Do FDI Inflows and ICT Affect Economic Growth? An Evidence from Arab Countries

Abstract

This article aims to examine the dynamic relationships between foreign direct investment inflows, information and communication technologies, and economic growth in a sample of 15 Arab countries over the period 1995–2019 by employing a panel ARDL approach. The results of estimation of the panel ARDL model reveal that ICT and FDI have positive and significant effects on economic growth in the long run, and ICT indicators have also positive impact on FDI inflows in the long run in the selected sample of Arab countries. From an empirical point of view, this study may have an important contribution. Its findings could be very interesting for better future management of ICT in Arab countries. Therefore, the Arab countries should further improve information and communication technology as an important infrastructure for receiving more foreign direct investment inflows and for better economic growth.

1. Introduction

The relationship between information and communication technology and economic growth is an issue that continues to receive great theoretical and empirical interest. By investing in ICT, developing and emerging countries can leapfrog development stages to catch up with developed countries. The explanation for the importance of investing in ICT infrastructure lies in how it attracts foreign investment and drives economic growth. The development of information and communication technology infrastructures and their increasing strength have led to a fundamental change in the nature of global economic relations, sources of competitive advantage, and opportunities for economic and social development that represent the main pillars of sustainability [1].

Technologies such as the Internet, personal computers, and cordless phones have created an interconnected global network of individuals, businesses, and governments. For the developed world, a modern telecommunications infrastructure is not only necessary for domestic economic growth but is also a prerequisite for participating in increasingly competitive global markets and for attracting foreign investment that, in turn, contributes to the dissemination of technology and wealth creation [2].

While there is ample evidence that new information technologies are transforming in many ways how modern economies function, the effects on productivity and economic growth have been much more difficult to detect. Although an increasing number of microeconomic studies have found a positive correlation between investment in information technology and various measures of economic performance across firms in industrialized countries, macroeconomic studies have been less flexible in finding any correlation, or even a negative association, between IT investment and economy-wide productivity (For a survey, see: [3].

Today, however, the role that ICTs play in economic growth, as one of the aspects of sustainability, is well documented. It is emerging today as a necessary factor for the development of the country’s productive capacity in all sectors of the economy, linking the country to the global economy and ensuring competitiveness. Like developing countries around the world, Arab countries seek to improve their investments in ICT and take advantage of the expected increases in economic activity, and it is often implicitly assumed that there is a positive relationship between the two [4]. ICT could affect various aspects of economic activities such as the creation of jobs, increasing incomes, improving business activities, providing accessible information and communication networks, improving education services, innovation, and human capital development. All these activities lead to social and economic sustainable development.

Based on the foregoing, the study will address the following research problem: How does information and communication technology affect economic growth in the group of Arab countries during the period 1995–2019?

Theoretically and realistically, the significance of this study lies in the fact that the mutual positive influence between information and communication technology on the one hand and economic growth and foreign investment on the other hand seems well known and familiar to the reader in general and the economic reader in particular. However, supplementing and strengthening the Arabic library with modern standard studies, especially with regard to Panel Data, is considered a qualitative addition to the Arab library and the economic researcher. From a practical point of view, this study can recommend to the governments of Arab countries to keep pace with recent developments in information and communication technology and pay attention to this element because of its role in the development of foreign investments and economic growth [5].

The main objective of this research is to study the relationships between information and communication technology, foreign direct investment, and economic growth for a sample of 15 Arab countries.

To answer this research question, the study relies on testing the following hypotheses:

Main hypothesis:

The increase in information and communication technology has a positive impact on economic growth in Arab countries.

Dependent hypotheses:

There is a disparity in the availability of information and communication technology among the Arab countries under study.

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Information and communication technology has a positive and moral impact on foreign direct investment in the short and long terms in the Arab countries under study.

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Information and communications technology and foreign direct investment have a positive and moral impact on economic growth in the short and long terms in the Arab countries under study.

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Foreign direct investment and economic growth positively and morally affect information and communication technology in the short and long term in the Arab countries under study.

The study focuses on the dynamic relationships between ICTs, foreign direct investment, and economic growth for a sample of 15 Arab countries over the period of 1995–2019. ICT could be a solution for the sustainable development in Arab countries mainly by its role of diversification of economic activities. The economic diversification can be a solution to sharp decline in oil prices.

The researcher will divide the research study into six themes. In the first theme, the researcher will present the research problem in general, its significance, objectives, and hypotheses. The second theme discusses the reality of information and communication technology and its development in the Arab countries. The third theme presents the theoretical framework and some previous studies that deal with the relationship between information and communication technology, foreign direct investment, and economic growth in developing and developed countries to a certain extent. The fourth theme exposes us to the data and its sources, in addition to presenting the models that were estimated, with a description of the study variables. The fifth theme includes analyzing, discussing, and comparing the results obtained with previous studies. Finally, the sixth theme presents the most important conclusions in addition to some recommendations.

2. Information and Communication Technology in the Arab Countries

ICTs are the most important key to strengthening transport, trade, and financial infrastructures and encouraging innovations that create an inclusive digital economy. ICT services, such as smart grids, integrated management systems, and intelligent transportation systems, are the main drivers of economic growth. These broadband networks facilitate the movement of goods, services, information, people, and money across borders in a more efficient manner. The countries of the Arab region have been making real progress in the process of adopting information and communication technology over the past decade, to the extent that the statistics show some of the rich countries as having ranked high in the indicators of the use of information and communication technology similar to developed countries.

In 2009, the United Nations (International: ITU Telecommunication Union) launched the Information and Communication Technology Development Index (IDI) to assess and measure ICT developments in various countries. It is a composite index (a compilation of the individual ICT indicators agreed upon internationally) that measures the accessibility of the latter as well as its skills, making it a valuable tool for measuring the most important indexes of the information society. It is also a standard tool that governments, operators, development agencies, researchers, and others can use to measure the digital divide and compare ICT performance within and between countries. The ICT Development Index is based on 11 ICT indicators, grouped into three groups: access, use, and skills. The most recent ICT Development Index was published in the [6].

As for the situation in the Arab countries, they are diverse in terms of the performance of the ICT Development Index (IDI). In

Table 1. ICT Development Index in Arab countries in 2011 and 2017.

