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Cuadernos de economía 2017;40:115-34 - DOI: 10.1016/j.cesjef.2016.02.003
Article
ICT-finance-growth nexus: Empirical evidence from the Next-11 countries
Nexo TIC-finanzas-crecimiento: hallazgos empíricos de los países de los Próximos 11
Rudra Prakash Pradhana,, , Mak Arvinb, Mahendhiran Nairc, Sara Bennettd, Sahar Bahmanie
a Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur 721302, India
b Department of Economics, Trent University, Peterborough, Ontario K9J 7B8, Canada
c School of Business, Monash University Malaysia, Jalan Lagoon Selatan 47500, Malaysia
d School of Business and Economics, Lynchburg College, Lynchburg, VA 24501, USA
e Department of Economics, University of Wisconsin at Parkside, Kenosha, WI 53144, USA
Recibido 03 octubre 2015, Aceptado 17 febrero 2016
Abstract

This study assesses the causal relationship between information and communication technology (ICT) penetration, financial development, and economic growth in Next-11 countries between 1961 and 2012. A panel vector auto-regressive (VAR) model is used to detect the direction of causality between ICT, financial sector development and economic growth for these countries. The results reveal that there is Granger-causality among the variables both in the short run and in the long run, although the exact nature of the results varies by the ICT penetration indicators for the sample countries. Empirical results from this study provide valuable insights on policies pertaining to ICT penetration, financial sector development and economic growth.

Resumen

El presente estudio evalúa la relación causal entre la penetración de las tecnologías de la información y comunicación (TIC), el desarrollo financiero y el crecimiento económico en los Próximos 11 entre 1961 y 2012. Se utilizó un modelo de panel de vectores autorregresivos para detectar la dirección de causalidad entre las TIC, el desarrollo del sector financiero y el crecimiento económico para estos países. Los resultados revelan que existe una causalidad de Granger entre las variables tanto a corto como a medio plazo, si bien la naturaleza exacta de los hallazgos varía conforme a los indicadores de penetración de las TIC para los países de la muestra. Los resultados empíricos de este estudio suponen una valiosa perspectiva acerca de las políticas de penetración de las TIC, el desarrollo del sector financiero y el crecimiento económico.

JEL classification
L96, O32, O33, O43
Palabras clave
Penetración de TIC, Desarrollo financiero, Crecimiento económico, Panel VAR, Próximos 11 países
1Introduction

Over the past five decades, there has been rapid transformation of the global economy and many of the changes have been powered by the information and communication technology (ICT) revolution. ICT has become an integral part of the global economic architecture. ICT is regarded as a foundational condition for a knowledge-intensive economy, where the following can be achieved: (1) strengthening of collaboration between economic agents, thereby enabling firms to pursue open innovation (Fitjar and Rodríguez-Pose, 2013); (2) enhancing the innovative capacity of countries (Nair, 2011; Nair and Shariffadeen, 2009); (3) improving firm level productivity (Sadun and Farooqui, 2006; Forman et al., 2005; Entner, 2008); (4) providing easy access to a wide range of affordable products and services (Brown and Goolsbee, 2002; Baye et al., 2004; Tang et al., 2007); (5) opening new employment opportunities (Nair and Vaithilingam, 2012); (6) improving corporate and public sector governance (Kalam, 2003); and (7) providing marginalized communities access to information and resources for business development (Bhatnagar, 2000; Quibria et al., 2002).

Several studies have examined the impact of network externalities on economic growth and vice versa (see, for instance, Pradhan et al., 2014a). Additionally, the role of financial sector development has been extensively studied in the literature (see for example, King and Levine, 1993; Levine and Zervos, 1996; Levine et al., 2000; Ulgen, 2015). While the relationship between ICT on economic development has been well established, the literature that examines the dynamics between ICT penetration, financial sector development and economic growth is not well-developed. Hence, the primary objective of this paper is to examine the causal relationships between ICT penetration, financial sector development and economic growth using a panel vector auto-regressive (VAR) model for the Next-11 countries from 1961 to 2012. The results from the empirical analysis will have important policy implications pertaining to ICT penetration, financial sector development and pro-growth strategies.

The paper is structured as follows. Section 2 provides a theoretical basis, Section 3 offers a brief review of the literature. Section 4 provides the methodology used in this study, outlining the conceptual model, hypotheses and empirical methodology. Section 5 discusses the empirical results and Section 6 provides the policy implications of the empirical results and concluding remarks.

2Theoretical basis

Explaining the sources of economic growth ranks among the most significant topics that economists have examined. Romer (1986) initially started a set of theoretical and empirical analyses focusing on the endogeneity of the growth process as compared to Solow-type neoclassical growth models (Solow, 1956), which used an aggregate function approach and endogenous technical changes (Roller and Waverman, 2001). Numerous papers since then have attempted to disentangle the elements of a national economy that create the economic growth (see, for instance, Aghion and Howitt, 1998). The factors that determine economic growth can be grouped into three different types: the first group basically considers the accumulation of broad capital, including human capital and different types of physical capitals; the second group basically considers spillovers or external economies; and third group stresses industrial innovation as the engine of growth (Grossman and Helpman, 1994).

This paper examines the impact of ICT infrastructure and financial development on per capita economic growth. We cover some key economies (namely, the Next-11 countries). Over recent decades, these policymakers in most of these countries have devoted considerable effort to developing their financial markets and ICT sectors to elevate their economic growth. Some of these efforts have included investment expansion, financial regulation, as well as improvements in the ICT infrastructure inter alia. Therefore, our study formally investigates whether the development of ICT and financial sectors can be causal factors of growth for these countries and whether they also cause each other. The balance of this section highlights the possible theoretical links between these variables.

ICT infrastructure is generally recognized as an important factor in determining economic growth. There are three reasons why this may be the case: (1) ICT are omnipresent in most business sectors, (2) ICT improves continuously and hence reduce costs for the users, and (3) ICT contributes to innovation and the development of the new products and processes (Andrianaivo and Kpodar, 2011). However, with the elevated role of ICT infrastructure, the issue of duality matters in the phases of economic growth (Dutta, 2001). That means there is the possibility of bidirectional causal relationships between ICT infrastructure and economic growth. Fig. C.1 in Appendix C summarizes the duality between ICT infrastructure and economic growth.

Analogously, financial development can be considered as an important factor in determining economic growth. The degree to which financial market activities are pervasive in a country is believed to be largely a function of five factors: (1) producing and processing information about possible investments and allocating capital based on these assessments, (2) monitoring individuals and firms and exerting corporate governance after allocating capital, (3) facilitating trade, (4) mobilizing and pooling savings, and (5) easing the exchange of goods, services, and financial instruments (Cihak et al., 2011). Again with the increasing importance of financial development, the issue of duality also matters in the phases of economic growth. That means there is possibility of bidirectional causality between financial development and economic growth (see Levine et al., 2000; King and Levine, 1993).

Finally, ICT infrastructure can be considered as an important factor for financial development. ICT generally allows expansion and access to financial services of the economy. It reduces transaction costs, especially the costs of running physical financial institution branches. Additionally, the increasing use of ICT services (especially mobile banking) has contributed to the emergence of branchless financial services, thereby improving financial inclusion in particular and financial development in general. In sum, ICT provides better information flows, particularly with reference to improving access to credit and deposit facilities, and can allow efficient allocation of credit, facilitate financial transfers, and boost financial development (Andrianaivo and Kpodar, 2011). Yet again with the increasing importance of ICT development, the issue of duality also matters here in the phases of financial development. That means there is possibility of bidirectional causality between ICT infrastructure and financial development (Pradhan et al., 2015).

3Literature review

The literature on ICT-growth nexus and financial sector development-growth nexus are extensive. Our paper is directly related to these two stands of the literature.

There are four schools of thought in the ICT-growth literature. The first supports a supply-leading hypothesis (SLH), which argues that ICT penetration is a pre-condition for economic development. The proponents argue that, as economic agents become more connected to the information highway, they are able to extend their reach to obtain more information, knowledge, products, services and markets. This enables them to improve their productivity, innovative capacity and wealth creation opportunities. Among the empirical studies that show this line of argument include: Cieslik and Kaniewsk (2004), Chakraborty and Nandi (2003), and Mehmood and Siddiqui (2013).

The second school of thought advances the demand-following hypothesis (DFH), which suggests that countries that experience higher economic growth tend to increase spending on ICT development. Supporters of this hypothesis argue that as wealth levels in a country grow, economic agents tend to demand more sophisticated technology to improve their socioeconomic statuses. To meet the demand of the various stakeholders, governments invest a significant amount of resources to develop and upgrade the ICT infrastructure, support systems and regulatory environment. That is, economic development causes ICT penetration. Empirical studies that have shown the causal linkage from economic growth to ICT penetration include Beil et al. (2005), Lee et al. (2012), Pradhan et al. (2013a) and Shiu and Lam (2008a).

A third school of thought is the feedback hypothesis (FBH), which argues that ICT can complement and can deepen the impact on economic growth and vice versa. Here, the proponents suggest that ICT improves economic productivity and economic growth opportunities. As the wealth of economic agents and countries increases, these countries tend to increase investments in ICT to continuously develop and upgrade not only the number of people using ICT, but also the quality of the ICT so as to enable firms to pursue economies of scale and scope and to move up the innovation value chain. The feedback between ICT development and economic growth has a reinforcing impact on each other, enabling the economy to gravitate to a higher stage of economic development. Empirical studies that supports the FBH include the following: Chakraborty and Nandi (2009, 2011), Wolde-Rufael (2007), and Zahra et al. (2008).

The final school of thought maintains that there is no causal link between ICT and economic growth, known as no linkage hypothesis (NLH). This hypothesis seems to be a minority in the literature. A study that has shown this result is Veeramacheneni et al. (2007).

A summary of the studies that have examined the causal link between ICT penetration and economic development is given in Table 1.

Table 1.

Summary of studies on the nexus between ICT penetration and economic growth.

