ICT diffusion, financial development, and economic growth: An international cross-country analysis

Journal Pre-proof ICT diffusion, financial development, and economic growth: An international crosscountry analysis Chih-Yang Cheng, Mei-Se Chien, Chien-Chiang Lee PII:

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DOI:

https://doi.org/10.1016/j.econmod.2020.02.008

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ECMODE 5150

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Economic Modelling

Received Date: 2 May 2019 Revised Date:

26 November 2019

Accepted Date: 3 February 2020

Please cite this article as: Cheng, C.-Y., Chien, M.-S., Lee, C.-C., ICT diffusion, financial development, and economic growth: An international cross-country analysis, Economic Modelling (2020), doi: https:// doi.org/10.1016/j.econmod.2020.02.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

ICT Diffusion, Financial Development, and Economic Growth:

An International Cross-Country Analysis

Chih-Yang Cheng Department of Finance, National Sun Yat-sen University, Kaohsiung, Taiwan E-mail: [email protected]

Mei-Se Chien Department of Finance and Information, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan E-mail: [email protected]

Chien-Chiang Lee* School of Economics and Management, Nanchang University, Nanchang, China E-mail: [email protected]

*

Corresponding author. Chien-Chiang Lee, Distinguished Professor, School of Economics and Management, Nanchang University, Nanchang, China. E-mail: [email protected] (C.-C. Lee). The authors are listed in alphabetical order and contributed equally to this manuscript.

ICT Diffusion, Financial Development, and Economic Growth: An International Cross-Country Analysis

ABSTRACT The purpose of this paper is to explore the relationship between financial development, information and communication technologies (ICT) diffusion, and economic growth by considering the interlinkage of finance and ICT. To capture the generalized effect of financial development on economic growth, we set up a broad index of financial development by employing principal component analysis. Based on panel data covering 72 countries from 2000 to 2015, the empirical results after applying dynamic GMM estimation with panel data can be generalized as the following. First, regardless of the national income level, the empirical results show that financial development is always unfavorable for economic growth, but this negative effect is greater in high-income countries. Second, ICT diffusion can improve economic growth in high-income countries, but the effect is ambiguous in middle & low-income countries. In middle & low-income countries, only mobile growth can raise economic growth, whereas increasing Internet or secure Internet servers cannot. Finally, the interaction effects between ICT and financial development are positive in both income-level countries, implying the interaction effects of ICT and finance can reduce the negative effects of financial development, but the effects are only significant for high-income countries. Keywords: ICT diffusion, Financial development, Economic growth, Dynamic panel GMM JEL Classification: O11, O33, C23

1

1. Introduction Financial development, one of the important growth factors in the national economy, has been extensively analyzed in related literature (Shen and Lee, 2006; Lee, 2013; Mishra and Narayan, 2013; Narayan and Narayan, 2013; Chiu and Lee, 2019). Levine (1997) indicated that the development of financial markets is one of the key factors in economic growth, because it helps make the economic system more efficient. Recently, there have been considerable transformations in the banking and financial sectors under the rapid development of information and communication technologies (ICT). Actually, the total expenditure of the global financial industry on ICT products and services was over US$197 billion in 2014, with this consistently the single largest buyer of ICT products and services ever since the mid-1990s. Since the late-1980s, the evolution of the financial industry has been based on the transmission and operation of digital information. Hence, the interlinkage of finance and technology impacts the relationship between financial development and economic growth. In order to better clarify this issue, we re-examine the relationship among financial development, ICT diffusion, and economic growth by considering the interlinkage of finance and technology. Most studies in the literature on economic growth, whether focusing on the impact of financial development or ICT diffusion, rarely considers the effects of the interaction between the two. The World Bank (2017) pointed out that the World Bank Group (WBG) has cooperated with customers and has successfully supported ICT industry reforms through technical assistance and loan business in recent years. This trend has also led many financial industries to combine ICT with internal process modernization and provide upgraded services such as paying bills, remittances, and cash transfers via mobile phones. Based on these considerations, the impact of ICT diffusion on financial evolution may have different effects on economic activity. After reviewing the theory of economic discussed in the model of Cobb and Douglas (1928), the total production function includes three production factors, labor, capital, and technology, and the changes of these factors all influence economic growth. In general, financial development is viewed as the representative of capital among production conditions, and ICT represent technological progress. Further, the role of the financial sector can gather savings, distribute resources into the highest 2

productive investments, decrease information and transaction costs, and promote the inter-industry trading. This leads to higher resource allocation efficiency, accelerated technology upgrades and material and human capital accumulation (see Greenwood and Jovanovic, 1990; Bencivenga and Smith, 1991; King and Levine, 1993a; Greenwood and Smith, 1997; Levine, 1997; Levine, 2005, among others). However, the 2008-2009 global financial crisis has shown the negative impacts of malfunctioning financial systems that can waste resources, depress savings, spur speculation, lower investment, and cause misallocation of scarce resources. Actually, some empirical studies indeed have negative and mixed views about financial-growth nexus , perhaps from a surge in financial crises, or because a stock market does not have enough listings to promote economic expansion, and due to the existence of non-linear relationships (Arcand et al., 2012; Loayza and Ranciere, 2002; Ram, 1999; Rousseau and Wachtel, 2000; Samargandi et al., 2015). The effect of ICT diffusion on economic growth has also widely discussed by scholars in recent years, because ICT is one of the main conditions for accelerating economic expansion. The information achieved by ICT can be transmitted through electronic coding and virtual movement, which impact the development and technology of different industries and thus alter economic activities. Although the positive relations between ICT diffusion and economic growth are expected based on the theoretic literature, the empirical works have presented inconsistent results. Some have offered positive effects (Jorgenson, 2001; Crandall and Jackson, 2001; Jorgenson et al., 2003; Nasab and Aghaei, 2009; Vu, 2011, and among others), while others showed mixed or negative effects (Dewan and Kraemer, 2000; Pohjola, 2001; Hassan and Islam, 2005; Lee et al., 2005; Yousefi, 2011; Ishida, 2015; among others). Against this backdrop, this study sets up an economic growth model covering the union effects of ICT diffusion and financial development to re-examine the connection among financial development, ICT diffusion, and economic growth. Although few studies have recently discussed changes between these three factors (see, for instance, Sassi and Goaied, 2013; Das et al., 2018), the main differences between them and ours go as follows. First, to discuss the issue Sassi and Goaied (2013) and Das et al. (2018) only used a narrow sense of financial development, applying for private sector bank credit in financial evolution. To improve this weak point, we employ principal component analysis (PCA) to establish a combined index 3

