Investigating the causal relationship between transportation infrastructure, financial penetration and economic growth in G-20 countries

Research in Transportation Economics xxx (xxxx) xxx

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Investigating the causal relationship between transportation infrastructure, financial penetration and economic growth in G-20 countries Rudra P. Pradhan Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur, WB, 721302, India

A R T I C L E I N F O

A B S T R A C T

Keywords: Transportation infrastructure Financial penetration Economic growth Temporal causality G-20 countries

This study examined the interactions between transportation infrastructure, financial penetration, and economic growth in the G-20 countries from 1961 to 2016. The aim was to investigate whether temporal causality exists between the variables. Using the panel vector error-correction model, we determined the long-run and short-run links between the variables. The study’s most robust finding is that both financial penetration and transportation infrastructure stimulate economic growth in the long run. Short-run results, on the other hand, were non-uniform and depended on the specific measure of financial penetration and transportation infrastructure utilized.

1. Introduction “Investing in and developing infrastructure typically improves people’s livelihoods by helping them to lead a high quality and convenient life. It also acts as an important engine to drive integrated regional development, as well as promoting global economic recovery and sustainable growth” (Jianqing, 2016). Transportation and its related infrastructures has played a pivotal role1 in economic growth (Arvin, Pradhan, & Norman, 2015; Beyzatlar, Karacal, & Yetkiner, 2014a, 2014b; Lenz, Skender, & Mirkovic, 2018; Pradhan & Bagchi, 2013). Hence, studying the relationship between the two has received much attention in the development literature (Banister, 2012; World Bank, 1994). Whether transportation infra­ structure and economic growth are connected has long been debated. A substantial number of empirical studies examined the relationship be­ tween transportation infrastructure and economic growth in both developed and developing countries (Achour & Belloumi, 2016; Button & Yuan, 2013; Calderon, Moral-Bemito, & Serven, 2015; Chi & Baek, 2013; Deng, Shao, Yang, & Zhang, 2014; Ding, 2013; Farhadi, 2015; Gillen, 1996; Hakim & Merkert, 2016; Hulten, Bennathan, & Srinivasan,

2006; Jiang, 2001; Khadaroo & Seetanah, 2007; Mohmand, Wang, & Saeed, 2016; Munnell, 1992; Oster, Rubin, & Strong, 1997; Phang, 2003; Vidyarthi & Sharma, 2014; World Bank, 1994). There are at least three ways we can justify the links between transportation infrastructure and economic growth. First, transportation infrastructure can enter the production process as direct input and, in many cases, as an unpaid factor of production. Second, transportation infrastructure may make other existing inputs more productive. Third, transportation infrastructure can act as a magnet for regional economic growth by attracting resources from other regions, which is called agglomeration (Fernald, 1999; Hong, Chu, & Wang, 2011; Kasarda & Green, 2005; Kawakami & Doi, 2004; Kayode, Babatunde, & Abiodun, 2013; Khadaroo & Seetanah, 2010; Linneker & Spence, 1996; Loo & Banister, 2016; Marazzo, Scherre, & Fernandes, 2010; Meersman & Van de Voorde, 2013; Nakamura, 2000; Njoh, 2000; Pradhan & Bagchi, 2013; Wang, 2002; Yamaguchi, 2007; Yao & Yang, 2012; Yu, De Jong, Storm, & Mi, 2012). The majority of the empirical studies used the production function approach to examine these links. In most cases, the causality direction observed was from the transportation infrastructure to economic

E-mail addresses: [email protected], [email protected]. There are numerous and different opinions concerning the role of transportation infrastructure in economic development (Hakim & Merkert, 2016; Icacono & Levinson, 2016; Lenz et al., 2018; Meersman & Nazemzadeh, 2017; Shabani & Safaie, 2018). 1

https://doi.org/10.1016/j.retrec.2019.100766 Received 30 June 2019; Received in revised form 26 September 2019; Accepted 28 October 2019 0739-8859/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Rudra P. Pradhan, Research in Transportation Economics, https://doi.org/10.1016/j.retrec.2019.100766

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growth. However, in reality, there is a feedback relationship, that is, there is a bidirectional causality between the two factors. Additionally, the development literature supports the notion that financial penetra­ tion2 is an important factor that can affect the nexus between trans­ portation infrastructure and economic growth. Transportation infrastructure is normally vital to foster countries’ economic development and prosperity (Khadaroo & Seetanah, 2008, 2009). It is considered that in today’s era of globalization, the compet­ itive advantage of each country exclusively depends on facilitating a more efficient transportation infrastructure, while a key obstacle can be the lack of an efficient and high quality transportation infrastructure (Lenz et al., 2018). The provision of this requirement depends on high investments in transportation infrastructure (Think 20, 2019). In­ vestments in transportation infrastructure contribute to higher produc­ tivity and growth, facilitate trade and connectivity, and promote economic inclusion. In short, to be able to supply an appropriate transportation infrastructure to enhance economic growth requires a considerable investment. However, the experiences of many countries show that it is difficult to depend only on the public sector3 to finance the country’s increasing transportation requirements,4 which has led to the development of the transportation infrastructure privatization concept (Navai, 1998; Shashikumar, 1998). This means that the devel­ opment of the financial sector is one of steps towards being able to finance the transportation infrastructure. Financial institutions can play a leading role in attracting more investments in the transportation infrastructure. There are at least three ways we can justify the financial penetration requirement for the nexus between transportation infra­ structure and economic growth.

freely exchanged, and earnings and risks can be shared (Jianqing, 2016). Hence, the objective of this paper is to examine how financial penetration can affect the nexus between transportation infrastructure and economic growth. This study may be crucial for the effective implementation of both financial and transportation policies to increase economic growth. For instance, the causality running from trans­ portation infrastructure to economic growth indicates that infrastruc­ ture impacts on the economy. Thus, economic development strategies should ensure a strong focus on creating an effective transportation infrastructure to boost the local economy. There is a possibility that the significance and magnitude of this relationship may differ, depending on the types of transportation infrastructure planned and built. Sound research should be undertaken before determining any transportation policies and strategies (Arvin et al., 2015; Baker, Merkert, & Kamruz­ zaman, 2015; Pradhan & Bagchi, 2013). The main contributions of this study are two-fold. First, we link transportation infrastructure and economic growth to financial pene­ tration. Second, we create and utilize the composite indices of trans­ portation infrastructure and financial penetration using principal component analysis to study the interrelationships between trans­ portation infrastructure, economic growth, and financial penetration. It may be noted that much of the existing research on the link between transportation infrastructure and economic growth used a bivariate format (see Table A1). Hence, identifying how the additional factor of financial penetration may impact the causal link between transportation infrastructure and economic growth represents a new research approach. Manifestly the use of a trivariate causal framework in a panel setting is not common in this literature. The study progresses as follows: Section 2 provides a literature re­ view, Section 3 describes the empirical strategy, Section 4 details and discusses the empirical results and Section 5 outlines the hypotheses validations and limitations of the study. Section 6 presents conclusions and recommendations for future research.

