Telecommunications Infrastructure and Economic Growth: Comparative Policy Analysis for the G-20 Developed and Developing Countries

Abstract

This paper examines the nexus between the development of telecommunications infrastructure (DTI), economic growth, and four key indicators of the operations of advanced economies. It employs a panel vector auto-regressive model to detect causality and examine long-run relationships between variables in the G-20 countries for the period 2001–2012. Evidence is found that DTI, measured through six indicators, may cause economic growth and that causation may be bidirectional. Not surprisingly, the exact nature of causality depends on the group of countries considered and on the definition of DTI.

Keywords:

• telecommunications infrastructure
• economic growth
• comparative policy analysis
• Granger causality
• G-20 countries

Introduction

Economists and policymakers regard technological change as the prime determinant of high economic growth and increasing labour productivity. The models that are used to measure the technological effects on economic growth vary significantly from the endogenous growth approach (Romer 1990) to earlier aggregate production functions with exogenous technological changes (Solow 1957). Other models seek to disentangle the determinants of economic growth (Madden and Savage 1998; Barro and Sala-i-Martin 2003). This paper, uniquely, focuses on how information and communication technology affects economic growth, especially the effects of modern telecommunications infrastructure: telephone, internet, and broadband usage. To do this, we examine, methodically, the structure of economic growth itself and its relationship with some other key macroeconomic variables.

Development of telecommunications infrastructure (DTI) plays a major role in economic growth (Cronin et al. 1991; McGovern and Hebert 1992; Madden and Savage 2000; Dutta 2001; Datta and Agarwal 2004; Lam and Shiu, 2010; Czernich et al., 2009; Bojnec and Ferto 2012; Prieger 2013; Rajabiun and Middleton 2013). DTI affects economic growth both directly and indirectly (see Figure 1). An efficient delivery of telecommunications infrastructure generates direct benefits through lower transaction costs and improved marketing information, as well as creating indirect benefits due to the accelerated diffusion of information (Antonelli 1991; Greenstein and Spiller 1995). Industrial, regional, and national economic systems which do not adopt such technologies in the near future will risk losing their position in the international market (Capello and Nijkamp 1996).

Figure 1. Impacts of telecommunications infrastructure on economic growth

Source: Thompson Jr. and Garbacz (2011).

In the absence of any specific deductive theory directly supporting our specific inquiry, two empirically based approaches characterize research on the relationship between DTI and economic growth: the correlation and regression approach and the Granger causality approach. The correlation approach documents the relationship between the two variables, while the Granger causality approach establishes the causal relationship and its direction between the variables of interest (Chakraborty and Nandi 2003; Lee et al. 2005). The works relating to the Granger causality approach, however, are very limited. Cornin et al. (1991) and Lee (1994) were the first to investigate it. They found a two-way causality between the two variables for both the USA and Korea.

The next section reviews the literature on the relationship between DTI and economic growth, summarizing relevant published studies. The following section delineates the hypotheses we test in this paper. We then define the variables, specify the data used, and outline our econometric approach. This is followed by a detailed discussion of our results. The final section concludes and provides pertinent policy prescriptions.

Literature Review

The supply-leading hypothesis (SLH) contends that DTI is a necessary precondition to economic growth. Thus, it is argued that the causality runs from DTI to economic growth. The proponents of this hypothesis (Dutta 2001; Röller and Waverman 2001; Chakraborty and Nandi 2003; Cieślik and Kaniewsk 2004; Yoo and Kwak 2004; Ahmed and Krishnasamy 2012; Mehmood and Siddiqui 2013) maintain that DTI induces economic growth by directly supporting other infrastructures and factors of production, thereby immensely improving economic growth.

A second proposition is the demand-following hypothesis (DFH), which suggests that causality runs, instead, from economic growth to DTI. Supporters of the demand-following hypothesis claim that DTI plays only a minor role in economic growth and that it is merely a by-product of the economic growth that has already taken place (Beil et al. 2005; Veeramacheneni et al. 2007; Pradhan et al. 2013a). The main idea throughout this body of work is that as an economy grows, additional telecommunications infrastructure emerges, thereby supporting the assertion that DTI is, in fact, only an outcome and that it plays a rather small role in economic growth.

Third, there is the feedback hypothesis (FBH), which suggests that economic growth and DTI can complement and reinforce each other, making economic growth and DTI mutually causal. The idea behind this argument, which is in favour of the bidirectional causality, is that telecommunications infrastructure is indispensable to economic growth and economic growth inevitably requires a solid telecommunications infrastructure (Cronin et al. 1993a; Chakraborty and Nandi 2003, 2011; Yoo and Kwak 2004; Zahra et al. 2008; Ramlan and Ahmed 2009; Pradhan et al. 2013b).

The fourth hypothesis is the absence of causality, arguing that there is a complete lack of any sort of relationship between telecommunications infrastructure and economic growth. The neutrality hypothesis (NLH) is supported by very few papers (Dutta 2001; Veeramacheneni et al. 2007; Shiu and Lam 2008).

Table 1 presents a synopsis of the literature on DTI and economic growth and all of the papers mentioned in this section with the direction of the causal effect noted as well as the methods used in each study, the countries that were focused on, the data used, and the inferences. It is important to note that the existing literature provides inconclusive results on the direction of causality between DTI and economic growth.

Table 1. A summary of relevant studies

Study	Method	Countries	Time periods	Inferences
Mehmood and Siddiqui (2013)	1	23 Asian	1990–2010	SLH
Pradhan et al. (2013a)	1	34 OECD	1961–2011	FBH, DFH
Pradhan et al. (2013b)	1	34 OECD	1990–2010	FBH
Ahmed and Krishnasamy (2012)	2	5 ASEAN	1975–2007	SLH
Chakraborty and Nandi (2011)	2	93 various	1985–2007	FBH
Lam and Shiu (2010)	1	105 various	1980–2006	FBH
Chakraborty and Nandi (2009)	2	Developing	1980–2001	FBH
Zahra et al. (2009)	2	24 various	17 years	FBH
Ramlan and Ahmed (2009)	2	Malaysia	1965–2005	NLH
Shiu and Lam (2008)	1	China	1978–2004	SLH, DFH, NLH
Zahra et al. (2008)	2	23 various	1990–2007	FBH
Veeramacheneni et al. (2007)	2	10 Latin American	1975–2003	FBH, SLH, DFH, NLH
Wolde-Rufael (2007)	2	USA	1947–1996	FBH
Dvornik and Sabolić (2007)	2	East European	1991–2001	SLH, DFH
Beil et al. (2005)	2 & 3	USA	1947–1996	DFH
Yoo and Kwak (2004)	2	Korea	1965–1998	SLH
Cieślik and Kaniewsk (2004)	2	Poland	1989–1998	SLH
Chakraborty and Nandi (2003)	2	12 Asian	1975–2000	FBH
Dutta (2001)	2	30 various	1960–1993	SLH
Cronin et al. (1991)	2	USA	1958–1988	FBH