of direct and indirect financial development, including M3/GDP, banking credit/GDP, and stock /GDP, so as to catch broad financial development; hence, this paper presents a generalized effect, not a narrow effect, of financial development on economic growth. Second, Sassi and Goaied (2013) and Das et al. (2018) just examined data of developing countries, and their empirical results did not compare different effects in different income-level countries. Our paper applies a wider dataset covering 72 countries, 34 high-income countries, and 38 middle & lower-income countries, thus allowing us to objectively analyze the impacts of ICT diffusion, financial development, and the interaction effect of both on economic growth for different income-level countries. The structure of this study is described as follows. Section 2 discusses the theoretical background and literature. Section 3 lists data sources and methodological selection. Section 4 presents the empirical results. Finally, Section 5 summarizes our findings and implications.

2. Theoretical Background and Literature Review 2.1 The Finance Diffusion-Growth Nexus The connection between financial development and economic growth has been widely discussed in the literature. The theoretical background of this literature can be traced back to the works of Schumpeter (1911), Gurley and Shaw (1960), and McKinnon (1973), who argued that the development of financial intermediaries, such as banks, can increase the efficiency of resource distribution and advance technological innovation in production, thus further enhancing economic growth. Many empirical works confirmed a positive linkage between financial development and economic growth. In line with cross-sectional material, King and Levine (1993a) believed that a more mature financial system will help increase productivity by building an endogenous growth model. Rousseau and Sylla (2001) adopted a cross-country regression by applying data covering six advanced countries from 1850 to 1997, and their results proved that financial development strongly dominated economic growth, especially in the 80 years before the Great Depression. Levine and Zervos (1999) analyzed 48 countries between 1976 and 1993, 4

supporting a strong and statistically significant relationship between stock market development and subsequent economic growth. Hondroyiannis et al. (2005) and Van Nieuwerburgh et al. (2006) also examined the impact of the stock market through the use of the VAR model and found that financial development led to long-term economic growth in Greece and Belgium, respectively. Furthermore, there are more papers confirming that financial development can cause a positive effect on economic growth (Jung, 1986; Demetriades and Hussein, 1996; Neusser and Kugler, 1998; Arestis et al., 2001; Xu, 2000; Christopoulos and Tsionas, 2004; Bekaert et al., 2005; Hassan, 2003; Hassan et al., 2011; Uddin et al., 2013; Jedidia et al., 2014). Conversely,

some

studies

indicated

that

the

connection

between

the

financial-growth nexus may be uncertain due to some financial liberalization policies (Sassi and Goaied, 2013). The 2008-2009 global financial crisis has shown the negative impact of malfunctioning financial systems that can waste resources, depress savings, spur speculation, lower investment, and cause misallocation of scarce resources. Actually, some empirical studies have negative or mixed views about the financial-growth nexus. By examining data of 95 countries, Ram (1999) noted insignificant or weakly negative linkages between financial development and economic growth based on cross-country data. Arcand et al. (2012) presented that if the private sector credit (%GDP) is higher than 100%, then financial development negatively impacts output growth. Samargandi et al. (2015) applied a threshold model to examine the non-monotonic effect in middle-income countries from 1980 to 2008, and their results displayed that excessive finance will have a negative influence on economic growth, because of the existence of an inverted U-shaped relationship between finance and growth in the long run. Asteriou and Spanos (2018) employed multiplicative dummies to study how the 2008 global financial crisis changed the effect of financial development on economic growth, based on a data sample of 26 European Union countries from 1990 to 2016. Their results displayed that the effect is positive before this financial crisis, but negative after this financial crisis. Hence, based on the above discussion, this paper makes the first hypothesis. Hypothesis 1: The effect of the financial development on economic growth is unambiguous. Furthermore, in light of the variables of financial development on the relative 5

