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Abstract

In this paper, the impact of Information and Communication Technologies (ICTs) on economic growth in South Asian countries is investigated during 1990–2014. For this purpose, the newly developed panel data estimation method Dynamic Seemingly Unrelated Regression (DSUR) is applied. According to the results, the impact of ICTs on economic growth remains positive in South Asian economies. In addition, education stimulates economic growth; however, foreign direct investment does not contribute to economic growth. The newly acquired result would guide policymakers and government officials to focus on the advancement of ICT for maintaining the sustainable economic growth. The findings imply that South Asian countries should raise the ICT level with proper technology education level to achieve the desired economic growth.

Keywords

information and communication technologies economic growth South Asian countries DSUR approach

Introduction

Recently, many countries have adopted the concept of “mainstreaming” ICT in sustainable economic development. For any country, sustainable economic growth is crucial for the sound and balanced development of the entire country. One of the important factors supporting sustainable economic growth is telecommunications technology and innovation, given its role and functions as an indispensable infrastructure of modern times. It provides economic development opportunities compatible with environmental protection (Maneejuk and Yamaka, 2020). More recently, ICT adoption has been widely accepted as a determinant of economic growth, human and sustainable development ((Appiah-Otoo and Song, 2021; Gómez-Barroso and Marbán-Flores, 2020). ICT and its applications remove the trade and investment barriers globally by providing a close linkage between customers, suppliers and firms (Pradhan et al., 2015). Furthermore, the ICT creates new opportunities in education and health, which accelerates the diffusion of information all over the world. In fact, the significance of ICT shows that the technologies are rationale (Fernback, 2018).

During the last few decades, ICT has changed societies dramatically and now these societies are more closely connected than past (García-Muñiz and Vicente, 2014). Finding the new sources of access and use of ICT make it more appropriate for the development of the other service sectors (Leidig et al., 2016). Access to ICT digital applications like smart phones and internet has significantly influenced social life of human beings and experts considered these applications as effective tools for economic development of a country (Houston et al., 2015; Qureshi, 2013). There are many time series and panel data studies about the nexus between ICT and growth which are available in existing literature. For instance, (Salahuddin and Gow, 2016) conduct study for South Africa; (Njoh, 2018) in Africa; (Pradhan et al., 2016) and (Pradhan et al., 2015) Asian economies; (Mohammed et al., 2017) for African countries; (Chakraborty and Nandi, 2011) in developing countries and (Liu et al., 2020)(Latif et al., 2018) for BRICS countries.

Education increases labor productivity and economic growth by increasing the human capital of the workforce (Benos and Zotou, 2014). Education may be one reason why most developing countries do not enjoy the full benefits of technological progress. For example, paying invoices online requires not only that the buyer has computer and internet knowledge, but also that the supplier develops and maintains this invoicing technology. The number of households using computers and the Internet has increased significantly. In the long run, education is an important force for technological innovation and economic growth in society. Therefore, in recent years, many countries are keen to build world-class universities to support their technological advancement and innovation-based development (Habibi and Zabardast, 2020). In the beginning of 20^th century, the world observed enormous foreign investment invested by multinational companies in the South Asian region. The main reason behind these investments was most likely to be the huge population, weak economy and dependence on foreign investment. In this regard, the work is done by examiningthe role of ICT in achieving the foreign investment. The results of these studies depict that with the increase in ICT investment results in higher level of foreign investment in some of South Asian countries.

South Asian economies have shown remarkable growth in penetration of ICT and became the fastest growing countries in the region (Hussain et al., 2021; Liu et al., 2021a). South Asian countries are densely populated having almost similar economic and social conditions. Among these countries India, Pakistan, Bangladesh and Sri Lanka privatized their ICT sector to attract investment and expand trade activities to ensure economic growth. Indian government recently decided to develop more than 100 cities into smart cities to strengthen the economy of the country (Chattopadhyay, 2017). Similarly, Pakistan, Sri Lanka and Bangladesh are also digitalizing their cities, which lead towards the enhancment of economic growth through information technology (Gries et al., 2009). Therefore, the situation is attracting to conduct a separate study whether develpoment in ICT contributes to economic growth for the South Asia region or not.