Study  Method  Study area  Study period  Finding 
Arvin and Pradhan (2014)  G-20 countries  1998–2011  FBH1 
Beil et al. (2005)  USA  1947–1996  DFH1 
Chakraborty and Nandi (2003)  12 ACs  1975–2000  SLH1 
Chakraborty and Nandi (2009)  DCs  1980–2001  FBH1 
Chakraborty and Nandi (2011)  93 countries  1985–2007  FBH1 
Cieslik and Kaniewsk (2004)  Poland  1989–1998  SLH1 
Cronin et al. (1991)  USA  1958–1988  FBH1 
Dutta (2001)  15DCs & 15 ICs  1960–1993  SLH1 
Lam and Shiu (2010)  105 countries  1980–2006  FBH1 
Lee et al. (2012)  3 NACs  1975–2009  DFH1 
Mehmood and Siddiqui (2013)  23 ACs  1990–2010  SLH1 
Pradhan et al. (2013b)  34 OECD countries  1961–2011  DFH1, FBH1 
Pradhan et al. (2014a)  G-20 countries  1991–2012  FBH1 
Pradhan et al. (2014b)  G-20 countries  2001–2012  SLH1, DFH1, FBH1 
Ramlan and Ahmed (2009)  Malaysia  1965–2005  NLH1 
Shiu and Lam (2008a)  China  1978–2004  SLH1, DFH1, NLH1 
Shiu and Lam (2008b)  105 countries  1980–2006  FBH1 
Veeramacheneni et al. (2007)  10 LACs  1975–2003  NLH1 
Wolde-Rufael (2007)  USA  1947–1996  FBH1 
Yoo and Kwak (2004)  Korea  1965–1998  SLH1 
Zahra et al. (2008)  23 countries  1990–2007  FBH1 

Note 1: SLH1: supply-leading hypothesis: unidirectional causality is present from ICT to economic growth; DFH1: demand-following hypothesis: unidirectional causality from economic growth to ICT is present; FBH1: feedback hypothesis: bidirectional causality between ICT and economic growth is present; and NLH1: neutrality hypothesis: no causality between ICT and economic growth is present.

Note 2: DCs: developing countries; ACs: Asian countries; CEE: Central and Eastern Europe; ICs: industrialised countries; NACs: Northeast Asian countries; LACs: Latin American countries.

Note 3: (a) Granger causality; and (b) Dynamic Panel Data Model.

There has also been extensive work in the literature examining the causal relationship between financial sector development and economic growth. Similar to the ICT-growth literature, there are four schools of thoughts on the causal relationship between financial sector development and economic growth. The first is the SLH, which suggests that financial sector development is important to facilitate economic growth. Schumpeter (1934) is one of the earliest works to postulate the impact of financial sector development on economic growth. Several other studies have also argued that financial sector development is a key catalyst for stimulating economic growth via the following channels: (1) reallocating resources from less productive sectors to more productive, growth-inducing sectors (Patrick, 1966); (2) providing valuable information and analysis of firms and markets to investors, hence reducing economic risks and uncertainties (Greenwood and Jovanovic, 1990); (3) increasing the ability of economic agents to accurately evaluate investment initiatives, mobilizing households savings for innovative activities and diversifying risks to enhance the innovation of intermediate goods (King and Levine, 1993); and (4) lowering costs of new investment, thus facilitating backward linkages with foreign firms and increasing rates of intermediate input and economic growth (Alfaro et al., 2004).

There are a number of studies that support DFH, which suggests that economic growth has a positive impact on financial development (Wolde-Rufael, 2009; Gries et al., 2009; Ang, 2008a,b). Accumulation of wealth due to robust economic growth will provide adequate resources to improve the technology, human capital, institutions, corporate governance, linkages between economic agents and innovation. These factors are important for improving the quality of the financial sector and increasing demand for more sophisticated financial systems, instruments and products. Studies that have shown the causal linkage between economic growth and financial sector development include Ang and McKibbin (2007), Liang and Teng (2006), Odhiambo (2008, 2010), Omri et al. (2015) and Panopoulou (2009). There have also been studies in the literature that support the FBH (Chow and Fung, 2011; Craigwell et al., 2001; Uddin et al., 2014; Wolde-Rufael, 2009) and the NLH (Mukhopadhyay et al., 2011; Pradhan et al., 2013c, 2014c).

A comprehensive summary of the studies that support the four hypotheses are given in Table 2.

Table 2.

Summary of studies on the nexus between financial development and economic growth.

Study  Method  Study area  Study period  Finding 
Abu-Bader and Abu-Qarn (2008)  Egypt  1960–2001  SLH2 
Ang and McKibbin (2007)  Malaysia  1960–2001  DFH2 
Bojanic (2012)  Bolivia  1940–2010  SLH2 
Boulila and Trabelsi (2004)  Tunisia  1962–1987  SLH2 
Calderon and Liu (2003)  109 countries  1960–1994  SLH2 
Chaiechi (2012)  South Korea, Hong Kong, UK  1990–2006  SLH2 
Chow and Fung (2011)  69 countries  1970–2004  FBH2 
Craigwell et al. (2001)  Barbados  1974–1998  FBH2 
Dritsakis and Adamopoulos (2004)  Greece  1960–2000  FBH2 
Fase and Abma (2003)  9 Asian countries  1978–1999  SLH2 
Hsueh et al. (2013)  Ten Asian countries  1980–2007  SLH2 
Jalil et al. (2010)  China  1977–2006  SLH2 
Kar et al. (2011)  15 MENA countries  1980–2007  SLH2, DFH2 
Liang and Teng (2006)  China  1952–2001  DFH2 
Menyah et al. (2014)  21 African countries  1965–2008  SLH2, DFH2 
Mukhopadhyay et al. (2011)  7 Asian countries  1979–2009  NLH2 
Naceur and Ghazouani (2007)  MENA region  1979–2003  SLH2 
Odhiambo (2008)  Kenya  1969–2005  DFH2 
Odhiambo (2010)  South Africa  1969–2006  DFH2 
Omri et al. (2015)  MENA countries  1990–2011  DFH2 
Panopoulou (2009)  5 countries  1995–2007  DFH2 
Pradhan et al. (2013c)  Asian countries  1960–2011  NLH2 
Pradhan et al. (2014c)  25 ARF Countries  1960–2012  NLH2 
Pradhan et al. (2014d)  35 Asian Countries  1960–2011  SLH2 
Uddin et al. (2014)  Bangladesh  1975–2011  FBH2 
Wolde-Rufael (2009)  Kenya  1966–2005  FBH2 
Wu et al. (2010)  European Union  1976–2005  SLH2 

Note 1: SLH2: supply-leading hypothesis: unidirectional causality is present from financial development to economic growth; DFH2: demand-following hypothesis: unidirectional causality from economic growth to financial development is present; FBH2: feedback hypothesis: bidirectional causality between financial development and economic growth is present; and NLH2: neutrality hypothesis: no causality between financial development and economic growth is present.

Note 2: (a) Bivariate Granger causality; (b) trivariate Granger causality; (c) quadvariate Granger causality; (d) multivariate Granger causality; MENA: Middle East and North Africa.

The relationship between ICT penetration and financial sector development has received some attention in the literature. Improvements in ICT over the years have enabled financial institutions to obtain and disseminate information to various stakeholders, reducing information asymmetries and associated market failures (Morck et al., 2000). High speed communication networks have also enabled financial institutions to de-couple the ‘space-time continuum’, leading to the development of more efficient online financial, banking and brokerage platforms.

There have been very few studies that have examined the causal relations between ICT penetration and financial sector development. One of the studies that have examined this relationship is Shamim (2007), which shows that for a sample of 61 countries, 49 countries had causality from ICT connectivity to financial sector development. The study also shows that causality ran from financial sector development to ICT connectivity for the Netherlands, Nigeria, Spain, Switzerland, Thailand and the USA. In the case of Austria, China, France, Italy, Korea, and Malaysia the causal relations is bi-directional. A more recent study by Lechman and Marszk (2015) argues a strong relationship between ICT penetration and exchange traded funds for Japan, Mexico, the United States and Korea.

However, the trivariate-relationship between ICT penetration, financial development and economic growth is scarce in the development literature. To our knowledge, there are only two studies which have examined the above-mentioned relationship. Shamim (2007) used the Generalised Method of Moments (GMM) approach for a sample of 61 countries to show that connectivity variables deepen the impact of financial sector development on economic growth. Sassi and Goaied (2013) examine MENA countries using the GMM method and show that ICT development contributes positively to economic growth. On the other hand, they find that financial sector development has a negative impact on growth and that ICT development reinforces the role of financial sector development on economic growth in these countries.

The above brief review of the literature highlights that the relationships between ICT penetration, financial development and economic growth have been mixed. Hence, there is a need for further analysis to understand the dynamics between these variables so that effective policy measures to ensure sustainable economic development can be introduced. In the light of the research gap identified in the literature, this paper examines the causal relationship between ICT penetration, financial development and economic growth for a panel of the Next-11 countries – a group of nations that have not been studied before in this literature.

4Methodology

The contribution of our paper is three-fold. First, this study integrates ICT penetration with finance-growth nexus, which has not received much coverage in the literature. Second, we use a sample of 11 developed and developing countries, which have not been previously studied in the literature. We also utilize a long span time period (1961–2012). Third, this study utilizes advanced panel cointegration and Granger causality tests to capture the dynamics between the three variables. This method, while common in the economics literature, has not gained much prominence in the information systems literature. In this section, the theoretical framework, hypothesis development, empirical model, description of the sample data and the empirical methodology are presented.

4.1Statement of the hypotheses

In this paper, we examine the possible patterns of causal relationships between ICT penetration,1 financial development, and economic growth. The null hypotheses we will test are given below and summarized in Fig. 1:

  • H1A, B: Telephone line penetration Granger-causes economic growth, and vice versa

  • H2A, B: Telephone line penetration Granger-causes financial development, and vice versa

  • H3A, B: Mobile phone penetration Granger-causes economic growth, and vice versa

  • H4A, B: Mobile phone penetration Granger-causes financial development, and vice versa

  • H5A, B: Internet user penetration Granger-causes economic growth, and vice versa

  • H6A, B: Internet user penetration Granger-causes financial development, and vice versa

  • H7A, B: Internet server penetration Granger-causes economic growth, and vice versa

  • H8A, B: Internet server penetration Granger-causes financial development, and vice versa

  • H9A, B: Broadband penetration Granger-causes economic growth, and vice versa

  • H10A, B: Broadband penetration Granger-causes financial development, and vice versa

  • H11A, B: ICT penetration Granger-causes economic growth, and vice versa

  • H12A, B: ICT penetration Granger-causes financial development, and vice versa

  • H13A, B: Economic growth Granger-causes financial development, and vice versa

Figure 1.
(0.13MB).