Notes: 1: Dynamic panel data model; 2: Granger causality test; 3: Modified Sims test; SLH: Supply-leading hypothesis; DFH: Demand-following hypothesis; FBH: Feedback hypothesis; NLH: Neutrality hypothesis.

Research Questions and Proposed Hypotheses

This paper is the first to study the nature of the long-run relationship between the development of telecommunications infrastructure and economic growth, side-by-side, but conjointly with four other missing macroeconomic variables that have been neglected in previous studies. We test the following critical hypotheses:

• H_1A: DTI Granger-causes economic growth.

• H_1B: Economic growth Granger-causes DTI.

• H_2A: A macroeconomic variable Granger-causes economic growth.

• H_2B: Economic growth Granger-causes a macroeconomic variable.

• H_3A: DTI Granger-causes a macroeconomic variable.

• H_3B: A macroeconomic variable Granger-cause DTI.

Figure 2 summarizes the direction of possible causality among the variables

Figure 2. Proposed model and hypotheses

Note: Four macroeconomic variables are considered.

Data, Variables, and the Econometric Approach

Our analysis utilizes annual time series data over the period 2001–2012. The data are abstracted and transformed from two main sources: (i) World Development Indicators, published by the World Bank, and (ii) World Investment Reports, published by the United Nations. We consider three samples of countries. The countries considered comprise the G-20.¹ Our first broad sample consists of the ten countries among the G-20 that are ranked lowest on the basis of the purchasing power parity of their income per capita. The developing countries are: Argentina, Brazil, China, India, Indonesia, Mexico, the Russian Federation, Saudi Arabia, South Africa, and Turkey. Our next broad sample consists of more developed countries in the G-20, namely Australia, Canada, France, Germany, Italy, Japan, the Korean Republic, the United Kingdom, and the United States. Our full sample includes all member nations of the G-20.

DTI is measured through six different indicators: number of telephone land lines per thousand of population (TML), number of mobile phone subscribers per thousand of population (MOB), number of internet users per thousand of population (INU), number of internet servers per thousand of population (INS), number of fixed broadband per thousand of population (FIB), and a composite index (TEL), which is derived through principal component analysis utilizing the first five indicators.²

We are interested in the nature of causality between six variables: DTI, which is measured in six ways, as described above; economic growth, measured by the percentage change in gross domestic product per capita (GDP); gross capital formation, as a percentage of gross domestic product (GCF); foreign direct investment inflows, as a percentage of gross domestic product (FDI); the urbanization rate, measured by the percentage of the population residing in urban areas (URB); and the degree of trade openness, measured by total trade as a percentage of gross domestic product (OPE). These variables are summarized in Table 2.

Table 2. Definition of variables

Variables	Definition
TEL	Composite index of telecommunications infrastructure: This index is derived from five variables: TML, MOB, INU, INS, and FIB, using principal component analysis.
TML	Telephone landlines: Telephone landlines per thousand of population.
MOB	Mobile phones: Mobile phone subscribers per thousand of population.
INU	Internet users: Internet users per thousand of population.
INS	Internet servers: Internet servers per thousand of population.
FIB	Fixed broadband: Fixed broadband per thousand of population.
GDP	Per capita economic growth: Percentage change in per capita gross domestic product, used as our indicator of economic growth.
GCF	Gross capital formation: Measured as a percentage of gross domestic product.
FDI	Foreign direct investment: Measured as FDI inflows as a percentage of gross domestic product.
URB	Urbanization rate: Percentage of urban population residing in urban areas.
OPE	Trade openness: Measured as total trade (exports plus imports) as a percentage of gross domestic product.

Note: All monetary measures are in real US dollars.

The descriptive statistics of the panel data used in this study and the correlation between the variables are summarized in Tables 3 and 4, respectively.

Table 3. Summary statistics for G-20 countries (combined)

Variables	Mean	Standard deviation	Skewness	Kurtosis	Jarque Bera statistics
TEL	1.05	0.03	1.52	1.85	11.10
TML	1.45	0.35	–1.17	3.74	47.40
MOB	1.81	0.34	–2.36	11.3	713.0
INU	1.45	0.45	–1.03	3.37	34.60
INS	1.57	1.11	–0.33	2.12	9.520
FIB	0.60	0.89	–1.12	3.62	42.40
GDP	0.98	0.23	–3.33	20.1	2640
URB	1.84	0.12	–1.83	5.65	160.0
FDI	0.77	0.15	–1.84	17.8	1826
OPE	1.72	0.17	–0.59	2.86	10.90
GCF	1.33	0.11	1.08	4.01	44.40

Note: Values reported here are the natural logarithms of the variables. Natural logarithmic forms are used in our estimations.

