Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=reme20

Download by: [Indian Institute of Technology - Kharagpur] Date: 14 November 2016, At: 06:11

Macroeconomics and Finance in Emerging MarketEconomies

ISSN: 1752-0843 (Print) 1752-0851 (Online) Journal homepage: http://www.tandfonline.com/loi/reme20

Broadband penetration, financial development,and economic growth nexus: evidence from theArab League countries

Rudra P. Pradhan, Mak B. Arvin, Sahar Bahmani & Sara E. Bennett

To cite this article: Rudra P. Pradhan, Mak B. Arvin, Sahar Bahmani & Sara E. Bennett (2016):Broadband penetration, financial development, and economic growth nexus: evidence fromthe Arab League countries, Macroeconomics and Finance in Emerging Market Economies, DOI:10.1080/17520843.2016.1250800

To link to this article: http://dx.doi.org/10.1080/17520843.2016.1250800

Published online: 14 Nov 2016.

Submit your article to this journal

View related articles

View Crossmark data

Broadband penetration, financial development, andeconomic growth nexus: evidence from the Arab LeaguecountriesRudra P. Pradhana, Mak B. Arvinb, Sahar Bahmanic and Sara E. Bennettd

aVinod Gupta School of Management, Indian Institute of Technology, Kharagpur, India; bDepartment ofEconomics, Trent University, Peterborough, Canada; cDepartment of Economics, University of Wisconsin-Parkside, Kenosha, WI, USA; dSchool of Business and Economics, Lynchburg College, Lynchburg, VA, USA

ABSTRACTThis paper examines the mutual relationship between broadbandpenetration, financial development, and economic growth in the22 Arab League countries for the period between 2001 and 2013.Financial development (represented by broad money supply,claims on the private sector, domestic credit to the private sector,domestic credit provided by the banking sector, market capitaliza-tion, turnover ratio, and traded stocks) is assessed both individu-ally, and by a composite index. Our results reveal that there is along-run equilibrium relationship between broadband penetra-tion, financial development, and economic growth. Additionally,we use a panel vector autoregression model to reveal the natureof Granger causality between the covariates. The most importantinsight of this study is the presence of bidirectional causality fromeconomic growth to broadband penetration in the long run. Inaddition, we find that financial development together with broad-band penetration Granger-cause economic growth in the long run.

ARTICLE HISTORYReceived 1 December 2015Accepted 13 October 2016

KEYWORDSBroadband penetration;financial development;economic growth; ArabLeague countries

JEL CLASSIFICATIONG10; O16; O53

1. Introduction

In this paper, we attempt to answer a relatively straightforward question: how arefinancial development1 and economic growth2 impacted by broadband penetration3?More generally, we seek out the direction of causality between these three variables.Here, broadband refers to the provision of high-speed data transmission, which enableshigh-speed internet services including streaming media, internet phone, gaming, andinteractive services (see, for instance, OECD 2002).

Broadband has been adopted by countries worldwide in recent decades. The trans-formative power of broadband, which enables the internet, is changing the way indivi-duals live, work, communicate, and collaborate (Mayer et al. 2015; Arvin and Pradhan2014; Mack and Rey 2014). It also has a profound impact on firms, markets, andgovernment operations. The internet has levelled the playing field between countries,giving developing countries opportunities to gather resources and expertise like never

CONTACT Rudra P. Pradhan rudrap@vgsom.ilitkgp.ernet.in

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES, 2016http://dx.doi.org/10.1080/17520843.2016.1250800

© 2016 Informa UK Limited, trading as Taylor & Francis Group

before. This, in turn, has allowed these countries to reorganize economic activity andachieve higher levels of economic growth.

A large body of literature has examined the effect of broadband penetration oneconomic growth, using an array of econometric techniques, such as cross-country, timeseries, panel data, and firm-level studies (Ng, Lye, and Lim 2013; Lin and Wu 2013; Bojnecand Fertő 2012; Czernich et al. 2011; Cambini and Jiang 2009; Holt and Jamison 2009). Byand large, most of the empirical studies have demonstrated that there is a positive long-runassociation between broadband penetration and economic growth (Bacache et al. 2014;Ruz, Varas, and Villena 2013; Bojnec and Fertő 2012; Kolko 2012; Czernich et al. 2011;Crandall, Lehr, and Litan 2007; Bouras, Giannaka, and Tsiatsos 2009; Holt and Jamison 2009;Brewer et al. 2005). In general, where we observe a positive correlation, we can demon-strate that increased broadband penetration is growth-enhancing and, hence, consistentwith the proposition of ‘the more broadband penetration, the more growth’ (Ishida 2015).However, these studies solely investigate the relationship between broadband penetrationand economic growth with less attention in evaluating the directions of causality.4

A main objective of this paper is to overcome this deficiency and examine, morebroadly, the causes and consequences of broadband penetration. In particular, this paperexamines the short-run and long-run causal relationships between broadband penetrationand economic growth for the Arab League countries, using a panel vector autoregression(VAR) model. However, a debate exists, within this framework, on whether financial devel-opment can influence the broadband penetration-growth nexus. In general, financialdevelopment impacts the economy in the following ways (Samargandi, Fidrmuc, andGhosh 2015; Ngare, Nyamongo, and Misati 2014; Jedidia, Boujelbène, and Helali 2014;Hsueh, Hu, and Tu 2013; Pradhan et al. 2014a): (1) it helps with savings mobilization,thereby increasing the savings rate, thus facilitating higher capital formation and economicgrowth; (2) it reduces investment risks due to the ease with which equities are traded,implying that financial development plays a central role in economic performance. Variousstudies have supported this association (see, for example, Levine 1997; King and Levine1993). However, the causality between financial development and broadband penetrationhas spawned considerable interest among economists and policymakers due to theinherent policy implications (see, for instance, Lechman and Marszk 2015; Pradhan et al.2014b; Sassi and Goaied 2013; Hassan, Sanchez, and Yu 2011).

Our working hypothesis is that both financial development and economic growthhave contributed significantly to broadband penetration. Our alternative hypothesis isthat the expansion of broadband penetration is simply a cause of both financial devel-opment and economic growth. We also examine the possible direct causal link betweenfinancial development and economic growth as a corollary.

The rest of this paper is organized as follows: the literature review is offered in Section2; the data and empirical model are presented in Section 3; the econometric methodol-ogy is explained in Section 4; the results are presented and discussed in Section 5; andSection 6 concludes the paper.

2. The literature review

We study the links between both broadband penetration and financial development toeconomic growth. The possible role of both broadband penetration and financial

2 R. P. PRADHAN ET AL.

development in contemporary business, social, and political spheres is well recognized(see, for example, Levine 1997). In most of the countries studied, these two services arerapidly used to advance economic growth. However, the question is the direction withregards to financial growth. That is, does one factor cause another (depending on thedirection of causality this is either supply-leading or demand-following), do they causeeach other (the feedback hypothesis), or is there no causal relationship (the neutralityhypothesis)?

The first relationship that we investigate is the Granger causality between broadbandpenetration and economic growth. While it is recognized that there is Granger causalitybetween the two variables, the evidence on the direction is rather mixed (Ruz, Varas, andVillena 2013). The supply-leading hypothesis states that broadband penetration deter-mines economic growth. The rationale for this hypothesis is that multiple channels, suchas wider dissemination of market information, more timely market information, lowercoordination costs in markets, and improved public services such as education, healthcare, and social linkages (Mayer et al. 2015; Bauer, Madden, and Morey 2014; Czernichet al. 2011; Majumdar 2010; Holt and Jamison 2009) determine economic growth. Thedemand-following hypothesis states that economic growth determines broadbandpenetration as demand for wider access for broadband services, which is a function ofeconomic growth (Hauge and Prieger 2010).