The relationship between ICT, financial development and economic growth. Note. GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

4.2Model specification and data

In order to examine the empirical relationships between ICT penetration, financial development and economic growth, the following model is considered:

The logarithmic transformation of Eq. (1) is given by

where β0=ln(B0GROWTH); i (=1, 2, …, N) represents a country in the sample; t (=1, 2, …, T) denotes the time period for each country; and βi (for i=1, 2) represents parameters of the model.

The task is to estimate the parameters in Eq. (2) and conduct some panel tests on the causal relationships between GROWTH, FINANC and ICTPEN. It is postulated that β1GROWTH>0, which can justify that an increase in FINANC will likely cause an increase in GROWTH. Similarly, we expect β2GROWTH>0, which can again justify that an increase in ICTPEN will likely cause an increase in GROWTH.

The variable GROWTH is the change in per capita gross domestic product, expressed in percentage. ICTPEN is information communication technology (ICT) penetration, which is measured by a composite index (variable: ICTIND) and five individual ICT penetration indicators: telephone landline penetration (variable: TELLIN), mobile phone penetration (variable: MOBILE), internet user penetration (variable: INTUSE), internet server penetration (variable: INTSER), and fixed broadband penetration (variable: FIXBRO). The final variable is FINANC which is a composite index of financial development and is the weighted average of nine financial development indicators: broad money supply (variable: BMONEY), claims on the private sector (variable: CLAIMS), domestic credit to the private sector (variable: DCPRIS), domestic credit provided by the banking sector (variable: DCBANK), domestic credit to the financial sector (variable: DCFINS), market capitalization (variable: SMACAP), traded stocks (variable: STMTRS), stock market turnover (STMTUR), and the number of listed companies in the stock market (variable: NLCOMP). Appendix A provides the detailed description of these variables (see Tables A.1 and A.2). The detailed descriptions about the formulation of the two composite indices (ICTIND and FINANC) are available in Appendix B.

We use the natural log of annual data on the Next-11 countries, covering the period from 1961 to 2012. The countries in our sample are: Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Nigeria, Pakistan, Philippines, Turkey, and Vietnam. Since the ICT revolution over the years has occurred in several waves (from basic telephony, followed by internet, mobile phones and broadband technology), the dynamics between different ICT, financial development and economic growth are investigated for the following six different time periods: Case 1 (1961–2012) uses telephone line penetration; Case 2 (1991–2012) uses mobile phone penetration; Case 3 (1991–2012) uses internet user penetration; Case 4 (2001–2012) uses internet server penetration; Case 5 (2001–2012) uses fixed broadband penetration; Case 6 (2001–2012) uses the composite index of ICT penetration.

4.3Panel unit root test

Studies have shown that most of the economic and financial time series were found to be non-stationary (Engle and Granger, 2003). If two time series are non-stationary, regressing one series against the other will lead to spurious regression results (Granger and Newbold, 1974). Hence, before studying any empirical relationships, the stationarity properties of each time series should be ascertained. In this context, unit root test should be conducted to determine the order of integration of a series. The order of integration is determined by the number of times a non-stationary series is differenced until the series becomes stationary (Christopoulos and Tsionas, 2004).

In our study, the panel unit root test is deployed to estimate the degree (i.e., the order) of integration for GROWTH, ICTPEN and FINANC. While several panel unit root tests are available, we specifically use four panel unit root tests [the Levine-Lin-Chu test (LLC; Levine et al., 2002), the Im-Pesaran-Shin test (IPS; Im et al., 2003), the Maddala and Wu-Fisher Augmented Dickey Fuller (ADF) test, and the Maddala and Wu-Fisher Phillips and Perron (PP) test (Maddala and Wu, 1999)] to check the stationarity of the variables. The LLC is a homogeneous panel unit root test, while IPS, ADF and PP are panel heterogeneous unit root tests. The tests employed in this study are widely used in the empirical literature and standard books on time series; hence the technical details of the tests are not included in this paper.

4.4Panel cointegration test

Cointegration is a statistical concept that explains the long-run relationships between non-stationary variables. If the difference between two non-stationary series is itself stationary, then the two series are regarded as being cointegrated. If two or more series are cointegrated, then there exist long-run equilibrium relationships between these series. If the series are not cointegrated, then it is regarded that there is no long-run relationship between the series and the series may diverge from each other. In our study, the panel cointegration test is deployed to ascertain the long-run equilibrium relationships between GROWTH, ICTPEN and FINANC.

While several panel cointegration tests are available in the literature, we specifically use Pedroni's panel cointegration method (Pedroni, 2004) to determine the existence of cointegration among these three series. The technique starts with the following regression equation:

where β0GROWTHi is a member-specific intercept or fixed-effects parameter which is allowed to vary across individual cross-sectional units. β1GROWTHi is a deterministic time trend specific to the individual countries in the panel. The slope coefficients (β2GROWTHi and β3GROWTHi) can vary from one country to another, allowing the cointegrating vectors to be heterogeneous across the panel members. Pedroni (2000) proposed seven different statistics for the cointegration test in the panel data setting. Since these statistics are again common in the literature, we do not provide the technical details of these statistics in this paper.

4.5FMOLS and DOLS estimation

In the literature, there are a number of estimators for estimating a cointegration vector using panel data, including with- and between- group, such as ordinary least squares (OLS) estimators, fully modified OLS (FMOLS) estimators and the dynamic OLS (DOLS) estimators. Although simple OLS estimators of the cointegrated vectors are super-convergent, their distribution was found to be asymptotically biased and depends on nuisance parameters associated with the presence of serial correlation in the data (Pedroni, 2001). Many problems that exist in time-series analysis may also arise in panel data analysis and tend to be more prevalent in the presence of heteroskedasticity (Kao and Chiang, 2000). Therefore, several other estimators have been proposed in the cointegrated literature. However, this study uses two panel cointegration estimators, namely the between-group fully modified OLS (FMOLS)2 and dynamic OLS (DOLS)3. Both FMOLS and DOLS were found to give consistent estimates of standard errors, which will render them to be robust for statistical inference. According to Kao and Chiang (2000), both FMOLS and DOLS estimators have normal limiting properties. In this study, we are interested in studying the nature of the relationships (positive or negative) between the variables. Since both FMOLS and DOLS estimators are very common and readily available in most of the financial time series econometrics books, the technical details of these estimators are not included in this paper.

4.6VECM estimation

Engle and Granger (1987) demonstrate that when variables are cointegrated, an error-correction model necessarily describes the data-generating process. Therefore, on the basis of the unit root and cointegration test results above, we use vector error-correction models (VECMs) to determine the causal relationships between our three sets of variables. In other words, we seek to determine which variable causes the other in the presence of the third variable. We are able to determine this causal link for both the short run and the long run. Following the methodological approach proposed by Canning and Pedroni (2008) and Holtz-Eakin et al. (1988), the panel Granger causality test is employed to ascertain the direction of causality between the three variables. The following econometric model is used:

where p is the lag lengths for the differenced variables; Δ is the first difference filter (IL); i=1, …, N; t=1, …, T; and, ??j (j=1, 2, 3) are independently and normally distributed random variables for all i and t, with zero means and finite heterogeneous variances (σi2).

The ECTs are error-correction terms derived from the cointegrating equations. The ECTs represent long-run dynamics, while differenced variables represent short-run dynamics between the variables. We look for both short-run and long-run Granger causal relationships. Short-run Granger causal relationships are measured using F-statistics and the significance of the lagged changes in the independent variables, whereas long-run Granger causal relationships are measured using the significance of the t-test of the lagged ECTs. The coefficient δsGROWTH (for s=1, 2, 3) measures the speed of adjustment to long-term equilibrium. The higher the coefficient, the faster the adjustment to the long-run equilibrium is. Based on Eq. (4), Table 3 summarizes the testable hypotheses on causal relationships among ICT penetration, financial development, and economic growth.

Table 3.

Testable hypotheses.

Causal flows  Testing restrictions 
FINANCGROWTH  γ1GROWTHik0; δ1GROWTHi
GROWTHFINANC  β2GROWTHik0; δ2GROWTHi
ICTPENGROWTH  λ1GROWTHik0; δ1GROWTHi
GROWTHICTPEN  β3GROWTHik0; δ3GROWTHi
ICTPENFINANC  λ2GROWTHik0; δ2GROWTHi
FINANCICTPEN  γ3GROWTHik0; δ3GROWTHi

Note 1: GROWTH: per capita economic growth; FINANC: composite index of financial development; and ICTPEN: ICT penetration.

Note 2: ICT penetration is measured through five individual indicators (TELLIN, MOBILE, INTUSE, INTSER, FIXBRO) and the composite index (ICTIND).

Note 3: TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

Note 4: The definitions of these variables are in Appendix A.

5Empirical findings

We begin with a discussion of the integration and cointegration properties of the variables. The estimated results confirm that the variables are integrated of order one (see Table 4) and are cointegrated (see Table 5). This is true for all the cases that we consider (Cases 1–6). The combined results of both integration and cointegration indicate the presence of a long-run equilibrium relationship between financial development, ICT penetration, and economic growth.

Table 4.

Results from panel unit root test.