Table 4. Correlation matrices

Variables	TEL	TML	MOB	INU	INS	FIB	GDP	URB	FDI	OPE	GCF
Panel A: G-20 developing group
TEL	1.00
TML	–	1.00
MOB	–	–	1.00
INU	–	–	–	1.00
INS	–	–	–	–	1.00
FIB	–	–	–	–	–	1.00
GDP	–0.16	–0.13	–0.12	–0.11	–0.23	–0.01	1.00
URB	0.56*	0.72*	0.54*	0.69*	0.67*	0.46*	–0.28	1.00
FDI	0.39*	0.37*	0.46*	0.46*	0.14	0.51*	0.03	0.21	1.00
OPE	0.06	–0.02	0.29	0.09	–0.10	0.09	0.03	–0.00	0.23	1.00
GCF	–0.11	–0.19	–0.12	–0.17	–0.55*	0.11	0.34	–0.70*	0.23	0.18	1.00
Panel B: G-20 developed group
TEL	1.00
TML	–	1.00
MOB	–	–	1.00
INU	–	–	–	1.00
INS	–	–	–	–	1.00
FIB	–	–	–	–	–	1.00
GDP	–0.06	0.14	–0.18	–0.00	–0.08	–0.09	1.00
URB	0.40	0.18	–0.37*	0.53*	0.42*	0.27	0.08	1.00
FDI	0.04	0.07	0.05	–0.03	0.09	–0.0	0.02	–0.09	1.00
OPE	0.19	0.21	0.30	0.09	–0.07	0.15	0.19	–0.41*	0.15	1.00
GCF	0.04	–0.23	–0.17	0.10	–0.17	0.14	0.20	0.34*	–0.24	0.11	1.00
Panel C: G-20 combined group
TEL	1.00
TML	–	1.00
MOB	–	–	1.00
INU	–	–	–	1.00
INS	–	–	–	–	1.00
FIB	–	–	–	–	–	1.00
GDP	–0.21	–0.20	–0.19	–0.20	–0.25	–0.13	1.00
URB	0.59*	0.74*	0.59*	0.74*	0.68*	0.60*	–0.29	1.00
FDI	–0.01	0.02	0.17	0.04	–0.05	0.08	0.04	0.02	1.00
OPE	0.14	0.06	0.24	0.10	–0.00	0.11	0.07	–0.04	0.17	1.00
GCF	–0.17	–0.25	–0.18	–0.21	–0.39*	–0.03	0.33*	–0.54*	0.02	0.13	1.00

Note: * indicates significance at the 5% level.

The following empirical model describes the causal relationship between DTI, economic growth, and the four other macroeconomic variables. We also entertain the possibility that causation may proceed in the other direction or in both directions simultaneously. Thus, in other variations of equation (1) the other five variables (besides DTI) take turns to act as the dependent variable.

(1)

The long-run causal nexus is examined by applying the Granger procedure within the vector error-correction model (VECM) framework.

The estimation procedure uses the following equation.

(2)

where ∆ is first difference filter (I – L); i = 1,… N; t = 1,… T; and ξ_j (j = 1,… 6) are independently and normally distributed random variables for all i and t, with zero means and finite heterogeneous variances (σ_i²). The ECTs are error-correction terms, derived from the cointegrating equations. The ECTs represent the long-run dynamics, akin to an equilibrium process, while differenced variables represent the short-run adjustment dynamics between the variables. The above model is meaningful if the time series variables are I(1) and are cointegrated. If the time series variables are I(1) and are not cointegrated, then the ECT component will be removed in the estimation process. We look for both short-run and long-run causal relationships. The short-run causal relationship is measured through F-statistics and the significance of the lagged changes in independent variables, whereas the long-run causal relationship is measured through the significance of the t-test of the lagged ECTs. However, the first procedure under VECM is to determine the unit root and the nature of cointegration among the six variables.

Panel Unit Root Test

The panel unit root test is deployed to ascertain the order of integration where a time series variable attains its stationary. The LLC (Levine–Lin–Chu: Levine et al. 2002) test is deployed for determining the order of integration, where the time series variable attains stationarity. A detailed description of this test is available from the authors upon request and is omitted due to space constraints.

Panel Cointegration Test

The concept of cointegration, introduced by Granger (see, for example, Granger 1988), is relevant to the problem of the determination of the long-run relationship between variables. Pedroni (2000) proposed seven different statistics for the cointegration test in the panel data setting. All seven tests are based on asymptotically standard normal distributions given by the respective panel/group cointegration statistic. Each of these tests is able to accommodate individual-specific short-run dynamics, country-specific fixed effects, deterministic trends, as well as country-specific slope coefficients. These tests, which follow Pedroni (2000), are not described here but are available from the authors upon request.

Empirical Results and Discussion

The estimation process involves treating three different samples of countries: the G-20 combined group, the G-20 developing group, and the G-20 developed group. The countries involved in each group are described in the previous section. Under each sample, we consider six cases, one for each of our measures of DTI. Hence, Case 1 considers the causal relationship between TEL, GDP, URB, FDI, OPE, and GCF. Case 2 replaces TEL with TML. Analogously, Cases 3–6 use MOB, INU, INS, and FIB, respectively.

The investigation proceeds with the integration and cointegration properties of time series variables. The estimated results confirm that the variables are integrated of order one [1(1)] and are cointegrated (see Tables 5 and 6, respectively). This indicates the presence of a long-run equilibrium relationship between DTI, economic growth, and the other four macroeconomic variables. Remarkably, this is true in all of the three samples.

Table 5. Panel unit root tests

	TEL	TML	MOB	INU	INS	FIB	GDP	URB	FDI	OPE	GCF
Panel A: G-20 developing group
LLC	–6.36*	–2.93*	–3.02*	–2.33*	–8.4*	–5.97*	–8.36*	–5.86*	–7.33*	–5.09*	–6.00*
ADF	48.8*	35.1*	48.8*	20.4*	72.6*	64.1*	71.1*	45.3*	63.5*	52.0*	48.7*
PP	100*	58.6*	105*	82.7*	125*	100*	156*	60.5*	110.9*	95.5*	67.4*
Panel B: G-20 developed group
LLC	–5.33*	–6.94*	–5.74	–7.4*	–6.5*	–14.5*	–9.1*	–2.4*	–14.0*	–6.16*	–6.1*
ADF	44.4*	55.1*	47.5*	70.3*	47.4*	124*	76.5*	45.1*	93.3*	51.3*	51.4*
PP	66.9*	86.0*	114*	121*	80.4*	171*	181*	60.8*	121*	106*	80.1*
Panel C: G-20 combined group
LLC	–3.52*	–1.97*	–5.19*	–7.73*	–4.2*	–14.7*	–12*	–5.4*	–16*	–7.7*	–8.4*
ADF	49.3*	90.1*	96.3*	90.7*	66.2*	188*	148*	90.4*	157*	103*	100*
PP	75.2*	145*	219*	204*	118*	271*	336*	121*	232*	202*	148*

Notes: The unit root test statistics at level data are not reported due to space constraints.