Researchers such as Pradhan et al. (2014a), Ng, Lye, and Lim (2013), Czernich et al.(2011), Majumdar (2010), and Bojnec and Fertő (2012) assert the validity of the hypoth-esis that broadband penetration leads to economic growth (supply-leading hypothesis).Bauer, Madden, and Morey (2014), Arvin and Pradhan (2014), and Pradhan et al. (2014b)present support for the validity of causality in the opposite direction (demand-followinghypothesis). Arvin and Pradhan (2014) and Pradhan et al. (2014a, 2014b) support thepresence of a mutual causal relationship between broadband penetration and economicgrowth (feedback hypothesis). On the other hand, Pradhan et al. (2014a) maintain thatthere is no causal relationship between the two variables (neutrality hypothesis). Table 1presents a summary of these studies.

The second relationship that we investigate is the Granger causality between financialdevelopment and economic growth. The empirical evidence is mixed with some studiessupporting the demand-following hypothesis, some supporting the supply-leadinghypothesis, and others supporting the feedback hypothesis. In the case of the supply-leading hypothesis, financial development determines economic growth. The demand-following hypothesis holds that it is economic growth that determines financial devel-opment in the economy. Additionally, there is support for the feedback hypothesis(bidirectional causality) between financial development and economic growth.

In regards to the causal relationship between financial development and economicgrowth, researchers such as Owusu and Odhiambo (2014), Menyah, Nazlioglu, andWolde-Rufael (2014), Uddin, Sjö, and Shahbaz (2013), Chaiechi (2012), Kar, Nazlıoğlu,and Ağır (2011), and Yang and Yi (2008) all find evidence in support of the hypothesisthat financial development leads to economic growth (supply-leading hypothesis). Incontrast, Menyah, Nazlioglu, and Wolde-Rufael (2014), and Kar, Nazlıoğlu, and Ağır(2011) find evidence in favour of the hypothesis that economic growth leads to financialdevelopment (demand-following hypothesis). Other studies, such as those by Pradhanet al. (2014c), Marques, Fuinhas, and Marques (2013), and Wolde-Rufael (2009) support

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 3

the hypothesis of bidirectional causality between the two variables (feedback hypoth-esis). Mukhopadhyay, Pradhan, and Feridun (2011) maintain that there is no causalrelationship between the two variables (neutrality hypothesis). Finally, some studiessupply mixed evidence. Table 2 presents a synopsis of research on the causal nexusbetween financial development and economic growth.

Finally, we investigate the relationship between broadband penetration and financialdevelopment.5 It has been argued that broadband constitutes an important factor incontributing to the strengthening of financial systems and financial development (Yartey2008). This relationship can also be considered in terms of the supply-leading anddemand-following hypotheses. It has been argued that financial markets can beregarded as ‘information markets’ and therefore broadband access reshapes their func-tioning by enabling information and data dissemination. This, in turn, decreases theincidence of market failures, such as time delays or information asymmetries. In thiscase, broadband penetration enhances the flow of information which, in turn, is aprerequisite for decentralized financial markets to work efficiently (feedback hypothesis).On the other hand, broadband penetration provides opportunities for dynamic financialdevelopment, which is enabled by the eradication of information asymmetries and thefree flow of data (supply-leading effect). Finally, amplified price and volume fluctuations,greater exposure to the risk of financial volatility, and a widening of differences in accessto financial services constitute threats that should not be overlooked in the demand-following hypothesis (Lechman and Marszk 2015).

This paper contributes to the literature in several ways. One contribution is that weexamine how both financial development and economic growth are impacted bybroadband penetration. The relationship between broadband penetration and financialdevelopment has largely been ignored. Additionally, we seek to determine the Grangercausality between these three variables rather than focusing on the relationship

Table 1. Summary of studies on the Granger causal connection between broadband penetration andeconomic growth.Study Approach Sample Data span

Studies supporting supply-leading hypothesisPradhan et al. (2014a) 1 G-20 countries 2001–2012Ng, Lye, and Lim (2013) 2 ASEAN countries 1998–2011Czernich et al. (2011) 2 OECD countries 1996–2007Majumdar (2010) 2 USA 1988–2001Bojnec and Fertő (2012) 2 34 OECD 1998–2009

Studies supporting demand-following hypothesisBauer, Madden, and Morey (2014) 1 30 OECD countries 2001–2009Arvin and Pradhan (2014) 1 Developing countries within G-20 1998–2011Pradhan et al. (2014b) 1 G-20 countries 2001–2012

Studies supporting feedback hypothesisArvin and Pradhan (2014) 1 Developed countries within G-20 1998–2011Pradhan et al. (2014a, 2014bb) 1 G-20 countries 2001–2012

Study supporting neutrality hypothesisPradhan et al. (2014a) 1 G-20 countries 2001–2012

Note 1: Supply-leading hypothesis: if unidirectional causality is present from broadband penetration to economicgrowth; Demand-following hypothesis: if unidirectional causality is present from economic growth to broadbandpenetration; Feedback hypothesis: if bidirectional causality is present between broadband penetration and economicgrowth; and Neutrality hypothesis: if no causality is present between broadband penetration and economic growth.

Note 2: AEAN is the Association of Southeast Asian Nations; OECD is the organisation of Economic Cooperation andDevelopment.

Note 3: 1: Granger Causality approach; and 2: GMM approach.

4 R. P. PRADHAN ET AL.

between broadband penetration and economic growth or the relationship betweenfinancial development and economic growth. Finally, we examine these relationshipsfor the Arab League nations, which have been ignored in the studies on the relation-ships between financial development, economic growth, and/or broadband penetration.

3. Data and empirical model

3.1. Data structure

Annual data, covering the years 2001–2013, for 22 Arab League (AL6) countries (or theLeague of Arab states) were obtained from the World Development Indicators of theWorld Bank.7 The time frame was dictated by data availability, since broadband was notadopted by most countries until the late 1990s. In the case of Arab countries, publisheddata exists only from 2001.

The multivariate framework encompasses per capita economic growth in percentage(variable: PGDP), broadband users per one thousand population,8 expressed as a per-centage (variable: BBND), broad money supply in percentage (variable: BRMS), claims onthe private sector in percentage (variable: CLPS), domestic credit to the private sector inpercentage (variable: DCPS), domestic credit provided by the banking sector in percen-tage (variable: DCBS), market capitalization in percentage (variable: MACA), turnoverratio in percentage (variable: TURN), and traded stocks in percentage (variable: TRAD). Adetailed description of these variables is available in Table 3.

Table 2. Summary of studies on the Granger causal connection between financial development andeconomic growth.Study Approach Sample Data span

Studies supporting supply-leading hypothesisOwusu and Odhiambo (2014) 1 Nigeria 1969–2008Menyah, Nazlioglu, and Wolde-Rufael (2014) 1 21 African countries 1965–2008Uddin, Sjö, and Shahbaz (2013) 1 Kenya 1971–2013Hsueh, Hu, and Tu (2013) 1 Ten Asian countries 1980–2007Chaiechi (2012) 1 South Korea, Hong Kong, UK 1990–2006Kar, Nazlıoğlu, and Ağır (2011) 1 15 MENA countries 1980–2007Yang and Yi (2008) 1 Korea 1971–2002

Studies supporting demand-following hypothesisMenyah, Nazlioglu, and Wolde-Rufael (2014) 1 21 African countries 1965–2008Kar, Nazlıoğlu, and Ağır (2011) 1 15 MENA countries 1980–2007

Studies supporting feedback hypothesisPradhan et al. (2014c) 1 ASEAN countries 1961–2012Marques, Fuinhas, and Marques (2013) 1 Portugal 1993–2011Wolde-Rufael (2009) 1 Kenya 1966–2005

Study supporting neutrality hypothesisMukhopadhyay, Pradhan, and Feridun (2011) 1 7 Asian countries 1979–2009

Note 1: Supply-leading hypothesis: if unidirectional causality is present from financial development to economicgrowth; Demand-following hypothesis: if unidirectional causality is present from economic growth to financialdevelopment; Feedback hypothesis: if bidirectional causality is present between financial development and economicgrowth; and Neutrality hypothesis: if no causality is present between financial development and economic growth.