Test statistics  GROWTHFINANCICTPEN
  NIT  INT  BIT  NIT  INT  BIT  NIT  INT  BIT 
Case 1: between GROWTH, FINANC, and TELLIN (1961–2012)
Levine-Lin-Chu (LLC)  −14.7*  −8.90*  −6.82*  −7.91*  −2.80*  −2.26*  −11.8*  −6.51*  −4.64* 
Maddala and Wu-Fisher ADF  193.9*  130.4*  93.89*  90.50*  48.11*  37.52*  146.1*  96.87*  78.58* 
Maddala and Wu-Fisher PP  243.3*  1438*  1729*  156.9*  106.8*  87.12*  1244*  454*  366* 
Inference  I [1]  I [1]  I [1]             
Case 2: between GROWTH, FINANC, and MOBILE (1991–2012)
Levine-Lin-Chu (LLC)  −14.6*  −9.96*  −7.39*  −8.36*  −6.57*  −4.54*  −3.88*  −3.88*  −2.37* 
Maddala and Wu-Fisher ADF  192.9*  128.5  92.93*  96.50*  52.23*  78.58*  41.89*  48.42*  47.79* 
Maddala and Wu-Fisher PP  232.2*  1146*  1148*  141.8*  96.50*  91.72*  55.67*  61.86*  56.87* 
Case 3: between GROWTH, FINANC, and INTUSE (1991–2012)
Levine-Lin-Chu (LLC)  −13.5*  −8.68*  −6.23*  −7.08*  −5.89*  −4.37*  −6.65*  −4.53*  −3.36* 
Maddala and Wu-Fisher ADF  170.1*  107.2*  74.99*  81.30*  47.29*  46.49*  76.3*  46.2*  38.54* 
Maddala and Wu-Fisher PP  192.9*  914.6*  612.6*  142.0*  102.4*  101.6*  120.1*  105.1*  92.5* 
Case 4: between GROWTH, FINANC, and INTSER (2001–2012)
Levine-Lin-Chu (LLC)  −10.2*  −8.89*  −8.82*  −5.50*  −4.26*  −3.41*  −6.52*  −6.50*  −4.06* 
Maddala and Wu-Fisher ADF  106.3*  59.79*  37.25*  55.9*  41.61*  32.34*  61.73*  45.44*  50.93* 
Maddala and Wu-Fisher PP  169.2*  130.9*  97.06*  88.6*  90.79*  80.79*  64.1*  53.29*  41.19* 
Case 5: between GROWTH, FINANC, and FIXBRO (2001–2012)
Levine-Lin-Chu (LLC)  −9.04*  −4.44*  −3.96*  −4.43*  −3.29*  −2.70**  −20.8*  −14.0*  −13.3* 
Maddala and Wu-Fisher ADF  81.7*  46.96*  28.87**  40.6*  29.0**  24.2**  92.8*  73.8*  57.2* 
Maddala and Wu-Fisher PP  141.8*  127.3*  106.0*  87.8*  78.9*  72.3*  63.9*  53.8*  47.2* 
Case 6: between GROWTH, FINANC, and ICTIND (2001–2012)
Levine-Lin-Chu (LLC)  −7.36*  −5.87*  −3.92*  −5.12*  −4.65*  −2.29**  −7.63*  −3.26*  −2.40** 
Maddala and Wu-Fisher ADF  66.7*  30.72**  25.9**  46.3*  38.55*  28.32**  61.38*  32.14*  23.28** 
Maddala and Wu-Fisher PP  131.4*  91.09*  76.67*  76.28*  57.79*  54.71*  103.1*  80.68*  69.4* 

Note 1: GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; ICTIND: composite index of ICT penetration; ADF: Augmented Dickey Fuller; and PP: Phillips and Perron.

Note 2: The figures are reported here at the first difference level.

Note 3: NIT is no trend and intercept, INT is intercept only, and BIT is both intercept and trend.

*

Statistical level of significance at 1% level

**

Statistical level of significance at 5% level.

Table 5.

Results of Pedroni panel cointegration test.

Test statistics  No
Intercept
With
Intercept
With
Intercept & trend
Case 1: between GROWTH, FINANC, and TELLIN (1961–2012)
Panel v-statistics  −0.50  [0.69]  −1.36  [0.91]  −3.54  [0.99] 
Panel ρ-statistics  −5.96*  [0.00]  −5.55*  [0.00]  −3.48*  [0.00] 
Panel PP-statistics  −7.31*  [0.00]  −8.86*  [0.00]  −8.97*  [0.00] 
Panel ADF-statistics  −4.17*  [0.00]  −4.63*  [0.00]  −3.88*  [0.00] 
Group ρ-statistics  −3.68*  [0.00]  −2.78*  [0.00]  −0.99  [0.16] 
Group PP-statistics  −8.25*  [0.00]  −10.6*  [0.00]  −11.2*  [0.00] 
Group ADF-statistics  −3.58*  [0.00]  −4.81*  [0.00]  −3.73*  [0.00] 
Inference  Cointegrated
Case 2: between GROWTH, FINANC, and MOBILE (1991–2012)
Panel v-statistics  2.07**  [0.02]  −1.13  [0.12]  −1.49  [0.93] 
Panel ρ-statistics  −6.14*  [0.00]  −5.37*  [0.00]  −3.24*  [0.00] 
Panel PP-statistics  −7.56*  [0.00]  −8.67*  [0.00]  −9.37*  [0.00] 
Panel ADF-statistics  −4.57*  [0.00]  −4.53*  [0.00]  −4.70*  [0.00] 
Group ρ-statistics  −4.04*  [0.00]  −2.18*  [0.00]  −0.27  [0.39] 
Group PP-statistics  −9.24*  [0.00]  −11.6*  [0.00]  −14.2*  [0.00] 
Group ADF-statistics  −5.62*  [0.00]  −5.26*  [0.00]  −4.52*  [0.00] 
Inference  Cointegrated
Case 3: between GROWTH, FINANC, and INTUSE (1991–2012)
Panel v-statistics  −0.62  [0.27]  −0.43  [0.66]  −2.59  [0.99] 
Panel ρ-statistics  −4.77*  [0.00]  −3.94*  [0.00]  −3.43*  [0.00] 
Panel PP-statistics  −6.98*  [0.00]  −7.27*  [0.00]  −10.5*  [0.00] 
Panel ADF-statistics  −4.43*  [0.00]  −3.74*  [0.00]  −5.24*  [0.00] 
Group ρ-statistics  −2.25*  [0.00]  −0.90  [0.18]  −0.97  [0.83] 
Group PP-statistics  −7.41*  [0.00]  −8.90*  [0.00]  −11.6*  [0.00] 
Group ADF-statistics  −5.17*  [0.00]  −4.01*  [0.00]  −3.65*  [0.00] 
Inference  Cointegrated
Case 4: between GROWTH, FINANC, and INTSER (2001–2012)
Panel v-statistics  −0.46  [0.67]  −1.21  [0.91]  −3.24  [0.99] 
Panel ρ-statistics  −2.88*  [0.01]  0.14  [0.56]  1.75  [0.96] 
Panel PP-statistics  −4.34*  [0.00]  −4.32*  [0.00]  −2.87*  [0.00] 
Panel ADF-statistics  −2.62*  [0.00]  −3.26*  [0.00]  −0.97  [0.16] 
Group ρ-statistics  0.48  [0.69]  1.80  [0.96]  3.03  [0.96] 
Group PP-statistics  −6.25*  [0.00]  −7.00*  [0.00]  −7.64*  [0.00] 
Group ADF-statistics  −3.95*  [0.00]  −3.07*  [0.00]  −1.59**  [0.05] 
Inference  Cointegrated
Case 5: between GROWTH, FINANC, and FIXBRO (2001–2012)
Panel v-statistics  −0.61  [0.72]  −1.33  [0.90]  −3.04  [0.99] 
Panel ρ-statistics  −2.58*  [0.00]  −1.94**  [0.03]  0.89  [0.81] 
Panel PP-statistics  −4.78*  [0.00]  6.01  [0.00]  −6.01*  [0.00] 
Panel ADF-statistics  −1.94  [0.02]  −1.79**  [0.05]  −2.71*  [0.00] 
Group ρ-statistics  0.13  [0.55]  0.47  [0.68]  2.02  [0.97] 
Group PP-statistics  −4.86*  [0.00]  −8.20*  [0.00]  −13.6*  [0.00] 
Group ADF-statistics  −2.42*  [0.00]  −2.27*  [0.01]  −3.64*  [0.00] 
Inference  Cointegrated
Case 6: between GROWTH, FINANC, and ICTIND (2001–2012)
Panel v-statistics  −0.87  [0.80]  −1.68  [0.95]  −3.40  [0.99] 
Panel ρ-statistics  −1.08  [0.14]  0.61  [0.73]  2.42  [0.99] 
Panel PP-statistics  −3.12*  [0.00]  −2.53*  [0.01]  −1.84**  [0.03] 
Panel ADF-statistics  −2.14*  [0.00]  −1.35  [0.09]  −0.66  [0.25] 
Group ρ-statistics  1.18  [0.88]  1.99  [0.98]  2.73  [0.99] 
Group PP-statistics  −4.71*  [0.00]  −6.17*  [0.00]  −8.92*  [0.00] 
Group ADF-statistics  −3.90*  [0.00]  −2.28**  [0.05]  −2.20*  [0.01] 
Inference  Cointegrated

Note 1: GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

Note 2: Figures in square brackets are probability levels indicating significance.

*

Statistical level of significance at 1% level.

**

Statistical level of significance at 5% level.

Having confirmed the existence of cointegration, the next step is to estimate the associated long-run cointegration parameters by FMOLS and DOLS procedures. The estimated results are presented in Table 6. The results indicate that economic growth is significantly influenced by both financial development and ICT penetration. This is again true for all six cases (Cases 1–6 as shown in Table 6).

Table 6.

Panel FMOLS and DOLS results.

Dependent variable  Independent variables  Method
    FMOLSDOLS
    Coefficients  t-Statistic  Coefficients  t-Statistic 
Case 1: between GROWTH, FINANC, and TELLIN (1961–2012)
GROWTHFINANC  0.16  3.37*  0.35  4.24* 
TELLIN  0.10  2.58*  0.15  3.24* 
Case 2: between GROWTH, FINANC, and MOBILE (1991–2012)
GROWTHFINANC  0.15  4.85*  0.22  3.36* 
MOBILE  0.04  4.11*  0.03  2.89* 
Case 3: between GROWTH, FINANC, and INTUSE (1991–2012)
GROWTHFINANC  1.12  2.52*  0.40  2.87* 
INTUSE  0.03  6.60*  0.01  2.61* 
Case 4: between GROWTH, FINANC, and INTSER (2001–2012)
GROWTHFINANC  1.13  6.87*  1.11  2.53* 
INTSER  0.05  2.88*  0.04  2.17* 
Case 5: between GROWTH, FINANC, and FIXBRO (2001–2012)
GROWTHFINANC  1.31  3.76*  1.13  7.76* 
FIXBRO  0.02  3.65*  0.04  3.18* 
Case 6: between GROWTH, FINANC, and ICTIND (2001–2012)
GROWTHFINANC  1.21  4.37*  1.35  9.78* 
ICTIND  0.03  3.89*  0.04  3.96* 

Note. GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

*

Rejection of the null hypothesis at 1% level.

After confirming cointegration, the next step is to check the direction of causality between these three variables. The panel Granger causality test, based on the panel vector error-correction model, is used for this purpose. Table 7 presents the Granger causal relationships among the variables based on the estimation of Eq. (4). These results are summarized below.

Table 7.

Panel Granger causality test results.