� Financial penetration can generate credit funds and wide-ranging financial solutions for transportation infrastructure investment. � Financial penetration can promote information sharing through the wide-ranging networks of financial institutions. On the one hand, they can help by identifying market opportunities in transportation infrastructure investment, evaluate the associated risks, and allocate resources. On the other hand, they can provide feasible financing solutions and consultation services to countries during their infra­ structure planning and development phases. � Financial penetration can promote cooperation through openminded collaboration, innovation and communication. An expanded financing mechanism is necessary when funding any infrastructure development. It requires wide-ranging cooperation between financial institutions, equity funds and multilateral inter­ national organizations, so that information and resources can be

2. Literature review The aim of this study was to identify the causality direction between financial penetration, transportation infrastructure and economic growth. Therefore, we reviewed three strands of literature. The first strand of literature concerns transportation infrastructure and economic growth. There can be four equally possible complemen­ tary hypotheses between these two factors: (1) the supply-leading hy­ pothesis (SLH1) of the transportation infrastructure-economic growth nexus, where transportation infrastructure Granger causes economic growth5; (2) the demand-following hypothesis (DFH1) of the trans­ portation infrastructure-economic growth nexus, where economic growth Granger causes transportation infrastructure, (3) the feedback hypothesis (FBH1) of the transportation infrastructure-economic growth nexus, which suggests that both transportation infrastructure and eco­ nomic growth Granger cause each other, and (4) the neutrality hypothesis (NEH1) of the transportation infrastructure-economic growth nexus, where both transportation infrastructure and economic growth do not Granger cause each other. Shabani and Safaie (2018), Pradhan and Bagchi (2013), Pradhan, Norman, Badir, and Samadhan (2013), Sahoo and Dash (2012), and Pradhan (2010 a,b) provide brief summaries of the studies where they found support for these four hypotheses.

2 Financial penetration indicates the level of development of a country’s financial sector. As with the relationship between transportation infrastructure and economic growth, the link between financial penetration and economic growth is well described in the literature (Greenwood & Smith, 1997; Khanh, 2019; Levine, 2003; Pradhan, Arvin, & Bahmani, 2018). The financial pene­ tration/economic growth link is also found in various channels, such as (a) providing information about possible investments so as to allocate capital efficiently, (b) monitoring firms and exerting corporate governance, (c) risk diversification, (d) easing the exchange of goods and services, (e) mobilizing and pooling savings, and (f) technology transfers. Hence, if the financial sector development is not up to the mark, economic growth and development will be compromised (Owusu & Odhiambo, 2014; Patrick, 1966; Pradhan et al., 2018). 3 Examples of public modes of financing are tax revenues, bank loans and government bond revenues, while examples of private modes of financing are domestic bank loans, stock issues, and bond issues. (Wei & Chung, 2002). 4 Amongst others, challenges of the inherently complex quality infrastructure investments are the mobilization of long-term finance at reasonable costs, and the reformation of existing urban management systems. To encourage quality infrastructure investments, a G-20 task force will create a knowledge base that will include relevant best practices used in various G-20 member countries (Think20, 2019).

5 There are several channels through which transport infrastructure can cause economic growth, namely by: increasing productivity growth, reducing travel time, attracting foreign direct investment, expanding trade and linking together resources and markets in an integrated economy (Achour & Belloumi, 2016; Banister & Berechman, 2001; Eisner, 1991; Esfahani & Ramirez, 2003; Hong et al., 2011; Pradhan, Norman, Badir, & Samadhan, 2013b; Satya, 2003).

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Fig. 1. Possible causal relationships between transportation infrastructure, financial penetration and per capita economic growth.

The second literature strand is between financial penetration and economic growth. There can be four equally possible complementary hypotheses between these two factors: (1) the supply-leading hypothesis (SLH2) of the financial penetration-economic growth nexus, where financial penetration Granger causes economic growth,6 (2) the demandfollowing hypothesis (DFH2) of the financial penetration-economic growth nexus, where economic growth Granger causes financial pene­ tration, (3) the feedback hypothesis (FBH2) of the financial penetrationeconomic growth nexus, which suggests that both financial penetra­ tion and economic growth Granger cause each other, and (4) the neutrality hypothesis (NEH2) of the financial penetration-economic growth nexus, where both financial penetration and economic growth

do not Granger cause each other. Pradhan, Hall, Gupta, Nishigaki, Fillho, and duToit (2019), Pradhan (2018), Pradhan, Arvin, Bahmani, Hall, and Norman (2017), Pradhan, Arvin, Bennett, Nair, and Hall (2016), Pradhan, Arvin, Hall, and Nair (2016), Menyah, Nazlioglu, and Wolde-Rufael (2014), Pradhan et al. (2014), Hsueh, Hu, and Tu (2013), Pradhan, Arvin, Bele, and Taneja (2013), and Kar, Nazlioglu, and Agir (2011) provide brief summaries of various studies to support these four hypotheses. The third strand of literature concerns the link between financial penetration and transportation infrastructure. There can be four equally possible complementary hypotheses between these two factors: (1) the supply-leading hypothesis (SLH3) of the financial penetrationtransportation infrastructure nexus, where financial penetration Granger causes transportation infrastructure, (2) the demand-following hypothesis (DFH3) of the financial penetration-transportation infra­ structure nexus, where financial penetration Granger causes trans­ portation infrastructure, (3) the feedback hypothesis (FBH3) of the financial penetration-transportation infrastructure nexus, which sug­ gests that both financial penetration and transportation infrastructure

6

There are several ways that financial penetration can cause economic growth, for example by: supplying information about possible investments, supervising firms and exerting corporate governance, diversifying risk, mobi­ lizing/pooling savings, facilitating the exchange of goods and services, and managing technology transfer (Levine, 2003; Zhang, Wang, & Wang, 2012). 3

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Granger cause each other, and (4) the neutrality hypothesis (NEH3) of the financial penetration-transportation infrastructure nexus, which sug­ gests that there is no causality between financial penetration and transportation infrastructure. To the best of our knowledge, little or no research has been conducted on the third strand of literature. Thus we were unable to validate these four hypotheses (SLH3, DFH3, FBH3 and NEH3) in terms of previous research findings. Table A.1 provides a brief summary of the hypotheses that have been validated through previous empirical analysis (see Appendix A).