Note 2: AEAN is Association of Southeast Asian Nations; MENA is Middle East and North Africa.Note 3: 1 denotes Granger Causality. Studies vary by the number of covariates that are used in the investigationprocess; results vary by counties/regions that are studied.

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 5

These ten variables are all converted into their natural logarithms. Table 4 presentsthe descriptive statistics for all ten log variables. The correlation matrix in Table 4suggests that the financial development indicators (BRMS, CLPS, DCPS, DCBS, MACA,TURN, and TRAD) are highly correlated to one another and are statistically significant atthe 1–5% level. This means that we cannot use these financial indicators simultaneouslywhile addressing the broadband-finance-growth nexus in the Arab League countries.

3.2. Empirical model

A panel data analysis is most appropriate, given that the span of the data is over a 13-yearperiod. We propose the following empirical model to describe the relationship betweenbroadband penetration, financial development (hereafter, FINA), and economic growth.

BBNDit ¼ μit þ β1iPGDPit þ β2iFINAit þ εit; (1)

where:i = 1, 2, . . ., 22 represents each country in the panel;t = 2001, 2002, . . ., 2013 refers to the time period;β1 and β2 represent the parameters, relating the long-run elasticity estimates of BBND

with respect to PGDP and FINA, respectively9; and εit refers to the independently andnormally distributed random variables for all i and t with zero means and finite hetero-geneous variances (σi

2).In the other variations of Equation (1), the other two variables (PGDP and FINA) serve as the

dependent variable to allow for the possibility that causation may flow in the other direction.The variable FINA represents financial development and is captured by BRMS, CLPS,

DCPS, DCBS, MACA, TURN, TRAD, or FIND – where FIND is the composite index offinancial development,10 which is a weighted average of the other seven financialdevelopment indicators: BRMS, CLPS, DCPS, DCBS, MACA, TURN, and TRAD. The weights

Table 3. List of variables.No. Variable/Description Notation

1 Broad money supply BRMS[Expressed as a % of gross domestic product]

2 Claims on the private sector CLPS[Expressed as a % of gross domestic product]

3 Domestic credit to the private sector DCPS[Expressed as a % of gross domestic product]

4 Domestic credit provided by the banking sector DCBS[Expressed as a % of gross domestic product]

5 Market capitalization MACA[% change in the market capitalization of the listed companies]

6 Turnover ratio TURN[% change in the total value of traded stocks]

7 Traded stock TRAD[% change in the total value of traded stocks]

8 Composite index of financial development FIND[Index is derived from combining all financial development indicators]

9 Broadband penetration BBND[Fixed broadband per 1000 population – expressed as a %]

10 Economic growth PGDP[% change in per capita gross domestic product]

Note 1: All monetary measures are in constant US dollars.Note 2: Variables above are defined in the World Development Indicators and published by the World Bank.

6 R. P. PRADHAN ET AL.

are assigned using principal component analysis. All of our eight financial developmentmeasures are used one at a time, comprising models M1-M8.

Figure 1 summarizes the proposed hypotheses, which describes the direction ofpossible causality among these variables.

4. Econometric approach

The testing procedure consists of three steps. The first step is to examine the stationarityproperties of the individual series in the panel data sets using a battery of panel unitroot tests. The second step develops the long-run relationship using appropriate panellong-run estimates from fully modified ordinary least squares (FMOLS) and dynamic leastsquares (DOLS) procedures. Finally, the third step consists of estimating a panel vectorerror-correction model (VECM) in order to study Granger causal relationships.

4.1. Panel unit root test

Data generating for many economic variables are characterized by stochastic trendsthat might result in spurious inference if the time series properties are not investi-gated. A time series is said to be stationary if the mean and autocovariances of the

Table 4. Summary statistics for the variables.Variables PGDP BBND BRMS CLPS DCPS DCBS MACA TURN TRAD FIND

Part 1: Summary statisticsMean 1.18 −0.05 1.89 1.08 1.72 1.72 1.71 1.37 1.05 0.13Median 1.21 0.19 1.86 1.06 1.75 1.74 1.76 1.42 1.04 0.12Maximum 1.44 1.15 2.39 1.68 1.96 1.96 2.48 2.46 2.57 0.15Minimum −0.11 −3.57 1.48 −0.15 1.44 1.44 0.86 0.17 −0.22 0.11Std. deviation 0.18 0.87 0.23 0.30 0.14 0.14 0.36 0.49 0.70 0.01Skewness −4.54 −1.52 0.56 −0.51 −0.21 −0.15 −0.41 −0.09 0.07 0.38Kurtosis 30.0 5.60 2.71 4.91 1.89 1.98 2.65 2.61 2.06 2.56

Part 2: Correlation matrixPGDP 1.00BBND −0.15*** 1.00

[0.10]BRMS 0.14 0.21** 1.00

[0.12] [0.04]CLPS −0.17*** 0.21** −0.50* 1.00

[0.08] [0.03] [0.00]DCPS 0.01 0.15* 0.73* −0.16*** 1.00

[0.94] [0.14] [0.00] [0.10]DCBS −0.01 0.17*** 0.74* −0.17* 0.99* 1.00

[0.98] [0.09] [0.00] [0.08] [0.00]MACA 0.10 0.25* 0.30* 0.30* 0.20* 0.24* 1.00

[0.35] [0.01] [0.00] [0.00] [0.10] [0.05]TURN 0.07 0.05 −0.24* 0.26* 0.27* −0.28* 0.35* 1.00

[0.49] [0.64] [0.02] [0.01] [0.01] [0.01] [0.00]TRAD −0.01 0.18*** 0.17*** 0.35* 0.19* −0.24* 0.74* 0.88* 1.00

[0.93] [0.07] [0.08] [0.00] [0.05] [0.05] [0.00] [0.00]FIND 0.06 0.25* 0.63* −0.10*** 0.68 0.69* 0.57* 0.36* 0.54* 1.00

[0.54] [0.01] [0.00] [0.37] [0.00] [0.00] [0.00] [0.00] [0.00]

Note 1: Variables are defined in Table 3.Note 2: Values reported in the square brackets indicate the probability level of significance.Note 3: *, **, and *** indicate statistical significance at the 1%, 5%, and 10% level, respectively.

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 7

series do not depend on time. Any series that is not stationary has a unit root. Theformal method to test the stationarity is the unit root test (see, for instance, Lee andBrahmasrene 2014). The panel unit root test is conducted to determine the statio-narity of the series, i.e., to ascertain the degree (or order) of integration for broad-band penetration, financial development, and economic growth. Three unit root testsare conducted in this paper. These tests are Breitung (2001), Levine, Lin, and Chu

H1A, B H2A, B

H3A, B H4A, B

H5A, B H6A, B

H7A, B H8A, B

H9A, B

H10A, B H11A, B

H12A, B H13A, B

H14A, B H15A, B

H16A, B H17A, B

PGDPBBND

DCPS

CLPS

DCBS

TRAD

MACA

TURN

BRMS

FIND

Figure 1. Theoretical causality between broadband penetration, financial development, andeconomic growth.Note 1: PGDP: Per capita economic growth rate; BBND: Broadband penetration; BRMS; Broad money supply; CLPS:Claims on private sector; DCPS: Domestic credit to private sector; DCBS: Domestic credit provided to bankingsector; MACA: Market capitalization; TURN: Turnover ratio; TRAD: Traded stocks; and FIND: Composite index offinancial development.Note 2: H1A, B: Broad money supply Granger-causes economic growth and vice versaH2A, B:Broad money supply Granger-causes broadband penetration and vice versaH3A, B: Claims on the private sectorGranger-causes economic growth and vice versaH4A, B: Claims on the private sector Granger-causes broadbandpenetration and vice versaH5A, B: Domestic credit to the private sector Granger-causes economic growth and viceversaH6A, B: Domestic credit to the private sector Granger-causes broadband penetration and vice versaH7A, B:Domestic credit provided by the banking sector Granger-causes economic growth and vice versaH8A, B: Domesticcredit provided by the banking sector Granger-causes broadband penetration and vice versaH9A, B: Broadbandpenetration Granger-causes economic growth and vice versaH10A, B: Market capitalization Granger-causeseconomic growth and vice versaH11A, B: Market capitalization Granger-causes broadband penetration and viceversaH12A, B: Turnover ratio Granger-causes economic growth and vice versaH13A, B: Turnover ratio Granger-causes broadband penetration and vice versaH14A, B: Traded stocks Granger-causes economic growth and viceversaH15A, B: Traded stocks Granger-causes broadband penetration and vice versaH16A, B: Financial developmentindex Granger-causes economic growth and vice versaH17A, B: Financial development index Granger-causesbroadband penetration and vice versa.

