Introduction

The economic growth (EG) of any country depends on various determinants like inflation rate, market size, interest rate, population size, labor force, technology, capital goods, financial development (FD), foreign direct investment (FDI), and trade openness (TO). Among these determinants, FD, FDI, and TO are considered the most important (Adom et al., 2019; Dunne & Masiyandima, 2017). However, information and communication technology (ICT) is a more modern driver of EG than TO and FDI (Donou-Adonsou, 2019; Myovella et al., 2020). A wide range of studies can be seen with an emphasis on different measures of ICT, but our study mainly discusses telecommunication infrastructure because telecommunication infrastructures reduce transaction time and cost, increases efficiency, trade growth, enlarge innovation, development, and increase job opportunities by which increase EG of any country (Pradhan, Arvin, Hall, & Bennett, 2018; Pradhan, Arvin, & Norman, 2015; Datta & Agarwal, 2004; Shahiduzzaman & Alam, 2014).

According to United Nations (2019), ICT infrastructure affects job creation, GDP, increasing rate of return, and profitability by using skilled persons and increasing income levels in developing countries. Similarly, World Bank (2012) argues that ICT is a driving force in encouraging EG in developing countries. However, developing countries are gradually using more developed ICT infrastructure. Therefore, it shows that the number of Internet and mobile users is increasing significantly in developing countries (including low- and middle-income countries). For improving ICT infrastructure, it requires funds and is possible when investing in new technologies, including artificial intelligence, robotics, and AR/VR.

ICT is increasing with more positive EG. Nowadays, ICT plays a vital role in all sectors, with extensive ICT applications which help in cost-reducing, innovative thinking, economic reconstruction, and increased performance in all sectors make possible (Sharafat and Lehr, 2017). ICT makes it easy to do business and connects customers, suppliers, and manufacturers across the geographical and demographical barriers. Even though ICT plays a significant role in EG, it requires more financial support for further investment in new ICT projects and adaptation of more TO policies from particular countries. Moreover, making good ICT infrastructure requires high speed of Internet, costly installation and operation charges, consumes more electricity, etc. The rapid growth of ICT requires more FD and TO.

The country’s income level does not imply that FD is efficient for developing the ICT infrastructure; FD requires more TO, but this will be possible when policies are liberal. But in this field of research on the impact of ICT infrastructure, FD, and TO on EG in low-income countries (LICs) and high-income countries (HICs), there are some conflicting results, so their studies need to be considered. Thus, it is also essential to consider FD and TO as playing essential roles in the performance of ICT infrastructure which will also boast EG. Moreover, it is intuitive to rethink that there could be country-wide differences due to the relationships between ICT infrastructure and EG, FD and EG, and TO and EG; hence, a suitable analysis framework has been prepared to account for the country-level heterogeneity. At last, it is also essential to consider that the countries have different levels of TO and FD over a period of time; hence, the empirical framework of analysis shall also take into account the regimes switching aspects among ICT infrastructure, FD, TO, and EG.

Previous studies regarding the nexus between ICT and EG have been discussed exclusively without looking at the impact of FD and TO. Hence, our study has assessed the contribution of FD and TO to EG by studying new evidence. This study has hypothesized that ICT infrastructure, FD, and TO have actually influenced EG in LICs and HICs and whether the new policy appropriately highlights the FD for EG and upgrades the ICT infrastructure. Since ICT infrastructure and FD do not necessarily enhance the EG of any country, this study also examines the impact of TO on EG. This study contributes to the previous studies of FD, ICT infrastructure, and TO by incorporating the issue of disparity in policies related to TO as well as ICT infrastructure indicators/measures and FD. To our knowledge, this is the first study to check the joint impact of ICT infrastructure, FD, and TO on EG in LICs and HICs.