* indicates significance at the 1% level.

Table 6. Results of Pedroni tests

	Models
Test statistics	M1	M2	M3	M4	M5	M6
Panel A: G-20 developing group
Panel v-statistics	–3.23	–2.63	–2.92	–3.42	–3.55	–3.26
Panel ρ-statistics	3.47	3.29	2.61	2.83	3.56	2.96
Panel PP-statistics	3.04	2.80	–0.59	0.28	3.08	0.19
Panel ADF-statistics	5.54	4.29	2.13	3.83	4.65	3.24
Group ρ-statistics	4.07	4.35	4.12	3.68	3.83	3.96
Group PP-statistics	–3.25*	–0.81	–2.11*	–5.69*	–2.90*	–2.76*
Group ADF-statistics	3.28	2.38	0.58	1.42	3.11	1.23
Panel B: G-20 developed group
Panel v-statistics	–3.43	–2.53	–3.42	–2.96	–3.45	–3.16
Panel ρ-statistics	2.43	2.43	2.28	2.18	2.39	2.32
Panel PP-statistics	–2.48	–2.14	–3.78	–3.44	–3.39	–5.13
Panel ADF-statistics	0.92	0.57	0.24	2.07	–0.39	0.50
Group ρ-statistics	3.71	3.60	3.69	3.70	4.02	3.63
Group PP-statistics	–3.79*	–6.12*	–2.65*	–4.04*	–3.36*	–7.07*
Group ADF-statistics	0.42	0.56	2.21	1.83	0.73	1.80
Panel C: G-20 combined group
Panel v-statistics	–4.69	–3.69	–4.19	–4.75	–4.97	–4.56
Panel ρ-statistics	3.56	4.41	3.65	3.89	4.46	3.92
Panel PP-statistics	–2.62	2.76	–1.14	–0.25	1.38	–1.64
Panel ADF-statistics	1.78	5.23	2.86	5.21	4.66	3.87
Group ρ-statistics	5.41	5.61	5.53	5.22	5.55	5.36
Group PP-statistics	–5.19*	–4.9*	–3.37*	–6.88*	–4.43*	–6.95*
Group ADF-statistics	2.07	2.07	1.97	2.30	2.71	2.14

Notes: M₁: Cointegration between TEL, GDP, URB, FDI, OPE, and GCF; M₂: Cointegration between TML, GDP, URB, FDI, OPE, and GCF; M₃: Cointegration between MOB, GDP, URB, FDI, OPE, and GCF; M₄: Cointegration between INU, GDP, URB, FDI, OPE, and GCF; M₅: Cointegration between INS, GDP, URB, FDI, OPE, and GCF; M₆: Cointegration between FIB, GDP, URB, FDI, OPE, and GCF.

* indicates significance at the 1% level.

The existence of I(1) and cointegration among these variables imply the possibility of Granger causality among them. Hence, we perform a Granger causality test, using a vector error-correction model and using equation (2). The results for our three samples of countries are shown in Tables 7, 8, and 9. These three tables report the panel Granger causality test results for both the short run, represented by the significance of the F-statistic, and the long run, represented by the significance of the lagged error-correction term (ECT_–1). The results for the three samples are summarized below.

Table 7. Granger causality tests: G-20 developing group

Dep var	Independent variables						Lagged error correction
Case 1: TEL, GDP, URB, FDI, OPE, GCF
	∆GDP	∆TEL	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	7.38*	2.92	5.78*	0.60	0.34	–2.73**
∆TEL	0.59	–	1.13	3.78**	11.5*	0.80	–0.73
∆URB	0.54	2.17	–	0.97	2.40	0.34	2.29
∆FDI	1.23	10.9*	12.4*	–	0.38	1.87	4.13
∆OPE	3.18**	2.46	5.98*	3.83*	–	0.53	1.30
∆GCF	0.13	0.20	1.01	2.74	1.21	–	1.06
Case 2: TML, GDP, URB, FDI, OPE, GCF
∆GDP	∆TML	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.35	1.59	3.51**	1.44	0.94	–1.68
∆TML	1.14	–	2.12	0.49	2.79	0.86	–0.84
∆URB	2.99**	0.19	–	0.68	1.92	0.17	2.88
∆FDI	9.92*	0.67	13.4*	–	0.11	1.18	4.44
∆OPE	3.10**	4.36*	5.37*	3.15**	–	0.63	1.76
∆GCF	0.52	1.95	1.91	2.65	2.05	–	1.58
Case 3: MOB, GDP, URB, FDI, OPE, GCF
∆GDP	∆MOB	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.98	0.58	2.48	1.55	0.25	–2.85
∆MOB	4.72*	–	1.47	0.11	1.57	0.07	–1.67
∆URB	0.54	2.17	–	0.97	2.40	0.34	1.27
∆FDI	1.23	10.9*	12.4*	–	0.38	1.87	2.28
∆OPE	3.18**	2.46	5.98*	3.83**	–	0.53	0.24
∆GCF	0.13	0.20	1.01	2.74	1.21	–	1.19
Case 4: INU, GDP, URB, FDI, OPE, GCF
∆GDP	∆INU	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	2.76	2.90	6.12*	0.91	1.86	–2.22
∆INU	0.07	–	2.88	6.23*	3.86**	1.18	–0.33
∆URB	2.92	0.56	–	1.35	2.29	0.17	2.59
∆FDI	13.9*	2.21	12.5*	–	0.93	1.91	4.41
∆OPE	1.72	2.88	6.58*	4.96*	–	1.27	1.63
∆GCF	1.11	0.46	0.70	3.73**	2.10	–	1.76
Case 5: INS, GDP, URB, FDI, OPE, GCF
∆GDP	∆INS	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.29	0.98	2.98**	0.98	0.88	–0.77
∆INS	3.18**	–	2.60	0.49	14.9*	4.17	–0.46
∆URB	0.81	1.09	–	0.08	0.98	0.07	1.36
∆FDI	16.8*	4.94*	8.51*	–	0.85	1.97	5.76
∆OPE	3.56**	2.19	2.62	3.21**	–	0.89	1.92
∆GCF	0.46	3.24	0.54	3.54**	0.91	–	2.02
Case 6: FIB, GDP, URB, FDI, OPE, GCF
∆GDP	∆FIB	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	1.16	1.58	4.26*	1.21	1.56	–2.61
∆FIB	2.88	–	0.15	1.77	0.45	0.97	–1.46
∆URB	3.46**	3.55**	–	1.99	2.54	0.29	2.74
∆FDI	5.29*	0.57	4.81*	–	0.02	1.13	3.01
∆OPE	1.59	2.44	3.88**	3.49**	–	0.61	1.27
∆GCF	0.35	1.22	0.27	3.36**	1.30	–	1.26

Notes: Dep var denotes the dependent variable; ECT_–1 is the lagged error-correction term as per equation (2).

