Tables 5 and 6 show the correlation results of LICs and HICs. There is a positive association between the secinternet and telephone in LICs with a value of 0.375, which is quite good. It specifies that people who have a telephone subscription are more likely to use the secure internet in LICs. It also shows that these countries are still facing a lack of ICT infrastructure.
Similarly, from Table 6, we can see that the correlation between the secinternet and broadband is 0.146 in HICs which is relatively high compared to LICs. So, it shows that internet and broadband services are good in HICs. Most people who do not even use broadband and phone subscription also rely on broadband. Therefore, we can say that developed countries are more updated and well-structured in ICT infrastructure than LICs.
Table 5
Correlation matrix (LICs)
| EG | TO | telephone | capfor | consum | popugr | internet | secinternet | broadband | FD |
EG | 1.000 | | | | | | | | | |
TO | -0.512 | 1.000 | | | | | | | | |
Telephone | 0.504 | -0.145** | 1.000 | | | | | | | |
Capfor | 0.143** | -0.087** | 0.033** | 1.000 | | | | | | |
Consum | 0.036** | -0.059** | 0.120** | 0.019 | 1.000 | | | | | |
Popugr | 0.400** | -0.120** | 0.185** | 0.065** | -0.008** | 1.000 | | | | |
Internet | 0.208** | -0.085** | 0.142** | 0.024** | -0.140* | 0.286* | 1.000 | | | |
secinternet | 0.032** | 0.097** | 0.375** | -0.100** | -0.042 | 0.034* | 0.007 | 1.000 | | |
broadband | -0.059** | 0.157** | 0.264** | -0.135** | -0.071 | -0.065* | -0.073 | 0.9012** | 1.000 | |
FD | -0.049** | 0.020** | 0.087** | 0.143** | 0.560** | 0.067** | 0.151** | -0.144 | -0.220 | -1.000 |
Source: Authors estimation, Note: ** 5% and *1 level of significance |
Table 6
Correlation Matrix of HICs
| EG | TO | telephone | capfor | consum | popugr | internet | secinternet | broadband | FD |
EG | 1.000 | | | | | | | | | |
TO | -0.618** | 1.000 | | | | | | | | |
Telephone | 0.163** | 0.246** | 1.000 | | | | | | | |
Capfor | -0.077** | 0.127** - | 0.131** | 1.000 | | | | | | |
Consum | 0.035 | -0.155** | 0.032** | -0.134** | 1.000 | | | | | |
Popugr | -0.035** | 0.062** | -0.022** | -0.002 | 0.053** | 1.000 | | | | |
Internet | 0.384** | -0.342** | -0.031** | -0.067** | 0.025** | 0.009** | 1.000 | | | |
secinternet | 0.103** | -0.029** | -0.0468 | 0.002** | 0.092** | -0.002** | 0.049* | 1.000 | | |
Broadband | 0.173** | -0.173** | 0.0740 | -0.091** | 0.099** | -0.001** | 0.528* | 0.146** | 1.000 | |
FD | 0.361** | -0.287** | 0.4864 | -0.066** | -0.035** | -0.053** | 0.191** | 0.065** | 0.239** | 1.000 |
Source: Authors estimation, Note: ** 5% and *1 level of significance |
Table 7 shows the results of OLS, FER, and system GMM. Initially, we have applied the OLS method and FE regression method, but we found that the results were biased in terms of endogenous. Therefore further, we have applied the GMM method to overcome the endogeneity problem. So, here we discussed only the results of GMM and showed 12 different models related to the framed hypothesis. In the GMM Model, we found TO, secinternet and FD negatively related to EG in LICs so these findings did not support hypotheses 2 (a) and (b). For the HICs, we found almost all results were the same but ICT infrastructure proxy sign (+/-) changes according to the Model. In the case of HICs, TO, broadband, and FD have a negative relation with EG which is also not supported by hypotheses 2 (a) and (b). But, the majority of the ICT infrastructure variables have a positive relation with EG along with FD and TO, therefore we can conclude that ICT infrastructure has a positive relation with EG in LICs and HICs.
