Bulletin of Monetary Economics and Banking, Vol. 22, No. 1 (2019), pp. 103 - 122
DO INFORMATION AND COMMUNICATION
TECHNOLOGIES FOSTER ECONOMIC GROWTH IN
INDONESIA?
Badri Narayan Rath1 and Danny Hermawan2
1Department of Liberal Arts, Indian Institute of Technology Hyderabad, India.
Email: badri@iith.ac.in.
2Macroprudential Department, Bank Indonesia, Jakarta, Indonesia. Email: danny_h@bi.go.id.
ABSTRACT
This paper investigates, using annual data from 1980 to 2014, whether adoption of information and communication technologies (ICT) fosters economic growth in Indonesia. We employ an Autoregressive Distributed Lag cointegration technique on an augmented neoclassical growth model. The empirical results indicate a positive effect of ICT development on economic growth in both the
Keywords: ICT development; Economic growth; ICT exports; ARDL bound test; Indonesia.
JEL Classifications: C22; O47.
Article history: |
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Received |
: January 18, 2019 |
Revised |
: April 6, 2019 |
Accepted |
: April 20, 2019 |
Available online |
: April 30, 2019 |
https://doi.org/10.21098/bemp.v22i1.1041
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I. INTRODUCTION
The literature widely recognizes that digital innovation in the form of information and communication technologies (ICT) has a significant influence on economic growth and productivity (see, for instance, Cette et al., 2005; Farkhanda, 2007; Venturini, 2009; Dimelis and Papaioannou, 2010; Heeks, 2010; Cortes and Navarro, 2011; Ahmed and Ridzuan, 2013; Rath, 2016; Niebel, 2018). As a consequence, rapid adoption of ICT in terms of access and use is observed worldwide (Chinn and Fairlie, 2007; Lam and Shiu, 2010; ITU, 2018). Adoption of ICT in advanced countries is much faster compared to underdeveloped and developing nations (Caselli and Coleman, 2001; Chinn and Fairlie, 2007). However, adoption of ICT, particularly the use of mobile phones and internet, has been rapidly increasing over time and now drives economic activity (see, for instance, Katz, 2009; Byrne et al., 2011; Avgerou et al., 2016; Rath, 2016).
While there is a considerable literature on the link between ICT development and economic growth in
First, Indonesia is the largest country by population in Southeast Asia and it is an emerging country on the world stage. The country ranks 17th in GDP and 7th in GDP on the basis of purchasing power parity. Despite a gradual increase in economic growth and improvement in ICT infrastructure in recent years, Indonesia still lags in ICT as compared to neighboring Southeast Asian countries like Singapore and Malaysia. Indonesia is the fourth most populous country in the world. Figure 1 shows Indonesia’s ICT investment growing at an annual average of 20% over the
Second, many studies examine the role of ICT in propelling productivity or output per worker (Cronin et al., 1993; Dewan and Kraemer, 2000; Oulton, 2002; Hu and Quan, 2005; O’Mahony and Vecchi, 2005; Atzeni and Carboni, 2006; Kumar et al., 2016). Thus, examining the effect of ICT on per capita output for Indonesia
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calls for investigation because the growth in ICT (as shown in Figure 1) both quantitatively (coverage, capacity, and accessibility) and qualitatively (adoption of advanced technologies, efficiency, and service quality) has been impressive.
Third, a plethora of studies examines the effect of ICT on productivity and economic growth, particularly via panel data sets (see, for instance, Roller and Waverman, 2001; Aker and Mbiti, 2010; Commander et al., 2011; Yousefi, 2011; Cardona et al., 2013; Jorgenson and Vu, 2016; Niebel, 2018). These studies find mixed evidence. This could be due to different methodologies, data sample periods, and measurement of different ICT indicators. However, studies on the
Fourth, many studies investigate the causal relationship between ICT development and economic growth (see, for instance, Cronin et al., 1993; Datta and Agarwal, 2004; Shiu and Lam, 2008; Koutroumpis, 2009). Most of these studies employ bivariate analysis (i.e., only using ICT and economic growth). However, this bivariate approach may lead to bias due to omitted variables (Gross, 2012; Ishida, 2015). Therefore, the present study uses other important variables, such as total factor productivity (TFP), human capital, and ICT exports, that potentially affect economic growth.
Fifth, the International Telecommunications Union (ITU, 2009) provides a detailed methodology for constructing an ICT development index using 11 indicators. This ICT development index is very useful for comparing ICT development across countries as well as within a country over time. Numerous studies use either
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fixed broadband subscriptions) employing principal component analysis.(Park et al., 2015; Rath, 2016).