* indicates significance at 1% level; ** indicates significance at 5% level.

Table 8. Granger causality tests: G-20 developed group

Dep var	Independent variables						Lagged error correction
Case 1: TEL, GDP, URB, FDI, OPE, GCF
	∆GDP	∆TEL	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.66	1.65	8.35*	4.13*	3.72**	–1.50
∆TEL	2.68	–	1.66	0.04	0.17	1.50	–1.37
∆URB	0.89	6.28*	–	1.34	2.24	4.75*	–1.99
∆FDI	11.7*	1.50	12.0*	–	4.55*	3.68**	–3.82*
∆OPE	2.00	5.99*	0.42	4.99*	–	9.29*	0.39
∆GCF	4.69*	4.06*	4.86*	4.78*	0.59	–	–3.18
Case 2: TML, GDP, URB, FDI, OPE, GCF
	∆GDP	∆TML	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	3.59**	0.98	8.42*	6.19*	11.1*	–2.09**
∆TML	7.30*	–	1.38	3.60**	2.02	3.58**	1.74
∆URB	1.68	4.52*	–	0.98	0.62	2.79	0.44
∆FDI	5.73*	4.36*	6.78*	–	14.2*	8.52*	4.59
∆OPE	1.68	2.81	0.47	6.46*	–	7.03*	0.15
∆GCF	0.63	0.50	0.64	4.60*	2.09	–	–0.66
Case 3: MOB, GDP, URB, FDI, OPE, GCF
	∆GDP	∆MOB	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	5.29*	2.92	8.49*	3.97*	4.93*	–2.29
∆MOB	1.00	–	0.96	4.90*	0.44	5.46*	0.63
∆URB	0.70	0.69	–	2.04	1.07	3.02**	–0.65
∆FDI	2.40	5.38*	0.23	–	10.2	8.04*	2.36
∆OPE	1.02	9.57*	3.37**	13.3*	–	6.49*	1.21
∆GCF	3.24**	3.78**	1.77	3.23**	1.27	–	–1.64
Case 4: INU, GDP, URB, FDI, OPE, GCF
	∆GDP	∆INU	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.02	1.07	9.05*	6.38*	5.19*	–1.67
∆INU	10.3*	–	3.67**	0.72	3.98**	3.18**	–4.13*
∆URB	0.90	1.02	–	1.56	1.53	2.09	–1.06
∆FDI	2.85	10.2*	0.95	–	4.33*	3.14*	–0.48
∆OPE	1.16	1.39	0.82	7.32*	–	7.91*	1.11
∆GCF	4.10*	0.71	0.70	5.03*	2.18	–	–2.21
Case 5: INS, GDP, URB, FDI, OPE, GCF
	∆GDP	∆INS	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	0.33	0.51	8.38*	5.97*	7.46*	–1.28
∆INS	3.06**	–	3.81**	1.03	1.39	1.39	–3.45*
∆URB	1.38	0.73	–	1.89	1.69	1.68	–0.88
∆FDI	1.71	6.47*	0.24	–	10.2*	2.38	1.53
∆OPE	4.24*	18.5*	3.01**	8.32*	–	5.23*	2.03
∆GCF	1.50	1.56	0.11	4.39*	3.40**	–	–0.51
Case 6: FIB, GDP, URB, FDI, OPE, GCF
	∆GDP	∆FIB	∆URB	∆FDI	∆OPE	∆GCF	ECT–1
∆GDP	–	3.20**	1.93	7.04*	5.18*	4.85*	–0.54
∆FIB	30.2*	–	1.10	10.5*	3.24**	4.63*	6.70
∆URB	0.87	0.94	–	1.52	0.49	1.57	–0.21
∆FDI	2.81	6.35*	0.69	–	6.14*	4.40*	2.40
∆OPE	7.37*	2.94	1.57	3.34**	–	8.12*	–0.69
∆GCF	1.41	3.35**	2.36	4.77*	2.27	–	–0.01

Notes: Dep var denotes the dependent variable; ECT_–1 is the lagged error-correction term as per equation (2).

* indicates significance at 1% level; ** indicates significance at 5% level.