In the aforementioned studies we have found several deficiencies. First, from the literature, the previous studies mainly discuss the impact of ICT on economic growth in different region of the world. However, for South Asian region, this growth and ICT relationship is studied by Hussain et al., (2021) and (Usman et al., 2021) for Asian countries, however they ignored the potential factor. Meanwhile, (Donou-Adonsou, 2019) and (Habibi and Zabardast, 2020) investigated the role of education in the relationship between information technology and economic growth for Sub-Saharan African and Middle east and (Habibi and Zabardast, 2020) OECD countries only. So, the nexus between information technology, education and economic growth for Asian countries is not explored so far. Further, the existing literature mostly examined the ICT effect on economic growth in various dimension; such as several studies consider mobile phone and internet as an indicator for ICT sector. On the other hand, some studies investigate various indictors for ICT sector, such as landline, fixed broadband, internet and mobile usages. However, none of the studies has examined that how ICT sector contribute to economic growth with the passage of time and change with technology.

This study contributes in the following ways; it investigates the effect of ICT on economic growth considering the potential role of education in South Asian countries. We use two additional variables education and foreign direct investment (FDI) in the nexus of ICT and economic growth those have been ignored in the literature for South Asia. The sample data over a long period has been taken for analysis i.e. (1990–2014). Finally, the study employs recently developed panel integration and long-run panel estimation tests to obtain most significant results. Moreover, most of the panel studies do not take cross sectional dependence in their econometric analysis, ignoring the issue may produce biased and inconsistent results. It will further eliminate the issue of cross-sectional dependence during the analysis, because ignoring it would give unreliable and inconsistent estimates.

Related studies

Relationship between ICT and economic growth

Baliamoune-lutz, (2003) found positive relationship between ICT and growth incorporating trade, education, financial development and freedom indicators employing panel of 46 developing countries with the help of Gompertz model. The results show that the trade and income influences ICT with the passage of time. However, freedom indicators have a neutral effect on ICT. Furthermore, (Erumban and Kusum, 2016; Ishida, 2015; Shahiduzzaman and Alam, 2014) used panel data series and found a significant positive impact of ICT investment on economic growth. (Latif et al., 2018) also analyzed the nexus of ICT and economic growth by incorporating trade and globalization for BRICS countries by using Dynamic Ordinary Least Square techniques (DOSL). In present days, ICT become a core part of the economy, and all the business stakeholders comprising firms and customers use mobile phones and internet for trading activities like providing consumers’ different product varieties and improving quality of products. Resultantly, the influence of ICT in societies increased during the last few decades. Apart from this, nexus between information technology and economic growth is widely debated across region and for panel group of studies. Pradhan et al., (2021) for G-20 countries; Asongu and Odhiambo (2020) for Sub-Saharan African countries; Appiah-Otoo and Song, (2021) compared rich and poor countries; Maneejuk and Yamaka, (2020) for developed and developing countries. These studies have concluded that information technology induce overall positive net effects on all three economic growth dynamics.

ICT and education nexus

The existing literature gives a possible link between ICT and education (Berrío-Zapata and Rojas-Hernández, 2014; Turner-Cmuchal and Aitken, 2016). The authors concluded that with introduction of ICT and its application in education sector, a positive impact is observed on education. On the other hand, Bolton Palumbo, (2014) depicted an indirect effect of ICT utilization in education. He argued that the effective utilization of ICT tools by teachers can lead towards a better education system and reduce the flaws in conventional education system. Recently, Habibi and Zabardast, (2020) investigated linkage between digitalization, education and economics growth comparing Middle East and OECD countries. The empirical results suggested that both technology and education are positively associated with the economic growth. Further, (Donou-Adonsou, 2019) estimated whether technology promote economic growth in Sub-Saharan African countries with better education. The result showed that internet user with better education contribute to economic growth, whereas, mobile phone user does not seem to do so.

Model specification and data source

Model specification

ICT sector is rapidly growing in both developing and developed countries during the last two decades. The term ICT has been used extensively adopted as a core variable in previous studies (Danish et al., 2018a; Latif et al., 2018). The rapid innovations in the ICT sector are considering a key tool in the economic development of a country. The change in technology means a various type of changes in ICT sector, i.e., landline telecom (LLT), fixed broadband (FBB), a number of internet users and a number of mobile users. The empirical estimation is based on the following model for each measure of ICT:

\begin{aligned} L o g G D P_{i t} & = α_{0} + α_{1} L o g (I C T_{L L})_{i t} + α_{2} L o g F D I_{i t} \\ + α_{3} L o g E D U_{i t} + ω_{0} \end{aligned}

(1)

\begin{aligned} L o g G D P_{i t} & = α_{0} + α_{1} L o g (I C T_{F B})_{i t} \\ + α_{2} L o g F D I_{i t} + α_{3} L o g E D U_{i t} + ω_{0} \end{aligned}

