Nexus between telecommunication infrastructures, economic growth and development in Africa: Panel vector autoregression (P-VAR) analysis

Telecommunications Policy 43 (2019) 101816

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Nexus between telecommunication infrastructures, economic growth and development in Africa: Panel vector autoregression (PVAR) analysis

T

Oladipo Olalekan Davida,b,∗ a b

School of Economic Sciences, Faculty of Economic and Management Sciences, North-West University, Vaal Campus, South Africa Department of Economics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic

ARTICLE INFO

ABSTRACT

Keywords: Telecommunication infrastructures Economic growth-development Panel causality Principal component analysis (PCA) Variance decomposition and impulse response analysis

This study examines the causal-effect relationship between telecommunication infrastructures, economic growth and development in selected African countries. It further estimates the trivariate impacts of telecommunication infrastructures, economic growth and development in the region. The analysis considers a panel of forty-six African countries from 2000 to 2015. To measure economic growth, real gross domestic product serves as the proxy, while economic development is measured by the Human Development Index, and telecommunication infrastructures by a composite index of telecommunication computed from mobile line, fixed line and internet access penetration via principal component analysis (PCA). The empirical results suggest the existence of a bidirectional long-run relationship between telecommunication infrastructures, economic growth and development. The causality tests reveal that there is feedback causality between telecommunication infrastructures, economic growth and development. Telecommunication infrastructures promote economic growth and development in Africa and vice versa. Thus, there is need to promote inclusive and holistic policies that will enhance digital provide, economic growth and development simultaneously in Africa. An increase in telecommunication infrastructures will encourage aggregate output and standard of living to move in the same direction in Africa.

JEL Classification: L96 O10 O40 C23 C38 C20

1. Introduction Innovation and technology are catalysts of globalisation that ensure closeness of trade partners and prompt trading. The world is rapidly advancing in the direction of an economic system based on continuous and widespread innovations that rely heavily on information and communications technology (ICT), of which telecommunications forms an integral component (Schumpeter, 1942). The progress in telecommunications technology has been an essential vehicle for enabling information exchange to develop as a valuable commodity. Since the invention of the telephone by Graham Bell in 1876, in slightly more than a century, the telephone as a new form of telecommunications technology has penetrated practically every corner of modern society. As the growth of telephones has been most remarkable in industrialized societies, it has been widely asserted that the use and availability of the telephone, as a component of telecommunication services, is an essential element of economic growth and development (Wellenius, 1977).

∗ Corresponding author. School of Economic Sciences, Faculty of Economic and Management Sciences, North-West University, Vaal Campus, South Africa. E-mail address: [email protected].

https://doi.org/10.1016/j.telpol.2019.03.005 Received 5 July 2018; Received in revised form 29 March 2019; Accepted 30 March 2019 Available online 16 April 2019 0308-5961/ © 2019 Elsevier Ltd. All rights reserved.