Table 9. Granger causality tests: G-20 countries combined

Dep var	Independent variables						Lagged error correction
Case 1: TEL, GDP, URB, FDI, OPE, GCF
	∆GDP	∆TEL	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	2.25	2.84**	2.10	3.41**	2.30	–4.18*
∆TEL	1.84	–	2.68	0.95	1.82	0.00	1.23
∆URB	3.36**	4.62*	–	5.99*	3.32**	1.36	2.25
∆FDI	3.96**	1.01	5.76*	–	1.36	3.82**	3.06
∆OPE	0.58	8.77*	5.80*	4.12*	–	5.53*	0.18
∆GCF	0.99	1.91	0.01	6.85*	0.85	–	2.07
Case 2: TML, GDP, URB, FDI, OPE, GCF
	∆GDP	∆TML	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	2.84	0.59	1.56	3.62**	4.23*	–4.33*
∆TML	1.68	–	1.90	5.53*	1.40	0.28	0.85
∆URB	3.08**	2.70	–	4.32*	3.88**	2.63	2.17
∆FDI	3.72**	0.79	6.97*	–	1.25	3.37**	2.97
∆OPE	1.30	0.55	4.01*	2.68	–	4.59*	0.32
∆GCF	0.54	3.30	0.50	6.05*	1.04	–	1.78
Case 3: MOB, GDP, URB, FDI, OPE, GCF
	∆GDP	∆MOB	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	3.61**	0.37	1.35	4.88*	4.37*	–5.55*
∆MOB	8.83*	–	2.91	0.94	2.95	0.19	–2.24**
∆URB	2.13	4.87*	–	4.44*	4.25*	0.61	1.54
∆FDI	0.57	5.97*	2.04	–	0.63	3.18**	1.58
∆OPE	2.20	1.27	2.72	2.40	–	5.76*	–0.46
∆GCF	0.25	7.59*	0.39	4.57*	1.24	–	0.72
Case 4: INU, GDP, URB, FDI, OPE, GCF
	∆GDP	∆INU	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	1.49	1.82	2.05	4.90*	2.71	–1.49
∆INU	27.4*	–	3.98*	2.04	5.87*	0.48	5.40
∆URB	3.37**	0.43	–	5.01*	4.38*	0.10	3.02
∆FDI	0.29	0.40	4.84*	–	0.90	2.71	1.84
∆OPE	0.66	2.31	5.59*	2.64	–	5.74*	0.58
∆GCF	0.74	0.10	0.05	5.50*	1.21	–	2.97
Case 5: INS, GDP, URB, FDI, OPE, GCF
	∆GDP	∆INS	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	0.31	5.34*	1.17	4.34*	1.76	–4.05*
∆INS	6.86*	–	1.39	1.83	2.78	0.67	2.15
∆URB	2.64	1.07	–	2.69	4.12*	1.46	2.04
∆FDI	3.01**	1.12	7.93*	–	1.79	3.48**	2.82
∆OPE	1.26	15.3*	2.83	4.56*	–	4.45*	–0.37
∆GCF	0.87	0.31	0.10	5.39*	0.92	–	2.04
Case 6: FIB, GDP, URB, FDI, OPE, GCF
	∆GDP	∆FIB	∆URB	∆FDI	∆OPE	∆GCF	ECT-1
∆GDP	–	3.56**	0.70	0.80	5.10*	4.43*	–1.39
∆FIB	18.6*	–	0.75	0.91	2.21	6.39*	6.01
∆URB	2.55	1.99	–	4.07*	3.98*	0.32	2.18
∆FDI	0.70	5.34	1.99	–	0.64	3.69**	1.93
∆OPE	1.76	3.61**	3.96**	3.08	–	6.19*	–0.19
∆GCF	0.57	3.21	0.34	4.54*	1.24	–	2.13

Notes: Dep var denotes the dependent variable; ECT_–1 is the lagged error-correction term as per Equation (2).

* indicates significance at 1% level; ** indicates significance at 5% level.

Sample 1: The G-20 Developing Group

Case 1: Causality between TEL, GDP, URB, FDI, OPE, and GCF

In this case, for the G-20 developing group, we find the existence of bidirectional causality between telecommunications infrastructure and foreign direct investment (TEL < = > FDI). Moreover, we find unidirectional causality from both trade openness (OPE = > GDP) and foreign direct investment (FDI = > GDP) to economic growth. A unidirectional causality between urbanization to foreign direct investment (URB = > FDI) is also discovered. Also, economic growth, urbanization, and foreign direct investment are all found to cause trade openness. In fact, a clear and distinct unidirectional causality is found from economic growth (GDP = > OPE), urbanization (URB = > OPE), and foreign direct investment (FDI = > OPE) to trade openness.

Case 2: Causality between TML, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between economic growth and foreign direct investment (GDP < = > FDI). Moreover, we find unidirectional causality from urbanization to foreign direct investment (URB = > FDI), economic growth to trade openness (GDP = > OPE), telecommunications infrastructure to trade openness (TML = > OPE), and the urbanization rate to trade openness (URB = > OPE). Therefore, an increase of openness to trade is found to clearly be caused by economic growth, telecommunications infrastructure, and the urbanization rate.

Case 3: Causality between MOB, GDP, URB, FDI, OPE, and GCF

We find the existence of unidirectional causality from economic growth to telecommunications infrastructure (GDP = > MOB). Both telecommunications infrastructure (MOB = > FDI) and urbanization (URB = > FDI) have unidirectional causality to foreign direct investment. Three variables are found to cause trade openness.

Case 4: Causality between INU, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between economic growth and foreign direct investment (GDP < = > FDI), meaning that economic growth causes foreign direct investment, and vice versa. In turn, foreign direct investment causes telecommunications infrastructure, which is needed to help facilitate foreign direct investment. Intuitively, it makes sense that unidirectional causality is found from foreign direct investment to telecommunications infrastructure. In turn, trade openness causes telecommunications infrastructure (OPE = > INU), while urbanization causes trade openness (URB = > OPE) as well as foreign direct investment (URB = > FDI).

Case 5: Causality between INS, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between economic growth and foreign direct investment (GDP < = > FDI). Moreover, we find unidirectional causality from economic growth to telecommunications infrastructure (GDP = > INS). We also find unidirectional causality from telecommunications infrastructure to gross capital formation (INS = > GCF), and foreign direct investment to gross capital formation (FDI = > GCF). Telecommunications infrastructure has a unidirectional causality to foreign direct investment (INS = > FDI), as does urbanization to foreign direct investment (URB = > FDI). Economic growth has a unidirectional causality to trade openness (GDP = > OPE), as does foreign direct investment to trade openness (FDI = > OPE).

Case 6: Causality between FIB, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between economic growth and foreign direct investment (GDP < = > FDI). Moreover, we find unidirectional causality from economic growth to urbanization (GDP = > URB) and telecommunications infrastructure to urbanization (FIB = > URB). Urbanization causes foreign direct investment (URB = > FDI) as well as trade openness (URB = > OPE). Foreign direct investment is found to cause both trade openness (FDI = > OPE), and gross capital formation (FDI = > GCF).

Sample 2: The G-20 Developed Group

Case 1: Causality between TEL, GDP, URB, FDI, OPE and GCF

Bidirectional causality is found between:

• telecommunications infrastructure and foreign direct investment (TEL < = > FDI);

• gross capital formation and economic growth (GCF < = > GDP);

• gross capital formation and urbanization (GCF < = > URB);

• gross capital formation and foreign direct investment (GCF < = > FDI);

• trade openness and foreign direct investment (OPE < = > FDI);

Unidirectional causality is found from:

• telecommunications infrastructure to urbanization (TEL = > URB);

• urbanization to foreign direct investment (URB = > FDI);

• telecommunications infrastructure to gross capital formation (TEL = > GCF);

• telecommunications infrastructure to trade openness (TEL = > OPE);

• gross capital formation to trade openness (GCF = > OPE).