8 R. P. PRADHAN ET AL.

(2002; Levine-Lin-Chu: LLC), and Im, Pesaran, and Shin (2003; Im-Pesaran-Shin: IPS).We do not present the details of these tests here as they are covered in most timeseries and/or econometric textbooks

4.2. Panel cointegration test

If each of the variables contains a panel unit root, the next step is to test whetherbroadband penetration, financial development, and economic growth are cointegrated.The available techniques for panel cointegration tests are, in essence, an application ofthe Engle and Granger (1987) cointegration analysis. As in the analysis of single timeseries, these approaches test the residuals from the estimation for stationarity. Kao(1999) and Pedroni (1999, 2000) provide different statistics for this purpose, both ofwhich assume homogenous slope coefficients across the countries. These tests areadequately described in advanced econometric textbooks.

4.3. Panel FMOLS and DOLS estimators

If existence of cointegration of our panel is confirmed, the next step is to estimate theassociated long-run cointegration parameters. Many types of problems that exist in the timeseries analysis may also arise in the panel data analysis and tend to bemore noticeable, evenin the presence of heterogeneity (Kao and Chiang 2000). For this reason, several estimatorshave been proposed. The study uses two panel cointegration estimators: the betweengroup FMOLS andDOLS. Both these estimators provide consistent estimates of the standarderrors that can be used for inference. According to Kao and Chiang (2000), FMOLS and DOLSestimators have normal limiting properties. In the context of our empirical model, we beginwith the estimation of the following regression equation

Yit ¼ αi þ βiXit þXKik¼�Ki

γikΔXit � k þ εit; (2)

where Yit represents log BBND and Xit represents log PGDP and log FINA. Both Yit and Xitare cointegrated with slopes βi, which may or may not be homogenous across i.

Let �it ¼ εitΔXit be a stationary vector consisting of the estimated residuals from thecointegrating regression.

Also, let Ωit ¼ lim T ! 1 T�1 PTt¼1

�it

� � PTt¼1

�it

� �0" #be the long-run covariance for this

vector process which can be decomposed into Ωit ¼ Ω0it þ Γi þ Γ0i , where Ω0

it is thecontemporaneous covariance and Γii is a weighted sum of autocovariances.

Thus, the panel FMOLS estimator will be given by

β�FMOLS ¼ N�1XNi¼1

XTt¼1

Xit � �Xið Þ2 !�1 XT

t¼1

Xit � �Xið ÞY�it � T γi

!" #; (3)

where

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 9

Y�it ¼ ðYit � �YÞ � Ω21i :Ω22i

. �ΔXit

�and

γi ¼ Γ21i þ Γ021i � Ω21i.Ω22i

� �T22i.Ω

022i

� �:

On the contrary, in the DOLS approach, as developed by Kao and Chiang (2000) andMark and Sul (2003), the concerning estimator includes advanced and delayed values(ΔXiT ) in the cointegrated relationship, in order to eliminate the correlation between theregressor and the error terms. Hence, the panel DOLS estimator can be defined as

β�DOLS ¼ N�1XNi¼1

XTt¼1

ZitZ0it

!�1 XTt¼1

ZitYit

!" #; (4)

where Zit ¼ Xit � �X;ΔXit�ki ; ::::ΔXtþKi½ � is the vector of regressors, and Yit ¼ ½Yit � �Y�.

4.4. The VECM estimation procedure

On the basis of the above unit root and cointegration test results, the next step is toestimate the short-run fluctuations and long-run equilibrium between broadband pene-tration, financial development, and economic growth. Following the Holtz-Eakin, Newey,and Rosen (1988) estimation procedure, we can establish the causal nexus between thevariables by employing a VECM of the form

Δ ln BBND it

Δ ln PGDP it

Δ ln FINA it

264

375 ¼

λ1j

λ2j

λ3j

264

375

þXpk¼1

d11ik Lð Þd12ik Lð Þd13ik Lð Þd21ik Lð Þd22ik Lð Þd23ik Lð Þd31ik Lð Þd32ik Lð Þd33ik Lð Þ

264

375

Δ ln BBND it�k

Δ ln PGDP it�k

Δ ln FINA it�k

264

375

þδ1iECT it�1

δ2iECT it�1

δ3iECT it�1

264

375þ

�1it

�2it

�3it

264

375;

(5)

whereΔ is first difference filter (I − L);i = 1, . . ., N; t = 1, . . ., T; and ξj (j = 1, . . ., 3) are independently and normally distributed

random variables for all i and t, with zero means and finite heterogeneous var-iances (σi

2).FINA is financial development and is captured by eight measures, including a com-

posite index, which we are using one at a time. The ECT−1 s are lagged error-correctionterms, derived from the earlier cointegrating equation. The error-correction terms (ECTs)represent the long-run dynamics, while differenced variables represent the short-runadjustment dynamics between the variables. The short-run causal relationship is mea-sured through F-statistics and the significance of the lagged changes in the independent

10 R. P. PRADHAN ET AL.

variables, whereas the long-run causal relationship is measured through the significanceof the t-test of the lagged ECTs.

5. Empirical results and discussion

5.1. Stationarity of the variables

Table 5 reports the results of the panel unit root tests for each variable. It is observedthat the test statistics for the level of each time series (PGDP, BBND, FIND, BRMS, CLPS,DCPS, DCBS, MACA, TURN, and TRAD) have a probability value greater than one,implying that each time series is panel non-stationary. However, when applying thepanel unit root tests to the first difference of the series, the null hypothesis of each seriescan be rejected at the 1% significance level. This means all series with the first differenceare stationary, which confirms that they are integrated of order one [i.e., I (1)]. Hence, we

Table 5. Results of panel unit roots test (LLC statistic).

Variable Level M1 M2 M3 M4 M5 M6 M7 M8Unit rootinferences

URTPGDP LD −0.15 −0.01 −0.03 −0.01 −0.29 −0.25 −0.25 −0.05 I (1)

FD −11.0* −10.8* −10.9* −10.8* −9.44* −9.11* −9.11* −7.95*BBND LD −0.59 −0.589 −0. 589 −0.58 −0.45 −0.43 −0.43 −0.49 I (1)

FD −18.9* −18.9* −18.8* −19.1* −19.7* −18.2* −18.2* −17.5*BRMS LD 2.11 I (1)

FD −7.24*CLPS LD −0.08 I (1)

FD −12.1*DCPS LD 1.62 I (1)

FD –7.79*DCBS LD 1.79 I (1)

FD −10.8*MACA LD −0.29 I (1)

FD −6.02*TURN LD −0.32 I (1)

FD −5.43*TRAD LD −0.16 I (1)

FD −7.52*FIND LD 1.75 I (1)

FD −4.37*Cointegration

KCT −3.85* −2.73** −2.17** −3.30* −2.22** −2.13** −2.34* −2.38*Cointegrationinferences

Y Y Y Y Y Y Y Y

Sample size 222 224 215 224 137 133 133 133

Note 1: Variables are defined in Table 3.Note 2: * and ** indicate statistical significance at the 1% and 5% level, respectively; I (1) indicates the integration oforder one; and ‘Y’ indicates the presence of cointegration between these variables from M1 to M8. LD refers to leveldata; FD is first difference data; and URT is unit root test.