(2)

\begin{aligned} L o g G D P_{i t} & = α_{0} + α_{1} L o g (I C T_{I n t e r n e t})_{i t} \\ + α_{2} L o g F D I_{i t} + α_{3} L o g E D U_{i t} + ω_{0} \end{aligned}

(3)

\begin{aligned} L o g G D P_{i t} & = α_{0} + α_{1} L o g (I C T_{m o b i l e})_{i t} \\ + α_{2} L o g F D I_{i t} + α_{3} L o g E D U_{i t} + ω_{0} \end{aligned}

(4)

In the above equations, ICT_LL, ICT_FB, ICT_Internet, and ICT_Mobile represents for information technology of landline, fixed broadband, number of internet user and number of the mobile user respectively. GDP shows economic growth, FDI stands for foreign direct investment and EDU means education. ω₀ indicates the stochastic error term, i show country and t is a number of years. The remaining control variables are FDI and trade openness in our study. The ICT equipment is transfered through trade and FDI from one country to the other countries. The increasing FDI and trade highly contribute to economic growth.

Data source and Variable description

The secondary data spanning from 1990–2014 was collected for ICT variables for our econometric analysis. Data has been taken from the World Bank Indicator (WBI) for the time period of 1990–2014¹ and has been chosen on the availability of data. The change in ICT is measured through, internet penetration per 100 users, mobile use penetration per 100 users, number of the users of landline and fixed broadband. The measure is selected based in existing literature. Danish et al. (2018a) (Liu et al., 2021b) used internet and mobile phone penetration as a measure of ICT. The variables are converted into their natural log for further necessary analysis. The variable details are illustrated in Table 1.

Table 1.

Description of variables.

Variable	Representation	Description	Data Sources
Landline telephone	ICT_LL	Landline telephone connections per hundred population.	World Bank
Mobile/Cell phone	ICT_mobile	Mobile phone customer per hundred population.	World Bank; International Telecommunications Union, ITU
Internet users	ICT _{internet user}	Internet users per hundred population.	World Bank
Fixed Broadband	ICT _FB	Fixed broadband per hundred population.	World Bank; International Telecommunications Union, ITU
Economic growth	GDP	Percentage change in the real per capita gross domestic product, it is used as an indicator of economic growth.	World Bank
Foreign direct investment	FDI	FDI status in the host country (in a million US dollars).	World Bank
Education	EDU	Higher/Tertiary education	World Governance Indicators

Econometric methodology

Cross-sectional dependence test

Generally empirical estimation starts with the investigation to find the cross-sectional dependence among the target countries. In our case, the selected Asian countries have different characteristics; there may be a possibility of the existence of cross-sectional dependence. In advance to analyzing stationary properties of economic growth (GDP), FDI, EDU, ICT_LL, ICT_FB, ICT_internet and ICT_mobile, the paper first employs cross-sectional dependence suggested by (Pesaran H, 2004),because ignoring the cross-sectional dependence lead to misleading and unreliable results.

Panel unit root test

In second step of analysis, for finding the unit root, this study employs advanced CIPS and CADF unit root tests for panel data estimation. South Asian countries have different economic structure and varied level of ICT infrastructure. Hence, there may be a possibility of cross-sectional dependence, therefore we prefered to use second-generation CIPS and CADF test because it generates efficient estimates and overcome the possibility of cross-sectional dependence.

Panel cointegration tests

After unit root estimation, the next step is to confirm the level of relationship among underlying variables. The purpose of cointegration test helps to reach the long-run estimation of non-stationary variables. Therefore, in this study we prefered to use the Westerlund panel cointegration test by (Westerlund, 2007), which gives robust values in the presence of heterogeneity.

DSUR long run estimation method

Based on the evidence of cointegration, we pursued estimation of long-run dynamics. In the existing literature, most of the panel data studies did not address the issue of heterogeneity properly while using latest estimation methods. Due to which the results produced are biased and unreliable. The dynamic seemingly unrelated regression (DSUR) method by (Mark et al., 2005) is preferred to measure the long-run estimation among the underlying variables. DSUR is slightly different from the DOLS and FMOLS, as DSUR method correct the problem of endogeneity with the help of leas and lags of the first difference. In addition, DSUR is more efficient than DOLS because the estimators in DSUR are more consistent and produces fewer biases than OLS model estimation. Based on the above argument the selection DSUR method is the most appropriate one for the current study.

