Call me maybe: the impact of telecommunications on economic growth in the asean region

MCREY BANDERLIPE II, MSc (CPA)DLSU - SOE

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 developmentTarget 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 of real GDP per capita (constant 2000 US$) of country i at year t;

= proxy for expenditure (ln of gross national expenditure per capita at constant 2000 US$) for country i at year t;

= labor force participation rate of country i at year t; = teledensity or penetration rate for country i at year t,

and= a variable that captures the essence of time trend for

country i (1 = 1992, 2 = 1993,…, 19 = 2010).

iti5it4it3it21it tPENRLFPRGNEPClnGDPPCln

itGDPPCln

itGNEPCln

itLFPR

itPENR

it

Where:

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.

_cons -.2935006 .1587204 -1.85 0.067 -.6075561 .0205549 t .0037928 .0028799 1.32 0.190 -.0019056 .0094911 TPENR -.0085078 .041167 -0.21 0.837 -.0899638 .0729481 LFPR -.2934206 .1430431 -2.05 0.042 -.5764559 -.0103854 lnGNEPC 1.07074 .0108887 98.34 0.000 1.049195 1.092286 lnGDPPC Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 273.908581 132 2.07506501 Root MSE = .09149 Adj R-squared = 0.9960 Residual 1.07131962 128 .008369685 R-squared = 0.9961 Model 272.837262 4 68.2093154 Prob > F = 0.0000 F( 4, 128) = 8149.57 Source SS df MS Number of obs = 133

. reg lnGDPPC lnGNEPC LFPR TPENR t

Results

LSDV - 1 Used to determine whether differences in

country-specific characteristics would affect economic growth.

Only lnGNEPC and t are significant at α = 0.001

No final interpretations can be made.

_cons 4.078197 .6552452 6.22 0.000 2.781073 5.37532 _ICcode_7 -1.514455 .1312605 -11.54 0.000 -1.774298 -1.254611 _ICcode_6 -.8794029 .0771899 -11.39 0.000 -1.032208 -.7265979 _ICcode_5 .1273716 .0273375 4.66 0.000 .0732544 .1814888 _ICcode_4 -1.224684 .1128189 -10.86 0.000 -1.44802 -1.001348 _ICcode_3 -.6501931 .0776878 -8.37 0.000 -.8039838 -.4964024 _ICcode_2 -1.239876 .1227452 -10.10 0.000 -1.482862 -.9968893 t .0128985 .0022314 5.78 0.000 .0084812 .0173158 TPENR -.0037141 .0318014 -0.12 0.907 -.0666682 .05924 LFPR -.5698494 .5507311 -1.03 0.303 -1.660077 .5203777 lnGNEPC .6262811 .0419855 14.92 0.000 .5431666 .7093955 lnGDPPC Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 273.908581 132 2.07506501 Root MSE = .05751 Adj R-squared = 0.9984 Residual .403511953 122 .003307475 R-squared = 0.9985 Model 273.505069 10 27.3505069 Prob > F = 0.0000 F( 10, 122) = 8269.30 Source SS df MS Number of obs = 133

i.Ccode _ICcode_1-7 (naturally coded; _ICcode_1 omitted). xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode

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.

_cons -.0639059 .1570168 -0.41 0.685 -.3750451 .2472333 _IYear_19 0 (omitted) _IYear_18 .0105699 .0445308 0.24 0.813 -.0776708 .0988106 _IYear_17 -.0093344 .0435074 -0.21 0.831 -.0955473 .0768784 _IYear_16 .0437834 .0429758 1.02 0.311 -.0413761 .1289428 _IYear_15 .0877139 .0432233 2.03 0.045 .002064 .1733638 _IYear_14 .0852526 .0427814 1.99 0.049 .0004785 .1700268 _IYear_13 .085108 .0423277 2.01 0.047 .0012327 .1689832 _IYear_12 .1055273 .0421588 2.50 0.014 .0219868 .1890678 _IYear_11 .0811382 .0419736 1.93 0.056 -.0020352 .1643116 _IYear_10 .0885448 .0414348 2.14 0.035 .006439 .1706507 _IYear_9 .0905919 .0415225 2.18 0.031 .0083123 .1728716 _IYear_8 .0966194 .0418604 2.31 0.023 .0136701 .1795686 _IYear_7 .0619383 .0419483 1.48 0.143 -.0211851 .1450617 _IYear_6 -.0246595 .0423118 -0.58 0.561 -.1085031 .0591841 _IYear_5 -.0477755 .0426636 -1.12 0.265 -.1323162 .0367652 _IYear_4 -.0298723 .0430474 -0.69 0.489 -.1151736 .055429 _IYear_3 -.0201903 .0437163 -0.46 0.645 -.106817 .0664365 _IYear_2 -.0231725 .0446049 -0.52 0.604 -.1115602 .0652151 t -.0059815 .0037546 -1.59 0.114 -.0134214 .0014585 TPENR .0952411 .0459443 2.07 0.040 .0041993 .1862828 LFPR -.3755887 .1354749 -2.77 0.007 -.6440412 -.1071361 lnGNEPC 1.050066 .0114607 91.62 0.000 1.027356 1.072776 lnGDPPC Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 273.908581 132 2.07506501 Root MSE = .08557 Adj R-squared = 0.9965 Residual .812788732 111 .007322421 R-squared = 0.9970 Model 273.095792 21 13.0045615 Prob > F = 0.0000 F( 21, 111) = 1775.99 Source SS df MS Number of obs = 133

note: _IYear_19 omitted because of collinearityi.Year _IYear_1-19 (naturally coded; _IYear_1 omitted). xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Year

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

Dilemma: LSDV-1 or LSDV-3?