Note 3: M1: indicates Model 1 (causal nexus between PGDP, BBND, and BRMS); M2: indicates Model 2 (causal nexusbetween PGDP, BBND, and CLPS); M3: indicates Model 3 (causal nexus between PGDP, BBND, and DCPS); M4:indicates Model 4 (causal nexus between PGDP, BBND, and DCBS); M5: indicates Model 5 (causal nexus betweenPGDP, BBND, and MACA); M6: indicates Model 6 (causal nexus between PGDP, BBND, and TURN); M7: indicates Model7 (causal nexus between PGDP, BBND, and TRAD); M8: indicates Model 8 (causal nexus between PGDP, BBND, andFIND).

Note 4: The study conducted three panel unit root tests: Breitung (2001), Levine, Lin, and Chu (2002), and Im, Pesaran,and Shin (2003). However, only the Levine, Lin, and Chu (2002) test is reported here at both intercept and trend.

Note 5: The study conducted both the Kao (1999) and Pedroni (1999, 2001) cointegration tests. However, due to spaceconstraints, the results reported here are for the Kao (1999) cointegration test only.

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 11

can implement a test for panel cointegration between broadband penetration, financialdevelopment, and economic growth.

5.2. Cointegration between the variables

Table 5 shows the results of the panel cointegration tests. The test statistics reject thenull hypothesis of no cointegration at the 1% significance level. Moreover, there exists atleast one cointegrating vector at the 5% significance level. These cointegrated variablesimply the presence of a long-run relationship among the variables.

5.3. FMOLS and DOLS representation

In this sub-section, we are mostly interested in determining the nature of the relation-ship (positive or negative) between the three variables BBND, PGDP, and FINA (the latterbeing measured by BRMS/CLPS/DCPS/DCBS/MACA/TURN/TRAD/FIND). It can be seenthat the estimated coefficients of economic growth and financial development arepositive for broadband penetration. The finding of the presence of a highly significantpositive impact of economic growth and financial development on broadband penetra-tion for the Arab League countries implies that both economic growth and financialdevelopment play a critical role in boosting broadband adoption in the region. Theresults of this subsection are presented in Table 6.

5.4. Short-run and long-run dynamics

Based on the estimation of Equation (5), the estimated results are presented in Table 7.Table 7 reports the panel Granger causality test results for both the long run, as

Table 6. Panel FMOLS and DOLS results (BBND as dependent variable).Panels

FMOLS DOLS

Model Independent variables Coefficients t-Statistic Coefficients t-Statistic

1 PGDP 0.85 1.93** 4.90 4.99*BRMS 7.86 7.50* 4.81 4.69*

2 PGDP 0.25 2.55** 1.02 2.10**CLPS 1.61 4.37* 1.62 2.32**

3 PGDP 1.85 1.89** 1.41 6.05*DCPS 4.35 6.83* 9.96 11.9*

4 PGDP 4.52 2.04** 3.30 5.92*DCBS 5.10 7.34** 10.1 12.3*

5 PGDP 0.93 4.28* 0.83 6.78*MACA 1.31 5.64* 1.10 9.36*

6 PGDP 0.51 2.79** 0.63 12.5*TURN 0.81 3.53* 1.03 3.98*

7 PGDP 0.67 2.85* 1.21 2.82**TRAD 0.91 6.19* 1.10 4.64*

8 PGDP 2.02 3.98* 3.56 3.28*FIND 18.7 3.86* 3.35 3.15*

Note 1: Variables are defined in Table 3.Note 2: * and ** indicate statistical significance at 1% and 5%, respectively.

12 R. P. PRADHAN ET AL.

Table 7. Granger causality test results.Dep var Independent variables ECT−1 coefficient

Model 1: VECM with PGDP, BBND, BRMSΔPGDP ΔBBND ΔBRMS ECT−1

ΔPGDP – 0.53 25.5* −0.03*[–] [0.80] [0.00] (−5.27)

ΔBBND 4.12** – 3.98*** −0.21*[0.05] [–] [0.10] (−6.57)

ΔBRMS 11.3* 4.31** – −0.01[0.00] [0.05] [–] (−0.94)

Model 2: VECM with PGDP, BBND, CLPSΔPGDP ΔBBND ΔCLPS ECT−1

ΔPGDP – 1.32 12.1* −0.86*[–] [0.68] [0.00] (−8.66)

ΔBBND 5.69* – 23.8* −1.04**[0.01] [–] [0.00] (−3.20)

ΔCLPS 6.41* 4.98* – −1.13[0.01] [0.01] [–] (−2.32)

Model 3: VECM with PGDP, BBND, DCPSΔPGDP ΔBBND ΔDCPS ECT−1

ΔPGDP – 0.43 3.93*** −0.49**[–] [0.92] [0.10] (−4.79)

ΔBBND 4.48* – 3.72*** −1.54**[0.05] [–] [0.10] (−4.03)

ΔDCPS 3.84*** 0.25 – −0.64[0.10] [0.85] [–] (−0.88)

Model 4: VECM with PGDP, BBND, DCBSΔPGDP ΔBBND ΔDCBS ECT−1

ΔPGDP – 0.21 3.87*** −0.98*[–] [0.97] [0.10] (−7.58)

ΔBBND 5.39* – 3.90*** −1.07***[0.01] [–] [0.10] (−3.80)

ΔDCBS 5.71* 0.44 – −0.12[0.01] [0.72] [–] (−0.74)

Model 5: VECM with PGDP, BBND, MACAΔPGDP ΔBBND ΔMACA ECT−1

ΔPGDP – 2.17 8.89* −0.18***[–] [0.53] [0.00] (−3.82)

ΔBBND 8.71* – 3.62*** −0.69***[0.00] [–] [0.10] (−3.39)

ΔMACA 3.31*** 3.95*** – −0.68[0.10] [0.10] [–] (−1.71)

Model 6: VECM with PGDP, BBND, TURNΔPGDP ΔBBND ΔTURN ECT−1

ΔPGDP – 0.87 5.58* −0.31***[–] [0.73] [0.01] (−3.12)

ΔBBND 14.2* – 4.36** −0.43***[0.00] [–] [0.05] (−3.62)

ΔTURN 6.99* 3.98*** – −0.59[0.01] [0.10] [–] (−0.74)

Model 7: VECM with PGDP, BBND, TRADΔPGDP ΔBBND ΔTRAD ECT−1

ΔPGDP – 0.98 3.67*** −0.16***[–] [0.93] [0.01] (−3.12)

ΔBBND 12.1* – 4.16** −0.80***[0.00] [–] [0.05] (−3.34)

ΔTRAD 10.5* 3.86*** – −0.89[0.00] [0.10] [–] (−1.43)

Model 8: VECM with PGDP, BBND, FINDΔPGDP ΔBBND ΔFIND ECT−1

ΔPGDP – 0.97 4.19** −0.57***[–] [0.86] [0.05] (−3.16)

ΔBBND 7.21* – 11.8* −0.99***

(Continued )

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 13

represented by the significance of the lagged error-correction term, and the short run, asrepresented by the significance of the F-statistic.

5.4.1. Long-run Granger causalityFrom Table 7, in Models 1–8, when ΔBBND serves as the dependent variable, thelagged error-correction terms (ECT−1′ s) are statistically significant at the 10% level.This implies that BBND tends to converge to its long-run equilibrium path inresponse to changes in its regressors (PGDP and FINA). The significance of theECT−1 coefficient in the ΔBBND equation in each of the eight models confirms theexistence of long-run equilibrium between broadband penetration and its determi-nants, which are economic growth and the different measures of financial devel-opment (BRMS/CLPS/DCPS/DCBS/MACA/TURN/TRAD/FIND). In other words, we cangenerally conclude that economic growth and financial development Granger-causebroadband penetration in the long run.