Empirical results and discussion

The descriptive statistics of underlying variables for South Asian countries are given in Table 3.

Table 3.

Results of cross sectional dependence test.

	LOGGDP	LOGFDI	LOGEDU	LOGLL	LOGFB	LOGmobile	LOGinternet
CD-test	0.54	−0.82	11.03^a	2.01^b	13.32^a	15.65^a	15.73^a
P-value	0.588	0.413	0.000	0.044	0.000	0.000	0.000

Note. ^a is the level of rejection at 1% level of significance.

Table 2

Table 2.

Results of the descriptive statistic of variables.

Country/Variables		LOGGDP	LOGEDU	LOG ICT_FB	LOG ICT _mobile	LOG ICT _internet	LOG ICT _LL
Afghanistan	Mean	2.68257	0.45144	0.17190	1.11413	3.31672	0.5025
	StDev.	0.09391	0.48036	0.11186	0.82536	2.69381	0.4803
Bangladesh	Mean	2.852341	1.094573	0.676633	1.241032	4.091034	0.3580
	StDev.	0.085062	0.036222	0.155020	0.716315	4.718210	0.4226
Bhutan	Mean	3.283297	0.950471	3.879839	1.339415	12.24108	6.5305
	StDev.	0.099348	0.100621	0.656844	0.688493	11.51935	4.7858
India	Mean	3.078957	0.895982	3.152210	1.346428	8.292438	2.1671
	StDev.	0.109538	0.086689	0.768514	0.599798	7.755439	2.0837
Maldives	Mean	3.811736	1.072480	9.280537	1.976429	24.81576	41.246
	StDev.	0.139091	0.044110	2.465783	0.360949	17.07226	33.527
Nepal	Mean	2.749839	0.896131	2.564178	0.941511	5.901748	6.3282
	StDev.	0.675394	0.129157	0.660247	1.015433	6.301737	17.915
Pakistan	Mean	3.022576	0.721927	2.965547	1.015433	8.398231	0.8366
	StDev.	0.025399	0.076687	0.544139	0.655118	3.862256	0.6830
Sri Lanka	Mean	3.418354	0.850341	11.80542	1.595949	10.69421	4.9200
	StDev.	0.098533	0.186025	4.941676	0.467848	9.952042	4.1209

Table 3 contains the results derived from cross-sectional dependence test In Table 3, the probability value of the model depicts that all variables tend towards normality and thus we can pursue the regression test Further, the presence of cross-sectional dependence can be seen for underlying variables. Table 4 consists of the results derived from CIPS and CADF unit root tests. It depicts that we reject the null hypothesis of non-stationary at first difference and hence, the underlying variables are integrated at first difference I(1).

Table 4.

Results of CIPS and CADF panel unit root tests.

	CIPS		CADF
Variables	Level	1^st Difference	Level	1^st Difference
LOG GDP	−2.292	−2.859**	−3.463	−4.267*
LOG FDI	−2.580	−3.292**	−1.439	−2.369***
LOG ICT_LL	−2.303	−4.833*	−2.431	−3.085**
LOG ICT _mobile	−2.114	−4.786*	−1.995	−4.103*
LOG ICT_internet	1.321	−3.601**	−2.978*	−4.929*
LOG ICT_FB	−1.307	−3.382**	−2.712	−2.960***

Note. *,**and*** indicates the level of significant at 1%, 5% and 10% respectively.

Panel cointegration

According to the values derived from Westerlund cointegration test given in Table 5, we reject the null hypothesis of no cointegration for all four models. Hence, it implies that the cointegration exists between the subject variables.

Table 5.

The result of westerlund panel cointegration test.

	ICT Landline			LOG ICT (Fixed broadband)
Statistic	Value	Z-value	P-value	Value	Z-value	P-value
G_t	−2.699^a	−2.734	0.003	−8.226^a	−17.996	0.000
G_a	−2.290	2.500	0.994	−1.552	2.834	0.998
P_t	−2.982	0.566	0.715	−6.376	−2.023	0.022
P_a	−0.180	1.832	0.967	−1.218	1.367	0.914
	ICT internet			ICT mobile
Statistic	Value	Z-value	P-value	Value	Z-value	P-value
G_t	−3.044^a	−3.687	0.000	−1.488	0.611	0.729
G_a	−0.912	3.124	0.999	−0.508	3.307	1.000
P_t	−10.22^a	−4.959	0.000	−12.956^a	−5.587	0.000
P_a	−1.275	1.341	0.910	−0.982	1.473	0.930

Note. ^a is the level of rejection at 1% level of significance.