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Telecommunications infrastructure services enables the setup, maintenance and consulting for electronic communications, examples of telecommunications infrastructure services include: optical fiber installation, cell tower site location, radio antenna testing, installation of standard phone equipment and data networks.1 According to Kumar, Kumar, and Patel (2015), telecommunication services have paved the way for greater advancements and spread of technology that changed the digital landscape in many parts of the world, from which Africa has greatly benefitted. However, in a number of small and developing countries, telecommunications is still a growing sector undergoing major reforms, in an effort to develop to a level where it can efficiently interlink industries and speed up production processes in a cost-effective manner. Economies and sectoral component units equipped with the requisite telecommunications systems are rapidly engaging in post-industrial and information-based economic growth due to the leapfrog in development stages as a result of digital advancements (Noah & David, 2013). The modern telecommunications infrastructure is essential for radical economic transformation and a progressive competitiveness of an economy. For emerging economies, a modern telecommunications infrastructure is not only important to domestic economic growth, but is also a key determinant of participation in increasingly competitive world markets, and for attracting new investments, both domestic and foreign, in order to stimulate sustainable development. In the advanced industrial countries of Europe and America, universal telecommunications services have penetrated every sector of society. The trend in mobile telecommunications penetration in Europe has increased from 91.7 in 2005 to 120.6 in 2015 but declines marginally to 120 in 2018, per 100 inhabitants, and in America from 52.1 in 2005 to 112.2 in 2015 and further rises to 112.8 in 2018 per 100 inhabitants. While in Africa, mobile telecommunications has grown from 12.4 in 2005 to 75.3 in 2015 and further booms to 76 in 2018 per 100 inhabitants (International Telecommunications Union [ITU], 2016; 2019).2 These clearly show that mobile telecommunications penetration in Europe and America is higher than that of Africa in 2005–2015. Though, low penetration of telecommunications services hampered information sharing speed which is one of the key determinant of economic prosperity. The limited availability of telecommunications services in underdeveloped countries has been one of the underlying factors hindering economic growth and development. Africa is no exception, as a continent with low levels of infrastructure and telecommunication density, inadequate power supply, and low levels of economic transformation (Alleman et al., 2004; Akanbi, 2013). However, Africa also experienced growth in mobile telecommunications penetration between 2005 and 2015, though at a slower rate when compared to Europe and America. This implies that in terms of mobile telecommunications penetration, Africa is still below the level at which Europe was in 2005 (ITU, 2016). As telecommunications services increase, electronic communication rises and triggers efficient business communication that promotes economic progress. Traditionally, effective communication spurs business development that contributes to gross output and economic development of a society. In achieving effective business communication, technology plays pivotal function in that regard, which is known as information and communication technology (ICT). Among components of ICT, telecommunication has been the driving force of technology spread mostly in developing countries including Africa. As the economic activities evolve, level of technology advances to accommodate sustainable economic growth and development. This suggests interdependence of relationships between technology (telecommunication), economic growth and development. In recent times, the movement of investments (domestic and foreign) towards telecommunications development in Africa has occurred, mostly in terms of mobile telecommunications. However, there are daily challenges facing the industry, such as frequent network problems, high costs of calls and internet service charges and poor penetration of fixedlines. The telecommunications operators attribute poor and costly services to a hostile business environment created by institutional factors such as unfavourable government policies and inadequate social infrastructures (energy, security, water etc.) in Africa. In Africa, the network architecture is different, and the integration of networks is less advanced, with networks having traditionally been built as stand-alone, end-to-end networks. Mobile networks are more likely to be entirely wireless, rather than being fiber-optic at their core, and fixed wireless technologies are often used to provide the last-mile links to businesses and homes in place of copper. These technology-based distinctions are starting to become obsolete. Fixed wireless networks are becoming mobile, wireless networks are being upgraded to fiber, and networks that were once used to provide voice services are increasingly being used to provide a full range of ICT services. For now, however, traditional concepts of fixed and mobile, and wireline and wireless, remain useful from an analytical point of view, and are used throughout this study. Studies on telecommunications infrastructures, economic growth and development show that there is a significant impact of telecommunication infrastructures (investment) on economic growth and development (Castells, 1999; Kim, Lestage, Flacher, Kim, & Kim, 2013; Mansell & Wehn, 1998; Nasab & Aghaei, 2009; Nulens & Van-Audenhove, 1998; Osotimehin, Akinkoye, & Olasanmi, 2010; Pradhan, Arvin, & Hall, 2016; Shiu & Lam, 2008; Zahra, Azim, & Mahmood, 2008). Most of these studies, however, were conducted in developed countries. Only a few studies were carried out in developing countries, including African economies. These studies either employed a time series or cross-sectional an alysis, while some attempted to do a panel data analysis, but only considered either economic growth or economic development as a standalone measure for economic prosperity. Investment in telecommunication is mostly used as a measure for telecommunication development and this is supply side approach to telecommunication operation that may not be sufficient to spur growth and development. This study validates the propositions of the past studies on telecommunication infrastructures, with economic growth and development been dealt with separately. The study

1 https://www.globalspec.com/learnmore/engineering_services/telecommunications_infrastructure_services/telecommunications_infrastructure_ services. 2 https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.

