV - 1
 Used
to determine whether differences in countryspecific characteristics would affect economic growth.
 Only lnGNEPC and t are significant at α = 0.001
 No final interpretations can be made.
. xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
Source
SS
df
MS
Model
Residual
273.505069
.403511953
10
122
27.3505069
.003307475
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_cons
.6262811
-.5698494
-.0037141
.0128985
-1.239876
-.6501931
-1.224684
.1273716
-.8794029
-1.514455
4.078197
Std. Err.
.0419855
.5507311
.0318014
.0022314
.1227452
.0776878
.1128189
.0273375
.0771899
.1312605
.6552452
t
14.92
-1.03
-0.12
5.78
-10.10
-8.37
-10.86
4.66
-11.39
-11.54
6.22
Number of obs
F( 10,
122)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.303
0.907
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
=
133
= 8269.30
= 0.0000
= 0.9985
= 0.9984
= .05751
[95% Conf. Interval]
.5431666
-1.660077
-.0666682
.0084812
-1.482862
-.8039838
-1.44802
.0732544
-1.032208
-1.774298
2.781073
.7093955
.5203777
.05924
.0173158
-.9968893
-.4964024
-1.001348
.1814888
-.7265979
-1.254611
5.37532
Results

LSDV - 2
 Used
to account for structural change of the model over
time for the study period between 1992 and 2010.
 Only lnGNEPC and LFPR are significant at α = 0.001.
TPENR is significant at α = 0.01.
 No final interpretations can be made.
. xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Year
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.095792
.812788732
21
111
13.0045615
.007322421
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
1.050066
-.3755887
.0952411
-.0059815
-.0231725
-.0201903
-.0298723
-.0477755
-.0246595
.0619383
.0966194
.0905919
.0885448
.0811382
.1055273
.085108
.0852526
.0877139
.0437834
-.0093344
.0105699
0
-.0639059
Std. Err.
.0114607
.1354749
.0459443
.0037546
.0446049
.0437163
.0430474
.0426636
.0423118
.0419483
.0418604
.0415225
.0414348
.0419736
.0421588
.0423277
.0427814
.0432233
.0429758
.0435074
.0445308
(omitted)
.1570168
t
Number of obs
F( 21,
111)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 1775.99
=
0.0000
=
0.9970
=
0.9965
=
.08557
[95% Conf. Interval]
91.62
-2.77
2.07
-1.59
-0.52
-0.46
-0.69
-1.12
-0.58
1.48
2.31
2.18
2.14
1.93
2.50
2.01
1.99
2.03
1.02
-0.21
0.24
0.000
0.007
0.040
0.114
0.604
0.645
0.489
0.265
0.561
0.143
0.023
0.031
0.035
0.056
0.014
0.047
0.049
0.045
0.311
0.831
0.813
1.027356
-.6440412
.0041993
-.0134214
-.1115602
-.106817
-.1151736
-.1323162
-.1085031
-.0211851
.0136701
.0083123
.006439
-.0020352
.0219868
.0012327
.0004785
.002064
-.0413761
-.0955473
-.0776708
1.072776
-.1071361
.1862828
.0014585
.0652151
.0664365
.055429
.0367652
.0591841
.1450617
.1795686
.1728716
.1706507
.1643116
.1890678
.1689832
.1700268
.1733638
.1289428
.0768784
.0988106
-0.41
0.685
-.3750451
.2472333
Results