Restricted Unrestricted Variable Critical F P-Value Decision

Naïve LSDV-1 CPPENR 33.40 0.0000*** LSDV-1

FLPENR 35.31 0.0000*** LSDV-1

TPENR 33.65 0.0000*** LSDV-1

Naïve LSDV-2 CPPENR 1.92 0.0227* LSDV-2

FLPENR 1.67 0.0600 Naïve

TPENR 2.08 0.0125* LSDV-2

Naïve LSDV-3 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

Results of BP Poolability Test

Decision: Random Effects over Naïve Model

Prob > chibar2 = 0.0000 chibar2(01) = 27.43 Test: Var(u) = 0

u .0033599 .0579649 e .0033075 .0575107 lnGDPPC 2.075065 1.440509 Var sd = sqrt(Var) Estimated results:

lnGDPPC[Ccode,t] = Xb + u[Ccode] + e[Ccode,t]

Breusch and Pagan Lagrangian multiplier test for random effects

. xttest0

Results of Hausman Test (LSDV-1)

(V_b-V_B is not positive definite) Prob>chi2 = 0.0000 = 145.39 chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from regress t .0128985 .006279 .0066195 . TPENR -.0037141 -.0256078 .0218937 . LFPR -.5698494 -.0097186 -.5601308 .3256738 lnGNEPC .6262811 1.007785 -.3815038 .0337386 fixed random Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

. hausman fixed random

. est store random

. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re

. est store fixed

. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode

Results of Hausman Test (LSDV-3)

Decision: Fixed Effects over Random Effects, but use LSDV -1

see suest for a generalized test assumptions of the Hausman test; data fails to meet the asymptotic = -127.96 chi2<0 ==> model fitted on these chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from regress t .0034859 .006279 -.0027931 .0016765 TPENR .109665 -.0256078 .1352728 . LFPR -.9099692 -.0097186 -.9002506 .3652789 lnGNEPC .6578491 1.007785 -.3499357 .0387671 fixed random Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

. hausman fixed random

. est store random

. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re

. est store fixed

. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode i.Year

Final Regression - CPPENR

_cons -.3378212 .140515 -2.40 0.016 -.6132255 -.0624169 t .0060003 .002995 2.00 0.045 .0001303 .0118704 CPPENR -.0463243 .0448773 -1.03 0.302 -.1342821 .0416336 LFPR -.2851512 .1378695 -2.07 0.039 -.5553704 -.014932 lnGNEPC 1.074407 .0082609 130.06 0.000 1.058216 1.090598 lnGDPPC Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = 132.4166 Prob > chi2 = 0.0000 Wald chi2(4) = 34132.64Estimated coefficients = 5 Time periods = 19Estimated autocorrelations = 0 Number of groups = 7Estimated covariances = 1 Number of obs = 133

Correlation: no autocorrelationPanels: homoskedasticCoefficients: generalized least squares

Cross-sectional time-series FGLS regression

. xtgls lnGDPPC lnGNEPC LFPR CPPENR t

Final Regression - FLPENR

_cons -.0466192 .1624281 -0.29 0.774 -.3649725 .2717341 t .0028489 .0014156 2.01 0.044 .0000743 .0056234 FLPENR .2627731 .1200319 2.19 0.029 .027515 .4980313 LFPR -.3948661 .1422361 -2.78 0.006 -.6736438 -.1160885 lnGNEPC 1.044135 .0129355 80.72 0.000 1.018782 1.069488 lnGDPPC Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = 134.2401 Prob > chi2 = 0.0000 Wald chi2(4) = 35085.23Estimated coefficients = 5 Time periods = 19Estimated autocorrelations = 0 Number of groups = 7Estimated covariances = 1 Number of obs = 133

Correlation: no autocorrelationPanels: homoskedasticCoefficients: generalized least squares

Cross-sectional time-series FGLS regression

. xtgls lnGDPPC lnGNEPC LFPR FLPENR t

Final Regression - TPENR

_cons -.2935006 .1557084 -1.88 0.059 -.5986834 .0116822 t .0037928 .0028252 1.34 0.179 -.0017446 .0093301 TPENR -.0085078 .0403858 -0.21 0.833 -.0876625 .0706468 LFPR -.2934206 .1403286 -2.09 0.037 -.5684596 -.0183817 lnGNEPC 1.07074 .010682 100.24 0.000 1.049804 1.091677 lnGDPPC Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = 131.9081 Prob > chi2 = 0.0000 Wald chi2(4) = 33871.64Estimated coefficients = 5 Time periods = 19Estimated autocorrelations = 0 Number of groups = 7Estimated covariances = 1 Number of obs = 133

Correlation: no autocorrelationPanels: homoskedasticCoefficients: generalized least squares

Cross-sectional time-series FGLS regression

. xtgls lnGDPPC lnGNEPC LFPR TPENR t

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 IIPhD in Economics Student School of Economics, De La Salle University

PhD Research ApprenticeDLSU Jesse M. Robredo Institute of

Governance

E-mail: mc_banderlipe@dlsu.phTwitter: @mcreyeconomics

Call me maybe: the impact of telecommunications on economic growth in the asean region

Thank You