literature, some of them used banking sector variables, some of them applied stock market variables, and some employed both. For the purpose of catching a more complete appearance and comprehensive of financial development, we use PCA to establish a financial development index combining three indicators: M3/GDP, banking credit/GDP, and stock /GDP. 2.2 The ICT Diffusion-Growth Nexus ICT diffusion has accelerated rapidly in recent decades, with many research studies having explored its impact on economic growth. The information achieved by ICT can be transmitted through electronic coding and virtual movement, which impact the development and technology of different industries and thus alter economic activities. Quah (2003) indicated that ICT overcome time and space constraints to accelerate the delivery of information and also increase market transparency as well as reduce information asymmetry. However, the empirical works until the early 1990s did not show a significant evidence to support the impact of ICT on economic growth. Some later empirical researches, such as Daveri (2002) and Colecchia and Schreyer (2002), displayed that ICT capital increase significantly affected economic growth, during the 1990s, in the U. S. and several EU and OECD countries. The empirical results of Jorgenson and Vu (2005) indicated that higher ICT investment can improve economic growth in all areas around the world from 1989 to 2003, and particularly in the advanced countries and the developing Asia. Focusing on OPEC countries, Nasab and Aghaei (2009) noted the positive effect of ICT on economic growth by using GMM in the countries over the period 1990-2007. Applying a wider dataset covering 102 countries from 1996 to 2005, the empirical work of Vu (2011) supports that positive and significant economic growth is affected due to ICT diffusion. Some other empirical works also agree with the positive relationship between ICT diffusion and economic growth, including Crandall and Jackson (2001), Jorgenson et al. (2003), Roller and Waverman (2001), Nour and Satti (2002), and Seo et al. (2009), among others. Conversely, other papers have argued that ICT could trigger a negative effect on economic growth. If the rapid growth of ICT reduces the employment of unskilled workers, then ICT diffusion may negatively impact labor markets, which could further lead to unfavorable effects on economic growth (Freeman et al., 1995; Aghion 6

et al., 1998). Therefore, another line of empirical literature provided mixed evidence. Pohjola (2001) found that a positive relationship between ICT and economic growth existed in 23 OECD countries, and not in the other 16 countries, based on data covering 39 countries from 1980 to 1995. Hassan and Islam (2005) also presented a mixed conclusion, supporting the positive effect of ICT diffusion on economic growth in the whole sample covering 95 countries, but exceptional results are found in 8 MENA(Middle Eastern and North African) countries. Yousefi (2011) examined contributions of ICT and other production factors on economic growth in 62 countries, displaying that ICT can improve economic growth in high- and upper-middle income countries, but not in lower-middle income countries. Focusing on Japan, Ishida (2015) found that ICT investment does not lift GDP by using the autoregressive distributed lag boundary test. Moreover, we made the second hypothesis. Hypothesis 2: The spread of ICT could cause an inconsistent impact on economic growth in different income-level countries.

2.3 The joint effect between ICT and Financial Development-Growth Nexus Most studies in the literature on economic growth, whether focusing on the impact of financial development or ICT diffusion, rarely considers the effects of the interaction between the two. Compared with many industries, the financial industry has a deeper and wider application of ICT, because ICT diffusion can significantly improve the operating efficiency of financial institutions. Shamim (2007) indicated that finance technologies decrease processing costs and information costs and further improve economic growth. Hence, in recent years some articles have concentrated on the joint effects of financial development and ICT diffusion on economic growth. Shamim (2007) argued that the financial sector along with a better telecommunication infrastructure improves economic growth in the long run. Using panel data of 61 countries from 1990 to 2002, her empirical results found that a greater number of mobile phone subscribers and Internet users can increase financial depth, which is an important factor for economic growth. However, Shamim (2007) lacked the interaction effect of ICT and financial development on economic growth. Sassi and Goaied (2013) added the interaction effects of ICT and financial 7

development into the economic growth model and found that it is positively significant in the use of the GMM approach in 17 MENA countries. Das et al. (2018) also consider the interaction effect by setting up an economic growth model. By employing the GMM method in 43 developing countries from 2000 to 2014, they indicated that the interaction effect of ICT and financial development can improve economic growth in low-income countries, but not in lower middle-income countries. Thus, based on the above empirical results, we make the following hypothesis: Hypothesis 3: The joint effect between ICT and Financial Development could improve economic growth. However, although the models of Sassi and Goaied (2013) and Das et al. (2018) include the combined influence of ICT and financial development, the two papers only used the bank credit as a variable in financial development. Furthermore, Sassi and Goaied (2013) examined the data of 17 MENA countries, and Das et al. (2018) focused only on samples from countries below the level of lower-middle-income. Hence, to improve the weak points of the above literature, our paper utilizes a broad index of financial development, including M3/GDP, banking credit/GDP, and stock/GDP to catch a generalized effect of financial development on economic growth. Next, this paper applies a wider dataset covering 72 countries to compare and analyze the differences income-level countries.

3. Data Sources and Methodological Issues 3.1 Data This article adopts a panel data sample covering annual data of 72 countries (34 high-income and 38 middle & lower-income countries) from 2000 to 2015. The variable includes real GDP per capita growth (GDPPC), which is used to measure economic growth. Based on the model developed by Sassi and Goaied (2013), the independent variables also involve the initial level of GDP per capita (IGC) to illustrate convergence, gross capital formation to GDP (INV) as a representative of the investment, financial development index by principal component analysis, and ICT indicators that are measured by three different ICT variables: mobile users (MU), the percentage of Internet users (IU), and secure Internet servers per 1 million people (IS). As to financial development, we use PCA to establish a financial development 8

index, which is illustrated in the next section. The control variables cover the ratio of exports to imports versus GDP (TEI) and inflation (INF). The variables other than indices and ratios are processed in logarithmic form. The definition and description of the variables are presented in Table 1. [Table 1 about here] 3.2 Measures of Financial Development For variety of reasons, building a more complete variable to present financial development is a daunting task. Because of the fairly broad level of financial services, both banking and stock markets are the main foundations. In order to catch a generalized effect of financial development on economic growth, we set up a broad index of financial development (denoted FDP) that combines three indicators:

M3

to GDP, the ratio of the private sector credit divided by GDP, and the whole value of the stock traded to GDP. The data of the three financial development indicators come from the World Development Indicators (WDI) and the World Bank Financial Development and Structure Database. Following the works of Ang and McKibbin (2007), Gries et al. (2009), and Campos and Kinoshita (2010), we use the PCA to represent FDP that combines the three financial variables mentioned above. The reasons why this article mainly constructs this single variable are as follows. First, inconsistent results are obtained when all three financial variables are included into the estimating model, which may be due to a high correlation among financial development variables. Therefore, this paper solves the problem of multicollinearity by using an index. Second, there is no uniform argument to investigate financial development-economic growth nexus. As for which financial development variable is best suited to capture this connection, the literature has chosen different variables and subsequently produced various outcome (see King and Levine, 1993a; Savvides, 1995; Chuah and Thai, 2004; Khan and Senhadji, 2003, and among others).