The novel feature of our study is stated in terms of four aspects: firstly, to examine the individual impact of ICT infrastructure, FD, and TO on EG; secondly, to examine the joint impact of ICT infrastructure, FD, and TO on EG. In other words, we consider the three most significant variables ICT infrastructure, FD, and TO which affect the EG of a country. Thirdly, we use a large sample of developing (LICs) and developed countries (HICs) over a recent span of time. Fourthly, we employ sophisticated econometrics and certain approaches that were not adopted in past studies to find out the answers to questions concerning the kind of impact of independent variables on the dependent variable. Therefore, our study investigates the joint impact of ICT Infrastructure, FD, and TO on EG for a sample of 85 countries (including 27 low- and 58 high-income countries) from 2000 to 2019. After considering the contrast grouping of countries according to their income level, we have framed various hypotheses and then applied the ordinary least square (OLS) regression, fixed effect regression (FER), and generalized method of moments (GMM) method.

This study is structured in the following way. Past studies are discussed in the “Literature Review” section. How to collect and choose the data for this study are explained in the “Data Collection and Methodology” section. Econometric results and analysis are described in the “Results” section. The “Analysis” section discusses the conclusion and policy implications.

Literature Review

ICT Infrastructure and EG

The impact of ICT on EG is discussed in the previous studies (Appiah-Otoo and Song, 2021; Habibi and Zabardast, 2020; Parwantoa and Wulansari, 2020; Adeleye and Eboagu, 2019; Albiman and Sulong, 2016; Bertschek and Niebel, 2016; Ghosh, 2016; Das, Khan, and Chowdhury, 2016; Ahmed and Ridzuan 2013; Chavula, 2013), and they have found a positive association between ICT and EG. But, most of the studies considered only developed countries for the empirical analysis to determine the impact of ICT on EG. Only a few studies (Yousefi, 2011) have been done on developing countries because of the lack of data availability on ICT variables. Thus, a brief literature review related to the impact of ICT infrastructure on FD or FDI inflows, TO, and EG at the contrast country level has been presented to address the significant research gaps and identify the primary objectives of this paper.

Internet, mobile, and fixed broadband users are encouraged to EG in high-, middle-, and low-income countries (Appiah-Otoo and Song, 2021). Similarly, these results in the study of Ghosh (2016) found a positive association between the mobile telephone and EG in the Indian context. Habibi and Zabardast (2020) examined the effects of education and ICT on EG in the 17 Middle East and 24 OECD countries. They have taken 18 years (2000 to 2017) for the dataset and applied OLS, fixed-effect, and GMM methods. Results of the panel analysis show that ICT is positively affecting the EG in both groups of countries (the Middle East countries and OECD). Parwantoa and Wulansari (2020) found that middle-income countries (MICs) used more ICT facilities than LICs and HICs countries. Pohjola (2000) observed that the countries that have invested in ICT sectors/ICT infrastructure/technology encourage the EG. But they have found that those countries that have not invested found a negative and insignificant relationship between ICT and EG. ICT capital played a significant role in the EG of upper-, middle-, and low-income countries (Yousefi, 2011). ICT capital helps to develop ICT infrastructure and increase EG (Chavula, 2013; Datta & Agarwal, 2004; Lam & Shiu, 2010; Lee et al., 2012; Pradhan et al., 2015, Pradhan et al., 2018). Based on the previous studies, we framed hypothesis 1.