different transportation infrastructure indicators (GAT/PAT/GRA/PRA/ RAL). We used principal component analysis (PCA) to create these composite indices. Detailed descriptions of how to formulate these indices are available in Pradhan et al. (2017, 2018). In addition, we used per capita gross domestic product as an indicator of economic growth. Tables C.1 and C.2 in Appendix C provide the PCA analyses used to compute the composite indices of financial penetration (CIF) and transportation infrastructure (CIT). For the analysis all of the variables were converted into their natural logarithms to normalize the data. Tables D.1 and D.2 in Appendix D provide the descriptive statistics, unit root test statistics and correlations for all of the variables used in the empirical analysis. The results indicate a large variation (in terms of the standard deviation) between financial market penetration and transportation infrastructure across the sample countries. The negative skewness values apply to almost all of the var­ iables. The kurtosis value indicates the presence of a fat-tailed distri­ bution for all of the series. Furthermore, the Jarque-Bera (JB) test rejects the null of Gaussian distribution for all of the variables. The correlation statistics indicate that financial penetration indicators are correlated with transportation infrastructure and economic growth. The effect is positive in some cases, while it is negative in other cases. The results of the correlation matrix also indicate that financial penetration and transportation infrastructure indicators are highly correlated. Thus, the problem of multicollinearity would arise if these variables were used simultaneously in our empirical analysis. This confirms our belief that, for our analysis, only one financial penetration and transportation infrastructure variable should be used at a time. The study considered eight specifications and six cases, covering different indicators of financial penetration and transportation infra­ structure. The following vector error correction model (VECM) was deployed to investigate the possible directions of causality between financial penetration, transportation infrastructure and economic growth. 2 3 2 3 α1j Δ ln PEGit 4 ΔFIPit 5 ¼ 4 α2j 5

3. Empirical strategy This study aimed to test the following hypotheses: H1A. Transportation infrastructure (TRI) Granger causes economic growth. This is called the TRI-led PEG hypothesis. H1B. Economic growth Granger causes transportation infrastructure. This is termed the PEG-led TRI hypothesis. H2A. Financial penetration (FIP) Granger causes economic growth (PEG). This is named the FIP-led PEG hypothesis. H2B. Economic growth Granger causes financial penetration. This is designated as the PEG-led FIP hypothesis. H3A. Financial penetration Granger causes transportation infrastruc­ ture. This is termed the FIP-led TRI hypothesis. H3B. Transportation infrastructure Granger causes financial penetra­ tion. This is called the TRI-led FIP hypothesis. Fig. 1 presents these three hypotheses relating to the causal nexus between transportation infrastructure, financial penetration and eco­ nomic growth. This study used annual data from 1961 to 20167 for the G-20 countries, which were obtained from the World Development Indicators of the World Bank. The G-20 consists of 19 member countries and the European Union. Thus, the G-20 group consists of both developed and emerging economies. We only used data relating to the 19 member countries for this investigation, that is, we excluded the European Union. The G-20 group has two divisions, based on the World Bank’s purchasing power parity data regarding the countries’ per capita income. First, the G-20 developing countries group, which consists of Argentina, Brazil, China, India, Indonesia, Mexico, the Russian Federation, Saudi Arabia, South Africa, and Turkey. Second, the G-20 developed group, which entails Australia, Canada, France, Germany, Italy, Japan, the Korean Republic, the United Kingdom, and the United States. Figure B.1 pro­ vides a map of these countries, while Table B.1 indicates their macro­ economic profiles (see Appendix B). The G-20 countries were selected as a study sample due to the mix of both emerging and developed economies. This study used seven different indicators for financial penetration (FIP), namely: commercial bank branches (CBB), depositors with com­ mercial banks (DCB), borrowers from commercial banks (BCB), auto­ mated teller machines (ATM), bank accounts (BAA), bank concentration (BAC), and bank branches (BAB). The five indicators of transportation infrastructure (TRI) used were: goods carried by air transportation (GAT), passengers carried by air transportation (PAT), goods carried by railways (GRA), passengers carried by railways (PRA), and railway lines (RAL). The study also used two composite indices, namely, the com­ posite index of financial penetration (CIF) and the composite index of transportation infrastructure (CIT). CIF is the weighted average between the seven different indicators of financial penetration (CBB/DCB/BCB/ ATM/BAA/BAC/BAB), while CIT is the weighted average of the five

α3j Δ ln TRIit 2 3 32 μ11ik ðLÞμ12ik ðLÞμ13ik ðLÞ Δ ln PEGit k p X 4 μ21ik ðLÞμ22ik ðLÞμ23ik ðLÞ 54 Δ ln FIPit k 5 þ k¼1 μ31ik ðLÞμ32ik ðLÞμ33ik ðLÞ Δ ln TRIit k 2 3 2 3 δ1i ECTit 1 ξ1it þ4 δ2i ECTit 1 5 þ 4 ξ2it 5 δ3i ECTit 1 ξ3it

(1)

where i is the country specification, t is the time specification, and ε is the error term. FIP is defined as CBB, DCB, BCB, ATM, BAA, BAC, BAB, or CIF, and TRI is defined as GAT, PAT, GRA, PRA, RAL, or CIT. ECT-1 is the lagged error-correction term, which represents the longrun dynamics between the variables. However, the inclusion of ECT in the model depends on the specification of the time series variables, which need to be integrated of order one (I (1)) and cointegrated. The null hypotheses of this study tested the following: H1A:μ12ik 6¼ 0; μ13ik 6¼ 0; and δ1i 6¼ 0 for k ¼ 1, 2, ……, p H1B:μ21ik 6¼ 0; μ23ik 6¼ 0; and δ2i 6¼ 0 for k ¼ 1, 2, …. …, p H1C:μ31ik 6¼ 0; μ32ik 6¼ 0; and δ3i 6¼ 0 for k ¼ 1, 2, ……, p To determine the direction of causality between financial penetra­ tion, transportation infrastructure and economic growth, there exist a number of possible situations. In the first instance, financial penetration and transportation infrastructure can cause economic growth, if μ12ik and μ13ik are significantly different from zero. Second, economic growth and transportation infrastructure Granger cause financial penetration, if μ21ik and μ23ik are statistically different from zero. Third, financial penetration and economic growth Granger cause transportation

7 It represents an unbalanced panel, as data for these variables are not uni­ formly available for all of the countries and for all of the years of the study period.