Table 7
Comparative Models impact of ICT infrastructure, FD and TO on EG
| LICs | HICs |
Dependent variable: EG | OLS | FE | GMM | OLS | FE | GMM |
GDP per capita (1st diff) | - | - | 0.900*** | - | - | 0.925*** |
(0.212) | | | (0.025) |
TO | -0.043** | -0.019* | -0.006** | -0.319*** | -0.088** | -0.034** |
(0.003) | (0.003) | (0.009) | (0.018) | (0.014) | (0.011) |
telephone | 0.033** | 0.002** | 0.004* | 0.010* | -0.347** | 0.036** |
(0.003) | (0.006) | (0.007) | (0.027) | (0.027) | (0.013) |
capfor | 0.007** | 0.003* | 0.000* | 0.002* | 0.075** | 0.016* |
(0.002) | (0.002) | (0.002) | (0.030) | (0.019) | (0.014) |
consum | 0.006** | -0.001* | 0.003** | -0.012 | -0.167*** | -0.046** |
(0.003) | (0.003) | (0.002) | (0.030) | (0.037) | (0.021) |
popugr | 0.025** | 0.009*** | 0.001** | 0.267*** | 0.092** | 0.025** |
(0.003) | (0.003) | (0.005) | (0.029) | (0.021) | (0.015) |
internet | 0.005** | 0.018*** | 0.001** | 0.125*** | 0.054** | 0.006* |
(0.002) | (0.004) | (0.001) | (0.016) | (0.009) | (0.007) |
secinternet | -0.043*** | 0.072*** | -0.020** | 0.199*** | -0.032** | 0.041*** |
(0.064) | (0.041) | (0.037) | (0.054) | (0.027) | (0.025) |
broadband | -0.012*** | -0.146*** | 0.007** | -0.033** | 0.041** | -0.003*** |
(0.060) | (0.040) | (0.038) | (0.015) | (0.011) | (0.008) |
FD | -0.011** | 0.007** | -0.003** | 0.172*** | 0.180*** | -0.014 |
(0.002) | (0.003) | (0.003) | (0.020) | (0.019) | (0.012) |
Constant | 0.015** | 0.014** | 0.002** | 0.030*** | 0.199*** | 0.018** |
(0.001) | (0.001) | (0.003) | (0.024) | (0.021) | (0.013) |
| | - | - | .479 | - | - |
R-squared | 0.558 |
| | - | - | 0.493 | - | - |
Adj R-squared | 0.551 |
Within | - | 0.529 | - | - | 0.481 | - |
Between | - | 0.056 | - | - | 0.067 | - |
Overall | - | 0.199 | - | - | 0.145 | - |
Arellano- Bond Test for AR(1) | - | - | -1.89 | - | - | -3.670** |
Arellano- Bond Test for AR (2) | - | - | 0.08 | - | - | -4.210 |
Saragan test H0: Not robust, but not some instruments are weak | - | - | 75.81 | - | - | 543.160 |
Hansen test H0: robust, but some instruments are weak | - | - | 21.56 | - | - | 34.860 |
No of observations | 540 | 540 | 513 | 1,160 | 1,160 | 1,160 |
No of countries | 27 | 27 | 27 | 58 | 58 | 58 |
Source: Authors’ estimations, |
Note: Std. Dev shown in bracket and *10%, ** 5% and *** 1 level of significance |
Table 8 shows the individual and joint effect of ICT infrastructure and FD on EG. When we check the individual effect of ICT infrastructure (internet, secinternet, broadband, and telephone) on EG, we found that internet and telephone have a positive impact on EG and are statistically significant at the level of 5%. On the other hand, secinternet and broadband have a negative impact on EG but are statistically significant at 10% and 5% level. AR (1) and AR (2) prove that all estimated versions of this model meet Areellono bond criteria for the valid estimations, so do not reject the hypothesis of the GMM Model, and it supports hypothesis 1. Moreover, Table 8 also revealed that the joint impact of internet*FD (0.025), secinternet*FD (0.101), broadband*FD (0.056) found a positive impact on EG and significant at 5% and 1% level of significance. Only telephone*FD (-0.023) have a negative effect on EG. Our findings support Sassi and Goaied (2013) for MENA countries. On the other hand, in the individual analysis, the internet (0.002), secinternet (0.05), and telephone (0.003) have found positive relation with EG and significant at 5% level of significance. In contrast, broadband (-0.006) has a negative relation with EG in LICs but is significant at a 5% level of significance. Hypothesis 2 (a) has been failed to prove that FD encourages EG in LICs. When we check the individual impact of FD than found that it has a negative impact on EG. But on the other hand, we proved hypothesis 2 (b) because jointly ICT infrastructure and FD positively correlate with EG in both countries. TO, capfor, and positively affect EG in the entire models. These findings support Sassi and Goaied, (2013) and Das, Khan, and Chowdhury, (2016). As we thought interaction of FD and ICT infrastructure is always positive and significant. FD motivates economic growth through the ICT infrastructure in LICs. When FD is considered, the marginal effects of the internet, broadband, and secured internet are more potent than that of the telephone 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 (.638), FD* secinternet* TO (0.103), FD* broadband* TO (0.096) and FD* telephone* TO (0.031) is positive relation with EG in LICs and statistical significance at 1% and 10% level of significance, respectively. But, when we analyzed the individual impact of FD and TO on EG then found a negative relationship with EG in LICs but statistically significant at 5% & 1% levels of significance. Our study proved hypothesis 3 (b) and supported the joint effects of ICT infrastructure, FD, and TO encouraging EG in LICs (Sassi and Goaied, 2013).