Dependent variable  Independent variablesLagged ECT
Case 1: between GROWTH, FINANC, and TELLIN (1961–2012)
  ΔGROWTHΔFINANCΔTELLINECT−1
ΔGROWTH  –  –  5.10**  [0.05]  1.66  [0.19]  −1.12**  [−5.71] 
ΔFINANC  6.01**  [0.05]  –  –  11.3*  [0.00]  −0.02  [−0.70] 
ΔTELLIN  10.5*  [0.00]  15.1*  [0.00]  –  –  −0.13  [−2.29] 
Case 2: between GROWTH, FINANC, and MOBILE (1991–2012)
  ΔGROWTHΔFINANCΔMOBILEECT−1
ΔGROWTH  –  –  7.63*  [0.05]  2.45  [0.18]  −0.91**  [−7.45] 
ΔFINANC  6.03*  [0.00]  –  –  7.70*  [0.00]  −0.05  [−2.66] 
ΔMOBILE  21.5*  [0.00]  7.31*  [0.00]  –  –  −0.19  [−1.49] 
Case 3: between GROWTH, FINANC, and INTUSE (1991–2012)
  ΔGROWTHΔFINANCΔINTUSEECT−1
ΔGROWTH  –  –  22.2*  [0.05]  5.18*  [0.05]  −0.55**  [−5.15] 
ΔFINANC  8.10*  [0.00]  –  –  6.97*  [0.00]  −0.04  [−1.52] 
ΔINTUSE  9.13*  [0.00]  5.71*  [0.00]  –  –  −0.67  [−1.57] 
Case 4: between GROWTH, FINANC, and INTSER (2001–2012)
  ΔGROWTHΔFINANCΔINTSERECT−1
ΔGROWTH  –  –  5.50**  [0.05]  5.54**  [0.05]  −1.32**  [−5.47] 
ΔFINANC  7.07*  [0.01]  –  –  5.74**  [0.05]  −0.22  [−2.08] 
ΔINTSER  10.7*  [0.00]  4.80**  [0.05]  –  –  −0.38  [−0.66] 
Case 5: between GROWTH, FINANC, and FIXBRO (2001–2012)
  ΔGROWTHΔFINANCΔFIXBROECT−1
ΔGROWTH  –  –  7.11*  [0.01]  5.22**  [0.05]*  −0.95*  [−5.96] 
ΔFINANC  8.17*  [0.01]  –  –  4.71**  [0.05]  −0.03  [−0.61] 
ΔFIXBRO  5.62**  [0.00]  37.0*  [0.00]  –  –  −0.55  [−1.47] 
Case 6: between GROWTH, FINANC, and ICTIND (2001–2012)
  ΔGROWTHΔFINANCΔICTINDECT−1
ΔGROWTH  –  –  22.6*  [0.00]  7.24*  [0.00]  −2.12**  [−5.52] 
ΔFINANC  7.06*  [0.01]  –  –  35.1*  [0.00]  −0.22  [−1.97] 
ΔICTIND  9.67*  [0.00]  16.7*  [0.00]  –  –  −0.94  [−0.94] 

Note. GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

*

Statistical level of significance at 1% level.

**

Statistical level of significance at 5% level.

5.1Long-run Granger causality results

From Table 7, when ΔGROWTH serves as the dependent variable, the lagged error-correction term is statistically significant at the 1% level. This implies that economic growth tends to converge to its long-run equilibrium path in response to changes in its regressors, namely ICT penetration and financial development.

The estimated lagged ECT (in all situations: Cases 1–6) carries a negative sign. This implies that the change in the level of economic growth (ΔGROWTH) does in fact rapidly respond to any deviation in the long-run equilibrium (or short-run disequilibrium) for the t1 period. In other words, the effect of an instantaneous shock to ICT penetration and financial development on economic growth will be completely adjusted in the long run.

The speeds of adjustment between GROWTH, FINANC and ICTPEN in these six cases vary between 0.55% and 2.12%. However, when we considered ΔFINANC or ΔICTPEN as the dependent variable, the ECTs are not statistically significant. The statistical insignificance of the ECTs suggests that economic growth (or FINANC or ICTPEN) does not respond to deviations from long-run equilibrium.

5.2Short-run Granger causality results5.2.1Dynamics between financial development and economic growth

We find evidence of bidirectional causality between financial development and economic growth for all the cases (see Table 8, Cases 1–6). This supports the feedback hypothesis of finance-growth nexus and is consistent with the findings of Chow and Fung (2011), Dritsakis and Adamopoulos (2004), Pradhan et al. (2014a,b), Uddin et al. (2014), and Wolde-Rufael (2009) for the various regions and countries that they study.

Table 8.

The summary of short-run inference between ICT penetration, financial development and economic growth in the Next-11 countries.

Causal relationships tested in the model  FINANC vs. GROWTH  ICTPEN vs. GROWTH  FINANC vs. ICTPEN 
Case 1 (1961–2012)  FINANCGROWTH  TELLINGROWTH  FINANCTELLIN 
Case 2 (1991–2012)  FINANCGROWTH  MOBILEGROWTH  FINANCMOBILE 
Case 3 (1991–2012)  FINANCGROWTH  INTUSEGROWTH  FINANCINTUSE 
Case 4 (2001–2012)  FINANCGROWTH  INTSERGROWTH  FINANCINTSER 
Case 5 (2001–2012)  FINANCGROWTH  FIXBROGROWTH  FINANCFIXBRO 
Case 6 (2001–2012)  FINANCGROWTH  ICTPENGROWTH  FINANCICTPEN 

Note 1: GROWTH: per capita economic growth; FINANC: financial development; TELLIN; telephone lines penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSERV: internet server penetration; FIXBRO: fixed broadband penetration; and ICTIND: composite index of ICT penetration.

Note 2: → or ←: unidirectional causality in one direction or another; ↔: bidirectional causality.

5.2.2Dynamics between ICT penetration and economic growth

Here, we find evidence of both bidirectional and unidirectional causality between ICT penetration and economic growth. The bidirectional causality is visible between INTUSE and GROWTH, INTSER and GROWTH, FIXBRO and GROWTH and ICTPEN and GROWTH (see Table 8, Cases 3–8). This supports the feedback hypothesis of ICT-growth nexus and is congruent with the findings of Arvin and Pradhan (2014), Chakraborty and Nandi (2009, 2011), Cronin et al. (1991), Lam and Shiu (2010), Pradhan et al. (2013b), Shiu and Lam (2008a, b), Wolde-Rufael (2007), and Zahra et al. (2008) for the various regions and countries that they study.

In addition, the study finds unidirectional causality between TELLIN and GROWTH and MOBILE and GROWTH (see Table 8, Cases 1–2). This supports the demand-following hypothesis of ICT-growth nexus and is consistent with the findings of Beil et al. (2005), Lee et al. (2012), Pradhan et al. (2013b), and Shiu and Lam (2008a,b) for the various regions and countries that they study.

The above analysis suggests that as income levels increase, the demand for all the ICTs will be on an upward trend. The empirical results also show that ICTs that provide ubiquitous high speed connectivity tend to provide a more effective platform for economic agents to pursue economies of scale and scope. This will result in improved economic productivity and economic growth, thus supporting the feedback hypothesis.

5.2.3Dynamics between ICT penetration and financial development

The empirical evidence suggests both bidirectional and unidirectional causality between ICT penetration and financial development. Bidirectional causality is visible between FINANC and TELLIN, FINANC and MOBILE, FINANC and INTUSE and FINANC and ICTPEN (see Table 8, Cases 1–3 & Case 6). This supports the feedback hypothesis of ICT penetration-financial development nexus and is consistent with the findings of Pandey (2014).

In addition, the study also finds unidirectional causality between FINANC and INTSER and FINANC and FIXBRO (see Table 8, Cases 4–5). This supports both the demand-following hypothesis of ICT penetration-financial development nexus (INTSERFINANC) and the supply-leading hypothesis of ICT penetration-financial development nexus (FIXBROFINANC). These results are congruent with the findings of Pandey (2014).

5.3Assessing shocks using generalized impulse response functions

The generalized impulse response functions (GIRFs) approach is used to trace the effect of a one-off shock to one of the data series on the current and future values of the series. The GIRFs can also be used to summarize the direction of causal patterns in a cointegrated system and to justify the strength of the causal relationships among these variables (Riezman et al., 1996). In this context, we will examine the impact of shocks to ICT penetration and financial development onto economic growth. This analysis provides additional support for the finding that there is demonstrated causality among a subset of variables in our vector error-correction model. The results of these GRIFs are graphed in Figs. 2–7. Our discussion of the impulse response functions mainly centers on the responses of ICT penetration, financial development and per capita economic growth to their own and other shocks.

Figure 2.
(0.21MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and telephone line penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and TELLIN; telephone lines penetration.

Figure 3.
(0.22MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and mobile phone penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and MOBILE: mobile phone penetration.

Figure 4.
(0.21MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and internet user penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and INTUSE: internet user penetration.

Figure 5.
(0.23MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and internet server penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and INTSERV: internet server penetration.

Figure 6.
(0.21MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and fixed broadband penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and FIXBRO: fixed broadband penetration.

Figure 7.
(0.25MB).

Response of variables to a shock when considering the relationship between economic growth, financial development, and the composite index of ICT penetration. Note. GROWTH: per capita economic growth; FINANC: financial development; and ICTIND: composite index of ICT penetration.

6Policy implications and conclusion

The paper examined the short-term and long-term dynamics between ICT penetration rates, financial sector development and economic growth for 11 countries over 6 sample time periods. The causal relationships between ICT penetration and economic growth in the short run vary depending on the types of ICTs. The more sophisticated ICTs, such as internet uses, internet servers and fixed broadband, seem to support the feedback hypothesis, while the traditional ICTs, such as telephone lines and mobile phones, are impacted by economic growth. In the case of the causal relationship between financial development and economic growth, the empirical analysis shows that they reinforce one another.

Similar results were obtained for ICT (telephone line penetration, mobile phone and internet use) and financial sector development. However, internet server penetration was found to have a significant impact on financial sector development and not vice versa. This is not surprising as many of the new financial instruments and services are highly dependent on internet services. On the other hand, increasing financial sector development seems to increase the demand for higher speed internet, hence financial sector development was found to impact fixed broadband penetration.

The study also found that there is a long-run relationship between the different types of ICT penetration (telephone line penetration, mobile phone penetration, internet use penetration, internet server penetration, fixed broadband penetration and the composite ICT indicator), financial development and economic growth. The empirical analysis shows that causality relationships are from ICT penetration and financial sector development to economic growth.