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Table 1 Empirical results of panel cointegration tests. Case 1 (GAT) Tra

Case 2 (PAT) Max

Specification 1: PEG, CBB, TRI None 275* 227* At most 1 101* 89.1* At most 2 82.2* 82.9* Specification 2: PEG, DCB, TRI None 169* 148* At most 1 73.8* 56.7* At most 2 51.9* 51.9* Specification 3: PEG, BCB, TRI None 32.9* 651* At most 1 19.9* 19.9* At most 2 18.7* 18.7* Specification 4: PEG, ATM, TRI None 295* 254* At most 1 127* 98.6* At most 2 95.2* 95.2* Specification 5: PEG, BAA, TRI None 43.8* 37.8* At most 1 36.8* 32.3* At most 2 16.5* 16.5* Specification 6: PEG, BAC, TRI None 121* 116* At most 1 39.4* 40.5* At most 2 23.5* 23.5* Specification 7: PEG, BAB, TRI None 388* 334* At most 1 126* 111* At most 2 64.2* 64.2* Specification 8: PEG, CIF, TRI None 253* 188* At most 1 123* 109* At most 2 97.9* 97.9*

Case 3 (GRA)

Case 4 (PRA)

Case 5 (RAL)

Case 6 (CIT)

Tra

Max

Tra

Max

Tra

Max

Tra

Max

Tra

Max

305* 103* 60.5*

256* 91.5* 60.5*

198* 112* 61.0*

167* 95.8* 61.0*

280* 108* 76.2*

248* 76.6* 76.2*

197* 68.7* 44.1*

150* 55.1* 44.0*

172* 62.5* 26.9*

153* 56.2* 26.9*

156* 64.7* 40.8*

135* 53.8* 40.8*

77.8* 67.9* 33.4*

69.1* 63.9* 33.4*

128* 61.6* 35.2*

101* 48.3* 35.2*

102* 59.7* 37.0*

86.1* 49.4* 37.0*

103* 59.2* 46.0*

88.3* 58.5* 46.0*

50.1* 16.8* 5.23

42.2* 16.8* 5.23

42.3* 8.18 1.57

37.7* 9.25 1.57

68.5* 15.8* 10.9*

58.8* 15.8* 10.9*

28.0* 8.61 5.45

27.9* 7.09 5.45

42.3* 16.8* 5.23

37.7* 16.8* 5.23

282* 122* 94.0*

210* 102* 94.0*

238* 200* 92.8*

199* 176* 92.8*

303* 123* 66.4*

249* 101* 66.4*

226* 68.6* 53.7*

193* 62.0* 53.7*

185* 94.0* 48.9*

152* 82.2* 48.9*

45.6* 32.8* 14.3

33.3* 30.5* 14.3

45.4* 13.2 2.35

39.5* 14.7 2.35

48.9* 10.97 3.65

41.2* 11.2 3.65

263* 22.3* 8.197

263* 19.5* 8.197

189* 161* 92.8*

184* 155* 92.8*

128* 70.0* 39.5*

95.5* 66.2* 39.5*

126* 60.4* 48.5*

95.6* 53.9* 48.5*

113* 55.6* 53.9*

92.8* 55.6* 53.9*

207* 65.7* 44.4*

178* 58.5* 44.4*

93.4* 30.9* 24.9*

83.4* 29.2* 24.9*

395* 137* 85.1*

344* 112* 85.1*

296* 122* 62.3*

260* 105* 62.3*

294* 115* 76.4*

259* 83.5* 76.4*

211.4* 69.4* 30.1*

180* 64.2* 30.1*

205* 75.4* 35.5*

182* 63.1* 35.5*

196* 114* 75.1*

146* 93.6* 75.1*

283* 121* 91.6*

218* 107* 91.6*

267* 109* 85.6*

198* 96.6* 85.6*

156* 74.7* 35.5*

123* 67.9* 35.5*

137* 80.0* 49.4*

89.1* 62.9* 49.4*

Note 1: CBB is commercial bank branches, DCB is depositors with commercial banks, BCB is borrowers from commercial banks, ATM is automated teller machines, BAA is bank accounts, BAC is bank concentration, BAB is bank branches, CIF is the composite index of financial penetration, GAT is goods carried by air transportation, PAT is passengers carried by air transportation, GRA is goods carried by railways, PRA is passengers carried by railways, RAL is railway lines, CIT is the composite index of transportation infrastructure, and PEG is per capita economic growth. Note 2: TRI indicates the use of GAT, PAT, GRA, PRA, RAl, or CIT. Note 3: Tra is trace statistics; Max is maximum eigenvalue statistics; and NOC is number of cointegrating vectors. Note 4: * indicates that the test statistics are significant at the 5% level.

infrastructure, if μ31ik and μ32ik are statistically different from zero.

at least one cointegrating relationship between all of the variables in the model (FIP, TRI and PEG) over time across all countries in the sample. This is true for all of the eight specifications and six cases in each specification, depending on the inclusion of different financial pene­ tration and transportation infrastructure indicators. Therefore, this unique order of integration of the variables and their cointegration re­ lationships help us to apply vector error-correction modeling (VECM) to examine the direction of causality between financial penetration, transportation infrastructure, and economic growth. Table 2 reports the results of VECM for the eight specifications and six cases we studied. We first refer to the long-run results, which were ascertained by examining the statistical significance of the ECT-1 coefficients. The test results show that when ΔPEG is the dependent variable, the coefficients are statistically significant at a 1% level. This implies that per capita economic growth tends to converge to its long-run equilibrium path in response to changes in both transportation infrastructure and financial penetration. This is true for all of the eight specifications and six cases that we investigated. However, the rate of convergence varied from case to case within a particular specification. For instance, for specification 1 and case 1, the ECT is 0.71, indicating that if per capita economic growth is below or above the equilibrium level, it regulates itself by 71% per year. Moreover, in the situation of case 4 using specification 1, per capita economic growth self-regulates by 90%. For all of the cases and all of the specifications, per capita economic growth varied from 10% to 96% (see Table 2). Therefore, the overall conclusion is that economic growth in G-20

4. Empirical results and discussion We first report the order of integration8 and cointegration9 between financial penetration, transportation infrastructure and economic growth. The Levine-Lin-Chu (LLC) panel unit root test was used at three levels10 to examine the order of integration of the variables, while the Fisher panel cointegration test was used to establish the existence of cointegration between FIP, TRI and PEG in our panel setting. All of these chosen variables were tested in level and first difference form. The test results confirm that all of these variables are non-stationary at their level, but they were found to be stationary at first difference form (see Table D.1 of Appendix D). Therefore, in our panel of G-20 countries, we concluded that all of the variables (FIP, TRI and PEG) are integrated of order one, that is I [1]. Further empirical evidence indicates that the null hypothesis of no cointegration was rejected by the Fisher panel cointe­ gration test (see Table 1). Therefore, there is support for the presence of

8 This is done by deploying panel unit root tests, which ensure whether a time series variable is non-stationary using VECM. 9 Cointegration ensures the existence of long-run equilibrium relationships between variables, even though short-term departures from equilibrium may exist. 10 These are with a constant and deterministic trend, with intercept only, and no trend and no intercept.