Country	ICT Development Index in 2017	Regional Ranking in 2017	ICT Development Index in 2017	Regional Ranking in 2017	Change in ICT Development Index 2011–2017
Bahrain	7.6	1	5.79	2	1.81
Qatar	7.21	2	6.41	1	0.8
United Arab Emirates	7.21	3	5.68	3	1.53
Saudi Arabia	6.67	4	5.46	4	1.21
Oman	6.43	5	4.8	5	1.63
Lebanon	6.3	6	4.62	6	1.68
Jordan	6	7	3.9	7	2.1
Tunisia	4.82	8	3.57	10	1.25
Morocco	4.77	9	3.59	9	1.18
Algeria	4.67	10	2.98	12	1.69
Egypt	4.63	11	3.64	8	0.99
Syria	3.34	12	3.12	11	0.22
Sudan	2.55	13	2.18	13	0.37
Djibouti	1.98	14	1.71	14	0.27
Comoros	1.82	15	1.68	15	0.14

Source: ITU’s Information Society Measurement Reports 2017 and 2011.

, we will discuss the values of the ICT Development Index and the ranking of Arab countries for the years 2011 and 2017. The table shows that the most significant improvements between 2011 and 2017 in the value of the ICT Development Index were for Jordan (the index increased by 2.1 points), Bahrain (the index increased by 1.81 points), Algeria (the index increased by 1.69 points) and Lebanon (the index increased by 1.69 points), and Oman (the index increased by 1.68 points). The table below shows that there are significant differences in the ICT Development Index between Arab countries because of differences in economic levels. This partly explains why there were small changes in the regional order of countries between 2011 and 2017. In the group of countries with high levels of GNI per capita, the Gulf countries top the rankings. Qatar moved from first place in 2011 to second place in 2017, Bahrain moved from second place in 2011 to first place in 2017, while the United Arab Emirates, Saudi Arabia, and Oman maintained the same third, fourth, and fifth positions, respectively. These five top-ranked countries improved their ICT Development Index values by an average of 1.40 points between 2011 and 2017. This made it the first place in this Index in the Arab region. These countries are followed by six middle-income countries: Lebanon, Jordan, Tunisia, Algeria, Morocco, and Egypt. This group of countries improved their performance by an average of 1.48 points during the same period, which indicates that middle-income countries in the region, especially Lebanon and Jordan, are approaching the performance of the countries of the Gulf Cooperation Council. At the end of the ranking, we find the lowest-income countries in the region: Sudan, Syria, Mauritania, Djibouti, and Comoros. They also improved in the average value of their ICT Development Index, but by only 0.25 points in the period 2011–2017. Sudan and Djibouti improved their values in the ICT Development Index by 0.37 points and 0.27 points, respectively, while Comoros and Syria achieved only weak growth in the value of the ICT Development Index during the same period.

The most significant improvement rates in the ICT Development Index were achieved throughout the Arab States region because of the improvement in the international Internet bandwidth and subscriptions to fixed and mobile phones. Referring to the [7] for the Arab region as a whole, the trends of the overall level in the development of information and communication technology in the Arab world presented in Figure 1 indicate that the proportion of individuals who use the Internet in the population as a measure of ICT development has witnessed a sharp upward trend over the period 1993–2019. However, it should be noted here also that the growth of information and communication technology slowed down a little in 2015 and then started to rise again in 2016. The trends of the overall level in the development of information and communication technology in the Arab world, which are presented in Figure 2, indicate that the number of mobile phone subscriptions (per 100 people) as a measure of the development of information and communication technology witnessed an upward trend over the period 2013–2019 and then declined slightly during the period 2014–2018, and rebounded in 2019. The trends of the overall level in the development of information and communication technology in the Arab world, which are shown in Figure 3, indicate that the number of fixed-telephone service subscriptions (per 100 people) as a measure also of the development of information and communication technology witnessed a sharp upward trend over the period 1993–2008 and then declined during the period 2009–2014, before resuming its rise again starting in 2015.

3. Literature Review

3.1. Theoretical Framework

The neoclassical theory is one of the most important theories concerning itself with the subject of the factors causing economic growth. The Solow growth model [8] is considered one of the most famous neo-classical models, and it is an entry point for most studies related to economic growth. In this model, Solow introduced an independent variable into the economic growth equation, which is the technological level, considered by him to be an external factor. According to this model, GDP growth can occur due to several factors, the most important of which is an improvement in the technological level. We can also refer to the model of Romer [9], the pioneer of the theory of internal growth, in relation to technical development and its impact on economic growth. Romer believes that human capital must be allocated between research and innovation activities on the one hand, and between production activities on the other. This is because increasing the proportion of human capital devoted to research and innovation activities enables the economy to achieve a high growth rate in the long run. According to Romer’s model, the output is always determined from within the model as determined by the level of technological development, which in turn depends on the balance of human capital allocated to research and development activities.

The economic growth theories in general consider that investment in information and communication technology plays an important role in driving economic growth [10]. However, empirical studies have yielded mixed results, varying according to the research methodology used and the country or sample of the study. Economic growth models consider investment in ICT as an important factor of production such as employment, human capital, and physical capital. ICT can affect economic growth on the one hand, as well as production on the other, in addition to productivity, through three main channels. First, ICT goods and services are part of the added value of the economy. Second, the use of ICT capital as an input in the production of all goods and services leads to an increase in production as well as productivity, and thus to an increase in economic growth. Finally, ICTs can cause economic growth through their contributions to technological change. If the growth of ICT production is based on the efficiency and productivity benefits of activities, it will lead to an increase in productivity growth at the macroeconomic level [10,11]. Many microeconomic studies have also proven a positive correlation between investment in information technology and various measures of economic performance for companies in developed countries [3].

In addition to the important role of information and communication technology in improving the productivity of the economy, it can allow developing countries to conduct commercial and economic activities with an efficiency similar to that achieved in developed countries. The development of information and communication technology infrastructures, and their increasing strength, have led to a fundamental change in the nature of global relations, sources of competitive advantage, and opportunities for economic and social development. Technologies such as the internet, personal computers, fixed telephones, and mobile phones have created an interconnected global network of individuals, businesses, and governments. For developed or developing countries, a modern telecommunications infrastructure is not only necessary for domestic economic growth but is also a prerequisite for participating in the increasingly competitive global markets and for attracting foreign investment. Ref. [12] analyzed the ICT infrastructure effects on economic growth in the context of theoretical approach. They reported that policy support combined with adequate funding, stable government, macroeconomic determinants, and an innovation environment is essential to ICT-induced prosperity. In addition, countries should promote e-commerce and e-governance activities with adequate support for research and development, technical support, knowledge dissemination on AI-based robots and chatbots, stand-alone computer applications, technology transfer to industry and society, application towards digitalization acceptance, and user-centric rewards.