Table 8
Individual and jointly effect of ICT infrastructure and FD on EG (LICs)
EG (dependent Variable) | \({Model}_{1}\) | \({Model}_{2}\) | \({Model}_{3}\) | \({Model}_{4}\) | \({Model}_{5}\) | \({Model}_{6}\) | \({Model}_{7}\) | \({Model}_{8}\) |
L1. EG | 0.946 | .929 (.134) | 0.930 | 0.908 | 0.968 | .926 | 0.923 | 0.930 |
(-0.198) | | ( -0.145) | (0.205) | (0.170) | (0.157) | (0.169) | (0.169) |
internet | 0.002** | - | - | - | -0.009** | - | - | - |
(0.001) | (0.012) |
FD*internet | - | - | - | - | 0.025** (0.029) | - | - | - |
secinternet | - | 0.05*** (.010) | - | - | - | -0.037 (0.028) | - | - |
FD* secinternet | - | - | - | - | - | 0.101*** (0.091) | - | - |
broadband | - | - | -0.006** (0.008) | - | - | - | -0.023 (0.031) | - |
FD* broadband | - | - | - | - | - | - | 0.056*** (0.100) | - |
telephone | - | - | - | 0.003** (0.006) | - | - | - | .983 (0.011) |
FD* telephone | - | - | - | - | - | - | - | -0.023*** (0.019) |
TO | -0.006*** | − .005*** (.004) | -0.006*** | -0.007*** | -0.004*** | -0.005*** | -0.006*** | -0.006*** |
(0.010) | (0.004) | (0.008) | (0.007) | (0.009) | (0.008) | (0.008) |
FD | -0.003*** | − .002** (.002) | -0.002** | -0.002** | -0.005** | -0.004** | -0.004** | -0.001** |
(0.002) | (0.002) | (0.003) | (0.002) | (0.003) | (0.003) | (0.002) |
capfor | 0.001* | .006** (.003) | 0.001* | 0.002* | 0.001* | 0.001* | 0.001* | 0.001* |
(0.002) | (0.002) | (0.002) | (0.001) | (0.001) | (0.002) | (0.001) |
consum | 0.003*** | .003* (.002) | 0.003* | 0.004* | 0.007* | 0.009* | 0.008* | 0.008* |
(0.003) | (0.003) | (0.003) | (0.002) | (0.002) | (0.003) | (0.002) |
popugr | 0.000* | .007** (.003) | 0.001* | 0.001* | 0.000* | 0.001* | 0.001* | 0.000* |
(0.007) | (0.004) | (0.006) | (0.005) | (0.005) | (0.006) | (0.005) |
_cons | 0.001* | .0028* (.003) | 0.002* | 0.002* | 0.002* | 0.002* | 0.002* | 0.001* |
(0.003) | (0.0020 | (0.003) | (0.0020 | (0.003) | (0.003) | (0.002) |
No of observations | 540 | 540 | 540 | 540 | 540 | 540 | 540 | 540 |
No of country | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 27 |
AR (1) | -1.91** | -2.00*** | -1.99*** | -1.89** | -1.91** | -1.98*** | -1.96** | -1.95** |
AR (2) | 0.04 | 0.06 | 0.06 | 0.05 | 0 | 0 | 0.05 | 0.07 |
Sargan test | 70.99 | 71.92 | 72.24 | 72.46 | 68.13 | 71.62 | 71.91 | 70.63 |
Hansen test | 23.44 | 23.74 | 23.45 | 24.26 | 21.92 | 24.12 | 24.99 | 24.02 |
Source: Authors, estimations based on data set. |
Note: Std. Dev shown in bracket and *10%, ** 5% and *** 1 level of significance |
Table 9
Joint effect of ICT infrastructure, FD and TO on EG (LICs)
Dependent variable: EG | \({Model}_{9}\) | \({Model}_{10}\) | \({Model}_{11}\) | \({Model}_{12}\) |
L1. EG | 0.952*** | 0.929*** | 0.930*** | .907*** |
(0.196) | (0.143) | (0.155) | (0.206) |
internet | 0.001* | - | - | - |
(0.001) |
secinternet | - | -0.008*** | - | - |
(0.010) |
broadband | - | | -0.009*** | - |
(0.009) |
telephone | - | - | - | 0.002** |
(0.006) |
FD | -0.003** | -0.003** | -0.003** | -0.003** |
(0.002) | (0.002) | (0.003) | (0.0030 |
FD*internet*TO | .638*** | - | - | - |
(0.015) |
FD* secinternet* TO | - | 0.103*** | - | - |
(0.205) |
FD* broadband* TO | - | - | 0.096*** | - |
(0.164) |
FD* telephone* TO | - | - | - | 0.031* |
(0.074) |
TO | -0.005** | -0.006* | -0.006** | -0.007** |
(0.009) | (0.006) | (0.006) | (0.008) |