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Table 2 Results of panel Granger causality test. Dependent Variable

Independent variables and ECT-1

Specification 1: PEG, CBB, TRI Case 1 ΔPEG ΔPEG – ΔCBB 6.10* ΔTRI 3.96** Case 4 ΔPEG ΔPEG – ΔCBB 6.91* ΔTRI 5.05* Specification 2: PEG, DCB, TRI Case 1 ΔPEG ΔPEG – ΔDCB 7.39* ΔTRI 4.14* Case 4 ΔPEG ΔPEG – ΔDCB 6.57* ΔTRI 1.85 Specification 3: PEG, BCB, TRI Case 1 ΔPEG ΔPEG – ΔCBB 3.86** ΔTRI 3.23** Case 4 ΔPEG ΔPEG – ΔBCB 0.66 ΔTRI 8.22* Specification 4: PEG, ATM, TRI Case 1 ΔPEG ΔPEG – ΔATM 13.2* ΔTRI 2.83 Case 4 ΔPEG ΔPEG – ΔATM 7.68* ΔTRI 8.38* Specification 5: PEG, BAA, TRI Case 1 ΔPEG ΔPEG – ΔBAA 1.56 ΔTRI 3.51** Case 4 ΔPEG ΔPEG – ΔBAA 3.93* ΔTRI 2.85 Specification 6: PEG, BAC, TRI Case 1 ΔPEG ΔPEG – ΔBAA 0.16 ΔTRI 5.55* Case 4 ΔPEG ΔPEG – ΔBAA 0.97 ΔTRI 15.4* Specification 7: PEG, BAB, TRI Case 1 ΔPEG ΔPEG – ΔBAB 5.23* ΔTRI 3.84** Case 4 ΔPEG

ΔCBB 0.72 – 0.70

ΔGAT 5.11* 4.60* –

ECT-1 0.71* 0.12 0.34

ΔCBB 0.02 – 4.12**

ΔPRA 6.24* 0.71 –

ECT-1 0.90* 0.10 0.04

ΔDCB 4.23** – 1.30

ΔGAT 4.23* 3.66** –

ECT-1 0.59* 0.08 0.11

ΔDCB 4.92* – 5.74*

ΔPRA 4.54* 0.27 –

ECT-1 0.54* 0.08 0.01

ΔBCB 1.06 – 0.94

ΔGAT 3.73** 0.36 –

ECT-1 0.53* 0.03 0.22

ΔBCB 0.62 – 1.07

ΔPRA 3.23** 1.27 –

ECT-1 0.46* 0.06 0.10

ΔATM 3.63** – 7.28*

ΔGAT 4.41* 25.2* –

ECT-1 0.72* 0.22 0.14

ΔATM 4.66* – 2.17

ΔPRA 3.53* 1.77 –

ECT-1 0.52* 0.14 0.01

ΔBAA 0.43 – 6.36*

ΔGAT 4.39* 10.9* –

ECT-1 0.93* 0.09 0.21

ΔCBB 4.05* – 3.69**

ΔPRA 0.24 3.82** –

ECT-1 0.95* 0.17 0.22

ΔBAA 1.48 – 1.77

ΔGAT 4.18* 0.25 –

ECT-1 0.70* 0.07 0.13

ΔBAA 4.03** – 0.30

ΔPRA 4.45** 1.03 –

ECT-1 0.46* 0.10 0.08

ΔBAB 0.52 – 2.01

ΔGAT 5.24* 3.30 –

ECT-1 0.95* 0.13 0.23

ΔBAB

ΔPRA

ECT-1

Case 2 ΔPEG – 7.87* 4.19** Case 5 ΔPEG – 3.99** 4.43** Case 2 ΔPEG – 4.37* 6.88* Case 5 ΔPEG – 5.40* 4.18** Case 2 ΔPEG – 3.94** 3.69** Case 5 ΔPEG – 4.93* 4.45* Case 2 ΔPEG – 20.6* 5.83* Case 5 ΔPEG – 15.5* 2.85 Case 2 ΔPEG – 1.30 1.23 Case 5 ΔPEG – 3.46* 2.22 Case 2 ΔPEG – 0.18 1.91 Case 5 ΔPEG – 1.41 8.25* Case 2 ΔPEG – 3.23 2.09 Case 5 ΔPEG

ΔCBB 0.42 – 1.04

ΔPAT 4.58* 12.9* –

ECT-1 0.78* 0.10 0.02

ΔCBB 0.89 – 1.03

ΔRAL 4.23** 0.61 –

ECT-1 0.74* 0.09 0.05

ΔDCB 4.19* – 5.15*

ΔPAT 2.63 0.79 –

ECT-1 0.70* 0.06 0.11

ΔDCB 3.94** – 0.73

ΔRAL 1.02 2.75 –

ECT-1 0.53* 0.08 0.05

ΔBCB 0.67 – 0.85

ΔPAT 6.05* 0.59 –

ECT-1 0.73* 0.08 0.04

ΔBCB 0.87 – 0.71

ΔRAL 4.17** 1.49 –

ECT-1 0.71* 0.01 0.02

ΔATM 32.6* – 2.48

ΔPAT 4.99* 24.5* –

ECT-1 0.75* 0.19 0.02

ΔATM 6.29* – 1.88

ΔRAL 0.84 0.81 –

ECT-1 0.36* 0.21 0.04

ΔCBB 0.71 – 9.61*

ΔPAT 4.71* 3.97* –

ECT-1 0.91* 0.10 0.22

ΔBAA 4.72* – 3.77**

ΔRAL 0.11 3.72** –

ECT-1 0.91* 0.05 0.09

ΔBAA 1.18 – 3.89**

ΔPAT 8.35* 2.39 –

ECT-1 0.72* 0.06 0.13

ΔBAA 2.48 – 0.21

ΔRAL 3.98** 0.35 –

ECT-1 0.38* 0.12 0.32

ΔBAB 0.13 – 3.33**

ΔPAT 9.32* 3.91** –

ECT-1 0.91* 0.12 0.13

ΔBAB

ΔRAL

ECT-1

Case 3 ΔPEG – 5.53* 5.94* Case 6 ΔPEG – 5.46* 1.43 Case 3 ΔPEG – 4.69* 0.67 Case 6 ΔPEG – 3.97** 7.53* Case 3 ΔPEG – 1.09 1.64 Case 6 ΔPEG – 1.97 4.28** Case 3 ΔPEG – 13.3* 2.71 Case 6 ΔPEG – 5.19* 1.27 Case 3 ΔPEG – 0.89 3.97** Case 6 ΔPEG – 6.97* 5.01* Case 3 ΔPEG – 1.77 3.89** Case 6 ΔPEG – 0.40 3.64* Case 3 ΔPEG – 5.56* 1.17 Case 6 ΔPEG