FD and EG

FD plays an important role in the EG of a nation (Shahbaz, 2020). Basically, FD implies the growth and development of financial institutions, which is recognized by the economy’s liquidity and the growing scale of the financial service sectors (Sawyer, 2015). The significance of financial institutions and their service sectors shows the stability of the real economy and society (Bagehot, 1873). The financial sectors play a major role in removing the financial crunch and other pandemic situations, but it will be possible when the country has maintained adequate liquidity. Former policies were focused on investing for profitability, while later they focused on the financial resources for further economic development. FD affects to firm’s ability to invest  Hou et al. (2014). Financialization is well described in the study of Moosa (2017, p.1) as “the increasing importance of financial markets, financial motives, financial institutions, and financial elites in the operation of the economy and its governing institutions, both at the national and international levels.” The size of the financial sector analyzes the economy’s growth, and as studied in previous work, real economy has always kept finance for growth. In this context, there are various studies that have found a positive relationship between FD and EG. Only a few studies (Sassi and Goaied, 2013; Dimelis and Papaioannou, 2010; Pradhan, Arvin, and Norman, 2015) have been done on the combined effects of ICT and FD on EG. These studies support the positive effect of jointly ICT and FD on EG in MENA regions (Sassi and Goaied, 2013), the insignificant impact of FD on EG in developing countries (Dimelis and Papaioannou, 2010), and short- and long-term causal relationships among the ICT infrastructure, FD, and EG in Asian countries (Pradhan, Arvin, and Norman, 2015). Thus, FD is important to developing and transitioning economies (Tasic & Valev, 2010). Based on these studies, we have framed hypotheses 2 (a) and 2 (b).

TO and EG

The relationship between TO and EG is discussed in previous studies (Fetahi-Vehapi, Sadiku, and Petkovski 2015; Chatterji, Mohan, and Dastidar 2014; Kaushal and Pathak, 2015) and received a great deal of attention both in conceptual and empirical literature during the last 3 decades. At the same time, there is no harmony on whether more openness to trade arouses EG. Trade liberalization encourages specialization in various sectors which has increased the economic scales that encourage productivity and efficiency. New endogenous growth models explain a positive association between TO and EG due to technology diffusion (Romer, 1994). Similarly, Fetahi-Vehapi et al. (2015) and Chatterji et al. (2014) found a robust association between TO and EG. Kumari et al. (2021) concluded that they had found bi-directional causality between FDI and EG. Still, in contrast, they found no bi-directional relationship between TO and EG in the Indian context. Bangake and Eggoh (2011) concluded bi-directional long-run causality between FD and EG in LICs, MICs, and HICs but did not find short-run causality between TO and EG in LICs and MICs. Only in HICs found a significant short-run causal relationship between FD and EG.

Based on the above literature review, we have framed the hypotheses to analyze the impact of ICT infrastructure, FD, and TO on EG in LICs and HICs. The framed hypotheses are as follows:

Hypothesis 1: good ICT infrastructure has a significantly positive impact on EG in LICs and HICs.

The ICT infrastructure has accelerated EG by developing and adapting innovation processes. The new EG theories have suggested the growth effects of modern communication processes in countries. The EG theories suggest that the ICT arrival of Internet technology may have different qualities. ICT infrastructure may accelerate the foster competition and develop new ideas, processes, and business models, which increase EG. Therefore, we also support the previous studies (Parwantoa and Wulansari, 2020; Adeleye and Eboagu, 2019; Albiman and Sulong, 2016) and expect ICT infrastructure significantly impacts EG in LICs and HICs.

Hypothesis 2(a): FD encourages EG in both countries (LICs and HICs).

FD is the most prominent independent variable that highly impacts the EG of any economy. Initially, only a few studies (Goldsmith, 1969) included FD and EG to analyze the relationship between them. Before economic growth theory, FD was not used as an endogenous variable in the regression model. But, after a growing number of previous studies related to FD and EG (including allocated resources, diversified risks, and intermediation savings) have shown how FD is an important contributor to EG (Greenwood and Jovanovic, 1990). Thus, we also support these studies and expect that FD will encourage EG in both countries (LICs and HICs countries).

Hypothesis 2(b): the joint ICT infrastructure and FD positively correlate with EG in both countries (LICs and HICs).

Hypothesis 2 (b) supports the ICT and FD to encourage the EG, but it has been checked individually only in the previous studies. Now, we expect jointly ICT infrastructure and FD to positively correlate with EG in both countries with the support of previous studies by Ghosh (2016), Habibi and Zabardast (2020), and Appiah-Otoo and Song (2021). This is because the joint use of these two significant factors may be highly affected EG in a positive manner.

Hypothesis 3 (a): the joint effect of ICT infrastructure, FD, and TO promote to EG in LICs and HICs.