Recently, there have been increasing numbers of scientific studies on the relationship between information and communication technology on the one hand and foreign direct investment on the other. This research views ICT as a “location” factor that attracts FDI and a factor that influences other determinants of FDI. Many studies conclude that ICT lowers transaction and production costs for foreign investors and improves their access to information on alternative investment opportunities.

Information and Communication Technology (ICT) is the major new determinant of foreign direct investment in a world that is rapidly moving towards an information-based economy. Economies with ICT infrastructure are moving towards an information-based economy. Among the main benefits of ICT, we can mention the reduction in transportation costs, the improvement of marketing information, and the increase in the efficiency of industrial production. A large number of studies show that telecommunications infrastructure is essential not only for domestic economic growth, but also for attracting foreign direct investment and participating in increasingly competitive global markets. As for the insufficient availability of ICT services, it constitutes an obstacle to economic growth in the least developed countries [13,14]. Advanced telecommunications services facilitate international communications between parent and subsidiaries abroad in the current trend of global economic integration driven by cross-border investment by multinational corporations. Technological developments, particularly in the field of information and communications technology, have facilitated new avenues for conducting business on a global scale [15].

When considering the relationship between information and communication technology and foreign direct investment, the researcher should find a connection to economic theory by considering the following: (i) skills and productivity, i.e., the human capital aspects of ICT; (ii) technology transfer; (iii) transaction cost implications; and (iv) the ICT infrastructure aspect or effects on the flow of foreign direct investment. As mentioned earlier, previous studies consider that ICTs lower international transaction costs [16,17,18,19]. In addition, human capital is important in terms of the ability to absorb both ICT and technology transfer.

3.2. Empirical Framework

Many previous studies dealt with the role of information and communication technology in foreign direct investment and, accordingly, in economic growth. Among these country-level studies, ref. [20] found a significant relationship between IT investment and productivity growth in 12 countries in the Asia-Pacific region. Ref. [21] used a data set of 36 countries for the period 1985–1993 and showed that investment in information technology is positive for developed countries but unimportant for developing countries. Refs. [10,22] conducted two 39- and 42-country studies during 1980–1995 and 1985–1999, respectively. The results of the two studies confirmed the conclusion of [23] that information and communication technology plays an important role in economic growth in developed countries but does not play any definite role in developing countries. However, single country studies (e.g., ref. [24,25,26,27,28]) showed that ICT contributed to the economic growth in all these countries. These studies have been influential in strengthening the consensus among many economists that ICTs enhance foreign direct investment and economic growth.

If there is a relationship between FDI and ICT, FDI may increase due to the ability of a country’s ICT infrastructure to further support its flow. FDI may encourage an increase in information and communication technology in intermediate inputs, particularly between parent and affiliate companies. While developed countries have the ability to attract foreign direct investment by relying on the development of the field of information and communication technology, in turn, such capabilities must be built in developing countries. In turn, the inflow of foreign direct investment increases the volume of investment in information and communications technology and enhances its ability to enhance economic growth. The rapid expansion of global foreign direct investment resulted from several factors, including technical progress in telecommunications services and the reorganization of major currencies. On the other hand, technical advances in telecommunications services facilitated international communications by involving parent companies and their subsidiaries abroad, whereas the reorganization of the major currencies provided companies with opportunities to make profits by undertaking foreign direct investment [29]. In the same vein, ref. [30] argued that the beneficial effect of FDI is only enhanced in an environment characterized by an open trading and investment system and macroeconomic stability. The relationship between investment and economic growth is evident, for example, in the case of the Southeast Asian tiger economies, where investment rates were the main driver of growth in these countries.

As for the studies that dealt with developing countries, especially Arab countries, we find, for example, the study of [31] which tested the direct and indirect impact of information and communication technology on economic growth in North African and Middle Eastern countries, based on data for the period 1992–2004, using the generalized method of moments (GMM). The study concluded that there is a direct negative impact of information and communication technology on economic growth. While [32] found quite the opposite result when they examined the impact of financial development and ICT on economic growth in North African and Middle Eastern countries, using dynamic panel data models. The results of the study indicated the presence of a positive impact of the ICT index on economic growth. This means that the countries of this region need to strengthen their ICT policies and further improve the use of new ICTs. In the same direction, a study by [4] found a similar result, where the researchers investigated the impact of information and communication technology on the economic growth of selected developing countries in North Africa, the Middle East, and sub-Saharan Africa, using the generalized method of moments for the combined data during the period 2007–2016. The results showed that the various indicators of information and communication technology represent one of the main drivers of economic growth in the selected sample countries. Ref. [33] examined the effects of information and communication technology on economic growth in a group of 50 developing countries during the period between 2005 and 2015, using panel data models. Through static analysis of panel data models, the researcher concluded that the impact of information and communication technology changes from one country to another. This study also found that the Internet index negatively affects the long-term economic growth of the group of developing countries under study. Ref. [34] study the impact of information and communication technology on economic growth in Palestine over the period from 2000 to 2018, using a multiple linear regression model. The researchers concluded that information and communication technology have a positive and significant impact on economic growth in Palestine.

The study by [35] dealt with the impact of information and communication technology on the financial development index of the Gulf Cooperation Council (GCC) group from 2000 to 2016, using static panel data models for fixed effects and dynamic panel data models. Their results showed that information and communication technology had a significant and positive impact on the variables of financial development in the Arab Gulf region. The study of [36] examined the impact of information and communication technology, foreign direct investment, and general government expenditures on the economic growth of the countries of the Middle East and North Africa during the period from 1998 to 2019, using the generalized method of moments. Their results reported that the impact of information and communication technology on economic growth is positive and important, but the effect of general government spending on economic growth is negative.