ΔCBB 7.49* – 9.66*

ΔGRA 6.54* 1.01 –

ECT-1 0.87* 0.10 0.04

ΔCBB 0.91 – 2.59

ΔCIT 2.09 2.65 –

ECT-1 0.84* 0.07 0.003

ΔDCB 3.94** – 1.37

ΔGRA 1.61 5.14* –

ECT-1 0.75* 0.09 0.03

ΔDCB 7.97* – 3.58**

ΔCIT 6.83* 16.3* –

ECT-1 0.65* 0.17 0.003

ΔBCB 1.43 – 9.95*

ΔGRA 4.27* 2.76 –

ECT-1 0.10* 0.05 0.09

ΔBCB 4.75* – 9.18*

ΔCIT 2.80 2.80 –

ECT-1 0.23* 0.012 0.01

ΔATM 4.16* – 0.28

ΔGRA 4.72* 0.71 –

ECT-1 0.11* 0.16 0.05

ΔATM 6.14* – 5.78*

ΔCIT 0.72 0.67 –

ECT-1 0.54* 0.14 0.002

ΔCBB 8.08* – 2.28

ΔGRA 2.25 2.07 –

ECT-1 0.94* 0.09 0.02

ΔBAA 0.25 – 7.13*

ΔCIT 2.52 2.80 –

ECT-1 0.13* 0.17 0.002

ΔBAA 3.87** – 1.28

ΔGRA 4.19** 1.92 –

ECT-1 0.59* 0.06 0.01

ΔBAA 3.67* – 1.28

ΔCIT 2.93 1.90 –

ECT-1 0.54* 0.14 0.01

ΔBAB 0.37 – 0.89

ΔGRA 4.42* 0.04 –

ECT-1 0.96* 0.14 0.05

ΔCIT

ECT-1

ΔBAB

(continued on next page)

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Table 2 (continued ) Dependent Variable

Independent variables and ECT-1

ΔPEG – ΔBAB 3.80* ΔTRI 4.41** Specification 8: PEG, CIF, TRI Case 1 ΔPEG ΔPEG – ΔCIF 6.41* ΔTRI 3.16 Case 4 ΔPEG ΔPEG – ΔCIF 7.25* ΔTRI 5.59*

0.09 – 3.59**

4.49* 0.29 –

0.90* 0.11 0.02

ΔCIF 8.47* – 3.13

ΔGAT 1.58 2.74 –

ECT-1 0.66* 0.01 0.08

ΔCIF 14.9* – 6.11*

ΔPRA 1.02 0.52 –

ECT-1 0.70* 0.01 0.003

– 5.05* 0.64 Case 2 ΔPEG – 6.19* 8.50* Case 5 ΔPEG – 10.4* 1.80

0.54 – 0.67

4.27* 0.97 –

0.80* 0.10 0.05

ΔCIF 3.52* – 2.53

ΔPAT 6.73* 0.57 –

ECT-1 0.72* 0.01 0.11

ΔCIF 14.1* – 9.82*

ΔRAL 0.86 7.56 –

ECT-1 0.68* 0.01 0.03

– 9.29* 1.70 Case 3 ΔPEG – 7.97* 0.12 Case 6 ΔPEG – 8.74* 4.84

0.01 – 2.50

2.15 2.28 –

0.77* 0.09 0.004

ΔCIF 15.3* – 0.23

ΔGRA 3.62** 1.19 –

ECT-1 0.64* 0.01 0.09

ΔCIF 9.11* – 1.51

ΔCIT 1.47 2.80 –

ECT-1 0.88* 0.03 0.08

Note 1: PEG is per capita economic growth, FIP is financial penetration, TRI is transportation infrastructure, CBB is commercial bank branches, DCB is depositors with commercial banks, BCB is borrowers from commercial banks, ATM is automated teller machines, BAA is bank accounts, BAC is bank concentration, BAB is bank branches, CIF is the composite index of financial penetration, GAT is goods carried by air transportation, PAT is passengers carried by air transportation, GRA is goods carried by railways, PRA is passengers carried by railways, RAL is railway lines, CIT is the composite index of transportation infrastructure, and ECT-1: lagged errorcorrection term. Note 2: FIP indicates CBB, DCB, BCB, ATM, BAA, BAC, BAB, IFB, or CIF; and TRI indicates GAT, PAT, GRA, PRA, RAL, or CIT. Note 3: * and ** indicate that parameter estimates are significant at the 5% and 10% levels respectively.

countries is significantly influenced by both financial penetration and transportation infrastructure. In other words, to stimulate economic growth it is necessary to improve both the transportation infrastructure and financial penetration in the G-20 countries. In the short run, however, the results are mostly non-uniform and they vary from specification to specification and case to case- and also within a particular case (see Table 3). From these non-uniform results, we deduced the following: For specification 1 (CBB), the common finding is that there is bidi­ rectional causality between transportation infrastructure and economic growth, and also that there is unidirectional causality from economic growth to financial penetration. These findings support both H2A,B, and H1B. For specification 2 (DCB), the common finding is that there is bidi­ rectional causality between financial penetration and economic growth. This finding supports H1A,B. For specification 3 (BCB), the common finding is that there is uni­ directional causality from economic growth to financial penetration. Also, there is bidirectional causality between transportation infrastruc­ ture and economic growth. These results support both H2A,B, and H1B. For specification 4 (ATM), the common finding is that there is bidi­ rectional causality between financial penetration and economic growth. This finding supports H1A,B. For specification 5 (BAA), the common finding is the existence of bidirectional causality between financial penetration and transportation infrastructure. This outcome supports H1A,B. For specification 6 (BAC), the common finding is that there is bidi­ rectional causality between transportation infrastructure and economic growth. This finding supports H2A,B. For specification 7 (BAB), the common finding is that there is uni­ directional causality from economic growth to financial penetration. This result supports H1B. For specification 8 (CIF), the common finding is that bidirectional causality exists between financial penetration and economic growth. This discovery supports H1A,B. To some extent, these short-run findings provided divergent results, which have been summarized in Table 3. In other words, the causal relationships between transportation infrastructure, financial penetra­ tion and economic growth for the G-20 countries may differ when using different transportation infrastructure and financial penetration in­ dicators. Hence, development policies of the G-20 countries may be different and can vary according to the different types of transportation infrastructure and financial penetration indicators used.