More TO attracts foreign investors for investment with the help of advanced ICT infrastructure. Combined, these three variables will encourage investment, employment, interest rate, foreign exchange rate, production growth, etc, which will highly contribute to the EG of any country. Thus, with the support of the previous study written by Salahuddin and Gow (2015), this work also expects the joint effect of ICT infrastructure, FD, and TO to promote EG in LICs and HICs.

Hypothesis 3 (b): joint effects of ICT infrastructure, FD, and TO encourage EG; it may vary between two groups of countries (LICs and HICs).

Joint effects of ICT infrastructure, FD, and TO encourage EG but may vary from country to country because countries have different policies, population size, geographical areas, etc. Thus, our work also expects joint effects of ICT infrastructure, FD, and TO to encourage EG may vary between two groups of countries (LICs and HICs). Hence, the impact of ICT, FD, and TO on EG in the existing studies are mixed (positive, negative, and insignificant); summarized results of previous studies are shown in Table 1, and framed hypothesis is represented in Fig. 1.

Data Collection and Methodology

Data Collection and Assessment Issues

Panel data analysis is becoming more popular among researchers because panel data provides multiple benefits compared to cross-section and time-series datasets. In this work, we have used the panel data considering LICs and HICs for the time frame from 2000 to 2019. All the variable data have been collected since 2000 because data related to the ICT variables for developing countries is available from 2000. The variable data has been collected from the World Development Indicators (WDI, 2019) except for ICT infrastructure variables. ICT infrastructure variable data has been collected from Worldwide Telecommunication Union (WTU, 2019).

This work applied the generalized method of moments (system GMM approach) to estimate the framed hypotheses. This approach has numerous benefits which are as follows: firstly, it does not find biased behavior. Secondly, this method is capable of controlling the country or sector-specific effects. Thirdly, and most importantly, it controls the endogeneity problem, which is not possible in OLS and fixed effect regression (FER) method. Therefore, our study applied the GMM approach. This technique is widely used when incorporating panel data including a couple of periods with an enormous number of observations (Hong and Jiang, 2022; Roodman, 2006; Baltagi, 2021). As suggested by Wang et al. (2022), Khan et al. (2021), Ledhem and Mekidiche (2021), Hoeffler (2002), and Das and Paul (2011), the GMM approach is utilized in both situations where variables are lagged and differenced versions of the regressors as instruments in obtaining coefficient estimates. In this method, Arellano-Bond shows that there is no serial correlation along with the hypothesis and error terms. This is supported by the selection of instrumental variables in the GMM model. In these studies, it is suggested that the GMM approach is preferred in comparison of the OLS and FER econometric model.

This study has applied a standard estimation for the economic growth model where the EG is determined by (Das, Khan, and Chowdhury, 2016; Kumari et al., 2021; Myovella, Karacuka, and Haucap, 2020) FD, TO, and ICT as suggested by Barro et al. (1991). Secure Internet servers, broadband servers, mobile users, and individual Internet users are used as proxies of ICT infrastructure. From the following (Myovella, Karacuka, and Haucap, 2020; Sassi and Goaied, 2013) literature, we have chosen government consumption, capital formation, and population growth as control variables; hence, the proposed model is framed as mentioned in Eq. (1) and explained in Eq. (2).

$$\boldsymbol{E}{\boldsymbol{G}}_{\boldsymbol{it}}=f\left(\ \textrm{ICT}\ {\textrm{infrastructure}}_{\textrm{it}},{FD}_{it},{TO}_{it}\right)$$
(1)
$$\begin{array}{c}\boldsymbol E{\boldsymbol G}_{\mathbf i\mathbf t}=\beta_0+\beta_1EG_{it-1}+\beta_2\;\text{ICT}\;{\text{infrastructure}}_{it}+\beta_3FD_{it}+\beta_4{TO}_{it}+\\\beta_5\text{ICT}\;{\text{infrastructure}}_{it}\ast{FD}_{it}+\beta_6{FD}_{it}\ast{TO}_{it}+\beta_7\text{ICT}\;{\text{infrastructure}}_{it}\ast{FD}_{it}\ast TO+IVE+\delta_{it}+\epsilon_{it}\\\end{array}$$
(2)