3.3 Model and Methodology Based on the model of Sassi and Goaied (2013), we set up the economic growth model covering ICT diffusion or financial development, represented by equations (1) and (2): 9


 =  +   −1 +    +   +    +   +  1
 =  +   −1 +    +   +   +   +  2

Here, i = 1, 2, ...., N (for the country), t = 1, 2, ...., T (for the number of periods); the explained variable
 represents the growth rate of actual per capita GDP (GDPPC). IGC is defined as an independent variable primarily to illustrate the convergence effects proposed by Barro (1998). FDP is the index of financial development. ICT is a variable to measure the growth of information and communication technology. This article uses three indicators:

mobile, Internet, and secure Internet servers. 

represents a set of control variables, including trade openness (TEI), and inflation rate (INF). Further, we consider the estimated economic growth model for ICT diffusion and financial development as follows (3):
 =  +   −1 +    +   +    +   +   +  3

Finally, the economic growth model with interaction effects between ICT and financial development is the following equation:


 =  +   −1 +    +   +   +   +    ∗  + "  +  (4)

As

for

the

estimating

method,

this

paper

applies

the

dynamic

Generalized-Method-of-Moments (GMM) of Arellano and Bond (1991) on the dynamic panel data. This method not only is used to solve problems in data containing potential endogeneity, heteroscedasticity, and autocorrelation, but also supply a more flexible variance-covariance structure under moment conditions. We also proposed a heteroscedastic-robust variance matrix estimate based on Huber (1967), Eicker (1967), and White (1980), which allowed our results to find robust standard errors in the case of heteroscedasticity. In addition, we used the lagged values (two and higher) of the regressors in the GMM dynamic framework as the estimation instrument to solve the potential bias due to reverse causality. The GMM method is better than the traditional OLS when 10

reviewing changes in financial variables. Furthermore, we employ the Arellano and Bond (1991) test to ensure that the first-order series AR(1) is correlated and the second-order sequence AR(2) is irrelevant. In addition, the Sargan test helps analyze whether the instrument variable is over-recognized and to confirm that the instrumental variables are not related to the error term.

4. Empirical Results 4.1 The Effects of ICT Diffusion This section explores the impact of ICT diffusion on economic growth by applying the GMM. Table 2 shows the results of estimating Eqs. (1)-(3) for the full sample, covering annual data for 72 countries from 2000 to 2015. In all estimating models of Table 2, the coefficient of IGC is negative and significant at the 5% significant level, indicating countries with higher initial incomes will be replaced by other countries with faster growth rates. The coefficient of INV is significantly positive for all models, implying higher investment can improve economic growth. For the coefficients of TEI and INF, the former is positive and the latter is negative; both are consistent with theoretic expectation. In light of the results of model (1), a model with FDP but without ICT variables, the coefficient of FDP, at the 1% level, is significantly negative which shows that higher financial development negatively impacts economic growth. Looking back at related literature, this argument is consistent with Ram (1999) and Arcand et al. (2012). Therefore, the empirical result supports hypothesis 1. Some past papers have discussed the reason why financial development causes negative effects on economic growth. Yong et al. (2009) put forward higher use of interest-rate derivative financial products causes higher long-term risks. Nijskens and Wagner (2011) also argued that bank risks result in higher long-term risks, because of excessive bank lending and lower capital holdings. In addition, due to excessive liquidity in the stock market, investors seek to obtain an abnormal return for short-term investment, neglecting to supervise a company’s business performance and hinder economic growth (Bhide, 1993). [Table 2 about here] 11

The results of models (2) ~ (4), being estimated based on equation (2), indicate the effects of three ICT variables on GDDPC. All of the three ICT variables’ coefficients are positive at the 5% significant level. In other words, higher economic growth comes from increasing ICT diffusion. Comparing with the coefficient of the ICT variables, the coefficients of MU, IU, and IS are 0.484, 0.371, and 0.084, respectively, showing the first factor causes the highest impact on GDPPC. The reason comes from the fact that mobile phones accelerate the way of transmitting information and improve the efficiency of information dissemination, thus boosting economic growth, which is consistent with the findings of Vu (2011) and Sassi and Goaied (2013). Our empirical results prove that ICT development is important for economic growth. ICT can promote the link between a country and the global economic system and spur more active economic activities through electronic coding and virtual motion communication. In Table 2 models (5), (6), and (7), based on Equation (3) and considering both financial development and ICT variables, show the same results, whereby the coefficients are statistically significant, FDP is negative and the three ICT variables are positive, thus also supporting Hypotheses 1 and 2(a). 4.2 The Interaction Effect of ICT and financial development To understand whether considering the two factors of ICT and financial development will further affect economic growth, we use equation (4) to explore the interaction effects and present the results in Table 3. In Table 3, the sign and significance of the coefficients of FDP and ICT variables are the same as the results in Table 2. Differently, IU shows the highest effect on GDPPC if the model considers the interaction effect of ICT and financial development. It is worth noting that the coefficients of all interaction effects of ICT and financial development, MU*FDP, IU*FDP, and IS*FDP, are positive, and the coefficients of MU*FDP and IU*FDP are significant at the 10% level. In other words, although financial development causes an unfavorable effect on economic growth, ICT diffusion can bring about some beneficial impacts on financial development, and the interaction effect can further increase economic growth. Based on the results in Table 3 confirm that ICT diffusion can reduce the unfavorable effects of financial development on economic growth, which is consistent with Hypothesis 3. In the past two decades, the use of telecommunications services has grown at an unprecedented rate. This growth is mainly driven by the liberalization of wireless technology and 12