Ref. [37] studied the impact of information and communication technology on inclusive growth, using the time series data methodology for cross-sectional data, and using regression models with fixed effects and regression models with random effects on a sample of developing countries and a sample of Arab countries during the period 2010–2018. The study concluded that there is a positive and significant impact of access and use of information and communication technology on comprehensive growth, whether in the sample of developing countries or in the sample of Arab countries. The impact of information and communication technology skills is negative and insignificant in the sample of developing countries, and negative and significant in the sample of Arab countries. Hence, the study emphasizes the importance of increasing investments in the infrastructure of the information and communication technology sector in order to support access and to access opportunities in all regions in the developing or Arab countries.

A recent study by [38] examined causal relationships between the internet and economic factors (GDP, FDI, imports, and exports) in Asian countries between 1997 and 2017, using the panel vector autoregressive model. The results of this study showed a two-way causal relationship between FDI and internet use in South Asia, a one-way causal relationship from internet use to FDI in East Asia, and a one-way causal relationship from FDI to internet use in West Asia. The results also indicated a one-way causal relationship from exports to internet use in East Asia, a one-way causal relationship from internet use to exports in South Asia, and a one-way causal relationship from internet use to GDP in West Asia. The researchers concluded from these findings that the use of the internet enhances economic performance in Asia. Therefore, they recommend decision makers to improve the use of the internet with a focus on economic growth, improving transaction efficiency, and facilitating foreign direct investment. In addition, ref. [39] noted a causal relationship between ICT and economic growth in 25 high- and middle-income Asian countries, using panel data over the period 2000–2018. Through this study, the researcher concluded that high-income Asian countries have achieved positive and significant economic development because of the high rate of internet penetration. In addition, middle-income countries are beginning to benefit from the development of the ICT index in boosting economic growth.

More recently, ref. [40] studied the effect of ICT and FDI inflows on the per-capita GDP in India using annual data during the period from 1991 to 2019 by estimating simultaneous equations models. Their results indicated that FDI inflows and information and communication technology represented by mobile density and internet density have positive and significant impact on the per-capita GDP. In the same line, ref. [41] analyzed the dynamic effects of ICT (e.g., telephone subscriptions, mobile subscriptions, broadband subscriptions, internet subscribers, and secure internet servers), FDI inflows, and trade openness on economic growth for the case of BRICS countries over the period 2000–2018 using generalized method of moments. Their findings reported that ICT affects positively economic growth for a few countries while FDI inflows have negative impact on economic growth. Ref. [42] investigated the impact of ICT infrastructure on FDI inflows in the group of eight countries of Bangladesh, Indonesia, Iran, Egypt, Nigeria, Malaysia, Pakistan, and Turkey over the period 1997–2018 by using fixed effects models. Their findings showed positive and significant impact of ICT on FDI inflows.

4. Research Data and Methodology

4.1. Data

The data needed to conduct the analysis for each Arab country were sourced from the World Bank’s World Development Indicators database [7]. The data set under study contains annual data for each Arab country for the following variables: GDP per capita (expressed in constant 2010 in USD), abbreviated as GDPC; foreign direct investment flows (% of GDP), abbreviated as FDI; number of subscriptions in fixed telephone service (per 100 people), abbreviated as FTS; number of mobile phone subscriptions (per 100 people), abbreviated as MCS; and individuals using the Internet (% of population), abbreviated as PIUI. Information and Communication Technology can be defined as a broad term for information technology, which refers to all communication technologies, including the Internet, fixed telephones, mobile phones, computers, software, medium devices, video conferencing, social networking, and other such Media applications and services [43]. On the other hand, economic growth represents the rates of increase in GDP per capita from year to year [33]. Following the tradition in the literature, we define FDI as the net inflows of FDI expressed as a percentage of GDP.

Data were selected based on the availability of ICT variables. According to the availability of information, we chose the following group of (15) Arab countries: Algeria, Morocco, Tunisia, Saudi Arabia, the United Arab Emirates, Bahrain, Kuwait, Oman, Egypt, Lebanon, Jordan, Iraq, Sudan, Yemen, and Mauritania. These countries were selected according to the availability of data for all study variables during the period 1995–2019. The following variables were used to represent ICT: FTS, MCS, and PIUI. The first two variables measure access to information and communication technology, while the third variable measures the extent of its use. The table in Appendix A summarizes definitions and data sources for all study variables.

The main advantage of panel data is that it has the particularity of taking into account temporal dynamics (adaptation time, expectations, etc.) in the interpretation of the dependent variable due to overlapping observations between sectors, thus improving the effectiveness of policies (decisions, actions, etc.). In addition, the use of panel data reduces the problem of heteroscedasticity, which often occurs when cross-sectional data are used [44]. In contrast, in the case of the stationary model, the immediate (or non-diffusive) explanation gives only a portion of the variance in the dependent variable.

In the first model, the dependent variable is economic growth, and the independent variables are foreign direct investment and information and communication technology. In the second model, the dependent variable is foreign direct investment, and the independent variables are economic growth and information and communication technology. In the third model, the dependent variable is one of the ICT variables, and the independent variables are economic growth and foreign direct investment. The focus here is on the study of the dynamic relationships between foreign direct investment, real GDP per capita, and information and communication technology. To obtain homogeneous data, we will use the natural logarithmic for GDP per capita, which is abbreviated as LGDPC. The natural logarithm of real GDP per capita measures economic growth.

The table in Appendix B shows the descriptive statistics of the Arab countries under study for the period from 1995 to 2019. The average GDP per capita in a sample of 15 Arab countries is $11,936.57, with the minimum for Yemen in 2018 amounting to $632.90, and the highest for the United Arab Emirates in 1997 with a value of about $64,864.74. The average value of FDI inflows as a percentage of GDP is 2.96%, with the minimum value for Mauritania reaching −11.62% in 2019, and the maximum value for Bahrain at 33.56% in 1996. The main variable of the study is information and communication technology represented by the variables: number of mobile phone subscriptions (per 100 people), number of fixed phone subscriptions (per 100 people), and the proportion of individuals who use the Internet (% of the population). The average value of mobile cellular subscriptions in Arab countries is about 65 to 100 people, ranging from 0 in Sudan, Mauritania, and Iraq in 1995 to about 212 for 100 people in the United Arab Emirates in 2016. The average value of fixed-line subscriptions (per 100 people) is 10.58, with a minimum value of 0.25 for Sudan in 1995 and a maximum value of 32.87 for the United Arab Emirates in 1999. The average value of the percentage of individuals using the internet was 26.27% with a minimum value of 0% for Yemen, Sudan, and Mauritania in 1995, and a maximum value of 99.7% for Bahrain in 2019.