Where β0 represents the coefficient of the parameters. EGit is represented by GDP per capita (constant in US$), the telephone is represented by the fixed telephone subscriptions (per 100 s people) [individuals using the Internet (% of the population)], secure Internet is represented by Secure Internet servers (per 1 million people)], broadband is represented by fixed broadband subscriptions (per 100 people). FD is represented by the total credit provided to the private sector (% of GDP). TO is represented by the total sum of imports and exports divided by GDP per capita, capfor is used to represent the capital formation (the gross domestic investment as a share of GDP), consum is represented by general government final consumption expenditure (1% of GDP), popugr is represented by the total population growth rate, i implies the number of observations (total countries considered), and t is the period. ϵit represents the error term.

This paper applied the system GMM approach after normalizing the data. A total of ten variables have been included in the empirical analysis, one variable has been taken as a dependent, and the other nine variables have been considered explanatory variables, including three control variables. All the framed hypotheses 1, 2 (a, b), and 3 (a, b) shown in Eq. (2) are well explained in the “Descriptive Statistics and Correlation Analysis” sub-section. We have described the variables, measured proxies, and data sources in Table 2.

Descriptive Statistics and Correlation Analysis

Tables 3 and 4 show the descriptive analysis of LICs and HICs. In LICs, the average of EG is 625.856 and std. dev is 371.990. The average and std. dev of Internet, secure Internet, broadband, and telephone is 9.741, 0.558, 0.793, 1.528, and 14.468, 0.860, 1.070, and 3.302, respectively. In HICs, as shown in Table 4, the average and std. dev of EG is 31,773.85 and 21,389.4, including min is 1659.908 and max is 118823.6. For HICs, we have observed an average of the Internet, secure Internet, broadband, and telephone as 60.731, 7194.66, 16.007, and 37.49, respectively.

Tables 5 and 6 show the correlation results of LICs and HICs. There is a positive association between the secure Internet and telephone in LICs with a value of 0.375, which is quite good. Table 6 shows that the correlation between secure Internet and broadband is 0.146 in HICs which is relatively high compared to LICs; hence, it shows that Internet and broadband services are good in HICs. In HICs, people who do not even use broadband and phone subscriptions also rely on broadband which shows that developed countries are more updated and well-structured in ICT infrastructure than LICs.

Fig. 1
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Framed hypothesis

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Table 1 Literature summary
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Table 2 Name of variables, proxies, measured, and data source
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Table 3 Descriptive analysis of LICs
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Results

Tables 7 and 8 shows the comparative results of OLS, FER, and system GMM towards LICs and HICs. Initially, we applied the OLS and FE regression methods but found that the results were biased in terms of endogenous. Therefore, further, we have applied the GMM method to overcome the endogeneity. Hence, we have discussed only the results of the GMM model here and 12 different models (as shown in Tables 9, 10, 11, and Appendix Table 12) related to the framed hypothesis. The GMM model found TO, secure Internet, and FD negatively related to EG in LICs; hence, these findings did not support hypotheses 1 and 2 (a). In HICs context, this paper found almost all results were the same, but the ICT infrastructure proxy sign (+/−) changes according to the model. TO, broadband, and FD have a negative relation with EG which is also not supported by hypotheses 1 and 2 (a). But, most of the ICT infrastructure variables have a positive relation with EG; hence, a comparison of Table 7 can conclude that ICT infrastructure has a positive relation with EG in LICs and HICs.