telecommunications markets. As King (2012) proposed, during the era of Bank 3.0, mobile financial services are allowing companies to realize broad access rights, including telecommunications, retail, and e-commerce, in order to provide payment of bills and other financial services. This trend will continue and radically change the rules of the game of traditional banks. [Table 3 about here] More specifically, the nuances between Sassi and Goaied (2013), the results in Table 3 show that the ICT coefficient and the interaction effect coefficient are positive. Conversely, Sassi and Goaied (2013) noted that the interaction effect’s coefficient is positive, but the ICT coefficient is negative. The reason could be that the sample of Sassi and Goaied is limited to countries in the MENA region. 4.3 The Effects in Different Income-Level Countries This section examines whether the effects of ICT diffusion cause varying impacts on economic growth in different income-level countries. We divide the entire sample into two groups on the basis of the income classifications of the World Bank in 2013:

GNI per capita is higher than US$12,236 in the high-income group

(denoted HIG) and lower than US$12,236 in the middle & low-income group (denoted MLIG). [Figure 1 about here] Figures 1 and 2 show the changes in the ratio of Internet population to a country’s population between HIG and MLIG in our sample from 2000 to 2015. Although there is a growing trend in both groups, it is interesting to note that the middle & low-income group shows significant growth. [Figure 2 about here] 4.3.1 The Estimating Results in the High-Income Group Based on equations (3) and (4) and by applying the data of the high-income group, Table 4 shows the estimating results of models (11) to (16). We can summarize the main points as follows. First, for all of the models in HIG, the coefficients of FDP are still significantly negative. This implies that the results also support Hypothesis 1. 13

Second, the coefficients of the three ICT variables, MU, IU, and IS, are all significantly positive at the 10% level, denoting ICT development is indeed helpful for economic growth in high-income countries. Comparing the results of the full sample in Tables 3 and 4, the coefficients of the three ICT variables are bigger in the high-income group, which means that ICT diffusion can bring about better effects to improve economic growth in high-income countries. Third, in HIG, each of the coefficients of the three interaction effects of ICT and FDP are significantly positive, with no exceptions, which match the results of the full sample. The empirical results of the high-income group are consistent with our Hypothesis 3. Hence, in high-income countries, although financial development is unfavorable for economic growth, the interaction effects of ICT diffusion and financial development can reduce this negative effect, and the interaction effects are higher than the full sample. [Table 4 about here] 4.3.2 The Estimating Results in the Middle & Low-Income Group As for the estimates of the middle & low-income group, based on equations (3) and (4), the results are shown in Table 5. The coefficients of the relevant control variables present similar results in Table 5. Compared to the results of the high-income group, some important finding of the coefficients of ICT and FDP are as follows. [Table 5 about here] First, in MLIG the coefficients of the FDP for all models are negative, but some of them are insignificant. Regardless of the national income level, our research finds that the impact of FDP on GDPPC is consistently negative. Different from the high-income group, the negative impacts of financial development on economic growth in the middle & low-income group might result from excessive lending caused by unsound financial institutions or regulations. Cecchetti and Kharroubi (2012) and Arcand et al. (2015) found that excessive private sector credit in banks will make financial development unfavorable for economic growth. Second, in light of the ICT variables, the coefficient of MU is positive, while the 14

coefficients of IU and IS are negative, displaying only that MU growth can raise economic growth, whereas increasing IU or IS cannot do so for MLIG, which is different from the results of HIG. Hence, the inconsistent empirical results of the two income-level groups confirm our Hypothesis 2. Yousefi (2011) stated that the role of ICT capital in economic growth is not significant in low- and middle-income countries due to insufficient human capital stocks and inadequate capital communications infrastructure. Aghion et al. (1998) found that the negative impact of the Internet may also come from rapid growth that reduces the employment of unskilled workers and further increases income inequality. Moreover, in middle & low-income countries, business activities are concentrated in a small number of companies with high assets, and so the demand for encrypted Internet transactions is not popular. Especially in low-income countries, an incomplete infrastructure will lead to a negative impact of ICT diffusion (Dewan and Kraemer, 2000). Third and finally, focusing on the coefficients of the interaction effects between ICT and FDP, all of them for the three models are positive but insignificant; in other words, the interaction effects between ICT and financial development can positively affect economic growth, but the effects are trivial. In general, in many middle & low-income countries, ICT diffusion can provide a convenient device to upgrade or improve financial transactions in many rural areas, which further bring about beneficial impacts on economic growth. For example, Nigeria and Ghana are respectively the second and third largest markets (after the U.S.) for Paxful, one of the world’s largest P2P bitcoin markets. This firm’s monthly bitcoin transactions in Africa now hit about $40 million, mainly to protect people’s savings there, restore purchasing power, and help facilitate business. In summary, ICT diffusion can improve economic growth and reduce the negative effects of financial development in HIG, but the influence of ICT development on economic growth is ambiguous in MLIG. Regarding the latter, the development of mobile growth can raise economic growth, but the development of Internet and secure Internet sever cause negative influence on economic growth. However, no matter for which of the above income-level groups, ICT diffusion does reduce the negative effects of financial development, which is consistent with our Hypothesis 3.

