Over the last three decades, the UK has experienced rapid development in ICT, which has attracted increasing attention among many researchers who have studied the effects of ICT development on UK economic growth. Various theories including neoclassic growth theory by Solow (1956) and neo-Schumpeterian theories by Schumpeter have indicated the existence of a meaningful association between economic growth and ICT development (Schumpeter & Backhaus, 2003). According to these theories, ICT enters in form of capital into economic supply resulting in the improved production process through labor force ability and technological advancement (Bodrozic & Adler, 2018; Bahrini & Qaffas, 2019). Besides, Aghaei and Rezagholizabeh (2017) and Hodrab and Maitah (2016) posit that ICT development leads to economic growth and improvement of productivity at a country level through the creation of added value at the firm level. Contrary to theoretical works, several empirical studies have produced mixed results and conclusions. However, most of the empirical studies have indicated the existence of a meaningful effect on economic growth by ICT development (Bahrini & Qaffas, 2019).
The Temporal and Geographical Dimension
The researcher aimed at employing a generalized method of the moment to investigating the ICT development’s effect on the economic growth in the UK. A panel data was used over the period 2010 to 2019.
Research Hypothesis
Ha0: There is no significant difference between real GDP per capita and the lagged real GDP per capita
Ha1: There is a significant difference between real GDP per capita and the lagged real GDP per capita
Hb0: There is no significant influence by mobile subscriptions on real GDP per capita
Hb1: There is a significant influence by mobile subscriptions on real GDP per capita
Hc0: There is no significant influence by mobile cellular subscriptions on real GDP per capita
Hc1: There is a significant influence by mobile cellular subscriptions on real GDP per capita
Hd0: There is no significant influence on real GDP per capita by the number of internet user
Hd1: There is a significant influence on real GDP per capita by the number of internet user
Detailed Research Method
The growth model used in this study was fitted as;
Where;
- GDPPCt represents the real gross domestic product per capita over period t
- GDPPCt-1 represents the lagged real gross domestic product per capita
- TEL, MOB, and INT were used to represents information and communication technology (ICT) development variable
- TEL represented the mobile subscriptions per 100 residents in the UK
- MOB represents the number of mobile cellular subscriptions among 100 residents in the UK
- INT represents the number of internet user among 100 residents in the UK
- Zt represents control variables
- ∝ is a constant variable
- ẞ1, ẞ2, and ẞ3, ẞ4, ẞ5 are the coefficients to be estimated to assess the association between the dependent and independent variables
- ϵt representserror terms
I used the GMM method to estimate the coefficients of the variables introduced in the research model. By using the GMM method, I was able to avoid all the country-based problems, endogeneity, and serial correlation/ multicollinearity (Su, Murtazashvili, & Ullah, 2013). The model includes a lagged dependent variable which is responsible for verifying the dynamics model’s process.
Over the last three decades, the UK has experienced rapid development in ICT, which has attracted increasing attention among many researchers who have studied the effects of ICT development on UK economic growth. Various theories including neoclassic growth theory by Solow (1956) and neo-Schumpeterian theories by Schumpeter have indicated the existence of a meaningful association between economic growth and ICT development (Schumpeter & Backhaus, 2003). According to these theories, ICT enters in form of capital into economic supply resulting in the improved production process through labor force ability and technological advancement (Bodrozic & Adler, 2018; Bahrini & Qaffas, 2019). Besides, Aghaei and Rezagholizabeh (2017) and Hodrab and Maitah (2016) posit that ICT development leads to economic growth and improvement of productivity at a country level through the creation of added value at the firm level. Contrary to theoretical works, several empirical studies have produced mixed results and conclusions. However, most of the empirical studies have indicated the existence of a meaningful effect on economic growth by ICT development (Bahrini & Qaffas, 2019).
The Temporal and Geographical Dimension
The researcher aimed at employing a generalized method of the moment to investigating the ICT development’s effect on the economic growth in the UK. A panel data was used over the period 2010 to 2019.
Research Hypothesis
Ha0: There is no significant difference between real GDP per capita and the lagged real GDP per capita
Ha1: There is a significant difference between real GDP per capita and the lagged real GDP per capita
Hb0: There is no significant influence by mobile subscriptions on real GDP per capita
Hb1: There is a significant influence by mobile subscriptions on real GDP per capita
Hc0: There is no significant influence by mobile cellular subscriptions on real GDP per capita
Hc1: There is a significant influence by mobile cellular subscriptions on real GDP per capita
Hd0: There is no significant influence on real GDP per capita by the number of internet user
Hd1: There is a significant influence on real GDP per capita by the number of internet user
Detailed Research Method
The growth model used in this study was fitted as;
Where;
- GDPPCt represents the real gross domestic product per capita over period t
- GDPPCt-1 represents the lagged real gross domestic product per capita
- TEL, MOB, and INT were used to represents information and communication technology (ICT) development variable
- TEL represented the mobile subscriptions per 100 residents in the UK
- MOB represents the number of mobile cellular subscriptions among 100 residents in the UK
- INT represents the number of internet user among 100 residents in the UK
- Zt represents control variables
- ∝ is a constant variable
- ẞ1, ẞ2, and ẞ3, ẞ4, ẞ5 are the coefficients to be estimated to assess the association between the dependent and independent variables
- ϵt representserror terms
I used the GMM method to estimate the coefficients of the variables introduced in the research model. By using the GMM method, I was able to avoid all the country-based problems, endogeneity, and serial correlation/ multicollinearity (Su, Murtazashvili, & Ullah, 2013). The model includes a lagged dependent variable which is responsible for verifying the dynamics model’s process.