Table 4 Descriptive analysis of HICs
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Table 5 Correlation matrix (LICs)
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Table 6 Correlation matrix of HICs
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Table 7 Comparative model impact of ICT infrastructure, FD, and TO on EG
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Table 8 Individual and joint effect of ICT infrastructure and FD on EG (LICs)
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Table 9 Joint effect of ICT infrastructure, FD, and TO on EG (LICs)
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Table 10 Individual and joint effect of ICT infrastructure and FD on EG (HICs)
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Table 11 Joint effect of ICT infrastructure, FD, and TO on EG (HICs)
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Table 8 shows the individual and joint effect of ICT infrastructure and FD on EG. When analyzing the individual effect of ICT infrastructure (Internet, secure Internet, broadband, and telephone) on EG, this study found that the Internet, secure Internet, and telephone have a positive impact on EG and are statistically significant at the level of 5%. On the other hand, broadband has a negative impact on EG but is statistically significant at 10% and 5% levels. AR (1) and AR (2) prove that all estimated versions of this model meet the Arellano-Bond criteria for valid estimations; hence, do not reject the hypothesis (1) of the GMM model, and it supports hypothesis 1. IT projects for the developed ICT infrastructure in their education sector, financial institutions (including banks and stock/share markets), and industries that help to encourage EG (Das, Khan, and Chowdhury, 2016; Rodas and Lopez, 2007). Hypothesis 2(a) has failed to prove that FD encourages EG in LICs; hence, the individual impact of FD than found that it has a negative impact on EG. On the other hand, the joint impact of internet*FD, secure internet*FD, and broadband*FD found a positive impact on EG and was significant at 5% and 1% levels of significance. Only telephone*FD has a negative effect on EG; hence, our findings support Sassi and Goaied (2013). Hypothesis 2(b) has been proved because jointly, ICT infrastructure and FD positively correlate with EG in LICs. TO, capfor, consum, and popugr positively affect EG in LICs. These findings support Sassi and Goaied (2013) and Das et al. (2016). Thus, FD motivates EG through the ICT infrastructure in LICs. When FD is considered, the effects of the internet, broadband, and secured internet are more important than telephones in LICs. Similarly, this finding was found in the study of Sassi and Goaied (2013), but they support more telephone use than the Internet and mobile.

Table 9 shows the joint impact of ICT infrastructure, FD, and TO on EG in LICs. Jointly impact of FD*internet*TO, FD* secure Internet* TO, FD* broadband* TO, and FD* telephone* TO is positive relation with EG in LICs and significant; hence, we support hypothesis 3 (a) and supported joint effects of ICT infrastructure, FD, and TO encouraging EG in LICs (Sassi and Goaied, 2013). Furthermore, we analyzed the individual impact of FD and TO on EG then find a negative relationship with EG in LICs and significant.

Table 10 shows the individual and joint impact of ICT infrastructure and FD on EG in HICs. In individual analysis, Internet, secure internet, broadband, telephone, and FD have found positive relations with EG and significance in HICs which have also been found in the LICs context. Thus, the majority of the findings support hypothesis 2(b). Table 10 also revealed that the joint impact of internet*FD, secure internet*FD, and telephone*FD found a positive impact on EG and was significant. Only broadband*FD has a negative relation with EG. On the other hand, in contrast, broadband and secure Internet have a negative relationship with EG in LICs but are significant. Therefore, we have failed to prove hypothesis 2(a) in the case of LICs but in the case of HICs we did not reject hypothesis 2(a), so our results are considered for the ICT infrastructure and FD more encouraged in HICs as a comparison to LICs.

Our results suggest that most of the different models found in FD have a positive relationship with EG in HICs. Moreover, TO has a negative impact on EG in HICs. More TO in any country is negatively affected by business activities because liberal legal rules and regulations increase FDI inflow which is harmful from the point of view of ownership transfer (Kumari and Sharma, 2017). Apart from TO, control variables (capital formation, consumption, and population growth) positively correlate with EG in all models. These findings were also found in the studies of Sassi and Goaied (2013) and Das et al. (2016).

As we expected, ICT infrastructure and FD have a positive relationship with EG, but in HICs jointly broadband and FD have a negative relationship with EG. Therefore, the results shown in Table 10 conclude that the marginal effect of the Internet, secure internet, and telephone with FD is stronger than the joint effect of broadband and FD. Our study proved hypothesis 3(a) and supported the joint impacts of FD and TO in encouraging EG in HICs (Sassi and Goaied, 2013).