Case 2: Causality between TML, GDP, URB, FDI, OPE, and GCF

Bidirectional causality is found between:

• telecommunications infrastructure and economic growth (TML < = > GDP);

• foreign direct investment and economic growth (FDI < = > GDP);

• telecommunications infrastructure and foreign direct investment (TML < = > FDI);

• gross capital formation and foreign direct investment (GCF < = > FDI);

• trade openness and foreign direct investment (OPE < = > FDI).

Unidirectional causality is found to flow from:

• trade openness to economic growth (OPE = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• gross capital formation to telecommunications infrastructure (GCF = > TML);

• telecommunications infrastructure to urbanization (TML = > URB);

• urbanization to foreign direct investment (URB = > FDI);

• gross capital formation to trade openness (GCF = > OPE).

Case 3: Causality between MOB, GDP, URB, FDI, OPE, and GCF

In this case, bidirectional causality is found to flow between:

• gross capital formation and economic growth (GCF < = > GDP);

• foreign direct investment and telecommunications infrastructure (FDI < = > MOB);

• trade openness and foreign direct investment (OPE < = > FDI);

• gross capital formation and telecommunications infrastructure (GCF < = > MOB);

• gross capital formation and foreign direct investment (GCF < = > FDI).

Unidirectional causality is found to flow from:

• telecommunications infrastructure to economic growth (MOB = > GDP);

• foreign direct investment to economic growth (FDI = > GDP);

• trade openness to economic growth (OPE = > GDP);

• gross capital formation to urbanization (GCF = > URB);

• telecommunications infrastructure to trade openness (MOB = > OPE);

• gross capital formation to trade openness (GCF = > OPE).

Case 4: Causality between INU, GDP, URB, FDI, OPE, and GCF

In this case, we find the existence of bidirectional causality between:

• economic growth and foreign direct investment (GDP < = > FDI);

• gross capital formation and economic growth (GCF < = > GDP);

• gross capital formation and foreign direct investment (GCF < = > FDI);

• trade openness and foreign direct investment (OPE < = > FDI).

Moreover, we find unidirectional causality to flow from:

• trade openness to economic growth (OPE = > GDP);

• economic growth to telecommunications infrastructure (GDP = > INU);

• trade openness to telecommunications infrastructure (OPE = > INU);

• urbanization to telecommunications infrastructure (URB = > INU);

• gross capital formation to trade openness (GCF = > OPE);

• telecommunications infrastructure to foreign direct investment (INU = > FDI);

• gross capital formation to trade openness (GCF = > OPE).

Case 5: Causality between INS, GDP, URB, FDI, OPE, and GCF

In this case, we find the existence of bidirectional causality to flow between:

• economic growth and trade openness (GDP < = > OPE);

• gross capital formation and trade openness (GCF < = > OPE);

• trade openness and foreign direct investment (OPE < = > FDI).

Moreover, we find unidirectional causality to flow from:

• foreign direct investment to economic growth (FDI = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• economic growth to telecommunications infrastructure (GDP = > INS);

• urbanization to telecommunications infrastructure (URB = > INS);

• telecommunication infrastructures to foreign direct investment (INS = > FDI);

• urbanization to trade openness (URB = > OPE);

• foreign direct investment to gross capital formation (FDI = > GCF).

Case 6: Causality between FIB, GDP, URB, FDI, OPE, and GCF

In this case, we find the existence of bidirectional causality between:

• telecommunications infrastructure and economic growth (FIB < = > GDP);

• trade openness and economic growth (OPE < = > GDP);

• gross capital formation and telecommunications infrastructure (GCF < = > FIB);

• gross capital formation and foreign direct investment (GCF < = > FDI);

• trade openness and foreign direct investment (OPE < = > FDI);

• telecommunications infrastructure and foreign direct investment (FIB < = > FDI).

Moreover, we find unidirectional causality from:

• trade openness to telecommunications infrastructure (OPE = > FIB);

• foreign direct investment to economic growth (FDI = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• gross capital formation to trade openness (GCF = > OPE).

Sample 3: The G-20 Combined Group

Case 1: Causality between TEL, GDP, URB, FDI, OPE, and GCF

In this case, we find the existence of bidirectional causality between:

• urbanization and trade openness (URB < = > OPE);

• foreign direct investment and urbanization (FDI < = > URB);

• gross capital formation and urbanization (GCF < = > URB);

• gross capital formation and foreign direct investment (GCF < = > FDI);

• trade openness and foreign direct investment (OPE < = > FDI).

Moreover, we find unidirectional causality from:

• telecommunications infrastructure to urbanization (TEL = > URB);

• trade openness to economic growth (OPE = > GDP);

• economic growth to foreign direct investment (GDP = > FDI);

• telecommunications infrastructure to gross capital formation (TEL = > GCF);

• telecommunication infrastructures to trade openness (TEL = > OPE);

• gross capital formation to trade openness (GCF = > OPE).

Case 2: Causality between TML, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between:

• foreign direct investment and urbanization (FDI < = > URB);

• trade openness and urbanization (OPE < = > URB);

• gross capital formation and foreign direct investment (GCF < = > FDI).

Moreover, we find unidirectional causality from:

• telecommunications infrastructure to economic growth (TML = > GDP);

• trade openness to economic growth (OPE = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• foreign direct investment to telecommunications infrastructure (FDI = > TML);

• gross capital formation to trade openness (GCF = > OPE)

• economic growth to urbanization (GDP = > URB);

• economic growth to foreign direct investment (GDP = > FDI);

• telecommunications infrastructure to gross capital formation (TML = > GCF).

Case 3: Causality between MOB, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between:

• telecommunications infrastructure and economic growth (MOB < = > GDP);

• trade openness and urbanization (OPE < = > URB);

• telecommunications infrastructure and urbanization (MOB < = > URB);

• gross capital formation and foreign direct investment (GCF < = > FDI).