capfor | 0.001* | 0.001* | 0.001* | 0.001* |
(0.002) | (0.024) | (0.029) | (0.031) |
consum | 0.003** | 0.023*** | 0.043** | 0.063** |
(0.003) | (0.003) | (0.003) | (0.003) |
popugr | -0.001** | 0.011* | 0.031* | 0.071** |
(0.007) | (0.004) | (0.005) | (0.006) |
_cons | 0.001* | 0.002* | 0.012* | 0.022* |
(0.013) | (0.012) | (0.102) | (0.113) |
AR (1) | -1.920** | -1.990** | -1.980** | -1.890** |
AR (2) | 0.030 | 0.050 | 0.060 | 0.050 |
Sargan test | 70.760** | 72.150** | 72.580** | 72.520** |
Hansen test | 23.220 | 23.780 | 23.420 | 24.220 |
Source: Authors’ estimations based on the dataset |
Note: Std. dev shown in bracket and *10%, ** 5% and *** 1 level of significance |
Table 10 shows the individual and joint impact of ICT infrastructure and FD on EG in HICs. The results of the GMM Model shows that AR (1) and AR (2) proved that there is no serial correlation under the predetermined variable and error terms of the different period. This helps to choose the set of lags that is used as instrumental variables in the GMM method. Moreover, Table 10 also revealed that the joint impact of internet*FD (0.003), secinternet*FD (0.015), telephone*FD (0.083) found a positive impact on EG and significant at 5% and 1% level of significance. Only broadband*FD (-0.035) has a negative relation with EG. On the other hand, in the individual analysis, internet (0.002), secineternet (0.040), broadband (0.001), telephone (0.034), and FD (0.001) have found positive relation with EG and significant at 5% level of significance in HICs. In contrast, broadband (-0.006) and secinternet (-0.037) have a negative relationship with EG in LICs but significant at 5% and 1% levels of significance, respectively as shown in Table 9. Therefore, we 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 comparison of LICs. When we check the individual impact of FD, we found it’s a positive impact on EG in and we found FD has a negative relationship with EG only in model 8. So, the majority of the different Models found FD have a positive relationship with EG in HICs. Moreover, TO has a negative impact on EG in HICs. More trade openness in any country is negatively affected to the business activities because liberal legal rules and regulations increase FDI inflow which is harmful at the point of view 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 study of (Sassi and Goaied, 2013) and (Das, Khan, and Chowdhury, 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, we can conclude that the marginal effect of the internet, secinternet, 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 encouraging EG in HICs (Sassi and Goaied, 2013).
Table 11 presents the joint effect of ICT infrastructure, FD, and TO on EG in HICs. Jointly impact of FD*internet*TO (-0.116), FD*secinternet*TO (-0.225), FD*broadband*TO (-0.160) and FD* telephone* TO (-0.194) are negative relations with EG in HICs and statistical significance at 1% and 10% level of significance, respectively. But, the Joint impact of FD*Internet*TO (0.638), FD*secinternet*TO (0.103), FD* broadband* TO (0.096) and FD* telephone* TO (0.031) are positive relations with EG in LICs and statistical significance at 1% and 10% level of significance, respectively as shown in Table 10. Therefore, we can conclude that ICT infrastructure plays a significant role in improving the EG of LICs.