We also present in the table in Appendix C the correlation matrix, which contains the correlation coefficients between the different research variables with an indication of their levels of significance at 1%, 5%, or 10%. It turns out that there is a direct statistically significant correlation at the 1% level of significance between the per capita GDP variable and the three variables that represent information and communication technology, while there is no correlation between the foreign direct investment variable and the information and communication technology variables. In addition, the results indicated that there is a direct and strong correlation between the three variables that represent information and communication technology.

4.2. Methods

The research methodology used in this research paper is based on estimating a heterogeneous dynamic panel model using annual data on real GDP per capita, foreign direct investment, and information and communication technology for a sample of Arab countries during the period 1995–2019. This methodology is implemented in three stages. In the first stage, unit root tests are applied to consider the stationarity of various variables. These tests are [45,46] and Fisher (ADF) and (PP) [47] tests. In the (LLC) (2002) test, the null hypothesis assumes a common root unit, whereas in the rest of the tests, the null hypothesis assumes the unity of the individual root. In the second stage, we conduct the [48,49,50] panel cointegration tests. Both tests are established on the null hypothesis of absence of cointegration against the alternative of existence of cointegration. However, the Kao test supposes a common cointegration vector across all countries in the panel whereas the Pedroni test permits for panel-specific cointegrating vectors. In the third step, we estimate panel ARDL models, using the (Pooled Mean Group) to reveal the effects of independent variables on the dependent variable in the short and long term. This methodology can enable us to identify and avoid spurious results, which may happen using a simple method such as the OLS method. This technique, as successfully applied in studies conducted by [29,51], proves its record-breaking toughness and ability to root out false relationships.

In this study, we will analyze the relationships between economic growth, foreign direct investment, and ICT in the short and long term without considering control variables. This allows us to get the overall effect of each variable on the other. The models that link the different variables take the following forms:

$$LGDP{C}_{i,t}={\beta }_0+{\mu }_i+{\beta }_1 FD{I}_{i,t}+{\beta }_2 IC{T}_{i,t}+{\epsilon }_{i,t}$$

(1)

$$FD{I}_{i,t}={\beta }_0+{\mu }_i+{\beta }_1 LGDP{C}_{i,t}+{\beta }_2 IC{T}_{i,t}+{\epsilon }_{i,t}$$

(2)

$$IC{T}_{i,t}={\beta }_0+{\mu }_i+{\beta }_1 LGDP{C}_{i,t}+{\beta }_2 FD{I}_{i,t}+{\epsilon }_{i,t}$$

(3)

where:

• GDPC: GDP per capita (at constant 2010 USD).
• FDI: FDI inflows as a percentage of GDP.
• ICT: Information and Communication Technology. It is represented here by three variables: the percentage of internet users, the number of mobile phone subscriptions per 100 people, and the number of fixed phone subscriptions per 100 people (PIUI, MCS, and FTS, respectively).
• The index (i = 1,…, N) i refers to the country i of our sample (N = 15).
• The index (t = 1,…, T) t represents the period or years (T = 25).
• β₀, β₁ and β₂: parameters to be estimated.
• µ_i: These are the country-specific fixed effects.
• ε_it: This is the random error term.

It can be said that we have a group of countries with some different properties represented by special fixed effects. On the other hand, it should be borne in mind that the economies of the Arab region have been characterized for decades by a similar development model that relies mainly on rentier activities. According to what many economists point out, all the economies of the Arab countries can be described as rentier economies, albeit to varying degrees. These countries depend on different sources of revenue, distributed mainly between the export of raw materials, especially oil, gas and phosphates, tourism, or remittances of immigrants and expatriates [52,53].

Macroeconomic variables are usually not stationary at levels (I (0)) but are more likely to be stationary at their first difference (I (1)). This means that the model is dynamic and assumes that lag-dependent variables are include explanatory variables. Hence, we will use the heterogeneous dynamic panel data model. In this case, the panel ARDL model is most appropriate. Various other approaches to dynamic modeling can lead to inconsistent estimates of the average value of the parameters when these are identical between countries. Moreover, the panel ARDL model is relatively more efficient in the case of a small sample size. Therefore, the panel ARDL models that we will estimate take the following forms [1,54]:

$$LGDP{C}_{i,t}={\propto }_0+{{\displaystyle \sum }}_{j=1}^P{\propto }_{j}LGDP{C}_{i,t-j}+{{\displaystyle \sum }}_{j=0}^q{\delta }_{j}FD{I}_{i,t-j}+{{\displaystyle \sum }}_{j=0}^r{\gamma }_{j}IC{T}_{i,t-j}+{\mu }_i+{\epsilon }_{it}$$

(4)

$$FD{I}_{i,t}={\propto }_0+{\displaystyle \sum }_{j=1}^P{\propto }_{j}FD{I}_{i,t-j}+{\displaystyle \sum }_{j=0}^q{\delta }_j LGDP{C}_{i,t-j}+{\displaystyle \sum }_{j=0}^r{\gamma }_{j}IC{T}_{i,t-j}+{\mu }_i+{\epsilon }_{i,t}$$

(5)

$$IC{T}_{i,t}={\propto }_0+{\displaystyle \sum }_{j=1}^P{\propto }_{j}IT{C}_{i,t-j}+{\displaystyle \sum }_{j=0}^q{\delta }_j LGDP{C}_{i,t-j}+{\displaystyle \sum }_{j=0}^r{\gamma }_{j}FD{I}_{i,t-j}+{\mu }_i+{\epsilon }_{i,t}$$

(6)

By reformulating the given models by Equations (4)–(6), they are converted into error correction models (re-parametrized ARDL (p, q, r)) as follows:

$$∆LGDP{C}_{it}={\alpha }_0+{\mathsf{\Phi }}_i \left(LGDP{C}_{i, t-1}-{\rho }_{i}FD{I}_{i,t}-{\upsilon }_{i}IC{T}_{i,t}\right)+{\displaystyle \sum }_{j=1}^{p-1}{\alpha }_{ij}∆LGDP{C}_{i, t-j}+{\displaystyle \sum }_{j=0}^{q-1}{\delta }_{ij}∆FD{I}_{i, t-j}+{\displaystyle \sum }_{j=0}^{r-1}{\gamma }_{ij}∆IC{T}_{i, t-j}+{\mu }_i+{\epsilon }_{it}$$