The ECT−1 coefficient is also statistically significant in the ΔPGDP equation in eachof the eight models. This shows the existence of long-run equilibrium between percapita economic growth and its determinants, which are broadband penetration andthe different measures of financial development (BRMS, CLPS, DCPS, DCBS, MACA,TURN, TRAD, and FIND). In other words, we can generally conclude that broadbandpenetration and financial development Granger-cause per capita economic growth inthe long run.

On the other hand, the lagged error-correction terms in the ΔBRMS, ΔCLPS, ΔDCPS,ΔDCBS, ΔMACA, ΔTURN, ΔTRAD, or ΔFIND equations under Table 7 are not statisticallysignificant in any of the eight models. Hence, there is no long-run causality frombroadband penetration and economic growth to financial development.

5.4.2. Short-run Granger causalityIn contrast to the long-run Granger causality results, our study reveals a wide spectrumof short-run causality results between the three variables (BBND, PGDP, and FINA).Although the results are clear, we first highlight the common findings across our eightmodels. The common insights are the existence of bidirectional causality betweenfinancial development and economic growth [FINA ↔ PGDP] and a unidirectionalcausality from economic growth to broadband penetration [BBND ← PGDP]. In othersituations, the findings are different from model to model.

Table 7. (Continued).

Dep var Independent variables ECT−1 coefficient

[0.01] [–] [0.00] (−3.59)ΔFIND 6.60** 3.61*** – −0.01

[0.01] [0.10] [–] (−0.02)

Note 1: Variables are defined in Table 3.Note 2: *, **, and *** indicate statistical significance at the 1%, 5%, and 10% level, respectively.Note 3: Models are defined in the text and in Note 3 of Table 5.Note 4: VECM: vector error-correction model; ECT−1: lagged error-correction term.Note 5: Values in square brackets represent probabilities for F-statistics.Note 6: Values in parentheses represent t-statistics.Note 7: Basis for the determination of long-run causality lies in the significance of the lagged ECT coefficient.

14 R. P. PRADHAN ET AL.

In Models 1, 2, 5, 6, 7, and 8 we observe bidirectional Granger causality from broad-band penetration to financial development. The financial development measures arebroad money supply [BBND ↔ BRMS], claims to the private sector [BBND ↔ CLPS],market capitalization [BBND ↔ MACA], turnover ratio [BBND ↔ TURN], traded stocks[BBND ↔ TRAD], and overall financial development [BBND ↔ FIND], respectively. Thesefindings support the feedback hypothesis of broadband penetration and the financialdevelopment nexus.

Alternatively, in Models 3 and 4, we observe unidirectional Granger causality fromfinancial development to broadband penetration. The financial development measuresare domestic credit to the private sector [BBND ← DCPS] and domestic credit providedby the banking sector [BBND ← DCBS], respectively. These findings support the supply-leading hypothesis between broadband penetration and the financial developmentnexus.

5.4.3. Discussion and perceptivityUnlike many of the earlier studies, this study makes a clear distinction between theshort-run and the long-run causal relationships between broadband penetration, finan-cial development, and economic growth. The short-run causal results describe theadjustment dynamics between the variables in the short run, whereas long-run causalresults depict the causal link between the variables in the long run.

We found uniform and robust results for the long-run equilibrium relationship amongthe variables, particularly when broadband penetration and per capita economic growthserve as the dependent variables. There are also two uniform results in the short-runanalysis: the existence of bidirectional causality between financial development andeconomic growth and between economic growth and broadband penetration. Inother situations, particularly between financial development and broadband penetra-tion, the findings are different across the eight models. In some cases, namely in Models1–2 and Models 5–8, we find the existence of bidirectional causality between financialdevelopment and broadband penetration, while we find the presence of unidirectionalGranger causality (i.e, the existence of supply-leading hypothesis) in other cases, namelyin Models 3–4.

6. Conclusion and policy implications

This paper investigates the relationships between broadband penetration, financialdevelopment, and economic growth. Using panel data of 22 Arab League countriesfrom 2001 to 2013, we find that broadband penetration, financial development, andeconomic growth are cointegrated. The panel Granger causality further confirms thatfinancial development Granger-causes both economic growth and broadband penetra-tion in the long-run. Furthermore, there is bidirectional Granger causality betweeneconomic growth and broadband penetration in the long run. These results are incontrast to those of Arvin and Pradhan (2014), who find unidirectional causality fromeconomic growth to broadband penetration for the developing countries within theG-20.11

Given the causal links uncovered in this study, future studies on the adoption ofbroadband that do not simultaneously consider financial development and economic

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 15

growth are likely to offer biased results. Similarly, subsequent studies on economicgrowth that do not simultaneously consider financial development and broadbandpenetration will likely offer unreliable results.

An important contribution of this paper is the consideration of the relationshipbetween broadband penetration and financial development. This relationship hasbeen largely ignored in earlier works. In the long run, financial development Granger-causes broadband penetration (the supply-leading hypothesis). In the short run, we findsupport for the feedback hypothesis between financial development and broadbandpenetration. Furthermore, in the long run, financial development and broadband pene-tration both Granger-cause economic growth.

Given these results, with regards to policy implications, decision makers in the ArabLeague countries wishing to encourage broadband adoption and economic growth inthe long run should pay close attention to developing their financial systems, andparticularly their financial markets. Increased participation in the financial markets, withregards to credit and stock ownership, should lead to improvements in economicgrowth. Imposing sufficient regulation and increasing the confidence of those whooperate in these markets should further develop financial systems of these nations andcould go a long way in enhancing the economic growth of Arab countries – as well ashelping them increase their broadband penetration in the long run. Additionally,increased investments in broadband adoption will improve economic development.

Finally, given the existence of bidirectional causality (feedback) between financialdevelopment and economic growth in the short run, promoting one will lead to furtherand ongoing elevation of the other through a self-perpetuating circular flow. As theArab League nations are furthering their economic development, policymakers shouldwork on ways to encourage participation of citizens and foreigners in their financialsystems and markets.

While this study provides further insight regarding the relationships between broad-band penetration, financial development, and economic growth, it should be noted thatthere are some limitations to this study. For instance, while we consider several variablesfor financial development, financial development measurements are limited to bank andstock market characteristics and do not include other markets such as bonds andinsurance. Future research might provide additional insight with respect to additionalmeasures of financial development. Additionally, regulatory issues are not addressed inthis study. As regulatory constraints and issues impact financial development, theinclusion of regulatory measures could add value to future research. Given the impor-tance of financial development on broadband penetration and economic growth, eval-uating measures of financial development beyond stock market and credit marketcharacteristics could give policymakers better insight regarding which areas of financialdevelopment they should pay particular attention to.

Notes

1. Financial development refers to a process that marks improvements in the quantity, quality,and efficiency of financial intermediary services (Chaiechi 2012). It covers a wide range offactors and includes both banking sector development and stock market development (see,for instance, Zaman et al. 2012).

16 R. P. PRADHAN ET AL.

2. Economic growth refers to the per capita growth of the gross domestic product ofindividual countries.

3. Broadband penetration is one of the non-monetary variables usually used in informationand communication technology (ICT) development studies, considering the casual relation-ship between ICT development and economic growth (see, for instance, Ishida 2015).

4. One exception is a study by Arvin and Pradhan (2014), which looks at the direction ofcausality between economic growth and broadband penetration. However, their studycovers the G-20 countries instead. Moreover, the focus of their study is on the link betweenbroadband penetration, economic growth, the degree of urbanization, and foreign directinvestment.

5. The relationship between broadband penetration and financial development is not avail-able in the literature and this is one of the major contributions of this study.