Sixth, the present study differs from the literature by employing an Autoregressive Distributed Lag (ARDL) model with two structural breakpoints in the data, following Narayan and Popp (2010). To the best of our knowledge, no earlier study, including those that focus on
Our approach provide the following insights. First, we find a
This paper proceeds as follows. Section II presents an overview of the performance of
II. ICT IN INDONESIA
Figure 1 presents the annual growth rates of real GDP and ICT for Indonesia from 1980 to 2014. The graph shows that the annual growth rate of real GDP is lower than the annual growth of the ICT development index in most of the study period, except 1989, 2000, and 2014. The graph also shows that the gap between ICT development and real GDP growth rates was lower during the period 1981 to 1988, but thereafter widened drastically. This is due to tremendous growth of ICT development as compared to real GDP growth in Indonesia. The average annual ICT growth rate was quite impressive particularly over the
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Figure 1. The Growth Rates of Real GDP and ICT in Indonesia
This figure exhibits the growth rates of real GDP and ICT in case of Indonesia from 1980 to 2014. The graph shows that the annual growth rate of real GDP is lower than the annual growth of ICT development index in most of the periods under the study except three years (1989, 2000 and 2014). The ICT development was growing slowly during 1980 to 1989, but thereafter, it has shown tremendous growth particularly from 2000 to 2008. The average annual ICT growth during
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1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 |
Source: World Development Indicators (WDI) database
Figure 2 shows several key indicators related to exports of ICT for Indonesia. Since there is no availability of ICT export data prior to 1990, this figure presents three indicators of ICT exports from 1990 to 2016. The
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Figure 2. ICT Exports of Indonesia
This figures portrays the ICT related exports of Indonesia. The trend line of
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Source: WDI database.
Figure 3 presents four key indicators pertaining to overall economic performance of Indonesia. First, the growth of output per worker shows a zigzag pattern from 1981 to 2014. Output per worker growth rate declined from 1981 to 1985, then began to increase and peaked at 9.4% in 1995. This shows a fluctuating trend with increases and decreases until 2014. Growth of capital per worker shows a similar pattern, except that prior to 1997, growth of capital per worker was higher than output per worker. Post 1997, growth of output per worker was slightly higher than growth of capital per worker. TFP growth shows a declining trend in recent years as compared to 1981. TFP growth was 1.1% in 1981 and it almost consistently maintained the same rate until 1995, but TFP growth declined from 1.1% in 1995 to 0.84% in 2000. Although it began to increase after 2000, growth was relatively lower most years
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Figure 3. Growth Rate of Key Indicators in the Production Process of Indonesia
This figure presents the growth rates of output per worker and capital per worker. Additionally, this figure also depicts ICT investment and TFP growth.
10
8
6
4
2
0
Output per worker (growth rate in %) ICT investment (%GDP)
Capital per worker (growth rate in %) TFP Growth
1981 |
1985 |
1990 |
1995 |
2000 |
2005 |
2010 |
2014 |
2017 |
Source: Authors own calculation based on Penn World data.
Table 1.
Basic Statistics of Key ICT Development Indicators
This table exhibits the key indicators pertaining to ICT development of Indonesia. The data relating to indicators such as fixed- telephone subscription, mobile cellular subscription, fixed broadband subscription, and internet users have consistently increased over the years except subscription
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Fixed- |
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Fixed |
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Household |
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telephone |
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broadband |
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ICT |
Internet |
with |
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Year |
subscriptions |
subscriptions per |
subscriptions |
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index |
users (%) |
computer |
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per 100 |
100 inhabitants |
per 100 |
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(%) |
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inhabitants |
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inhabitants |
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1980 |
0.25 |
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0.13 |
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1985 |
0.37 |
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0.18 |
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1990 |
0.59 |
0.01 |
0.3 |
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1995 |
1.67 |
0.11 |
0.89 |
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0.03 |
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2000 |
3.15 |
1.73 |
1.60 |
0.00 |
0.93 |
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2005 |
5.96 |
20.69 |
8.96 |
0.05 |
3.60 |
3.67 |
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2010 |
16.88 |
87.12 |
35.34 |
0.94 |
10.92 |
10.80 |
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2014 |
10.28 |
127.61 |
47.13 |
1.33 |
17.14 |
17.30 |
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2017 |
4.23 |
173.84 |
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2.29 |
32.29 |
19.11 |
Source: International Telecommunication Union (ITU) database.
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Table 1 presents basic data on key ICT development indicators for Indonesia. This table also clearly reflects the
III.FRAMEWORK AND METHODOLOGY
A. Framework
We employ the commonly used
Solow (1956) framework and following Kumar et al. (2016, 2015) and Rao (2010).
The equation for output per worker (yt) can be written as:
(1)
where At is technological progress, kt refers to capital per worker, and α is share of capital. The Solow (1956) model assumes that the progression of technology is given by
(2)
where g and A0 are the growth of technological progress and initial TFP, respectively. πt refers to aggregate technology, At. The impact of ICT development on TFP can be assessed when ICT development arrives as a shift in production function. Thus, we use an augmented Solow growth model by including the ICT- related variables in the production function.