LSDV - 3
 Used
to account for unobserved heterogeneity and
structural change of the model across cross-sectional
units and time periods.
 All variables of interest are significant at α = 0.001,
except for LFPR in all the three models.
 Wald’s Test shall be used to determine the suitable
model.
Results of Wald’s Test
Restricted
Unrestricted
Variable
Critical F
Naïve
LSDV-1
CPPENR
Naïve
Naïve
LSDV-2
LSDV-3
P-Value
Decision
33.40
0.0000***
LSDV-1
FLPENR
35.31
0.0000***
LSDV-1
TPENR
33.65
0.0000***
LSDV-1
CPPENR
1.92
0.0227*
LSDV-2
FLPENR
1.67
0.0600
Naïve
TPENR
2.08
0.0125*
LSDV-2
CPPENR
9.48
0.0000***
LSDV-3
FLPENR
9.07
0.0000***
LSDV-3
TPENR
9.85
0.0000***
LSDV-3
LSDV-1
LSDV-3
1.17
0.3043
LSDV-1
LSDV-2
LSDV-3
24.41
0.0000***
LSDV-3
Dilemma: LSDV-1 or LSDV-3?
Results of BP Poolability Test
. xttest0
Breusch and Pagan Lagrangian multiplier test for random effects
lnGDPPC[Ccode,t] = Xb + u[Ccode] + e[Ccode,t]
Estimated results:
Var
lnGDPPC
e
u
Test:
sd = sqrt(Var)
2.075065
.0033075
.0033599
1.440509
.0575107
.0579649
Var(u) = 0
chibar2(01) =
Prob > chibar2 =
27.43
0.0000
Decision: Random Effects over Naïve Model
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode
. est store fixed
Results of Hausman Test (LSDV-1)
. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
TPENR
t
.6262811
-.5698494
-.0037141
.0128985
1.007785
-.0097186
-.0256078
.006279
(b-B)
Difference
-.3815038
-.5601308
.0218937
.0066195
sqrt(diag(V_b-V_B))
S.E.
.0337386
.3256738
.
.
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
145.39
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode i.Year
. est store fixed
Results of Hausman Test (LSDV-3)
. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
TPENR
t
.6578491
-.9099692
.109665
.0034859
1.007785
-.0097186
-.0256078
.006279
(b-B)
Difference
-.3499357
-.9002506
.1352728
-.0027931
sqrt(diag(V_b-V_B))
S.E.
.0387671
.3652789
.
.0016765
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -127.96
chi2<0 ==> model fitted on these
data fails to meet the asymptotic
assumptions of the Hausman test;
see suest for a generalized test
Decision: Fixed Effects over Random Effects, but use LSDV -1
Final Regression - CPPENR
. xtgls lnGDPPC lnGNEPC LFPR CPPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_cons
1.074407
-.2851512
-.0463243
.0060003
-.3378212
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
132.4166
Std. Err.
.0082609
.1378695
.0448773
.002995
.140515
z
130.06
-2.07
-1.03
2.00
-2.40
P>|z|
0.000
0.039
0.302
0.045
0.016
=
=
=
=
=
133
7
19
34132.64
0.0000
[95% Conf. Interval]
1.058216
-.5553704
-.1342821
.0001303
-.6132255
1.090598
-.014932
.0416336
.0118704
-.0624169
Final Regression - FLPENR
. xtgls lnGDPPC lnGNEPC LFPR FLPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_cons
1.044135
-.3948661
.2627731
.0028489
-.0466192
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
134.2401
Std. Err.
.0129355
.1422361
.1200319
.0014156
.1624281
z
80.72
-2.78
2.19
2.01
-0.29
P>|z|
0.000
0.006
0.029
0.044
0.774
=
=
=
=
=
133
7
19
35085.23
0.0000
[95% Conf. Interval]
1.018782
-.6736438
.027515
.0000743
-.3649725
1.069488
-.1160885
.4980313
.0056234
.2717341
Final Regression - TPENR
. xtgls lnGDPPC lnGNEPC LFPR TPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_cons
1.07074
-.2934206
-.0085078
.0037928
-.2935006
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
131.9081
Std. Err.
.010682
.1403286
.0403858
.0028252
.1557084
z
100.24
-2.09
-0.21
1.34
-1.88
P>|z|
0.000
0.037
0.833
0.179
0.059
=
=
=
=
=
133
7
19
33871.64
0.0000
[95% Conf. Interval]
1.049804
-.5684596
-.0876625
-.0017446
-.5986834
1.091677
-.0183817
.0706468
.0093301
.0116822
Significance of FLPENR



Businesses still resort to Fixed Line modes of
telecommunications (Sridhar and Sridhar, 2009)
Basic services with lower ceiling costs
Restrictions on the use of landlines to ensure
productivity.
Non-Significance of CPPENR/TPENR





Other complementary factors (public infrastructure,
better business climate, education, training for
better use of telecommunications)
Telecommunication facilities as a measure of social
status
Developing countries have yet to fully realize the
benefits from such investment in these modes.
Need to make telecommunications accessible/
affordable esp. to those in remote areas.
Better statistical scrutiny is needed for future studies.
Conclusions and Policy Recommendations





Only Fixed Lines Teledensity and Penetration rates
of telecommunications is significantly related to
economic growth in the ASEAN Region.
Benefits of telecommunications, in general have yet
to be realized over time.
Need to make telecommunication services
affordable by tariff and subscription fees reduction.
Liberalization of telecommunications industry.
Continuous tapping of the private sector.
Postscript





Potential effects of the ASEAN Integration on
Telecommunications
Occurrence of disasters triggering the need to a
more integrated mechanism of telecommunications
Telecommunications as mechanism for social
responsibility, keeping people closer during these
difficult moments
In this regard, I dedicate this presentation to them.
Many thanks to Dr. Cesar Rufino for the guidance.
Author Details
MCREY BANDERLIPE II
PhD in Economics Student
School of Economics, De La Salle University
PhD Research Apprentice
DLSU Jesse M. Robredo Institute of Governance
E-mail: mc_banderlipe@dlsu.ph
Twitter: @mcreyeconomics
CALL ME MAYBE: THE IMPACT
OF TELECOMMUNICATIONS
ON ECONOMIC GROWTH IN
THE ASEAN REGION
Thank You