(7)

$$∆FD{I}_{it}={\alpha }_0+{\mathsf{\Phi }}_i \left(FD{I}_{i, t-1}-{\rho }_{i}LGDP{C}_{i,t}-{\upsilon }_{i}IC{T}_{i,t}\right)+{\displaystyle \sum }_{j=1}^{p-1}{\alpha }_{ij}∆FD{I}_{i, t-j}+{\displaystyle \sum }_{j=0}^{q-1}{\delta }_{ij}∆LGDP{C}_{i, t-j}+{\displaystyle \sum }_{j=0}^{r-1}{\gamma }_{ij}∆IC{T}_{i, t-j}+{\mu }_i+{\epsilon }_{it}$$

(8)

$$∆IC{T}_{it}={\alpha }_0+{\mathsf{\Phi }}_i \left(IC{T}_{i, t-1}-{\rho }_{i}LGDP{C}_{i,t}-{\upsilon }_{i}FD{I}_{i,t}\right)+{\displaystyle \sum }_{j=1}^{p-1}{\alpha }_{ij}∆IC{T}_{i, t-j}+{\displaystyle \sum }_{j=0}^{q-1}{\delta }_{ij}∆LGDP{C}_{i, t-j}+{\displaystyle \sum }_{j=0}^{r-1}{\gamma }_{ij}∆FD{I}_{i, t-j}+{\mu }_i+{\epsilon }_{it}$$

(9)

where:

• Φ_i is the group’s adaptive velocity coefficient (${\mathsf{\Phi }}_i<0).$
• ϑ and ρ: represent the coefficients that measure the effect of the independent variables on the dependent variable in the long run.
• δ_ij and γ_ij: They are the coefficients that represent the effect of the independent variables on the dependent variable in the short run.
• μ_i: They are the fixed effects that represent the specifies of each country, and they do not change in time.
• ε_it: This is the random error term.

ECT: This is the error correction term.

$$\mathrm{ECT}=\left[\left(LGDP{C}_{i, t-1}-\rho FD{I}_{i,t}-\upsilon IC{T}_{i,t}\right)\right]$$

in model (7)

$$\mathrm{ECT}=[\left(FD{I}_{i, t-1}-\rho LGDP{C}_{i,t}-\upsilon IC{T}_{i,t}\right)]$$

in model (8)

$$\mathrm{ECT}=[\left(IC{T}_{i, t-1}-\rho LGDP{C}_{i,t}-\upsilon FD{I}_{i,t}\right)]$$

in model (9).

According to [54,55], the models presented in Equations (7)–(9) can always be estimated using the mean-group method (Mean Group estimator: MG). This method relies on estimating the coefficients for each country and then estimating an average for the group. However, the researchers admit that if the long-run coefficients are not heterogeneous from one group to another, it is more appropriate to use a more efficient estimation method, the Pooled Mean Group: PMG. The method for estimating the PMG parameter allows it to differ from one country to another in the short term, but it is homogeneous in the long term. The use of both methods requires that the variables be stationary on the level or on their first differences. Hence, the next section of empirical results will first present the results of the root unit tests, and then, the results of the Pooled Mean Group method for the different study models.

This research studies the causal relationships between information and communication technology and foreign direct investment and between information and communication technology and economic growth for a group of Arab countries using time series data and error correction models. If the non-stationary time series do not coincide, a high degree of correlation between the two variables does not imply a causal relationship between the variables. The methodology used enables us to identify and avoid spurious outcomes, which may happen using a simple method such as the OLS method. It should be noted that the causality test requires extreme accuracy in estimation, as any deletion of previous information related to the study may give rise to misleading results, and the selection of the optimal lag periods has a very important role in estimating the model.

5. Result Analysis

In this study, we estimated the model equations and analyzed various results by using the econometric program Eviews 11.

5.1. Results of Unit Root Tests and Cointegration Tests

The first step in analyzing the results is to test the stationarity of the variables.

Table 2. Results of unit root tests.

Variable	Statistics of Tests								Order of Integ.
	LLC Test		IPS Test		ADF-Fisher Test		PP-Fisher Test
	H0: Common Unit Root		H0: Individual Unit Root
	At Level	At First Level	At Level	At First Level	At Level	At First Level	At Level	At First Level
LGDPC	−0.497	−4.50 *	0.437	−5.59 *	30.92	84.68 *	12.82	108.6 *	I (1)
FDI	−3.56 *	-	−5.60 *	-	88.51 *	-	88.00 *	-	I (0)
PIUI	3.51	−3.43 *	9.30	−4.82 *	3.76	95.27 *	0.85	138.2 *	I (1)
MCS	0.541	−3.92 *	−0.872	−4.09 *	32.61	66.01 *	7.60	76.77 *	I (1)
FTS	−3.22 *	−5.48 *	−1.96 **	−7.21 *	50.83 *	113.15 *	32.50	216.5 *	I (0) or I (1)

Note: *, ** significance levels at 1% and 5%, respectively. Automatic selection is made using Akaike information criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.

presents all the results of unit root tests for the different variables. In order to test the presence of unit roots in our data, we use the first-generation tests; Refs. [45,46] and Fisher type tests (ADF and PP).

In the LLC test (2002), the null hypothesis assumes a common unit root. In the rest of the tests, the null hypothesis assumes that the panel data model adheres to the individual unit root. In Table 2, we present the results of the various first-generation unit root tests. According to the results of the LLC, IPS, ADF-Fisher, and PP-Fisher tests, it is clear that all the variables are either stationary in their levels or in their first differences (I (0) or I (1)). In particular, the foreign direct investment variable is stationary at the level. The economic growth, the percentage of internet users, and the number of mobile subscribers’ variables are stationary at their first differences, while the variable number of fixed-line subscribers is stationary at the level or at the first differences. Therefore, it is necessary to use the panel ARDL model to estimate the various models in the short and long terms.