After confirmation of cointegration now it turns to estimate the long run results, which is the key focus of the current study. The DSUR panel data estimation method is applied for long-run estimation and is given in Table 6. The change is ICT refers to various kind of technology with the passage of time, such as ICT _Landline (Model 1), ICT _{fixed broadband} (Model 2), ICT_{Internet sources} (Model 3), ICT _mobile (Model 4). The results depicted from DSUR method show a significat relationship between ICT and economic growth for each model, i.e. (Model 1, Model2, Model 3 and Model 4).

Table 6.

DSUR-Dynamic seemingly unrelated regression (panel estimation).

	Model 1: ICT _Landline		Model 2: ICT _{fixed broadband}		Model 3:ICT_{Internet sources}		Model 4: ICT _mobile
Variables	Co-efficient	Probability	Co-efficient	Probability	Co-efficient	Probability	Co-efficient	Probability
Log FDI	0.0087	0.659	−0.0257	0.339	−0.0059	0.796	0.0162	0.501
Log EDU	0.5299*	0.000	0.5487*	0.000	0.4154*	0.001	0.6014*	0.000
Log ICT _LL	0.0561*	0.000	—	—	—	—	—	—
Log ICT _FB	—	—	0.2111*	0.000	—	—	—	—
Log ICT _Internet	—	—	—	—	0.0195*	0.000	—	—
Log ICT _Mobile	—	—	—	—	—	—	0.0099*	0.000
Constant	2.3373*	0.000	2.5667*	0.000	2.6131*	0.000	2.3756*	0.000
R²	0.6476		0.3832		0.5191		0.4754
F-statistics	66.15		22.37		38.86		32.63
Prob.	0.0000		0.0000		0.0000		0.0000

Note. * shows level of significance at 1%; LL = landline; FB = fixed broadband.

The finding indicates that a long-run relationship exists between ICT and growth, also the economic growth is elastic to ICT. In other words, with a change in technology, the effect on economic growth remains the same implying that change in ICT technology sustains the level of growth in South Asian region.

The landline telephone system is a core and earliest application of modern ICT technology. Generally, the ICT sector boost up with the advancement in landline telepone network during the last two decades. In South Asian coutries, India, Pakistan, Bangladesh and Sri Lanka liberilzed their telecom sector in the beginning of 21st century and received a huge amount in shape of foreign investment by providing voice communication all over the region. Similar results have also been derived by (Oseni and Pollitt, 2017). Moreover, the internet service providers (ISPs) can be also a possible reason in the penetration of internet users that attract enormous investment by providing ISPs lisences to different multinational vendors. These investments in the ICT sector in shape of various companies both in public and in private sectors reshaped infrastructure and created many job opportunities in the South Asian region. Furthermore, these advancements in ICT sector fasten the business dealings throughout the region. The previous studies conducted by (Asongu and Nwachukwu, 2016; Kodila-Tedika and Asongu, 2015) support the results depicted in our research study and added to the discussion that the de-regulation policies adopted by some of the South Asian countries results in a rapid increase in mobile phone users that further enhanced the country economic growth and created employment opportunities for the citizen of the region. Finally, from the results, it is very clear that for achieving the long-run sustainable economic growth, the ICT, and its applications need to be promoted in this region.

The findings also depict that FDI has an insignificant impact on economic growth. In another words, FDI does not contribute to the economic growth of South Asian countries. The reason could be that FDI outflow from south Asian countries towards developed countries is greater than FDI inflow, which gives more advantage to the developed countries instead of host countries.

Lastly, education positively affects the economic growth in the entire four models estimated in the study. Education can play an important role in the development of the living standards of the citizens in the South Asian region. By introducing internet and computer knowledge in colleges and universities of South Asian countries will lead towards the increase in knowledge and maturity of the citizens and they can actively participate in social development. The results of our study illustrate that with the introduction of the internet and smartphones in education system will lead to enhance the quality of education and produce a competent digital workforce that can contribute towards the development of a country. The results are consistent with the study of (Salam et al., 2018) in which authors highlighted that the ICT adoption has a significant positive impact on the education sector in the developing countries including digital workforce competency that further strengthen the economy.

Conclusions and recommendations

The purpose of this study is to investigate the impact of the change in ICT and economic growth controlling the model for FDI and education. To serve the purpose, the DSUR panel estimation method was used for South Asian economies from 1990 to 2014.