Let |
(3) |
Here θt is part of the technology component in equation (1), and At is redefined as follows:
(4) Thus, we can rewrite equation (1) as
(5)
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Taking the natural logarithm of equation (5) yields the linear general production function equation for estimating the model as
(6)
where δ is the intercept term, β is the elasticity coefficient of ICT development, and e is the error term.
Equation (6) can be further expanded by adding the control variables ICT exports and structural break dummy variables, which can be presented as:
(7)
where ICTEXP is ICT exports, y is the coefficient of elasticity of ICT exports, TD is the structural break dummy based on
B.Methodology B1. Unit Root Tests
Let us now empirically analyze the stationary property of all
(1988) unit root tests are employed to check stationarity. These conventional tests do not address the presence of structural breaks in the data. To account for structural breaks, we further employ the Narayan and Popp (2010) unit root test, which captures structural breaks.
B2. ARDL Model
After checking the unit root, we next use an ARDL bound testing approach (Pesaran et al., 2001) to inspect the
(ii)it can generate the unknown parameters for both the
Model I: |
(8) |
Model II: |
(9) |
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where y represents output per worker, k refers to capital per worker, ICTINDX is defined as ICT development index, ICTEXP is ICT exports, GDP is real gross domestic product, TFP is the TFP, and HC is the human capital in equation (8) and equation (9). Thus, we can write the error correction representation of the ARDL bound testing for model I and model II as, respectively:
(10)
(11)
In equations 10 and 11, refers to the first difference operator of the corresponding variable. To inspect the cointegration relationship between ICT development and economic growth, the null hypothesis (no cointegration) can be elaborated as: H0:δ1 = δ2 = δ3 = δ4 = 0, and the alternative hypothesis (presence of cointegration) as H1: δ1≠ δ2 ≠ δ3≠ δ4 ≠ 0 by conducting the F test developed by Pesaran et al. (2001) and later refined by Narayan (2005). If the calculated
Next, we examine the
(12)
(13)
In equations 12 and 13, σ is the speed of adjustment parameter and ECM is the residual series from the estimated model.
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IV. DATA AND RESULTS
A. Data
This study uses annual data for Indonesia for the period
B. Results and Discussion
This section presents the results of the effect of ICT on economic growth. The empirical analysis includes examination of unit root test, cointegration, and ECM.
Table 2.
Results for Narayan and Popp (2010) Unit Root Test with Two Structural Breaks
This table presents the unit root test with two endogenous structural breaks proposed by Narayan and Popp (2010). The results indicate that the TFP and ICTINDX are stationary at the level form and the remaining variables are I(1), i.e. nonstationary at levels. M1 stands for a model which includes an intercept whereas the M2 model includes both an intercept and a time trend. TB1 refers to the first break while TB2 denotes the second break. Critical values for M1 are: −5.259 (1%); −4.514 (5%); −4.143 (10%). Critical values for M2 are: −5.949 (1%); −5.181 (5%); −4.789 (10%). k stands for optimum lag. T refers to observations. Finally, *** (**) represent statistical significance at 1% (5%) levels.
Variable |
T |
M1 |
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M2 |
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Test Statistic |
TB1 |
TB2 |
k |
Test Statistic |
TB1 |
TB2 |
k |
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TFP |
35 |
1987 |
1996 |
0 |
1997 |
2004 |
3 |
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lnGDP |
35 |
1997 |
1999 |
0 |
1988 |
1997 |
2 |
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lny |
35 |
1994 |
1997 |
0 |
1994 |
1997 |
0 |
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lnk |
35 |
0.5849 |
1996 |
1998 |
0 |
1996 |
1998 |
0 |
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ICTINDX |
35 |
1997 |
1999 |
3 |
1999 |
2005 |
5 |
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lnICTINV |
35 |
0.7131 |
2000 |
2006 |
0 |
0.9176 |
2000 |
2006 |
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lnICTEXP |
35 |
1987 |
2003 |
5 |
1988 |
2003 |
0 |
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FTS |
35 |
2005 |
2007 |
2 |
2003 |
2006 |
4 |
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MPS |
35 |
1994 |
1997 |
0 |
3.534 |
1994 |
1998 |
5 |
1The empirical results in this paper stem from data prior to 2014 because of
2The factor scores and corresponding eigenvalues for constructing the ICT index are not presented here but the results are available upon request.
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Table 3.