15

4.4 Robustness Tests In order to eliminate periodic components and focus on the long-run relationship between financial development and ICT on economic growth to express the robustness of the estimation results, we convert all the dependent and independent variables into three-year average panels based on equations (3) and (4). Check whether the adjusted variables have changed the impact of ICT and financial development on economic growth. Table 6 shows the re-estimation of average economic growth for ICT, financial development, and its intersections. The results of Models (23) to (25) in Table 6 show that the sign and significance of the coefficients of FDP and ICT variables are the same as the results in Table 2. This implies that the results also support Hypothesis 1. Furthermore, each of the three interaction effects of ICT and FDP is significantly positive, and the results are consistent with the conclusions of Table 3. In other words, the interaction between ICT and financial development can have a positive impact on economic growth, which is consistent with our hypothesis 3. [Table 6 about here]

5. Conclusion and policy implications This paper explores how ICT diffusion and financial development affect economic growth based on an economic growth model with the joint effect of these two factors. To catch the generalized effect of financial development on economic growth, we use principal component analysis (PCA) and combine three variables of financial development into a broad index of such development. We apply a wider dataset, consisting of 72 countries from 2000 to 2015, to compare the differences of these effects in different income-level countries. Through the dynamic panel GMM technique, we generalize the empirical results as follows. First, regardless of the national income level, the empirical results present that the financial development-economic growth nexus is consistently negative, especially in high-income countries. Malfunctioning financial systems could waste resources, spur speculation, lower investment, cause a misallocation of scarce resources, and 16

such unfavorable effects could be caused by the crisis of the bank itself, poor quality of the institution, or by the incompleteness of financial regulation and supervision. Unlike the traditional regulatory environment, the financial institution's meshing makes the financial system too relevant, and various cross-market and cross-commodity behaviors that evade supervision interweave multiple risk factors. The stability of the financial system has brought great impact, and the effectiveness and timeliness of financial supervision in the future is facing great challenges. Hence, for the negative impact of this financial development, it is necessary to strengthen credit constraints from both ends of supply and demand, orderly regulate corporate debt and suppress the excessive, disorderly and excessive expansion of financial credit. Targeting the coordination and implementation of financial regulation and managing decision makers, especially policymakers in high-income countries. Second, ICT diffusion can improve economic growth in high-income countries, but the effect is ambiguous in middle & low-income countries. In middle & low-income countries, only mobile growth can raise economic growth, whereas increasing Internet and secure Internet sever cannot. The policy implications of these results are that middle & low-income countries should strengthen their development in mobile facilities in a short period of time, as this will be more cost-effective and beneficial. However, the lack of competition for Internet services could bring about unreasonable prices, and insufficient infrastructure investment plus high prices is a natural hindrance to the Internet in middle & low-income countries. In the long run, we suggest that policies in these countries should not only consistently facilitate mobile growth and encourage competition in ICT markets, but also increase infrastructure investment at the same time. Finally, the interaction effects between ICT diffusion and finance are positive in both sets of income-level countries, implying that the interaction effects of ICT and finance can reduce the negative effects of financial development, but the effects are only significant in high-income countries. Obviously, the development of ICT redefines the timing and location of our use of financial services, and more and more transactions are turning to mobile payments, personal-to-personal (P2P) financing. Banking services have become ubiquitous with the needs of customers and have become the main driving force for national economic activity. Therefore, reinforcing and upgrading ICT applications in the financial sector can help decrease the unfavorable impact from financial development on an economy, which is especially 17

helpful for high-income countries.

18

Acknowledgements We would like to thank the editor and the two anonymous referees for their helpful suggestions. The data that supported the findings of this study are available by request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. The authors are listed in alphabetical order and contributed to equally first authorship.

19

Appendix A.

Table A1 List of High-income and Middle & Low-income Countries High-Income group

Middle & Low-Income group

Australia

Malta

Argentina

Macedonia, FYR

Austria

Netherlands

Armenia

Malawi

Bahrain

Norway

Bolivia

Malaysia

Belgium

Oman

Brazil

Mauritius

Chile

Poland

Bulgaria

Mexico

Cyprus

Portugal

China

Morocco

Czech Republic

Saudi Arabia

Colombia

Namibia

Denmark

Singapore

Cote d'Ivoire

Nigeria

France

Slovak Republic

Croatia

Pakistan

Germany

Slovenia

El Salvador

Panama

Greece

Spain

Georgia

Peru

Hong Kong

St. Kitts and

Ghana

Philippines

Nevis Hungary

Switzerland

India

Romania

Ireland

United Kingdom

Indonesia

Russian Federation

Israel

United States

Jordan

South Africa

Italy

Kazakhstan

Sri Lanka

Japan

Kenya

Thailand

Korea, Rep.

Kyrgyz Republic Turkey

Luxembourg

Lebanon

20

Uganda

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27

Fig. 1 Evolution of individuals using the Internet (% of population) in the high-income group

28

Fig. 2 Evolution of individuals using the Internet (% of population) in the middle & low-income group

29

Table 1. Description Statistics Std. Variable

Definition

Mean

Median

Max

Min

Observations Dev.