The empirical analysis suggests that there is a close linkage between ICT penetration and financial sector development, with one reinforcing the other. As both the financial and ICT sectors become increasingly integrated, this will result in more sophisticated, user-friendly and value-creating financial products and services. This, in turn, will increase the reach of new financial services to a wider segment of the population, resulting in higher economic growth. Hence, to ensure long-term economic growth, there should be adequate investments and incentives to develop viable electronic financial ecosystems that reinforce greater ICT penetration rates and enhance wealth creation opportunities for all economic agents. Among the key investments and incentives include the following:

  • Adequate investment for ICT infrastructure development, including expanding network coverage to rural and remote communities;

  • Subsidies and tax-incentives to ensure ICT services are affordable for disadvantaged communities;

  • Access to ICT literacy and electronic training programs that will enable all segments of the population to use the digital platform to access electronic financial services and other e-services to improve their socioeconomic wellbeing; and

  • Development of a sound regulatory environment that ensures good governance of the digital platform to prevent market failures such as cybercrimes and other online criminal activities that hinder investor and user confidence.

In summary, the global economic landscape is undergoing rapid transformation powered by new innovations in the ICT and financial sectors. Increasing integration of ICT platforms and financial services is having a deeper impact on long-term economic growth of countries. For these reasons, development plans should include strategies to increase ICT penetration rates and development of modern electronic financial systems that can have a greater multiplier impact on national economic wealth. Effective policy measures that ensure sustainable economic development have been introduced in the hope of advancing the reinforcement of ICT penetration and financial sector development.

Acknowledgment

The constructive suggestions from a referee of this journal are gratefully acknowledged.

Appendix A
Definition of variables

Tables A.1 and A.2

Table A.1.

Definition of financial development variables.

Variables  Definition 
FINANC  Composite index of financial development: based on nine financial development indicators: broad money supply, claims on the private sector, domestic credit to the private sector, domestic credit provided by the banking sector, domestic credit provided by the financial sector, market capitalization, traded stocks, turnover ratio, and number of listed companies–derived through principal component analysis. 
BMONEY  Broad money supply, expressed as a % of gross domestic product. 
CLAIMS  Claims on the private sectors, expressed as a % of gross domestic product. 
DCBANK  Domestic credit provided by the banking sector, expressed as a % of gross domestic product. 
DCPRIS  Domestic credit to the private sector, expressed as a % of gross domestic product. 
DCFINS  Domestic credit provided by the financial sector, expressed as a % of gross domestic product. 
SMACAP  Market capitalization, expressed as a % change in the market capitalization of the listed companies, used as a proxy for the evolution in the size of the stock market. 
STMTRS  Traded stocks, expressed as a % change in the total value of traded stocks, used as a proxy for the evolution in stock market liquidity. 
STMTUR  Turnover ratio, expressed as a % change in the turnover ratio in the stock market, used as a proxy for the evolution in stock market turnover. 
NLCOMP  Number of listed companies, expressed as number of listed companies per 10,000 population. 

Note 1: All monetary measures are in real US dollars.

Note 2: The variables are chosen on the basis of data availability in the chosen Next-11 countries. Additionally, these nine variables are popular measures of financial development, particularly with reference to both banking sector development and stock markets development.

Note 3: See Pradhan et al. (2014c,d) and WDI for more details about these variables.

Table A.2.

Definition of ICT penetration and growth variables.

Variables  Definition 
ICTIND  Composite index of ICT penetration: based on a composite index derived – using principal component analysis and five individual ICT indicators: telephone line penetration, mobile phone penetration, internet user penetration, internet server penetration, and fixed broadband penetration 
TELLIN  Telephone line penetration: telephone landlines per thousand of population. 
MOBILE  Mobile phone penetration: mobile phone subscribers per thousand of population. 
INTUSE  Internet user penetration: internet users per thousand of population. 
INTSER  Internet server penetration: internet servers per thousand of population. 
FIXBRO  Fixed broadband penetration: Fixed broadband per thousand of population. 
GROWTH  Per capita economic growth, expressed as a % change in per capita gross domestic product, used as an indicator of economic growth. 

Note 1: ICT denotes Information Communication Technology.

Note 2: The ICT penetration variables are chosen on the basis of data availability in the chosen Next-11 countries.

Note 3: See Pradhan et al. (2014a,b) and WDI for additional information on these variables.

Appendix B
Formulation of two composite indices using principal component analysis (PCA)

We construct a composite index of ICT penetration, hereafter denoted by ‘ICTIND’ and a composite index of financial development, henceforth denoted by ‘FINANC’. These indices are derived through principal component analysis using the following steps44

A weakness of PCA is that it is a non-parametric approach. The estimation method is data-driven and is not dependent on the predilections of the users. Furthermore, PCA does not take into account any a-priori knowledge, as alternative parametric algorithms do. It also deals with linear relationships between the variables. Not surprisingly and evidently, PCA has limitations – just as every estimation procedure does – such as scaling problems, interpretation problems, and higher correlation problems.

: first, data are arranged in the same order to create an input matrix for the principal components, thereafter the matrix is normalized, based on the min-max method. Next, using principal component analysis, eigen values, factor loadings, and principal components are derived. Finally, the principal components are used to construct the two indices separately for each country for each year. These steps are discussed in detail in several papers including Hosseini and Kaneko (2011, 2012) and Pradhan et al. (2014a, c). The variables included for the construction of the two indices are set out in Tables A.1 and A.2 (see Appendix A). The two tables (Table B.1 and Table B.2) below present the statistical values from our principal component analysis, i.e., for ICT penetration and financial development, respectively.

Table B.1.

The summary of PCA-related information for ICT penetration.

Sl. numbers  Eigen value  Proportion variance  Cumulative 
Part A: Eigen analysis of correlation matrix
PCs  Eigen value  Proportion  Cumulative 
3.69  0.738  0.738 
0.75  0.150  0.889 
0.268  0.054  0.942 
0.194  0.039  0.981 
0.095  0.019  1.000 
Part B: Eigen vectors (component loadings)
Variables  PC1  PC 2  PC 3  PC 4  PC 5 
TELLIN  0.355  −0.811  0.314  −0.153  −0.307 
MOBILE  0.420  0.582  0.496  −0.251  −0.420 
INTUSE  0.480  0.045  −0.144  0.853  −0.137 
INTSER  0.467  0.038  −0.767  −0.416  −0.137 
FIXBRO  0.499  0.008  0.216  −0.111  0.832 

Note 1: PCs denotes principal components.

Note 2: TELLIN: telephone line penetration; MOBILE: mobile phone penetration; INTUSE: internet user penetration; INTSER: internet server penetration; and FIXBRO: fixed broadband penetration.

Table B.2.

The summary of PCA-related information for financial development.

Sl. numbers  Eigen value  Proportion variance  Cumulative 
Part A: Eigen analysis of correlation matrix
PCs  Eigen value  Proportion  Cumulative 
5.291  0.588  0.588 
1.445  0.161  0.784 
1.053  0.117  0.865 
0.694  0.077  0.942 
0.291  0.032  0.975 
0.164  0.018  0.993 
0.052  0.006  0.998 
0.013  0.002  0.999 
0.001  0.0001  1.000 
Part B: Eigen vectors (component loadings)
Variables  PC1  PC 2  PC 3  PC4  PC 5  PC6  PC7  PC 8  PC 9 
BMONEY  0.394  0.217  0.204  −0.001  0.254  0.373  0.722  −0.024  −0.176 
CLAIMS  0.089  0.439  −0.659  0.547  −0.186  0.177  −0.020  0.002  −0.001 
DCPRIS  0.395  0.279  0.118  0.011  −0.006  −0.520  −0.222  −0.027  −0.657 
DCBANK  0.396  0.284  0.125  −0.009  −0.066  −0.467  0.103  0.009  0.717 
DCFINS  0.401  0.149  0.145  −0.087  0.320  0.503  −0.641  0.039  0.143 
SMACAP  0.219  −0.525  0.152  0.668  0.193  −0.106  −0.001  0.399  0.013 
STMTRS  0.330  −0.453  −0.327  −0.005  0.158  −0.071  0.012  −0.740  0.029 
STMTUR  0.281  −0.220  −0.563  −0.494  0.092  −0.068  0.084  0.538  −0.031 
NLCOMP  0.357  −0.226  0.161  −0.055  −0.85  0.258  −0.016  0.018  −0.045 

Note 1: PCs denotes principal components.

Note 2: BMONEY: broad money supply; CLAIMS: claims on the private sector; DCPRIS: domestic credit to the private sector; DCBANK: domestic credit provided by the banking sector; DCFINS: domestic credit provided by the financial sector; SMACAP: stock market capitalization; STMTRS: stock market traded stocks; STMTUR: stock market turnover ratio; and NLCOMP: number of listed companies.

Appendix C
ICT and economic growth relationships

Fig. C.1

Figure C.1.
(0.22MB).

Information and communications technologies (ICT) infrastructure and economic growth: the duality.