Bound Test Results
This table depicts the bound test results of the ARDL cointegration model. Model I is based on per capita output as depicted in Equation (8) and Model II is based on real GDP as depicted in Equation (9). The
Test Statistic |
Model I |
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Model II |
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F - statistic |
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5.66 |
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7.45 |
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Significance level |
I(0)a |
I(1)a |
I(0)b |
I(1)b |
1% |
4.483 |
6.320 |
3.74 |
5.06 |
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5% |
3.120 |
4.560 |
2.86 |
4.01 |
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10% |
2.560 |
3.828 |
2.45 |
3.52 |
We first examine the stationarity of the variables using traditional ADF and Phillips and Perron unit root tests. The results of the unit root tests indicate that all variables are
After examining the unit root tests, we now employ an ARDL model to investigate the
Table 4.
Estimated
Based on the Schwarz Bayesian Criterion
This table depicts the
Variable |
Coefficient |
|
lnk |
0.28*** |
3.71 |
lnICTINDX |
0.08*** |
5.18 |
lnICTEXP |
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TD |
||
C |
19.13*** |
3Per capita output is synonymously used for economic growth.
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After identifying the cointegrating relationship, we now estimate the long- run coefficients using equation (10) for model I. The results of
Table 5.
Error Correction Representation for Selected ARDL for Model I
This table presents the error correction representation for the selected ARDL for model, and *** (**) indicate statistical significance at the 1% (5%) levels. The diagnostic tests are reported in the second half of the table.
Regressor |
Coefficient |
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Δlnk |
1.48*** |
7.30 |
ΔlnICTINDX |
0.06 |
1.47 |
ΔlnICTEXP |
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ΔlnTD |
||
Diagnostic tests |
Coefficient |
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R2 |
0.93 |
|
Adjusted R2 |
0.85 |
|
χ2 Auto(1) |
1.52 |
0.25 |
χ2 Norm(2) |
1.35 |
0.50 |
χ2 Hetero(1) |
0.20 |
0.65 |
CUSUM |
Not stable |
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CUSUM |
Stable |
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4We also run an ARDL model by replacing ICT imports with ICT exports in Equation (8). However, we obtain no robust results. The chosen model (4,4,3,3,4) based on the Schwartz criterion with 4 lags yields an
written as |
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. The error correction |
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coefficient turns out to be 0.02 with probability value of 0.06. |
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After studying the
Table 6.
Estimated
Based on Schwarz Bayesian Criterion
This table depicts the
*** indicates statistical significance at 1% level.
Variable |
Coefficient |
|
lnTFP |
1.53*** |
5.21 |
lnHC |
2.18*** |
4.31 |
lnICTINDX |
0.10*** |
2.72 |
lnICTEXP |
||
C |
25.47*** |
After discussing the empirical results of model I, let us now discuss the results obtained from model II. We use model II5 as an alternative specification in which real GDP is treated as the dependent variable and TFP growth, human capital, ICT development, and ICT exports are included as the regressor. The bound testing results presented in Table 3 reject the null of no cointegration at the 1% level of significance based on both Narayan (2005) and Pesaran (2001) critical values. Thus, our finding indicates a
5We also use an alternative form of model II, based on a reviewer suggestion. We run the model by taking labor and capital inputs along with other explanatory variables already presented in Equation (9). However, the results based on the ARDL bounds testing approach produces no convincing results. We select the ARDL (4,4,4,4,4) model based on Schwarz Criterion with 4 lags. The bound test result
as: The
error correction coefficient term is
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growth. In the literature, it is well established that TFP growth and human capital play a critical role in boosting national economic growth. Thus, the results in Table 6 are not surprising.
Table 7.
Error Correction Representation for Selected ARDL for Model II
This table presents the error correction representation for the selected ARDL for model and *** (*) indicate statistical significance at 1% (10%) levels. The diagnostic tests are reported in the second half of the table.
Regressor |
Coefficient |
|
ΔlnTFP |
0.79*** |
12.75 |
ΔlnHC |
0.43*** |
2.80 |
ΔlnICTINDX |
0.02* |
1.89 |
ΔlnICTEXP |
||
Diagnostic tests |
Coefficient |
|
R2 |
0.87 |
|
Adjusted R2 |
0.85 |
|
χ2 Auto(1) |
1.92 |
0.13 |
χ2 Norm(2) |
2.76 |
0.25 |
χ2 Hetero(1) |
1.76 |
0.25 |
CUSUM |
Stable |
|
CUSUM |
Stable |
|
After discussing the
V. CONCLUSIONS
This paper investigates the impact of ICT on economic growth in Indonesia. Although many studies examine the impact of ICT development on economic growth or TFP, our focus exclusively on Indonesia is our main contribution to the literature. A plausible reason that the nexus between ICT and economic growth is less explored for individual emerging countries is the
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show evidence of a
Acknowledgement: The authors gratefully acknowledge the suggestions from the Managing Editor and anonymous referees on an earlier draft of this paper. The first author is thankful to Dr. Dinh Phan for providing the ICT related data. The usual disclaimer applies.
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