6. AL is a regional organization of 22 Arab countries in and around North Africa, the Horn ofAfrica, and Southwest Asia.

7. The 22 AL countries included in the panel are Algeria, Bahrain, Comoros, Djibouti, Egypt,Eritrea, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Palestine, Qatar,Saudi Arabia, Somalia, Sudan, Tunisia, the United Arab Emirates, and Yemen. Additionally,the regional organization has had three observer countries: Brazil (in 2003), Venezuela (in2006), and India (in 2007). However, these three observer countries are not included in ourempirical investigation.

8. The choice of measurement for broadband penetration is guided by the literature (see, forexample, Bojnec and Fertő 2012) and data availability. Holt and Jamison (2009) offer adiscussion of some of the challenges of precisely analysing the impact of broadband oneconomic development in the U.S. It is likely that some of these challenges also exist withour data and analysis.

9. This follows given that our variables are in logarithmic forms.10. A detailed description of the construction of this index is available in Pradhan et al. (2014a).11. The developing countries within the G-20 are a reasonable comparator group to the Arab

League countries. Arvin and Pradhan (2014) also present results with respect to developedcountries within the G-20 (see Table 1).

Acknowledgement

The authors acknowledge the useful comments of two exceptionally helpful reviewers on anearlier draft of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Rudra P. Pradhan is a SAP Fellow and an Associate Professor at Indian Institute of Technology,Kharagpur, India, where he has been associated with Vinod Gupta School of Management andRCG School of Infrastructure Design and Management. Pradhan is affiliated with various profes-sional journals like Telecommunications Policy, Cities, Empirica, Neural Computing and Applications,and Review of Economics and Finance. He has been a visiting scholar to University of Pretoria,Republic of South Africa and a visiting professor to Asian Institute of Technology, Thailand.

Mak B. Arvin is a Full Professor of Economics at Trent University, Peterborough, Ontario, Canada,where he has been a faculty member for the past 30 years. Arvin’s research has resulted in over150 publications in journals, edited volumes, and books. He has served on the editorial board of

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 17

over a dozen professional journals and is the Editor-in-Chief of the International Journal ofHappiness and Development. Arvin has been a visiting professor to Boston College and a con-sultant to the IFO Institute for Economic Research, Germany. His latest book is the Handbook onthe Economics of Foreign Aid published in 2015 (with Byron Lew).

Sahar Bahmani is an Associate Professor at Department of Economics, University of Wisconsin atParkside, Kenosha, Wisconsin 53144, USA. Her area of interest includes ICT infrastructure andtechnology management. She has published extensively in monetary economics and was named aWisconsin Teaching Fellow for the 2013–2014 academic year.

Sara E. Bennett is an Assistant Professor of Finance in the School of Business and Economics atLynchburg College in Lynchburg, VA, USA. Her areas of interest include macroeconomics, financialdevelopment, ICT infrastructure, corporate financial management, and financial derivatives. She isthe author of several papers and reviews in refereed journals in the area of finance.

References

Arvin, M.B., and R.P. Pradhan. 2014. Broadband penetration and economic growth nexus: Evidencefrom cross-country panel data. Applied Economics 46, no. 35: 4360–4369. doi:10.1080/00036846.2014.957444.

Bacache, M., M. Bourreau, and G. Gaudin. 2014. Dynamic entry and investment in new infrastruc-tures: Empirical evidence from the fixed broadband industry. Review of Industrial Organization44, no. 2: 179–209. doi:10.1007/s11151-013-9398-4.

Bauer, J.M., G. Madden, and A. Morey. 2014. Effects of economic conditions and policy interven-tions on OECD broadband adoption. Applied Economics 46, no. 12: 1361–1372. doi:10.1080/00036846.2013.872765.

Bojnec, Š., and I. Fertő. 2012. Broadband availability and economic growth. Industrial Management& Data Systems 112, no. 9: 1292–1306. doi:10.1108/02635571211278938.

Bouras, C., E. Giannaka, and T. Tsiatsos. 2009. Identifying best practices for supporting broadbandgrowth: Methodology and analysis. Journal of Network and Computer Applications 32, no. 4: 795–807. doi:10.1016/j.jnca.2009.02.003.

Breitung, J. 2001. The local power of some unit root tests for panel data. Advances in Econometrics15: 161–177.

Brewer, E., M. Demmer, B. Du, M. Ho, M. Kam, S. Nedevschi, J. Pal, R. Patra, S. Surana, and K. Fall.2005. The case for technology in developing regions. Computer Society 38, no. 6: 25–38.doi:10.1109/MC.2005.204.

Cambini, C., and Y. Jiang. 2009. Broadband investment and regulation: A literature review.Telecommunications Policy 33, no. 10–11: 559–574. doi:10.1016/j.telpol.2009.08.007.

Chaiechi, T. 2012. Financial development shocks and contemporaneous feedback effect on keymacroeconomic indicators: A post Keynesian time series analysis. Economic Modelling 29, no. 2:487–501. doi:10.1016/j.econmod.2011.12.008.

Crandall, R., W. Lehr, and R. Litan. 2007. The effects of broadband deployment on output andemployment: A cross-sectional analysis of US data. Issues in Economic Policy 6, no. 7: 1–34.

Czernich, N., O. Falck, T. Kretschmer, and L. Woessmann. 2011. Broadband infrastructure andeconomic growth. The Economic Journal 121, no. 552: 505–532. doi:10.1111/j.1468-0297.2011.02420.x.

Engle, R.F., and C.W.J. Granger. 1987. Cointegration and error correction: Representation, estima-tion and testing. Econometrica 55, no. 2: 251–276. doi:10.2307/1913236.

Hassan, M.K., B. Sanchez, and J. Yu. 2011. Financial development and economic growth: Newevidence from panel data. The Quarterly Review of Economics and Finance 51, no. 1: 88–104.doi:10.1016/j.qref.2010.09.001.

Hauge, J., and J. Prieger. 2010. Demand-side programs to stimulate adoption of broadband: Whatworks? Review of Network Economics 9: 1–38. doi:10.2202/1446-9022.1234.

18 R. P. PRADHAN ET AL.

Holt, L., and M. Jamison. 2009. Broadband and contributions to economic growth: Lessons fromthe US experience. Telecommunications Policy 33, no. 10–11: 575–581. doi:10.1016/j.telpol.2009.08.008.

Holtz-Eakin, D., W. Newey, and H.S. Rosen. 1988. Estimating vector auto regressions with paneldata. Econometrica 56, no. 6: 1371–1395. doi:10.2307/1913103.

Hsueh, S., Y. Hu, and C. Tu. 2013. Economic growth and financial development in Asian countries:A bootstrap panel granger causality analysis. Economic Modelling 32: 294–301. doi:10.1016/j.econmod.2013.02.027.

Im, K.S., M.H. Pesaran, and Y. Shin. 2003. Testing for unit roots in heterogeneous panels. Journal ofEconometrics 115, no. 1: 53–74. doi:10.1016/S0304-4076(03)00092-7.

Ishida, H. 2015. The effect of ICT development on economic growth and energy consumption inJapan. Telematics and Informatics 32, no. 1: 79–88. doi:10.1016/j.tele.2014.04.003.

Jedidia, K.B., T. Boujelbène, and K. Helali. 2014. Financial development and economic growth: Newevidence from Tunisia. Journal of Policy Modeling 36, no. 5: 883–898. doi:10.1016/j.jpolmod.2014.08.002.

Kao, C. 1999. Spurious regression and residual based tests for cointegration in panel data. Journalof Econometrics 90, no. 1: 1–44. doi:10.1016/S0304-4076(98)00023-2.

Kao, C., and M.H. Chiang. 2000. On the estimation and inference of a cointegrated regression inpanel data. In Advances in econometrics: Non-stationary panels, panel cointegration and dynamicpanels, ed. B.H. Baltagi, vol. 15, 179–222.