Provided that the majority of variables are either stationary in their levels or in their first differences, the second step of the analysis is to test for the presence of cointegration between each dependent variable and the regressors using the Pedroni and Kao cointegration tests in all panels. The results of both cointegration tests are shown in

Table 3. Results of Cointegration Tests.

	LGDPC	FDI	MCS	PIUI	FTS
	Pedroni Residual Cointegration Test
	Alternative Hypothesis: Common AR Coefs. (Within-Dimension)
Panel v-Statistic	−4.41 *	−3.22 *	−1.79 ***	−0.98	−1.92 ***
Panel rho-Statistic	−1.02	−0.79	−0.36	−0.53	−0.17
Panel PP-Statistic	−7.45 *	−6.21 *	−2.44 **	−1.85 ***	−2.51 **
Panel ADF-Statistic	−8.66 *	−6.74 *	−2.51 **	−1.94 ***	−2.63 **
	Pedroni Residual Cointegration Test
	Alternative Hypothesis: Individual AR Coefs. (Between-Dimension)
Group rho-Statistic	−1.02	−0.79	−0.36	−0.53	−0.17
Group PP-Statistic	−7.71 *	−6.35 *	−2.29 **	−1.78 ***	−2.55 **
Group ADF-Statistic	−8.44 *	−6.88 *	−2.61 **	−1.84 ***	−2.59 **
	Kao Cointegration Test
Augmented Dickey Fuller	−5.67 *	−4.58 *	−0.81	−1.95 ***	−2.78 **

Notes: *, **, ***: significance levels at 1%, 5%, and 10%, respectively. Trend assumption: Without trend. Automatic lag selection is based on AIC with a maximum lag of 5. Source: Authors’ own estimations based on Eviews 11 Output.

. Their findings indicate the rejection of null hypothesis of absence of cointegration in all models considered. Hence, we can conclude the presence of long-run relationships between the various variables.

5.2. Analysis of The Results of Panel ARDL Model

The models presented in Equations (7)–(9) are estimated using the three panel estimators of Pooled Mean Group, Mean Group, and Dynamic Fixed Effect (DFE) and then we select the best one based on Hausman test. The null hypothesis of Hausman test states that the difference between PMG and MG or PMG and DFE estimators is not significant. If the null hypothesis is not rejected, the PMG estimator is more efficient. We use the PMG estimation if the p-value is higher than 5% level. Otherwise, we use the MG or DFE estimator. The results of Hausman test are reported in

Table 4. Results of Hausman test.

Dep. Var.	LGDPC		FDI		FTS		MCS		PIUI
	PMG vs. MG	PMG vs. DFE	PMG vs. MG	PMG vs. DFE	PMG vs. MG	PMG vs. DFE	PMG vs. MG	PMG vs. DFE	PMG vs. MG	PMG vs. DFE
Chi-squared statistic	1.60	1.73	1.14	1.65	7.23	8.56	1.62	0.00	1.17	0.00
p-value	0.90	0.82	0.95	0.84	0.19	0.14	0.89	1.00	0.96	1.00
Decision	The null hypothesis of homogeneity cannot be rejected
Best model	PMG estimation

Source: Authors’ own estimations.

. It is indicated that p-value is usually less than 5%, which implies that the PMG estimator is more efficient for all models. Thus, we consider the results of PMG estimation.

Through the analysis of the results of the dynamic model, it is possible to know the various influences and relationships between the variables of economic growth, the foreign direct investment inflows, and the variables of information and communication technology in the long and short terms. The short-term dynamic model details how to make adjustments between the various time series to re-establish long-term equilibrium. As for the relationship between variables in the long run, it is held by the error correction term coefficient (ECT). The ECT is the rate of adaptation, that is, the speed at which the system returns to equilibrium after a shock. When the ECT coefficient is negative and significant, this supports a long-term relationship between the variables. In

Table 5. Estimation results of model (7) using PMG method.

Independent Variables	Dependent Variable: D (LGDPC)
Use of the ICT Index: Proportion of Individuals Using the Internet
	Long run coefficients
FDI	0.025 *
PIUI	0.003 *
	Short run coefficients
ECT	−0.155 *
D(FDI)	−0.005
D(PIUI)	0.001
Use of the ICT Index: Number of Mobile Subscriptions per 100 Individuals
	Long run coefficients
FDI	0.018 *
MCS	0.0002
	Short run coefficients
ECT	−0.211 *
D(FDI)	−0.004
D(MCS)	0.001
Use of the ICT Index: Number of Fixed Telephone Subscriptions per 100 Individuals
	Long run Coefficients
FDI	0.026 *
FTS	0.048 *
	Short run coefficients
ECT	−0.088 *
D (FDI)	−0.004
D (FTS)	0.005

Note: *: significance level at 1%. Automatic selection is made using Akaike information criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.

, we present the results of the impact of the foreign direct investment inflows and information and communication technology variables on economic growth in the long and short terms, and the error correction term. In the long term, we find that the two variables, the percentage of internet users and the number of fixed-line subscribers, have a positive and significant effect at 1% on economic growth, while the variable number of mobile subscribers has a positive but not significant effect. In the short term, we find that the three variables of information and communication technology have a positive impact on economic growth in the Arab countries, but it is not significant, and thus there is no effect of information and communication technology on economic growth in the group of Arab countries in the short term. These results highlight the importance of information and communication technology in the development of electronic commerce, electronic marketing, electronic financial transactions, and digitization of electronic management in driving long-term economic growth in the Arab countries, especially the Arab Gulf countries. As for the foreign direct investment inflows, it has always had a positive and significant effect at 1% on economic growth in the long term, while it has no significant effect in the short term. As for the error correction term coefficient, we find its negative and significant sign at 1% as expected, which indicates the existence of a correlation between the study variables in the long run. The value of the error correction term coefficient ranged between 8.8% and 21.1%, which is medium, thus indicating that there is no return to the equilibrium position quickly in the long term in the variables of foreign direct investment flows and the variables of information and communication technology towards economic growth.

Table 6. Estimation results of model (8) using PMG method.