Main findings

The main findings of the study are:

ICT strengthens the economic growth, and the effect of a change in ICT on economic growth remains the same.

The impact of FDI on economic growth is significant.

Finally, education affects positively the economic growth in South Asian countries.

Policy recommendation

Most of the South Asian countries badly suffered due to poor law & order and terrorism issues. Particularly, Afghanistan and Pakistan during the last couple of decades underwent the worst chaotic situation. Also, a problem of weak political institutions and corruption affect almost all the South Asian region. Moreover, South Asian countries rely mainly on foreign investment for their economic development. ICT sector has a potential to attract enormous amount of foreign investments throughout the region, which will further lead towards the development of this most populated region of the world. Hence, ICT can be a game changer for the stainable development and growth.

In order to develop the ICT sector, South Asian countries must formulate their policies that can produce skilled ICT persons, up-to-date ICT applications and reduced the cost of latest ICT applications and equipment. Ensure and ease the complex rules and regulations in a legal environment and the respective governments in the region should provide easy access to ICT services to the people living in urban and rural areas.

Other than some interesting findings, the study has some limitations. Some more variables can be tested in future to analyze the influence on economic growth with change in ICT sector, such as population and inflation; imported technology (Danish et al., 2018b); financial development (Salahuddin and Gow, 2016; Xu et al., 2018) and corruption (Wang et al., 2018). The similar model can be used for another panel of countries and time series data to validate the study.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Natural Science Foundation of Zhejiang Province, (grant number LQ21G010007).

ORCID iDs

Zahid Latif

Shahid Latif

Notes

About the authors

Liu Ximei is a teacher at Zhejiang Sci-Tech University, China. She graduated from Beijing University of Posts and Telecommunications, China. Her research area includes financial forecasting, telecommunications, optimization and portfolio financing. She holds postgraduate qualifications in Business Administration as well as in different fields of telecom engineering.

Zahid Latif earned PhD degree from Beijing University of Posts and Telecommunications, China. He has been working in the telecommunication sector of Pakistan since 2003. He is the author of several publications in the field of management. His research area includes ICT diffusion, role of ICT in development of rural areas, Belt and Road initiative, CPEC, etc.

Danish is currently working as an associate professor at school of Economics and Trade, Guangdong University of foreign studies Guangzhou, China. He earned a PhD in Beijing Institute of Technology in 2018. Currently, his main research interests include environmental policy, ecological economics and energy economics. So far, he has published over 60 papers in peer-reviewed journals in these fields of research.

Shahid Latif obtained his PhD degree in Computer Science from Peshawar University, Pakistan. He earned his master’s degree from Gandhara University of Science and Technology, Peshawar, Pakistan. His research areas are Ad hoc Vehicular Network and VNET. He is working as an associate professor in Sarhad University of Science and Information Technology, Peshawar, Pakistan.

Kaif ul Wara is a PhD scholar at the Islamia College University, Peshawar, Pakistan. She graduated in Economics and Management Science from City University, Pakistan.

References

Appiah-Otoo

Song

(2021) The impact of ICT on economic growth-comparing rich and poor countries. Telecommunications Policy 45(2): 102082. https://doi.org/10.1016/j.telpol.2020.102082 .

Asongu

Nwachukwu

(2016) The role of governance in mobile phones for inclusive human development in Sub-Saharan Africa. Technovation 55–56: 1–13. https://doi.org/10.1016/j.technovation.2016.04.002 .

Asongu

Odhiambo

(2020) Foreign direct investment, information technology and economic growth dynamics in Sub-Saharan Africa. Telecommunications Policy, 44(1): 101838.

Baliamoune-lutz

(2003) SSRN-An Analysis of the Determinants and Effects of ICT Diffusion in Developing Countries by Mina Baliamoune-Lutz. 10(August 2002), 151–169.

Benos

Zotou

(2014) Education and economic growth: A meta-regression analysis. World Development 64: 669–689. https://doi.org/10.1016/j.worlddev.2014.06.034 .

Berrío-Zapata

Rojas-Hernández

(2014) The {digital} {divide} in the {university}: {the} {appropriation} of {ICT} in {higher} {education} {students} from {Bogota}, {Colombia}. Comunicar 22(43): 133–142. https://doi.org/10.3916/C43-2014-13 .

Bolton Palumbo

(2014) Mobile phones in Africa: Opportunities and challenges for academic librarians. New Library World 115(3–4): 179–192. https://doi.org/10.1108/NLW-01-2013-0008 .