5. Hypotheses validation and limitations of the study In addition to the previous analyses, we calculated further estima­ tions to supplement our analysis. First, the FMOLS11 was used to determine the magnitude of the impact of financial penetration and transportation infrastructure on economic growth. The results indicate that they have a positive effect on per capita economic growth in most cases (see Table E.1; Appendix E). This positive outcome confirms the supply-leading hypothesis between financial penetration and economic growth, as well as transportation infrastructure and economic growth. Hence, we can conclude that financial penetration and transportation infrastructure are the main drivers of economic growth in G-20 countries. Second, Generalized Impulse Response Functions (GIRFs) were used for describing the dynamics in a time series model by mapping out the reaction of financial penetration and transportation infrastructure to a one standard deviation shock to the residual in economic growth. Third, Variance Decomposition Analysis (VDA) was used for exam­ ining the relative effects of financial penetration and transportation infrastructure on economic growth. Using variance decomposition analysis, we found that the shocks of both financial penetration and transportation infrastructure have a significant impact on per capita economic growth. This supports the long-run results of VECM. Fourth, all of these estimations (VECM, FMOLS, GIRFs and VDAs) were also examined separately for the G-20 developing and G-20 developed groups to justify the robustness of our findings.12 These additional results largely support our VECM findings. Nevertheless, a brief discussion of some of the limitations of our study is in order. First, by deploying VECM to capture the dynamics between financial penetration, transportation infrastructure and eco­ nomic growth, our study excluded non-linearity tests and/or structural breaks, which may have led to biases in the results. Second, our study could not incorporate other indicators of financial

11 FMOLS is fully modified ordinary least squares, a non-parametric approach, which takes into account the possible correlation between the error term and first differences of regressor as well as presence of a constant term to deal with corrections for serial correlation (Maeso-Fernandez, Osbat, & Schnatz, 2006; Pedroni, 2001). 12 The results of the tests relating to the Generalized Impulse Response Functions (GIRFs), Variance Decomposition Analysis (VDA), as well as all of the estimations (VECM, FMOLS, GIRFs and VDAs) are not reported here due to space constraints. However, they can be made available on request.

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information and communication technologies (ICT), industrialization, and infrastructure investment, amongst others (Hakim & Merkert, 2016). Fourth, the issues of structural breaks and non-linearity were not incorporated in the panel data tests when analyzing the impact of financial penetration on the transportation-infrastructure-economic growth nexus. This may have resulted in biased findings. Fifth, this study did not allow for different environmental factors, such as the economic, investment, political, and institutional conditions that could affect the impact of transportation infrastructure and finan­ cial penetration on economic growth (Meersman & Nazemzadeh, 2017). Sixth, this study used only bank-based financial penetration to establish the nexus of transportation infrastructure, economic growth and financial penetration. Hence, the exclusion of securities markets data may have affected the findings. For instance, a well-developed se­ curities market may hinder the process of identifying innovative projects that promote economic growth (Stiglitz, 1985). In summary, when considering the potential policy insights from this study of G-20 countries over the period of 1961–2016, it is critical to keep some caveats in mind. First, data constraints may restrict the possibility of drawing fair conclusions from the analysis. Second, issues such as omitting structural breaks, non-linearity, time-varying variables and the proxies for both transportation infrastructure and financial penetration could have affected our results.

Table 3 Summary of short-run Granger causality results. Samples

Cases

1

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6

2

3

4

5

6

7

8

Short-run Causal Relationships Between FIP and TRI

FIP and PEG

TRI and PEG

CBB ← GAT CBB ← PAT CBB → GRA CBB → PRA CBB 6¼ RAL CBB 6¼ CIT DCB ← GAT DCB → PAT DCB ← GRA DCB → PRA DCB 6¼ RAL DCB ↔ CIT BCB 6¼ GAT BCB 6¼ PAT BCB → GRA BCB 6¼ PRA BCB 6¼ RAL BCB → CIT ATM ↔ GAT ATM ← PAT ATM 6¼ GRA ATM 6¼ PRA ATM 6¼ RAL ATM → CIT BAA ↔ GAT BAA ↔ PAT BAA 6¼ GRA BAA ↔ PRA BAA ↔ RAL BAA → CIT BAC 6¼ GAT BAC → PAT BAC 6¼ GRA BAC 6¼ PRA BAC 6¼ RAL BAC 6¼ CIT BAB 6¼ GAT BAB ↔ PAT BAB 6¼ GRA BAB → PRA BAB 6¼ RAL BAB 6¼ CIT CIF 6¼ GAT CIF 6¼ PAT CIF 6¼ GRA CIF → PRA CIF ↔ RAL CIF 6¼ CIT

CBB ← PEG CBB ← PEG CBB ↔ PEG CBB ← PEG CBB ← PEG CBB ← PEG DCB ↔ PEG DCB ↔ PEG DCB ↔ PEG DCB ↔ PEG DCB ↔ PEG DCB ↔ PEG BCB ← PEG BCB ← PEG BCB 6¼ PEG BCB 6¼ PEG BCB ← PEG BCB → PEG ATM ↔ PEG ATM ↔ PEG ATM ↔ PEG ATM ↔ PEG ATM ↔ PEG ATM ↔ PEG BAA 6¼ PEG BAA 6¼ PEG BAA → PEG BAA ↔ PEG BAA ↔ PEG BAA ← PEG BAC 6¼ PEG BAC 6¼ PEG BAC → PEG BAC → PEG BAC 6¼ PEG BAC → PEG BAB ← PEG BAB 6¼ PEG BAB ← PEG BAB ← PEG BAB ← PEG BAB ← PEG CIF ↔ PEG CIF ↔ PEG CIF ↔ PEG CIF ↔ PEG CIF ↔ PEG CIF ↔ PEG