Moreover, we find unidirectional causality from:

• trade openness to economic growth (OPE = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• trade openness to telecommunications infrastructure (OPE = > MOB);

• foreign direct investment to urbanization (FDI = > URB);

• telecommunications infrastructure to foreign direct investment (MOB = > FDI);

• gross capital formation to trade openness (GCF = > OPE);

• telecommunications infrastructure to gross capital formation (MOB = > GCF).

Case 4: Causality between INU, GDP, URB, FDI, OPE, and GCF

In this case, we find the existence of bidirectional causality between:

• urbanization and foreign direct investment (URB < = > FDI);

• trade openness and urbanization (OPE < = > URB).

Moreover, we find unidirectional causality from:

• trade openness to economic growth (OPE = > GDP);

• urbanization to telecommunications infrastructure (URB = > INU);

• economic growth to urbanization (GDP = > URB);

• economic growth to telecommunications infrastructure (GDP = > INU);

• trade openness to telecommunications infrastructure (OPE = > INU);

• gross capital formation to trade openness (GCF = > OPE);

• foreign direct investment to gross capital formation (FDI = > GCF).

Case 5: Causality between INS, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between:

• trade openness and urbanization (OPE < = > URB);

• telecommunications infrastructure and trade openness (INS < = > OPE);

• gross capital formation and foreign direct investment (GCF < = > FDI).

Moreover, we find unidirectional causality from:

• trade openness to economic growth (OPE = > GDP);

• urbanization to economic growth (URB = > GDP);

• economic growth to telecommunications infrastructure (GDP = > INS);

• economic growth to foreign direct investment (GDP = > FDI);

• urbanization to foreign direct investment (URB = > FDI);

• gross capital formation to trade openness (GCF = > OPE);

• foreign direct investment to trade openness (FDI = > OPE).

Case 6: Causality between FIB, GDP, URB, FDI, OPE, and GCF

We find the existence of bidirectional causality between:

• telecommunications infrastructure and economic growth (FIB < = > GDP);

• trade openness and urbanization (OPE < = > URB);

• gross capital formation and telecommunications infrastructure (GCF < = > FIB);

• gross capital formation and foreign direct investment (GCF < = > FDI).

Moreover, we find unidirectional causality from:

• trade openness to economic growth (OPE = > GDP);

• gross capital formation to economic growth (GCF = > GDP);

• foreign direct investment to urbanization (FDI = > URB);

• telecommunications infrastructure to trade openness (FIB = > OPE);

• gross capital formation to trade openness (GCF = > OPE);

• foreign direct investment to trade openness (FDI = > OPE);

• telecommunications infrastructure to foreign direct investment (FIB = > FDI).

Conclusion and Policy Implications

The results provide evidence that the development of telecommunications infrastructure generally causes economic growth and that the causation may be bidirectional, implying various feedback linkages which we document here. Not surprisingly, the exact nature of causality depends on the country groupings considered and how one defines telecommunications infrastructure. For example, in the case of the G-20 developed country group, mobile phone usage is at the maturity level and hence the development of telecommunications infrastructure (defined as mobile phone usage) makes a contribution to (i.e. Granger-causes) economic growth. However, in the case of the G-20 developing country group where mobile phone usage is less popular and has not reached the maturity level, it is economic growth which attracts higher mobile phone usage. Obviously, in the latter case, economic growth Granger-causes mobile phone usage.

Furthermore, this paper demonstrates that economic growth and the development of telecommunications infrastructure may also be causally linked to other macroeconomic variables. An important result here concerns the relationship between DTI and FDI. We find that in both the developing and developed country groups, internet servers bring FDI. This carries an important lesson especially for developing countries, namely that the existence of telecommunications infrastructure attracts FDI. Hence, further development of the former should be promoted in order to attract additional foreign direct investment.

Finally, the results confirm the presence of a long-run relationship between all variables considered, irrespective of whether the countries are developed or developing. Thus, if policymakers wish to promote long-run economic growth, our general findings augment the case for assisting the development of the telecommunications industry alongside the encouragement of gross capital formation, foreign direct investment, the urbanization rate, and freer trade. As always, a more stable macroeconomic environment can enhance all macroeconomic variables associated with economic growth. The main message from our study for researchers and policymakers alike is that inferences drawn from research that does not include the dynamic interrelation of all the macroeconomic and policy variables in our study will be unreliable. It is the conjoint interplay between gross capital formation, foreign direct investment, the urbanization rate, and the openness to trade that distinguishes our study and guides future research on this topic.

Additional information

Notes on contributors

Rudra P. Pradhan

Rudra P. Pradhan is a SAP Fellow and an Assistant Professor at the Indian Institute of Technology, Kharagpur, India, where he has been associated with the School of Management and the School of Infrastructure Design and Management. He is the author of over 80 papers in refereed journals as well as several books on many topics. Rudra is the Associate Editor of African Journal of Political Science and International Relations.

Mak B. Arvin

Mak Arvin is a Professor of Economics at Trent University, Canada where he has been a faculty member for the past 28 years. He is the author of over 130 papers and reviews in refereed journals as well as several books on a wide variety of topics. Arvin is the Editor of the International Journal of Happiness and Development and the International Journal of Public Policy.

Sahar Bahmani

Sahar Bahmani is an Assistant Professor of Economics at the University of Wisconsin at Parkside. She has been teaching economics courses since 2003. She is the author of over 20 papers in refereed journals as well as several book chapters. Bahmani is the Editor of Journal of International Business and Economics and the Journal of Business and Economic Research.

Neville R. Norman

Neville R. Norman is an Associate Professor and Honours Economics Co-ordinator at the University of Melbourne, Official Visiting Scholar in Economics at Cambridge University, UK and the 2010 Honorary Fellow of the Economic Society of Australia. He has a PhD from Cambridge, and Honours and Master’s degrees from Melbourne. Neville specializes in the trade policy-industrial economics intersection, with a focus on theory, econometrics, data generation and policy analysis, with publications, public addresses and evidence in these areas in many countries.

Notes

^1. The G-20 consists of 19 member countries plus the European Union, which is represented by the President of the European Council and by the European Central Bank. Thus, although we look at the G-20, within this group of industrialized and developing economies, we observe only 19 member nations, which are used for our analysis. To include the EU, the twentieth member, would have meant double-counting France, Germany, Italy, and the UK.

^2. Principal component analysis is a well-established approach used in calculating an index.