Yousefi (2011) study argues that there is no significant effect of ICT infrastructure on EG in LMICs. It may be due to the improper use of available human capital or labour; maybe they feel an extra burden with new technologies. Thus, people need higher salaries with higher ICT use and require a stronger financial sector. Without further enhancement in ICT uses, the country's overall development is not possible. Most of the financial intermediaries (including banks and stock markets) adopted ICT facilities like AI, machine learning, data mining, etc. If the banking sector and the stock market do not accept ICT, the number of changes to increase labour productivity will be minimal. Based on a comparison of LICs and HICs ICT infrastructure, we found that LIC will immediately require vital support of R&D, ICT infrastructure, IT skill enhancement program, human resource, more trade openness, reconstructive manual process, and new IT business Model (S.-Y.T. Lee, Gholami, and Tong, 2005). Although, few LICs adopted mobilization due to the down price in mobile but still internet and broadband penetration facilities remain unequal between developed and developing countries.
On the other hand, LMICs like Bolivia and Ghana have taken IT projects for the developed ICT infrastructure in their education sector, financial institutions (including bank and stock/share market), and industries that help to encourage EG (Das, Khan, and Chowdhury, 2016; Rodas and Lopez, 2007). Similarly, with the help of ICT facilities, 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 the people. In contrast, LICs are still struggling for ICT infrastructure, FD, and more TO because these countries only enjoy ICT infrastructure's as initial benefits (Das, Khan, and Chowdhury, 2016). Nevertheless, policymakers should pay attention to the growth in ICT infrastructure because after the situation of covid-19 without adaptation of ICT survival will be difficult.
Table 10
Individual and Joint effect of ICT infrastructure and FD on EG (HICs)
Dependent variable: EG | \({Model}_{1}\) | \({Model}_{2}\) | \({Model}_{3}\) | \({Model}_{4}\) | \({Model}_{5}\) | \({Model}_{6}\) | \({Model}_{7}\) | \({Model}_{8}\) |
L1. EG | 0.915*** (0.025) | 0.911*** (0.018) | 0.918*** (0.021) | 0.942*** (0.017) | 0.915*** (0.025) | 0.912*** (0.018) | 0.918*** (0.021) | 0.947*** (0.016) |
internet | 0.002** (0.006) | - | - | - | 0.003** (0.012) | - | - | - |
FD | 0.001** (0.014) | 0.031** (0.014) | 0.051** (0.013) | -0.015*** (0.012) | 0.002** (0.038) | 0.001** (0.018) | 0.010*** (0.017) | -0.040*** (0.028) |
FD*internet | | | | | - 0.003*** (0.289) | - | - | - |
Secinternet | - | 0.040** (0.027) | - | - | - | 0.029*** (0.054) | - | - |
FD* secinternet | | | | | - | 0.015*** (0.162) | - | - |
Broadband | - | - | 0.001** (0.007) | - | - | - | 0.104** (0.015) | - |
FD* broadband | | | | | - | - | -0.035 (0.052) | - |
Telephone | - | - | - | 0.034** (0.012) | - | - | - | 0.014** (0.023) |
FD* telephone | | | | | -0.032** (0.057) | - | - | 0.083*** (0.082) |
TO | -0.042** | -0.044** | -0.042** | -0.030** | -0.042** | -0.044** | -0.041** | -0.028** |
(0.012) | (0.011) | (0.012) | (0.010) | (0.012) | (0.012) | (0.011) | (0.010) |
Capfor | 0.013* | 0.012* | 0.012* | 0.015** | 0.013** | 0.010** | 0.011** | 0.016** |
(0.015) | (0.015) | 0.015 | 0.013 | 0.015 | 0.015 | 0.014 | 0.013 |
Consum | -0.043** | -0.045** | -0.044** | -0.043** | -0.043** | -0.046** | -0.044** | -0.044** |
(0.023) | (0.023) | (0.023) | (0.019) | (0.023) | (0.023) | (0.022) | (0.019) |
Popugr | 0.020** | 0.021** | 0.019*** | 0.023*** | 0.020*** | 0.022*** | 0.020** | 0.021** |