GAT ↔ PEG PAT ↔ PEG GRA ↔ PEG PRA ↔ PEG RAL ↔ PEG CIT 6¼ PEG GAT ↔ PEG PAT ← PEG GRA 6¼ PEG PRA → PEG RAL ← PEG CIT ↔ PEG GAT ↔ PEG PAT ↔ PEG GRA → PEG PRA ↔ PEG RAL ↔ PEG CIT ← PEG GAT → PEG PAT ↔ PEG GRA → PEG PRA ↔ PEG RAL 6¼ PEG CIT 6¼ PEG GAT ↔ PEG PAT → PEG GRA ← PEG PRA 6¼ PEG RAL 6¼ PEG CIT ← PEG GAT ↔ PEG PAT → PEG GRA ↔ PEG PRA ↔ PEG RAL ↔ PEG CIT ← PEG GAT ↔ PEG PAT → PEG GRA → PEG PRA ↔ PEG RAL → PEG CIT 6¼ PEG GAT 6¼ PEG PAT ↔ PEG GRA → PEG PRA ← PEG RAL 6¼ PEG CIT ← PEG

6. Concluding remarks and recommendations for future research The role of transportation infrastructure in economic growth has been well-recognized in many empirical studies. However, it is still an ongoing topic of discussion in scientific circles as some research results have been inconclusive. It is generally accepted, that in this era of globalization, economic progress depends, amongst other things, on the availability of an effective transportation infrastructure, while inade­ quate transportation infrastructures are significant obstacles to eco­ nomic growth (Lenz et al., 2018). The present study aimed to examine the causal relationships between transportation infrastructure, financial penetration and economic growth simultaneously. We found that all of the factors are cointegrated, thereby indicating the existence of long-run relationships. Most importantly, there is clear evidence that both financial penetration and transportation infrastructure are important for long-run economic growth. In the short run, however, there are numerous relationships between financial penetration, transportation infrastructure, and economic growth. Table 3 shows, that for the nexus between transportation and economic growth, 21 of the 48 cases support the feedback hypothesis,13 while eight support the supply-leading hypothesis, seven support the de­ mand-following hypothesis, and eight support the neutrality hypothesis. Regarding the nexus between financial penetration and economic growth, 21 of the 48 cases support the feedback hypothesis, while five support the supply-leading hypothesis, 13 support the demand-following hypothesis, and seven support the neutrality hypothesis. Finally, con­ cerning the nexus between transportation and financial penetration, eight of the 48 cases support the feedback hypothesis, while ten support the supply-leading hypothesis, five support the demand-following hypothe­ sis, and 24 support the neutrality hypothesis. Unlike the long-run results, which are homogenous, the short-run

Note 1: PEG is per capita economic growth, FIP is financial penetration, TRI is transportation infrastructure, CBB is commercial bank branches, DCB is de­ positors with commercial banks, BCB is borrowers from commercial banks, ATM is automated teller machines, BAA is bank accounts, BAC is bank concentration, BAB is bank branches, CIF is the composite index of financial penetration, GAT is goods carried by air transportation, PAT is passengers carried by air trans­ portation, GRA is goods carried by railways, PRA is passengers carried by rail­ ways, RAL is railway lines, and CIT is the composite index of transportation infrastructure. Note 2: FIP indicates CBB, DCB, BCB, ATM, BAA, BAC, BAB, IFB, or CIF; and TRI indicates GAT, PAT, GRA, PRA, RAL, or CIT. Note 3: ←/→/↔ indicate the direction of Granger causality; and 6¼ indicates no Granger causality.

penetration and transportation due to the lack of available data and also to avoid producing too many, and possibly confusing, results. Third, this study explored the relationships between transportation infrastructure and economic growth in a trivariate framework by inte­ grating only financial penetration as an additional factor. However, in reality, the relationship between transportation infrastructure and eco­ nomic growth may be affected by other time-dependent factors, such as foreign direct investment, trade openness, population growth,

13 This bidirectional causal relationship suggests that transportation infra­ structure and economic growth are jointly determined and impacted at the same time. As regional economies often fluctuate, investments should be delayed and saved for weaker economic times during economic upswings. However, during economic downswings, more investments should be made to ensure that the balance of transportation infrastructure to economic growth is maintained over time (Baker et al., 2015).

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results depend on the types of financial penetration and transportation infrastructure indicators we used for the analysis. On the whole, a rich set of short-run dynamics exists between the variables. However, in the long run they all produce the same result, namely that financial pene­ tration and transportation infrastructure are key drivers of economic growth. Our empirical results clearly suggest that, to promote long-term economic growth in the G-20 countries, priority should be given to strengthening a country’s financial penetration and transportation infrastructure. Furthermore, the results show that there are strong endogenous links between financial penetration, transportation infra­ structure and economic growth, even in the short run. In this context, financial penetration plays a key role in causing more transportation in the economy, which then generates more economic growth, both directly and indirectly through the transportation infrastructure (Meersman & Nazemzadeh, 2017; Mohmand et al., 2016; Shabani & Safaie, 2018; Song & Mi, 2016). In short, the empirical results suggest that, to stimulate economic growth in the G-20 countries, policy makers should give priority to both financial penetration and the transportation infrastructure. The overall findings further suggest that appropriate transportation and financial policies should be developed to enhance the transportation infrastructure and financial penetration, which will then positively affect economic growth. It is considered that well-built transportation infrastructures played an important role in achieving high economic growth in the G-20 developed group. However, a much healthier financial penetration is required to regulate the countries’ economic fluctuations. This can be achieved by consistently imple­ menting development policies based on these findings. However, with reference to the heterogeneous causality results, the G-20 countries should not blindly follow any policy without considering the types of transportation infrastructure relevant to their socioeco­ nomic conditions. Identifying a country’s appropriate transportation infrastructure and the required financial penetration is an extremely complex process. Hence, determining suitable measurement indicators more precisely for this process is an important area for future research. Additionally, future studies could incorporate other time-varying covariates (such as urbanization, industrialization, global competitive­ ness, regional economic integration, etc.) in their analysis by using multivariate econometric frameworks, which may produce more robust results. The findings of this study clearly imply that improvements in transportation infrastructure and financial penetration do play an important role in promoting economic growth in G-20 countries. We suspect that any uneven availability of financial penetration and trans­ portation infrastructure will result in regional economic disparities, which may ultimately hamper global economic growth.

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Acknowledgement The author appreciates the helpful comments and suggestions of the anonymous reviewers and the editor of this journal. An earlier version of this paper was presented in the session on ‘Transport Financing in Developing Countries’ at the 15th World Conference on Transport Research, 26–31 May 2019, Mumbai, India. We thank the session chair and session participants for their helpful comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.retrec.2019.100766. References Achour, H., & Belloumi, M. (2016). Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia. Renewable and Sustainable Energy Reviews, 56(C), 988–998.

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