Chakraborty

Nandi

(2011) Mainline ‘ telecommunications infrastructure, levels of development and economic growth: evidence from a panel of. Telecommunications Policy 35(5): 441–449. https://doi.org/10.1016/j.telpol.2011.03.004 .

Chattopadhyay

(2017) Performance Analysis of Solar Rooftop Power Plants in Smart Cities of India under changing Climatic Conditions. 6–11.

10.

Danish , Khan

Baloch

, et al. (2018a) The effect of ICT on CO2 emissions in emerging economies: Does the level of income matters? Environmental Science and Pollution Research 25(23): 22850–22860. https://doi.org/10.1007/s11356-018-2379-2 .

11.

Danish Wang

Wang

(2018b) Imported technology and CO2emission in China: collecting evidence through bound testing and VECM approach. Renewable and Sustainable Energy Reviews 82(3): 4204–4214. https://doi.org/10.1016/j.rser.2017.11.002 .

12.

Donou-Adonsou

(2019) Technology, education, and economic growth in Sub-Saharan Africa. Telecommunications Policy 43(4): 353–360. https://doi.org/10.1016/j.telpol.2018.08.005 .

13.

Erumban

Kusum

(2016) Information and communication technology and economic growth in India. Telecommunications Policy 40(5): 412–431. https://doi.org/10.1016/j.telpol.2015.08.006 .

14.

Fernback

(2018) Academic / Digital Work : ICTs, Knowledge Capital, and the Question of Educational Quality. 16(July 2017), 143–158.

15.

García-Muñiz

Vicente

(2014) ICT Technologies in Europe: A study of technological diffusion and economic growth under network theory. Telecommunications Policy 38(4): 360–370. https://doi.org/10.1016/j.telpol.2013.12.003 .

16.

Gómez-Barroso

Marbán-Flores

(2020) Telecommunications and economic development – the 20th century: the building of an evidence base. Telecommunications Policy 44(2): 101904. https://doi.org/10.1016/j.telpol.2019.101904 .

17.

Gries

Kraft

Meierrieks

(2009) Linkages between financial deepening, trade openness, and economic development: causality evidence from Sub-Saharan Africa. World Development 37(12): 1849–1860. https://doi.org/10.1016/j.worlddev.2009.05.008 .

18.

Habibi

Zabardast

(2020) Digitalization, education and economic growth: A comparative analysis of Middle East and OECD countries. Technology in Society 63(July): 101370. https://doi.org/10.1016/j.techsoc.2020.101370 .

19.

Houston

Hawthorne

Perreault

, et al. (2015) Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters 39(1): 1–22. https://doi.org/10.1111/disa.12092 .

20.

Hussain

Batool

Akbar

, et al. (2021) Is ICT an enduring driver of economic growth? Evidence from South Asian economies. Telecommunications Policy 45(8): 102202. https://doi.org/10.1016/j.telpol.2021.102202 .

21.

Ishida

(2015) Telematics and informatics the effect of ICT development on economic growth and energy consumption in Japan. Telematics and Informatics 32(1): 79–88. https://doi.org/10.1016/j.tele.2014.04.003 .

22.

Kodila-Tedika

Asongu

(2015) The effect of intelligence on financial development: A cross-country comparison. Intelligence 51: 1–9. https://doi.org/10.1016/j.intell.2015.04.010 .

23.

Latif

Mengke

Danish , et al. (2018) The dynamics of ICT, foreign direct investment, globalization and economic growth: panel estimation robust to heterogeneity and cross-sectional dependence. Telematics and Informatics 35(2): 318–328. https://doi.org/10.1016/j.tele.2017.12.006 .

24.

Leidig

Teeuw

Gibson

(2016) Data poverty: A global evaluation for 2009 to 2013 - implications for sustainable development and disaster risk reduction. International Journal of Applied Earth Observation and Geoinformation 50: 1–9. https://doi.org/10.1016/j.jag.2016.03.001 .

25.

Liu

Latif

, et al. (2020) Relationship between economic growth and CO2 emissions: Does governance matter? Environmental Science and Pollution Research 27: 17221–17228.

26.

Liu

Latif

, et al. (2021a) The corruption-emissions nexus: do information and communication technologies make a difference? Utilities Policy 72: 101244.

27.

Liu

Latif

Xiong

, et al. (2021b) The nexus among globalization, ICT and economic growth: an empirical analysis. Journal of Information Processing Systems 17(6): 1044–1056.