GDPPC

GDP per capita growth (annual %)

0.983

1.135

3.413

-3.796

0.902

933

IGC

Initial GDP per capita (constant 2010 US)

9.175

9.260

11.618

5.882

1.333

1152

INV

Gross capital formation (% of GDP)

3.124

3.119

4.074

1.548

0.279

1149

0.000

-0.008

4.248

-4.055

1.359

1152

Financial development index by principal FDP component analysis MU

Mobile subscribers (per 100 people)

4.069

4.493

5.462

-3.713

1.145

1152

IU

Individuals using the Internet (% of population)

3.119

3.568

4.578

-2.748

1.316

1148

IS

Secure Internet servers (per 1 million people)

3.351

3.493

8.040

-4.836

2.627

1080

TEI

The sum of trade (% of GDP)

4.382

4.335

6.121

2.986

0.578

1150

INF

Increase rate of consumer price index (annual %)

1.165

1.204

4.006

-3.275

0.980

1059

30

Table 2. Dynamic GMM Estimations: Full Sample Variable GDPPC(-1)

Model 1 0.153 *** (0.000)

IGC

-0.400 *** (0.001)

INV

1.999 *** (0.000)

FDP

Model 2 0.014 * (0.096) -3.623 *** (0.000) 1.830 *** (0.000)

Model 3

Model 4

0.017433 *** (0.006)

0.019 ** (0.049)

-3.322 *** (0.000)

-2.550 *** (0.000)

2.643 *** (0.000)

2.365 *** (0.000)

-0.220 ***

0.018 * (0.087) -1.142 *** (0.000) 2.264 *** (0.000) -0.212 ***

(0.000) MU

Model 5

(0.000) 0.484 ***

0.023 (0.118) -1.842 *** (0.000) 1.966 *** (0.000) -0.158 *** (0.000)

0.371 ***

INF

AR(2)

-0.087 ***

(0.930) -0.086 ***

(0.03) 2.527 *** (0.000) -0.362 *** (0.000)

0.233 ***

(0.034)

(0.000)

-1.077 **

0.293 ***

0.084 **

0.010

(0.000)

(0.000)

IS

0.185 ***

0.146 ***

(0.000)

(0.000)

TEI

Model 7

0.179 ***

(0.000) IU

Model 6

0.470 *** (0.000)

0.421 *** (0.000)

-0.072 ***

-0.080 ***

(0.001) 0.365 *** (0.000) -0.094 ***

1.503 *** (0.000) -0.068 ***

0.368 (0.368) -0.148 ***

(0.000)

(0.000)

(0.000)

(0.001)

(0.000)

(0.009)

(0.008)

0.883

0.886

0.975

0.944

0.7804

0.994

0.881

0.373

0.369

0.379

0.345

0.152

0.401

0.162

72

72

72

72

72

592

592

638

635

589

(P-value) Sargan Test (P-value) N.country N.obs

635

589

Notes: P-value is informed in brackets. The symbols of ***, ** and * represent significant levels of 1%, 5% and 10%. For AR(2): Represents the Arellano-Bond test, whose null hypothesis is that there is no second-order autocorrelation in the first difference. For the Sargan test: when p-values are closer to 1, indicating that the instrumental variables are valid.

31

Table 3. Dynamic GMM Estimates with the Interaction Terms: Full Sample Variable GDPPC(-1) IGC INV FDP MU

Model 8 0.026 (0.239) -1.952 *** (0.000) 2.480 *** (0.000) -0.559 *** (0.010) 0.395 *** (0.003)

IU

Model 9 0.116 *** (0.002) -1.439 ** (0.029) 2.932 *** (0.000) -0.813 *** (0.000)

0.605 *** (0.000)

IS MU*FDP

0.257 *** (0.001) 0.075 * (0.082)

IU*FDP

0.118 *** (0.001)

IS*FDP TEI INF

AR(2) Sargan Test N.country N.obs

Mode10 0.083 ** (0.028) -1.164 ** (0.040) 2.712 *** (0.000) -0.393 *** (0.000)

0.585 *** (0.000) -0.064 *** (0.140) 0.420 0.232 72 638

0.206 *** (0.540) -0.116 *** (0.077) 0.329 0.302 72 634

0.002 (0.899) 0.344 (0.388) -0.198 *** (0.003) 0.331 0.206 72 635

Note: P-value is informed in brackets. The symbols of ***, ** and * represent significant levels of 1%, 5% and 10%. For AR(2): Represents the Arellano-Bond test, whose null hypothesis is that there is no second-order autocorrelation in the first difference. For the Sargan test: when p-values are closer to 1, indicating that the instrumental variables are valid.

32

Table 4. Dynamic GMM estimates: high-income group Variable

Model 11

Model 12

Model 13

Model 14

Model 15

Model 16

GDPPC

0.018

0.070***

0.036

0.039**

0.020

0.014

(0.277)

(0.000)

(0.235)

(0.043)

(0.472)

(0.458)

-0.223

-2.044***

-6.060***

-1.914

-4.983***

-3.081***

(0.733)

(0.003)

(0.000)

(0.485)

(0.000)

(0.005)

INV

3.756***

2.399**

4.669***

1.077*

3.922***

4.221***

(0.000)

(0.000)

(0.000)

(0.097)

(0.000)

(0.000)

FDP

-0.293***

-0.196***

-0.414***

-6.819***

-2.167***

-0.618***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

IGC

MU

0.322*

1.113*

(0.087) IU

(0.075) 0.677***

2.117***

(0.000)

(0.000)

IS

0.484***

0.455***

(0.000) MU * FDP

(0.000) 1.317*** (0.000)