Source: Dutta (2001).
References
Abu-Bader and Abu-Qarn, 2008
Abu-Bader,S. Abu-Qarn
Financial development and economic growth: the Egyptian experience
Journal of Policy Modelling, 30 (2008), pp. 887-898
Aghion and Howitt, 1998
P. Aghion,P. Howitt
Endogenous Growth Theory
MIT Press, (1998)
Alfaro et al., 2004
L. Alfaro,A. Chanda,S. Kalemli-Ozcan,S. Sayek
FDI and economic growth: the role of local financial markets
Journal of International Economics, 64 (2004), pp. 89-112
Andrianaivo and Kpodar, 2011
M. Andrianaivo,K. Kpodar
ICT, Financial Inclusion, and Growth: Evidence from African countries
IMF Working Paper, No. WP/11/73. International Monetary Fund, (2011)
Ang, 2008a
J.B. Ang
Survey of recent developments in the literature of finance and growth
Journal of Economic Surveys, 22 (2008), pp. 536-576
Ang, 2008b
J.B. Ang
What are the mechanisms linking financial development and economic growth in Malaysia?
Economic Modelling, 25 (2008), pp. 38-53
Ang and McKibbin, 2007
Ang,J.B. McKibbin
Financial liberalization, financial sector development and growth: evidence from Malaysia?
Journal of Development Economics, 84 (2007), pp. 215-233
Arvin and Pradhan, 2014
B.M. Arvin,R.P. Pradhan
Broadband penetration and economic growth nexus: evidence from cross-country panel data
Applied Economics, 46 (2014), pp. 4360-4369
Baye et al., 2004
M. Baye,J. Morgan,P. Scholten
Temporal price dispersion: evidence from an online consumer electronics market
Journal of Interactive Marketing, 18 (2004), pp. 101-115
Beil et al., 2005
R.O. Beil,G.S. Ford,J.D. Jackson
On the relationship between telecommunications investment and economic growth in the United States
International Economic Journal, 19 (2005), pp. 3-9
Bhatnagar, 2000
S. Bhatnagar
Social implications of information and communication technology in developing countries: lessons from Asian success stories
Electronic Journal of Information Systems in Developing Countries, 1 (2000), pp. 1-10
Bojanic, 2012
A.N. Bojanic
The impact of financial development and trade on the economic growth of Bolivia
Journal of Applied Economics, 15 (2012), pp. 51-70
Boulila and Trabelsi, 2004
G. Boulila,M. Trabelsi
Financial development and long run growth: evidence from Tunisia: 1962–1997
Savings and Development, 28 (2004), pp. 289-314
Brown and Goolsbee, 2002
J. Brown,A. Goolsbee
Does the internet make markets move competitive? Evidence from the life insurance industry
Journal of Political Economy, 110 (2002), pp. 481-507
Calderon and Liu, 2003
C. Calderon,L. Liu
The direction of causality between financial development and economic growth
Journal of Development Economics, 72 (2003), pp. 321-334
Canning and Pedroni, 2008
C. Canning,P. Pedroni
Infrastructure, long run economic growth and causality tests for cointegrated panels
Manchester School, 76 (2008), pp. 504-527
Chaiechi, 2012
T. Chaiechi
Financial development shocks and contemporaneous feedback effect on key macroeconomic indicators: a post Keynesian time series analysis
Economic Modelling, 29 (2012), pp. 487-501
Chakraborty and Nandi, 2003
C. Chakraborty,B. Nandi
Privatization, telecommunication and growth in selected Asian countries: an econometric analysis
Communications and Strategies, 52 (2003), pp. 31-47
Chakraborty and Nandi, 2009
C. Chakraborty,B. Nandi
Telecommunications adoption and economic growth in developing countries: do levels of development matter
Journal of Academy of Business and Economics, 9 (2009), pp. 51-61
Chakraborty and Nandi, 2011
C. Chakraborty,B. Nandi
Mainline telecommunications infrastructure, levels of development and economic growth: evidence from a panel of developing countries
Telecommunications Policy, 35 (2011), pp. 441-449
Chow and Fung, 2011
W.W. Chow,M.K. Fung
Financial development and growth: a clustering and causality analysis
Journal of International Trade and Economic Development, 35 (2011), pp. 1-24
Christopoulos and Tsionas, 2004
D.K. Christopoulos,E.G. Tsionas
Financial development and economic growth: evidence from panel unit root and cointegration tests
Journal of Development Economics, 73 (2004), pp. 55-74
Cieslik and Kaniewsk, 2004
A. Cieslik,M. Kaniewsk
Telecommunication infrastructure and regional economic development: the case of Poland
Regional Studies, 38 (2004), pp. 713-725
Cihak et al., 2011
M. Cihak,A. Demirguc-Kunt,E. Feyen,R. Levine
Financial development in 205 economies, 1960–2010
Journal of Financial Perspectives, 2 (2011), pp. 17-37
Craigwell et al., 2001
R. Craigwell,D. Downes,M. Howard
The finance-growth nexus: a multivariate VAR analysis of a small open economy
Savings and Development, 25 (2001), pp. 209-223
Cronin et al., 1991
F.J. Cronin,E.B. Parker,E.K. Colleran,M.A. Gold
Telecommunications infrastructure and economic growth: an analysis of causality
Telecommunications Policy, 15 (1991), pp. 529-535
Dritsakis and Adamopoulos, 2004
N. Dritsakis,A. Adamopoulos
Financial development and economic growth in Greece: an empirical investigation with granger causality analysis
International Economic Journal, 18 (2004), pp. 547-559
Dutta, 2001
A. Dutta
Telecommunication and economic activity: an analysis of Granger causality
Journal of Management Information Systems, 17 (2001), pp. 71-95
Engle and Granger, 2003
R. Engle,C.W. Granger
Time series econometrics: cointegration and autoregressive conditional heteroscedasticity
Advanced Information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, (2003)pp. 1-30
http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2003/advanced-economicsciences2003.pdf
Engle and Granger, 1987
R.F. Engle,C.W.J. Granger
Cointegration and error correction: representation, estimation and testing
Econometrica, 55 (1987), pp. 251-276
Entner, 2008
R. Entner
The increasingly important impact of wireless broadband technology and services on the US economy, An Ovum Study for CTIA-The Wireless Association
Fase and Abma, 2003
M.M.G. Fase,R.C.N. Abma
Financial environment and economic growth in selected Asian countries
Journal of Asian Economics, 14 (2003), pp. 11-21
Fitjar and Rodríguez-Pose, 2013
R.D. Fitjar,A. Rodríguez-Pose
Firm collaboration and modes of innovation in Norway
Research Policy, 42 (2013), pp. 128-138
Forman et al., 2005
C. Forman,A. Goldfarb,S. Greenstein
Geographic location and the diffusion of internet technology
Electronic Commerce Research and Applications, 4 (2005), pp. 1-13
Granger and Newbold, 1974
C.W. Granger,P. Newbold
Spurious regressions in econometrics
Journal of Econometrics, 2 (1974), pp. 111-120
Greenwood and Jovanovic, 1990
J. Greenwood,B. Jovanovic
Financial development, growth and the distribution of income
The Journal of Political Economy, 98 (1990), pp. 1076-1107
Gries et al., 2009
T. Gries,M. Kraft,D. Meierrieks
Linkages between financial deepening, trade openness, and economic development: causality evidence from sub-Saharan Africa
World Development, 37 (2009), pp. 1849-1860
Grossman and Helpman, 1994
G.M. Grossman,E. Helpman
Endogenous innovation in the theory of growth
Journal of Economic Perspectives, 8 (1994), pp. 23-44
Holtz-Eakin et al., 1988
D. Holtz-Eakin,W. Newey,H.S. Rosen
Estimating vector auto regressions with panel data
Econometrica, 56 (1988), pp. 1371-1395
Hosseini and Kaneko, 2011
H.M. Hosseini,S. Kaneko
Dynamics sustainability assessment of countries at the macro level: a principal component analysis
Ecological Indicators, 11 (2011), pp. 811-823
Hosseini and Kaneko, 2012
H.M. Hosseini,S. Kaneko
Causality between pillars of sustainable development: global stylized facts or regional phenomena
Ecological Indicators, 14 (2012), pp. 197-201
Hsueh et al., 2013
S. Hsueh,Y. Hu,C. Tu
Economic growth and financial development in Asian countries: a bootstrap panel Granger causality analysis
Economic Modelling, 32 (2013), pp. 294-301
Im et al., 2003
K.S. Im,M.H. Pesaran,Y. Shin
Testing for unit roots in heterogeneous panels
Journal of Econometrics, 115 (2003), pp. 53-74
Jalil et al., 2010
A. Jalil,M. Feridun,Y. Ma
Finance-growth nexus in China revisited: new evidence from principal components and ARDL bounds tests
International Review of Economics and Finance, 19 (2010), pp. 189-195
Kalam, 2003
A.P.J. Kalam
Citizen Centric e-Governance: technology and management policy
Finance India, XVII (2003), pp. 1273-1277
Kao and Chiang, 2000
C. Kao,M.H. Chiang
On the estimation and inference of a cointegrated regression in panel data
Advances in Econometrics: Nonstationary Panels, Panel Cointegration and Dynamic Panels, 15 (2000), pp. 179-222
Kar et al., 2011
M. Kar,S. Nazlioglu,H. Agir
Financial development and economic growth nexus in the MENA countries: bootstrap panel Granger causality analysis
Economic Modelling, 28 (2011), pp. 685-693
King and Levine, 1993
R. King,R. Levine
Finance and growth: Schumpeter might be right
Quarterly Journal of Economics, 108 (1993), pp. 717-737
Lam and Shiu, 2010
P.L. Lam,A. Shiu
Economic growth, telecommunications development and productivity growth of the telecommunications sector: evidence around the world
Telecommunications Policy, 34 (2010), pp. 185-199
Lechman and Marszk, 2015
E. Lechman,A. Marszk
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 (2015), pp. 355-376
Lee et al., 2012
S.H. Lee,J. Levendis,L. Gutierrez
Telecommunications and economic growth: an empirical analysis of Sub-Saharan Africa
Applied Economics, 44 (2012), pp. 461-469
Levine et al., 2002
A. Levine,C.F. Lin,C.S. Chu
Unit root tests in panel data: asymptotic and finite sample properties
Journal of Econometrics, 108 (2002), pp. 1-24
Levine and Zervos, 1996
R. Levine,S. Zervos
Stock market development and long-run growth
World Bank Economic Review, 10 (1996), pp. 323-339
Levine et al., 2000
R. Levine,N. Loayza,T. Beck
Financial intermediation and growth: causality analysis and causes
Journal of Monetary Economics, 46 (2000), pp. 31-77
Liang and Teng, 2006
Q. Liang,J. Teng
Financial development and economic growth: evidence from China
China Economic Review, 17 (2006), pp. 395-411
Maddala and Wu, 1999
G.S. Maddala,S. Wu
A comparative study of unit root tests with panel data and new simple test
Oxford Bulletin of Economics and Statistics, 61 (1999), pp. 631-652
Maeso-Fernandez et al., 2006
F. Maeso-Fernandez,C. Osbat,B. Schnatz
Towards the estimation of equilibrium exchange rates for CEE acceding countries: methodological issues and a panel cointegration perspective