(0.016) | (0.015) | (0.015) | (0.013) | (0.016) | (0.015) | (0.015) | (0.012) |
_cons | 0.030*** | 0.031* | 0.031** | 0.019** | 0.029** | 0.032** | 0.029** | 0.024** |
(0.015) | (0.014) | (0.014) | (0.012) | (0.016) | (0.015) | (0.013) | (0.013) |
No of observations | 1102 | 1102 | 1102 | 1102 | 1102 | 1102 | 1102 | 1102 |
No of country | 58 | 58 | 58 | 58 | 58 | 58 | 58 | 58 |
AR (1) | -3.65 | -3.67 | -3.67 | -3.7 | -3.68 | -3.71 | -3.68 | -3.71 |
AR (2) | -4.2 | -4.22 | -4.2 | -4.22 | -4.21 | -4.22 | -4.21 | -4.22 |
Sargan test | 569.41 | 566.88 | 567.11 | 542.37 | 566.9 | 537.1 | 566.9 | 537.1 |
Hansen test | 36.69 | 35.55 | 36.67 | 36.23 | 36.07 | 36.05 | 36.08 | 36.06 |
Source: Authors’ estimations based on dataset |
Note: Std. Dev shown in bracket and *10%, ** 5% and *** 1 level of significance. |
Table 11
Joint effect of ICT infrastructure, FD, and TO on EG (HICs)
Dependent variable: EG | \({Model}_{9}\) | \({Model}_{10}\) | \({Model}_{11}\) | \({Model}_{12}\) |
L1. EG | 0.899*** | 0.911*** | 0.909*** | 0.937*** |
(0.028) | 0.018 | 0.023 | 0.019 |
internet | 0.009* | - | - | - |
(0.009) |
secinternet | - | .457** | - | - |
(0.034) |
broadband | - | - | 0.007* | - |
(0.010) |
telephone | - | - | - | 0.041* |
0.014 |
FD | 0.014* | 0.002** | 0.007** | -0.007*** |
(0.019) | (0.015) | (0.014) | (0.015) |
FD*internet*TO | -0.116*** | - | - | - |
(0.151) |
FD* secinternet* TO | - | -0.225*** | - | - |
(0.618) |
FD* broadband* TO | - | - | -0.160*** | - |
(0.215) |
FD* telephone* TO | - | - | - | -0.194**** |
(0.235) |
TO | -0.033*** | -0.043*** | -0.039*** | -0.022** |
(0.015) | (0.012) | (0.011) | (0.012) |
capfor | 0.011* | 0.012* | 0.010** | 0.014** |
(0.0160 | (0.015) | (0.015) | (0.013) |
consum | -0.050** | -0.045** | -0.050** | -0.045** |
(0.025) | (0.023) | (0.024) | (0.020) |
popugr | 0.025** | 0.021** | 0.022** | 0.025** |
(0.018) | (0.015) | (0.016) | (0.013) |
Cons | 0.027** | 0.031** | 0.032** | 0.017** |
(0.015) | (0.015) | (0.014) | (0.012) |
AR (1) | -3.65** | -3.67** | -3.66** | -3.7** |
AR (2) | -4.2 | -4.22 | -4.2 | -4.22 |
Sargan test | 577.11 | 567.14 | 570.52 | 542.66 |
Hansen test | 36.83 | 35.53 | 36.64 | 36.16 |
No of observations | 1102 | 1102 | 1102 | 1102 |
No of country | 58 | 58 | 58 | 58 |
Source: Authors’ estimations based on dataset |
Note: Std. Dev shown in bracket and *10%, ** 5% and *** 1 level of significance. |
The impact of ICT infrastructure and its joint implications are summarized in Figures 2, 3, and 4 using tables 8, 9, 10, and 11.
Figure 2 shows the individual impact of ICT infrastructure. We can see that the internet positively affects economic growth, and its influence is almost the same in low-income and high-income countries. Secure internet and broadband negatively influence the GDP per capita (EG) of LICs while the positive influence on HICs. Telephone lines are positively influencing the EG of LICs and HICs. From figure 2, it can be concluded that secure internet and telephone lines significantly impact the high-income countries' EG.
Figures 3 and 4 show the joint effects of ICT infrastructure with FD and FD*TO, respectively. Observations from Fig. 3 show that the joint effect of the Internet and secure internet is favourable on both LICs and HICs. When we look at the broadband and telephone lines, both are having a reverse correlation with EG in LICs and HICs. Observations from Fig. 4 show that when we look at the joint impact of ICT infrastructure with FD and TO, EG of LICs are positively influenced while HICs are negatively affected. Overall observation drawn from Figs. 2, 3, and 4 shows that individual ICT infrastructure and jointly 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.