Independent Variables	Dependent Variable: D (FDI)
Use of the ICT Index: Proportion of Individuals Using the Internet
	Long run coefficients
LGDPC	3.14 *
PIUI	0.059 *
	Short run coefficients
ECT	−1.31 *
D (LGDPC)	7.44
D (PIUI)	0.302
Use of the ICT Index: Number of Mobile Subscriptions per 100 Individuals
	Long run coefficients
LGDPC	0.174 *
MCS	0.001 *
	Short run coefficients
ECT	−1.061 *
D (LGDPC)	−2.30
D (MCS)	0.069
Use of the ICT Index: Number of Fixed Telephone Subscriptions per 100 Individuals
	Long run coefficients
LGDPC	0.207 *
FTS	0.026 **
	Short run coefficients
ECT	−0.623 *
D (LGDPC)	29.28
D (FTS)	−1.302 ***

Note: *, **, ***: significance levels at 1%, 5%, and 10%, respectively. Automatic selection is made using Akaike information criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.

presents the results of the impact of ICT variables and GDP per capita on foreign direct investment flows in the long and short terms, and the error correction term coefficient. Through the results, we note that information and communication technology have a positive and moral impact on foreign direct investment flows in the long term. While in the short term, there is one ICT variable (number of fixed-line subscribers) that negatively affects FDI inflows. These results highlight the importance of information and communication technology in attracting foreign investments to Arab countries in the long term. The results also show the importance of economic growth in driving foreign direct investment flows in the long term, especially since we note that the coefficient of the per capita GDP variable is always positive and significant at 1% in the long term, while it is not significant in the short term. As for the value of the coefficient of the error correction term, it was always negative and significant, and this confirms the existence of a relationship between the variables in the long term. The value of the coefficient of the error correction term ranged between 62.3% and 131%, which is considered high, which indicates a return to the equilibrium position quickly in the long term in the variables of GDP per capita and the variables of information and communication technology towards foreign direct investment.

Finally,

Table 7. Estimation results of model (9) using PMG method.

Independent Variables	Dependent Variables
	D (MCS)	D (PIUI)	D (FTS)
	Long run coefficients
LGDPC	57.04 *	6.45 *	0.11 *
FDI	5.83 *	19.42 *	1.92 *
	Short run coefficients
ECT	−0.09 *	−0.02	−0.83 *
D(LGDPC)	11.80	−0.54	−2.93
D(FDI)	−0.36	−1.07	−0.01

Note: *: significance level at 1%. Automatic selection is made using Akaike information criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.

reports the results of the impact of GDP per capita and foreign direct investment inflows on ICT variables in the long and short terms and the coefficient of the error correction term. These results highlight that the coefficient of GDP per capita and foreign direct investment inflows are positive and significant in the long run only. This indicates that the per capita GDP and FDI inflows have a positive impact on the long-term development of ICT in the Arab countries. This result is consistent with the descriptive analysis of data, where high income countries (particularly the Gulf countries) appear to have a high ICT Development Index as well. Therefore, it can be said that foreign direct investment and economic growth play an important role in the further development of information and communication technology in the group of Arab countries. As for the value of the coefficient of the error correction term, it ranges between 2% and 83%, which is considered weak to medium, which indicates a lack of return to the equilibrium position quickly in the long term in the variables of GDP per capita and foreign direct investment towards information and communication technology. In general, these results support the existence of a positive relationship between information and communication technology, foreign direct investment inflows, and economic growth.

It can be concluded that the findings of the study are the consensus presented by economists that information and communication technology enhances foreign direct investment and economic growth, especially the Solow model of economic growth [8] which considers the technological level as a positive external factor affecting economic growth, as well as Romer’s model [9], which considers technological development as an internal factor positively affecting economic growth. The results of the study are also consistent with many of the results of previous studies related to developing countries, such as the studies of [4,32,34,35,36,37].

6. Main Conclusions and Recommendations

The results of the panel ARDL model showed that information and communication technology have a direct and significant relationship in the long run with the foreign direct investment inflows and economic growth in the Arab countries group. This is demonstrated by the results of empirical estimates, which showed that information and communication technology and foreign direct investment inflows have a positive and significant impact on economic growth in the long run. In addition, all the variables of information and communication technology have a positive and significant impact on the foreign direct investment inflows to those countries in the long term. In addition, the variables of foreign direct investment inflows and economic growth positively and significantly affect the variables of information and communication technology in the long term only, while the results also proved that all ICT variables have no impact on foreign direct investment inflows and economic growth in the short term. These results support the existence of a direct relationship between information and communication technology, foreign direct investment inflows, and long-term economic growth in the study sample. This highlights that the positive effects of information and communication technology are not on the short level, but are usually on the medium and long levels, especially in attracting foreign investments, which in turn contribute to boosting economic growth [10]. These results show that the telecommunications infrastructure (fixed phones, mobile phones, the Internet, etc.) is important and essential to attract foreign direct investment and to drive economic growth, especially in the long term for the Arab countries that represent the study sample. Advanced telecommunications services facilitate international transactions and communications between parent companies and their subsidiaries abroad and create new ways to conduct business on a global scale [15]. These results are consistent with economic growth theories that consider investment in information and communication technology as one of the production factors that contribute to increasing its benefit.

From the practical point of view, this study can recommend to the governments of Arab countries to keep pace with recent developments in information and communication technology and pay attention to this element because of its role in the development of foreign investments and economic growth [5]. Consequently, some Arab countries can further improve the quality of information and communication technology as an important infrastructure to receive more inflows of foreign direct investment and to witness better economic growth. Thus, we have come to an important conclusion, that the least developed Arab countries must increase investment in information and communication technology because of the positive effect it has on the flow of foreign direct investment and economic growth, especially in the long term. We therefore recommend that these countries plan to increase investment in ICT capital sectors such as the Internet, mobile and broadband infrastructure, e-commerce practices, etc., as well as increase investment in ICT skills such as education. Therefore, Arab countries, especially weak and middle-income ones, are called upon to restructure strategies and policies related to investment in information and communication technology.

This study has some shortcomings. For example, we used data for 15 Arab countries, and therefore, we can find it difficult to generalize the results to all other Arab countries. However, this is due to the lack of data related to the variables of importance to the Arab countries that were not considered within the study sample.

In this research, we study the relationship between information and communication technology, foreign direct investment, and economic growth for a group of Arab countries only, which makes the results one-sided. We recommend that researchers in the future conduct a comparative study between the group of high-income countries, middle-income countries, and low-income countries. It is also possible that future studies will focus on the role of ICT development in inclusive growth.