28.

Maneejuk

Yamaka

(2020) An analysis of the impacts of telecommunications technology and innovation on economic growth. Telecommunications Policy 44(10): 102038. https://doi.org/10.1016/j.telpol.2020.102038 .

29.

Mark

Ogaki

Sul

(2005) Seemingly unrelated cointegrating regressions. Review of Economic Studies 72(3): 797–820.

30.

Mohammed

Sulong

Albiman

, et al. (2017) The linear and non-linear impacts of ICT on economic growth, of disaggregate income groups within SSA region. Telecommunications Policy 41(7–8): 555–572. https://doi.org/10.1016/j.telpol.2017.07.007 .

31.

Njoh

(2018) The relationship between modern Information and Communications Technologies (ICTs) and development in Africa. Utilities Policy 50(November 2017): 83–90. https://doi.org/10.1016/j.jup.2017.10.005 .

32.

Oseni

Pollitt

(2017) The prospects for smart energy prices: observations from 50 years of residential pricing for fixed line telecoms and electricity. Renewable and Sustainable Energy Reviews 70(December 2015): 150–160. https://doi.org/10.1016/j.rser.2016.11.214 .

33.

Pesaran H

(2004) General diagnostic tests for cross-section dependence in panels. 1229.

34.

Pradhan

Arvin

Hall

(2016) Economic growth, development of telecommunications infrastructure, and financial development in Asia, 1991–2012. Quarterly Review of Economics and Finance 59: 25–38. https://doi.org/10.1016/j.qref.2015.06.008 .

35.

Pradhan

Arvin

Nair

(2021) Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities 115(April): 103213. https://doi.org/10.1016/j.cities.2021.103213 .

36.

Pradhan

Arvin

Norman

(2015) Technology in society the dynamics of information and communications technologies infrastructure, economic growth, and fi nancial development : evidence from Asian countries. Technology in Society 42: 135–149. https://doi.org/10.1016/j.techsoc.2015.04.002 .

37.

Qureshi

(2013) What is the role of mobile phones in bringing about growth? Information Technology for Development 19(1): 1–4. https://doi.org/10.1080/02681102.2013.764597 .

38.

Salahuddin

Gow

(2016) Telematics and informatics the effects of internet usage, financial development and trade openness on economic growth in South Africa : A time series analysis. Telematics and Informatics 33(4): 1141–1154. https://doi.org/10.1016/j.tele.2015.11.006 .

39.

Salam

Zeng

Pathan

, et al. (2018) Impediments to the integration of ICT in public schools of contemporary societies: A review of literature. Search.EbscohostCom 14(1): 252–269.

40.

Shahiduzzaman

Alam

(2014) The long-run impact of information and communication technology on economic output: the case of Australia. Telecommunications Policy 38(7): 623–633. https://doi.org/10.1016/j.telpol.2014.02.003 .

41.

Turner-Cmuchal

Aitken

(2016) ICT As a tool for supporting inclusive learning opportunities. International Perspectives on Inclusive Education 8: 159–180. https://doi.org/10.1108/S1479-363620160000008010 .

42.

Usman

Ozturk

Hassan

, et al. (2021) The effect of ICT on energy consumption and economic growth in South Asian economies: an empirical analysis. Telematics and Informatics 58: 101537. https://doi.org/10.1016/j.tele.2020.101537 .

43.

Wang

Danish Zhang

, et al. (2018) The moderating role of corruption between economic growth and CO2 emissions: evidence from BRICS economies. Energy 148: 506–513. https://doi.org/10.1016/j.energy.2018.01.167 .

44.

Westerlund

(2007) Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics 69(6): 709–748. https://doi.org/10.1111/j.1468-0084.2007.00477.x .

45.

Baloch

Meng

, et al. (2018) Nexus between financial development and CO2 emissions in Saudi Arabia: Analyzing the role of globalization. Environmental Science and Pollution Research. 25(28): 28378–28390.

Estimating the impact of information technology on economic growth in south Asian countries: The silver lining of education

Abstract

Keywords

Introduction

Related studies

Relationship between ICT and economic growth

ICT and education nexus

Model specification and data source

Model specification

Data source and Variable description

Econometric methodology

Cross-sectional dependence test

Panel unit root test

Panel cointegration tests

DSUR long run estimation method

Empirical results and discussion

Panel cointegration

Conclusions and recommendations

Main findings

Policy recommendation

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Notes

About the authors

References