IU * FDP

0.428*** (0.001)

IS * FDP

0.043*** (0.002)

TEI

0.656** (0.024)

INF

-0.157** (0.015)

AR(2)

0.325

1.954*

2.156

0.490

0.531

(0.251)

(0.099)

-0.075**

-0.293***

(0.054)

(0.564)

(0.481)

-0.024

-0.139*

-0.070

(0.036)

(0.000)

(0.690)

(0.073)

(0.243)

0.7525

0.9362

0.9163

0.923

0.568

0.850

Sargan Test N.country

0.388306

0.328284

0.333832

0.515

0.361

0.445

34

34

34

34

34

34

N.obs

266

262

216

219

238

241

Notes: P-value is informed in brackets. The symbols of ***, ** and * represent significant levels of 1%, 5% and 10%. For AR(2): Represents the Arellano-Bond test, whose null hypothesis is that there is no second-order autocorrelation in the first difference. For the Sargan test: when p-values are closer to 1, indicating that the instrumental variables are valid.

33

Table 5. Dynamic GMM estimates: Middle & Low-Income group Variable

Model 17

Model 18

Model 19

Model 20

Model 21

Model 22

GDPPC

0.106***

0.013

0.018

0.050

0.104

0.046

(0.000)

(0.208)

(0.469)

(0.530)

(0.859)

(0.387)

IGC

-1.214***

-0.554

-0.668***

-1.120

8.688

-0.994

(0.002)

(0.250)

(0.001)

(0.621)

(0.459)

(0.358)

INV

0.670***

3.397***

1.679***

1.597*

7.528*

1.796 **

(0.000)

(0.000)

(0.000)

(0.054)

(0.060)

(0.015)

FDP

-0.050**

-0.200***

-0.064***

-0.451

-1.697

-0.167*

(0.017)

(0.000)

(0.000)

(0.343)

(0.408)

(0.067)

MU

0.186***

0.188

(0.000) IU

(0.516) -0.153***

-1.689***

(0.000)

(0.000)

IS

-0.138***

-0.349**

(0.000) MU * FDP

(0.012) 0.067 (0.504)

IU * FDP

0.410 (0.525)

IS * FDP

0.045 (0.367)

TEI

0.694***

0.123

0.842***

2.019**

3.456

INF

0.068

(0.002)

(0.471)

(0.000)

(0.011)

(0.432)

(0.940)

-0.092***

-0.326***

-0.124

-0.236

-0.180

-0.346***

(0.001)

(0.000)

(0.000) ***

(0.018) **

(0.800)

(0.000)

AR(2)

0.977

0.679

0.853

0.707

0.466

0.662

Sargan Test

0.434

0.246

0.260

0.143

0.458

0.348

N.country

34

34

34

34

34

34

N.obs

372

372

372

332

332

332

Notes: P-value is informed in brackets. The symbols of ***, ** and * represent significant levels of 1%, 5% and 10%. For AR(2): Represents the Arellano-Bond test, whose null hypothesis is that there is no second-order autocorrelation in the first difference. For the Sargan test: when p-values are closer to 1, indicating that the instrumental variables are valid.

34

Table 6. Dynamic GMM Estimates with the Interaction Terms: Full Sample Variable GDPPC(-1)

Model 23 0.623 *** (0.000)

IIC

-1.854 *** (0.000)

INV

0.808 *** (0.000)

FDP

-0.151 *** (0.000)

MU

Model 24 0.700 *** (0.000) -1.276 *** (0.000) 0.350 *** (0.000) -0.227 *** (0.000)

Mode1 25 0.475 *** (0.000) -1.816 *** (0.000) 2.106 *** (0.000) -0.195 *** (0.000)

0.295 ***

0.496 *** (0.000) -6.190 *** (0.000) 2.933 *** (0.008) -1.271 ** (0.026)

Model 27 0.956 *** (0.000) -0.514 (0.471) 0.199 (0.668) -0.620 *** (0.003)

Mode1 28 0.579 *** (0.000) -1.135 *** (0.000) 1.019 *** (0.000) -0.216 *** (0.000)

0.973 ***

(0.000) IU

Model 26

(0.001) 0.353 ***

0.287 **

(0.000) IS

(0.015) 0.181 ***

0.047 *

(0.000) MU*FDP

(0.084) 0.279 ** (0.031)

IU*FDP

0.110 ** (0.031)

IS*FDP

0.017 *** (0.000)

Trade

0.001 (0.993)

Inflation

Sargan Test N.country N.obs

-0.140 ***

0.870 *** (0.000) 0.008

0.970 *** (0.388) -0.150 ***

2.111 *** -0.004 -0.487 ***

-0.495 (0.226) -0.197 ***

0.184 ** (0.020) -0.056 ***

(0.000)

(0.5783)

(0.003)

(0.000)

(0.000)

(0.000)

0.340

0.281

0.10

0.161

0.130

0.311

72

72

72

72

72

72

864

864

864

864

792

792

Notes: The dependent variable is a three-year average of the growth rate of actual per capita GDP. The P-value is informed in brackets. The symbols of ***, ** and * represent significant levels of 1%, 5% and 10%. For the Sargan test: when p-values are closer to 1, indicating that the instrumental variables are valid.

35

Highlights:
Explore how ICT diffusion and financial development affect economic growth.





Set up a broad index of financial development by employing principal component analysis. The impact of financial development on economic growth is consistently negative. ICT diffusion can improve economic growth in high-income countries. The interaction effects of ICT and finance can reduce the negative effects of financial development.