Interestingly, we found reverse results of LICs in the case of HICs when we analyze the joint effect of ICT infrastructure, FD, and TO on EG. As shown in Table 11, our estimated results have clearly proved hypothesis 3(b), the joint effects of ICT infrastructure, FD, and TO encourage EG may vary between LICs and HICs. It is evident from the estimates that growth in Internet, secure internet, broadband, and telephone along with FD and TO coincides with higher EG in LICs but not in the HICs context.

Analysis

The work results clearly show that the effects of ICT infrastructure on EG in LICs are positive; hence, we support hypothesis 1: ICT infrastructure will encourage to economic growth. On the other hand, hypothesis 2 (a) was not proved because the individual effect of FD found negative relation with EG. Nevertheless, when we estimate the joint effect of ICT infrastructure and FD for both countries (i.e. hypothesis 2 (b)), we find positive effects on all four types of ICT (excluding telephone) in LICs but in HICs, excluding Internet and broadband, two types of ICT have a positive effect on EG. On average, the marginal contributions of secure Internet and broadband on EG are found to be greater than those of Internet and telephone.

The joint marginal effect of ICT infrastructure, FD, and TO on EG works differently in LICs and HICs. All four variables are significant in LICs and HICs, but the coefficient signs are contradictory because the coefficient shows a positive sign in LICs and a negative HICs context. Hence, the Internet, secure Internet, broadband with FD, and trade openness play a crucial role in LICs. But in the HICs context, Internet, secure Internet, broadband, and telephone with FD and TO are negatively significant. In this case, our study support Yousefi (2011) because he argues that the insignificant effect of ICT diffusion and finance on EG is similar to our results on ICT infrastructure with FD and TO significant but negative. It may be due to the improper use of available human capital or labor and lack of knowledge of new technologies. Thus, people need higher salaries with higher ICT use and require a stronger financial sector. Without further enhancement in ICT use, the country’s overall development is not possible. Most of the financial intermediaries (including banks and stock markets) have adopted ICT facilities like AI, machine learning, and data mining. If the banking sector and the stock market do not accept ICT, then human capital requires more to increase productivity.

Based on a result analysis, we have compared and summarized (as shown in Figs. 2, 3, and 4) the situation of the ICT infrastructure of two contrast countries (LICs and HICs). We have found that immediately LIC requires vital support from R&D, financial development, IT skill enhancement program, ICT infrastructure, human resources, more trade openness, reconstructive manual process, and a new IT business model for the EG (S.-Y.T. Lee, Gholami, and Tong, 2005). Although due to the down price of mobile sets, LICs adopted mobilization. But, due to the high Internet and broadband penetration cost, facilities remain unequal in developed and developing countries. ICT facilities in developed countries benefit from high-speed Internet, free Wi-Fi (selected places), developed e-commerce channels, sales distribution channels, etc. It also helps to create new job opportunities for people.

Fig. 2
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Impact of ICT infrastructure on EG. Source: Authors’ framed based on results

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Fig. 3
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Joint impact of ICT infrastructure and FD on EG. Source: Authors’ framed based on results

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Fig. 4
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Joint impact of ICT infrastructure, FD, and TO on EG. Source: Authors’ framed based on results

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In contrast, LICs are still struggling for ICT infrastructure, FD, and more TO because these countries only enjoy ICT infrastructure as an initial benefit (Das, Khan, and Chowdhury, 2016). Nevertheless, policymakers should pay attention to the growth in ICT infrastructure because after the situation of COVID-19, survival will be difficult without ICT adaptation. The impact of ICT infrastructure and its joint implications are summarized in Figs. 2, 3, and 4 using Tables 8, 9, 10, and 11.