Journal of Computational Economics, 34 (2006), pp. 499-517
Mark and Sul, 2003
C.N. Mark,D. Sul
Cointegration vector estimation by panel DOLS and long-run money demand
Oxford Bulletin of Economics and Statistics, 65 (2003), pp. 655-680
Mehmood and Siddiqui, 2013
B. Mehmood,W. Siddiqui
What causes what? Panel cointegration approach on investment in telecommunication and economic growth: case of Asian countries
Romanian Economic Journal, 16 (2013), pp. 3-16
Menyah et al., 2014
K. Menyah,S. Nazlioglu,Y. Wolde-Rufael
Financial development, trade openness and economic growth in African countries: new insights from a panel causality approach
Economic Modelling, 37 (2014), pp. 2014
Morck et al., 2000
R. Morck,B. Yeung,W. Yu
The information content of stock markets: why do emerging markets have synchronous stock price movement?
Journal of Financial Economics, 58 (2000), pp. 215-260
Mukhopadhyay et al., 2011
B. Mukhopadhyay,R.P. Pradhan,M. Feridun
Finance-growth nexus revisited for some Asian countries
Applied Economics Letters, 18 (2011), pp. 1527-1530
Naceur and Ghazouani, 2007
S.B. Naceur,S. Ghazouani
Stock markets, banks, and economic growth: empirical evidence from the MENA region
Research in International Business and Finance, 21 (2007), pp. 297-315
Nair, 2011
M. Nair
Inclusive innovation and sustainable development: leap-frogging strategies to a high income economy
ICT Strategic Review 2011/2012, pp. 225-257
(Chapter 11)
Nair and Shariffadeen, 2009
M. Nair,T.M.A. Shariffadeen
Managing innovation in the network economy: lessons for countries in the Asia-Pacific region
Digital Review of Asia Pacific 2009–2010, (2009), pp. 25-42
Nair and Vaithilingam, 2012
M. Nair,S. Vaithilingam
Innovative use of ICT among urban-poor communities: challenges and opportunities
ICT Strategic Review 2012/2013, pp. 181-191
Odhiambo, 2008
N.M. Odhiambo
Financial development in Kenya: a dynamic test of the finance-led growth hypotheses
Economic Issues, 13 (2008), pp. 21-36
Odhiambo, 2010
N.M. Odhiambo
Finance-investment-growth nexus in South Africa: an ARDL bounds testing procedure
Economic Change Restructuring, 43 (2010), pp. 205-219
Omri et al., 2015
A. Omri,S. Daly,C. Rault,A. Chaibi
Financial development, environmental quality, trade and economic growth: what causes what in MENA countries?
Energy Economics, 48 (2015), pp. 242-252
Pandey, 2014
S.K. Pandey
ICT Diffusion, Financial Development and Economic Growth: The Causal Nexus in Selected Asian Countries
Indian Institute of Technology, (2014)
(Ph.D. thesis)
Panopoulou, 2009
E. Panopoulou
Financial variables and euro area growth: a non-parametric causality analysis
Economic Modelling, 26 (2009), pp. 1414-1419
Patrick, 1966
H.T. Patrick
Financial development and economic growth in underdeveloped countries
Economic Development and Cultural Change, 14 (1966), pp. 174-189
Pedroni, 2004
P. Pedroni
Panel cointegration: asymptotic and finite sample properties of pooled time series tests, with an application to the PPP hypothesis
Econometric Theory, 20 (2004), pp. 597-625
Pedroni, 2000
P. Pedroni
Fully modified OLS for heterogeneous cointegrated panels
Advanced in Econometrics, 15 (2000), pp. 93-130
Pedroni, 2001
P. Pedroni
Purchasing power parity tests in cointegrated panels
Review of Economics and Statistics, 83 (2001), pp. 727-731
Pradhan et al., 2015
R.P. Pradhan,M.B. Arvin,N.R. Norman
The dynamics of information and communications technologies infrastructure, economic growth, and financial development: Evidence from Asian countries
Technology in Society, 42 (2015), pp. 135-149
Pradhan et al., 2014a
R.P. Pradhan,M.B. Arvin,N.R. Norman,S.K. Bele
Economic growth and the development of telecommunications infrastructure in the G-20 countries: a panel-VAR approach
Telecommunications Policy, 38 (2014), pp. 634-649
Pradhan et al., 2014b
R.P. Pradhan,M.B. Arvin,S. Bahmani,N.R. Norman
Telecommunications infrastructure and economic growth: comparative policy analysis for the G-20 developed and developing countries
Journal of Comparative Policy Analysis, 16 (2014), pp. 401-423
Pradhan et al., 2014c
R.P. Pradhan,M.B. Arvin,N.R. Norman,J.H. Hall
Causal nexus between economic growth, banking sector development, stock market development and other macroeconomic variables: the case of ASEAN countries
Review of Financial Economics, 23 (2014), pp. 155-173
Pradhan et al., 2014d
R.P. Pradhan,M.B. Arvin,N.R. Norman,J.H. Hall
The dynamics of banking sector and stock market maturity and the performance of Asian economies
Journal of Economic and Administrative Sciences, 30 (2014), pp. 16-44
Pradhan et al., 2013a
R.P. Pradhan,S. Bele,S. Pandey
Internet-growth nexus: evidence from cross country panel data
Applied Economics Letters, 20 (2013), pp. 1511-1515
Pradhan et al., 2013b
R.P. Pradhan,S. Bele,S. Pandey
The Link between telecommunication infrastructure and economic growth in 34 OECD countries
International Journal of Technology, Policy and Management, 13 (2013), pp. 278-293
Pradhan et al., 2013c
R.P. Pradhan,B. Mukhopadhyay,A. Gunashekar,S. Bele,S. Pandey
Financial development, social development, and economic growth: the causal nexus in Asia
Decision, 40 (2013), pp. 69-83
Quibria et al., 2002
M.G. Quibria,T. Tschang,M.-L. Reyes-Macasaquit
New Information and Communication Technologies and Poverty: some evidence from developing Asia
Journal of the Asia Pacific Economy, 7 (2002), pp. 285-309
Ramlan and Ahmed, 2009
J. Ramlan,E.M. Ahmed
Information and communication technology (ICT) and human capital management trend in Malaysia's economic development
Applied Economics Letters, 16 (2009), pp. 1881-1886
Riezman et al., 1996
R.G. Riezman,P.M. Summers,C.H. Whiteman
The engine of growth or its handmiden? A time series assessment of export-led growth
Empirical Economics, 21 (1996), pp. 77-113
Roller and Waverman, 2001
L. Roller,L. Waverman
Telecommunications infrastructure and economic development: a simultaneous approach
American Economic Review, 91 (2001), pp. 909-923
Romer, 1986
P.M. Romer
Increasing returns and long run growth
Journal of Political Economy, 94 (1986), pp. 1024-1037
Sadun and Farooqui, 2006
R. Sadun,S. Farooqui
Broadband availability, use and impact on returns to ICT in UK firms
OECD Working Party on Indicators for the Information Society,
Sassi and Goaied, 2013
S. Sassi,M. Goaied
Financial development, ICT diffusion and economic growth: lessons from MENA region
Telecommunications Policy, 37 (2013), pp. 252-261
Schumpeter, 1934
P. Schumpeter
The Theory of Economic Development
Harvard University Press, (1934)
Shamim, 2007
F. Shamim
The ICT environment, financial sector and economic growth: a cross-country analysis
Journal of Economic Studies, 34 (2007), pp. 352-370
Shiu and Lam, 2008a
A. Shiu,P.L. Lam
Causal relationship between telecommunication and economic growth in China and its regions
Regional Studies, 42 (2008), pp. 705-718
Shiu and Lam, 2008b
A. Shiu,P.L. Lam
Causal relationship between telecommunication and economic growth: a study of 105 countries
17th Biennial Conference of the International Telecommunications Society (ITS), pp. 24-27
Solow, 1956
R.M. Solow
A contribution to the theory of economic growth
Quarterly Journal of Economics, 70 (1956), pp. 65-94
Tang et al., 2007
Z. Tang,M.D. Smith,A. Montgomery
The impact of shopbot use on prices and price dispersion: evidence from online book retailing
Heinz Research, Paper 47, (2007),
(accessed 09.05.13)
Uddin et al., 2014
G.S. Uddin,M. Shahbaz,M. Arouri,F. Teulon
Financial Development and Poverty Reduction Nexus: a cointegration and causality Analysis in Bangladesh
Economic Modelling, 36 (2014), pp. 405-412
Ulgen, 2015
F. Ulgen
Schumpeterian innovations, financial innovations and instability: An institutional perspective
Cuadernos de Economía, 38 (2015), pp. 46-53
Veeramacheneni et al., 2007
B. Veeramacheneni,E.M. Ekanayake,R. Vogel
Information technology and economic growth: a causal analysis
Southwestern Economic Review, 34 (2007), pp. 75-88
Wolde-Rufael, 2007
Y. Wolde-Rufael
Another look at the relationship between telecommunications investment and economic activity in the United States
International Economic Journal, 21 (2007), pp. 199-205
Wolde-Rufael, 2009
Y. Wolde-Rufael
Re-examining the financial development and economic growth nexus in Kenya
Economic Modelling, 26 (2009), pp. 1140-1146
Wu et al., 2010
J. Wu,H. Hou,S. Cheng
The dynamic impacts of financial institutions on economic growth: evidence from the European Union
Journal of Macroeconomics, 32 (2010), pp. 879-891
Yoo and Kwak, 2004
S.H. Yoo,S.J. Kwak
Information technology and economic development in Korea: a causality study
International Journal of Technology Management, 27 (2004), pp. 57-67
Zahra et al., 2008
K. Zahra,P. Azim,A. Mahmood
Telecommunications infrastructure development and economic growth: a panel data approach
Pakistan Development Review, 47 (2008), pp. 711-726

ICT penetration (ICTPEN) is measured by telephone line penetration (TELLIN), mobile phone penetration (MOBILE), internet user penetration (INTUSE), internet server penetration (INTSER), fixed broadband penetration (FIXBRO) and a composite indicator of the ICT measure (ICTIND).

FMOLS is a non-parametric approach, which takes into account the possible correlation between the error term and the first differences of the regressors, as well as the presence of a constant term, to deal with corrections for serial correlation (Maeso-Fernandez et al., 2006; Pedroni, 2001).

DOLS is a parametric approach, which adjusts the errors by augmenting the static regression with leads, lags, and contemporaneous values of the regressor in first differences (Mark and Sul, 2003; Kao and Chiang, 2000).

A weakness of PCA is that it is a non-parametric approach. The estimation method is data-driven and is not dependent on the predilections of the users. Furthermore, PCA does not take into account any a-priori knowledge, as alternative parametric algorithms do. It also deals with linear relationships between the variables. Not surprisingly and evidently, PCA has limitations – just as every estimation procedure does – such as scaling problems, interpretation problems, and higher correlation problems.

Corresponding author. (Rudra Prakash Pradhan rudrap@vgsom.iitkgp.ernet.in)
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