Kar, M., Ş. Nazlıoğlu, and H. Ağır. 2011. Financial development and economic growth nexus in theMENA countries: Bootstrap panel Granger causality analysis. Economic Modelling 28, no. 1–2:685–693. doi:10.1016/j.econmod.2010.05.015.

King, R., and R. Levine. 1993. Finance and growth: Schumpeter might be right. The QuarterlyJournal of Economics 108, no. 3: 717–737. doi:10.2307/2118406.

Kolko, J. 2012. Broadband and local growth. Journal of Urban Economics 71, no. 1: 100–113.Lechman, E., and A. Marszk. 2015. ICT technologies and financial innovations: The case of

exchange traded funds in Brazil, Japan, Mexico, South Korea and the United States.Technological Forecasting and Social Change 99, no. 3: 355–376. doi:10.1016/j.techfore.2015.01.006.

Lee, J.W., and T. Brahmasrene. 2014. ICT, CO2 emissions and economic growth: Evidence from apanel of ASEAN. Global Economic Review: Perspectives on East Asian Economies and Industries 43,no. 2: 93–109. doi:10.1080/1226508X.2014.917803.

Levine, A., C.-F. Lin, and C.-S. Chu. 2002. Unit root tests in panel data: Asymptotic and finite sampleproperties. Journal of Econometrics 108, no. 1: 1–24. doi:10.1016/S0304-4076(01)00098-7.

Levine, R. 1997. Financial development and economic growth: Views and agenda. Journal ofEconomic Literature 35, no. 20: 688–726.

Lin, M., and F. Wu. 2013. Identifying the determinants of broadband adoption by diffusion stage inOECD countries. Telecommunications Policy 37, no. 4–5: 241–251. doi:10.1016/j.telpol.2012.06.003.

Mack, E.A., and S.J. Rey. 2014. An econometric approach for evaluating the linkages betweenbroadband and knowledge intensive firms. Telecommunications Policy 38, no. 1: 105–118.doi:10.1016/j.telpol.2013.06.003.

Majumdar, S.K. 2010. Fiber in the backbone! The impact of broadband adoption on firm growth innetwork markets. Economics of Innovation and New Technology 19, no. 3: 283–293. doi:10.1080/10438590903522366.

Mark, N.C., and D. Sul. 2003. Cointegration vector estimation by panel DOLS and long-run moneydemand. Oxford Bulletin of Economics and Statistics 65, no. 5: 655–680. doi:10.1111/obes.2003.65.issue-5.

Marques, L.M., J.A. Fuinhas, and A.C. Marques. 2013. Does the stock market cause economicgrowth? Portuguese evidence of economic regime change. Economic Modelling 32: 316–324.doi:10.1016/j.econmod.2013.02.015.

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 19

Mayer, W.J., G. Madden, Z. Jin, and T. Tran. 2015. Modelling OECD broadband subscriptions indisequilibrium. Technological Forecasting and Social Change 90: 476–486. doi:10.1016/j.techfore.2014.03.010.

Menyah, K., S. Nazlioglu, and Y. Wolde-Rufael. 2014. Financial development, trade openness andeconomic growth in African countries: New insights from a panel causality approach. EconomicModelling 37, no. 2: 386–394. doi:10.1016/j.econmod.2013.11.044.

Mukhopadhyay, B., R.P. Pradhan, and M. Feridun. 2011. Finance-growth nexus revisited for someAsian countries. Applied Economics Letters 18, no. 16: 1527–1530. doi:10.1080/13504851.2010.548771.

Ng, T.H., C.T. Lye, and Y.S. Lim. 2013. Broadband penetration and economic growth in ASEANcountries: A generalized method of moments approach. Applied Economics Letters 20: 857–862.doi:10.1080/13504851.2012.754538.

Ngare, E., E.M. Nyamongo, and R.N. Misati. 2014. Stock market development and economic growthin Africa. Journal of Economics and Business 74, no. 1: 24–39. doi:10.1016/j.jeconbus.2014.03.002.

Organization for Economic Cooperation and Development. 2002. Broadband infrastructure deploy-ment: The role of government assistance. Paris: Organization for Economic Cooperation andDevelopment.

Owusu, E.L., and N.M. Odhiambo. 2014. Financial liberalisation and economic growth in Nigeria: AnARDL-bounds testing approach. Journal of Economic Policy Reform 17, no. 2: 164–177.doi:10.1080/17487870.2013.787803.

Pedroni, P. 1999. Critical values for cointegration tests in heterogeneous panels with multipleregressors. Oxford Bulletin of Economics and Statistics 61, no. S1: 653–670. doi:10.1111/obes.1999.61.issue-S1.

Pedroni, P. 2000. Fully modified OLS for heterogeneous cointegrated panels. Advances inEconometrics 15, no. 1: 93–130.

Pedroni, P. 2001. Purchasing power parity tests in cointegrated panels. Review of Economics andStatistics 83, no. 4: 727–731. doi:10.1162/003465301753237803.

Pradhan, R.P., M.B. Arvin, S. Bahmani, and N.R. Norman. 2014a. Telecommunications infrastructureand economic growth: Comparative policy analysis for the G-20 developed and developingcountries. Journal of Comparative Policy Analysis: Research and Practice 16, no. 5: 401–423.doi:10.1080/13876988.2014.960227.

Pradhan, R.P., M.B. Arvin, N.R. Norman, and S.K. Bele. 2014b. Economic growth and the develop-ment of telecommunications infrastructure in the G-20 countries: A panel-VAR approach.Telecommunications Policy 38, no. 7: 634–649. doi:10.1016/j.telpol.2014.03.001.

Pradhan, R.P., M.B. Arvin, N.R. Norman, and J.H. Hall. 2014c. The dynamics of banking sector andstock market maturity and the performance of Asian economies. Journal of Economic andAdministrative Sciences 30, no. 1: 16–44. doi:10.1108/JEAS-06-2013-0022.

Ruz, G.A., S. Varas, and M. Villena. 2013. Policy making for broadband adoption and usage in chilethrough machine learning. Expert Systems with Applications 40, no. 17: 6728–6734. doi:10.1016/j.eswa.2013.06.039.

Samargandi, N., J. Fidrmuc, and S. Ghosh. 2015. Is the relationship between financial developmentand economic growth monotonic? Evidence from a sample of middle-income countries. WorldDevelopment 68: 66–81. doi:10.1016/j.worlddev.2014.11.010.

Sassi, S., and M. Goaied. 2013. Financial development, ICT diffusion and economic growth: Lessonsfrom MENA region. Telecommunications Policy 37, no. 4–5: 252–261. doi:10.1016/j.telpol.2012.12.004.

Uddin, G.S., B. Sjö, and M. Shahbaz. 2013. The causal nexus between financial development andeconomic growth in Kenya. Economic Modelling 35: 701–707. doi:10.1016/j.econmod.2013.08.031.

Wolde-Rufael, Y. 2009. Re-examining the financial development and economic growth nexus inKenya. Economic Modelling 26, no. 6: 1140–1146. doi:10.1016/j.econmod.2009.05.002.

Yang, Y.Y., and M.H. Yi. 2008. Does financial development cause economic growth? Implication forpolicy in Korea. Journal of Policy Modeling 30: 827–840. doi:10.1016/j.jpolmod.2007.09.006.

20 R. P. PRADHAN ET AL.

Yartey, C.A. 2008. Financial development, the structure of capital markets, and the global digitaldivide. Information Economics and Policy 20, no. 2: 208–227. doi:10.1016/j.infoecopol.2008.02.002.

Zaman, K., Z. Izhar, M.M. Khan, and M. Ahmad. 2012. RETRACTED: The relationship betweenfinancial indicators and human development in Pakistan. Economic Modelling 29, no. 5: 1515–1523. doi:10.1016/j.econmod.2012.05.013.

MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES 21