Figure 2 shows the summarized results of the individual impact of ICT infrastructure on EG. We can see that the Internet positively affects EG, and its influence is almost the same in LICs and HICs. Secure Internet and broadband negatively influence the EG in LICs while the positive influence on the EG of HICs. Telephone lines are positively influencing the EG of LICs and HICs. It can be concluded that secure Internet and telephone lines significantly impact the EG of HICs.

Figures 3 and 4 summarize the results and show the joint impact of ICT infrastructure and FD and ICT infrastructure, FD, and TO on EG. The Internet’s and secure Internet’s impact is favorable for both LICs and HIC. Broadband positively impacts LICs EG, while telephone has a negative. In the case of HICs, broadband has a negative impact on EG while telephones have a positive. Overall observation drawn from Figs. 2, 3, and 4 shows that individual ICT infrastructure and joint ICT infrastructure along with FD can play a crucial role in improving the GDP per capita of HICs when we invest in it. However, in the case of LICs, it can be observed that ICT infrastructure, FD, and TO show a positive relationship with GDP per capita. Therefore, to improve the GDP per capita of LICs, it is recommended to invest in ICT infrastructure along with FD and TO.

Conclusion and Policy Implication

Our study investigates the joint impact of ICT infrastructure, FD, and TO on EG for a sample of 85 countries (including 27 low- and 58 high-income countries) from 2000 to 2019. After considering the contrast grouping of countries according to their level of income, we framed various hypotheses and then apply the ordinary least square (OLS) regression, fixed effect regression (FER), and generalized method of moments (GMM) method. Our findings are fourfold: (a) the individual effects of ICT (Internet, secure Internet, broadband, and telephone) on EG are positive, excluding only telephone in LICs and excluding broadband in the HICs context; (b) in the case of individual effects, the majority of ICT proxies have a positive and significant relationship with EG in LICs and HICs; (c) the joint effect of ICT infrastructure and FD in both samples indicates that FD complements the positive impact of EG; (d) the result of the joint impact of ICT infrastructure, FD, and TO on EG in both countries is contradictory to each other. Thus, the results of the joint effect conclude that in the LICs context, the joint effect of ICT infrastructure, FD, and TO positively affects EG, but the results are reversed in the HICs context.

We then investigate the joint effect of ICT infrastructure and FD in both samples, and the results indicate that FD complements the positive impact of ICT infrastructure. FD works as a harmonizer for the ICT infrastructure. Further, we estimate the effect of ICT infrastructure, FD, and TO in both countries, but the results are contradictory to each other. Thus, the results of the joint effect conclude that in the LICs context, the joint effect of ICT infrastructure, FD, and TO positively affects EG, but the results are reversed in the HICs context.

We have framed a number of policies and suggestions for both countries (LICs and HICs). With the help of these policies, both countries would increase their potential output capital gains and maintain sustained GDP per capita. Against these findings, we recommended to policymakers in LICs to enhance ICT infrastructure by reducing Internet and telephone prices, similarly as advised by Hasbi and Dubus (2020) and Adeley and Eboagu (2019). In addition, policymakers should promote ICT infrastructure education and how we can improve the infrastructure of ICT, especially among adolescents (Hasbi and Dubus, 2020). Additionally, policymakers should promote ICT infrastructure along with FD and TO in LICs because these have a positive relationship with EG. We recommended policymakers to promote quality regulations, stable political policies, less corrupt business scenarios, develop a supportive environment for the ease of doing business, and maintain a proper legal system. Finally, we urge policymakers to make favorable TO policies for the public sector to encourage partnerships with private industries that support the high cost of ICT investment which would help to make easy access to all persons.

Limitations of this study

This study has some limitations like we have not considered the middle-income countries (MICs) data in our dataset. It is because of data constraints, as we have used only four proxies of ICT infrastructure. The improved ICT infrastructure is not possible to consider without the ICT revolution as it is in the form of cloud computing, machine learning, the Internet of Things (IoT), artificial intelligence, 5G, and many other components. Therefore, it will be quite interesting to observe the impact of these new measures in future studies.