2

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captures telecommunication infrastructures through a composite index of telecommunications, which is measured by the market penetration of fixed telephone lines, mobile telephone lines and internet access in Africa and this is the demand side approach to telecommunication infrastructures in which it spillover effects assumed to spread faster. This study investigates the existence of short - and long-run equilibrium, and direction of causality between telecommunication infrastructures, economic growth and development in Africa. Thus, a panel-vector autoregressive (PVAR) techniques are used to show the relationships that exist between telecommunication infrastructures, economic growth and development in selected African countries for the period 2000 to 2015. Section 2 reviews literature on telecommunication infrastructures, economic growth and development from three. Section 3 presents methodology employed to evaluate the relationship between telecommunication infrastructures, economic growth and development. Section 4 presents data and empirical results of the PVAR technique. Section 5 consists of the conclusion and policy recommendations for the study. 2. Empirical literature review The growing literature on the connection between telecommunications, economic growth and development recognizes the positive link between mainline teledensity and economic growth, mainly in the long run. Previous studies like Hardy (1980), Leff (1984), Norton (1992), Lichtenberg (1995), Roller and Waverman (1996), Datta and Agarwal (2004), Shiu and Lam (2008), Zahra et al. (2008), Nasab and Aghaei (2009), Osotimehin et al. (2010), Kim et al. (2013), Pradhan, Arvin, Norman, and Bele (2014) and Pradhan et al. (2016) contribute to this end by quantifying the positive spillover effect of telecommunications infrastructures on economic growth and development. However, this study evaluates the literature on nexus of telecommunication, economic growth and development on the global, regional and country-specific level in order to capture the empirical literature from diverse or mixed cultural background. The global studies consider panel analysis of similar countries in term of development or cultural features. But, regional studies take geographical bloc into consideration of the panel analysis while country specific is strictly a time-series studies. 2.1. Global studies Norton (1992) empirically investigated the role of telecommunication infrastructure on economic development for the period from 1957 to 1977. The study used the data of 47 countries and concluded that there is positive and significant impact of telecommunication infrastructure on economic development. The study further argued that telecommunication infrastructure reduces the transaction costs since “smart production” as a result of technology adoption reduces production cost and thereby improves aggregate economic growth and development. Dholakia and Harlam (1994) showed the relationship between investment in telephone infrastructure and economic growth by examining the connection among a number of factors such as education, energy, telephone, other physical infrastructure and economic growth. The result of their multiple regressions suggest that simultaneous investment in education, telecommunications and other physical infrastructure are complementary in helping to promote economic development. Canning and Pedroni (1999) conducted Granger causality test between investments in three types of economic infrastructure i.e., kilometres of paved road, kilowatts of electricity generating capacity, and number of telephones based on data from a panel of 67 countries for the period 1960–1990. They found strong evidence in favour of causality running in both directions between each of the three infrastructure variables and GDP among a significant number of the countries investigated. Literature on investment in telecommunications infrastructure, economic growth and development also exists and identifies telecommunications service availability as a crucial element in the accumulation of factors boosting economic growth and development at both the regional and sectoral specific level (Cohen, 1992; Cronin, Parker, Colleran, & Gold, 1991; Datta & Agarwal, 2004; Hardy, 1980; Yilmaz, Haynes, & Dinc, 2001). However, as Ding and Haynes (2006) summarize, most of the existing literature suffer the endogeneity problem of the telecommunications variable, ignore the rapid development in mobile communications and lack consideration of the spatial dependence problem. Spatial implications are inherent in the development of telecommunications infrastructure due to the significant impact of telecommunications service availability on interregional economic activities. New “network neighborhoods” may possibly form with advancements in telecommunications technologies and the relatively diminishing advantages of some forms of geographic proximity. This geographical imbalance should be even more important in analyses conducted upon developing and newly integrating economies like China and India, which exhibit dramatic location-sensitive differences in terms of regional development. Theoretically, better availability of traditional telecommunications infrastructure and more advanced telecommunication technologies liberate economic activities from geographical limitations, and allow them to decentralize from the core to the periphery while maintaining necessary connections (Abler, 1970; Stough & Paelinck, 1998). The regional disparity of telecommunications infrastructure endowments has been studied extensively. The imbalanced telecommunications technologies penetration among populations and regions in developed economies has been examined by studies focusing on the topics of “universal service” and the “digital divide” (Dinc, Haynes, Stough, & Yilmaz, 1998; Duwadi, 2003; Norris, 2000). Wilson, Mann, and Otsuki (2005), when extending the gravity model to trade facilitation measures and to a larger sample of 75 economies, posited that port efficiency and the proxies for infrastructure quality for the services sector, such as the use, speed, and cost of the internet, significantly affected trade flows. Wilson et al. (2005) also found that improving port and airport efficiency could positively impact intra- Asia-Pacific Economic Cooperation (APEC) trade. Zahra et al. (2008) show that telecommunication can actively participate in the growth of an economy. In most of developing countries, the telecommunication sector is facing; low teledensity especially in rural areas, low standard of services and shortage of 3

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quality human resource in information and communication technology (ICT) sector. The study employed convergence hypothesis Granger Causality and autoregressive model for world countries categorised into income groups: low, medium and high. Portugal-Perez and Wilson (2012) assessed the impact of four indicators related to trade facilitation—physical infrastructure, ICT, border and transport efficiency, and the business and regulatory environment—on the export performance of 101 developing economies. The study used factor analysis to derive the aggregate indicator for trade. Accordingly, physical infrastructure was found to have the greatest impact on exports. Investment in telecommunication is linked with liberalisation and drive for stronger competitions by state or privately owned firms (Kim et al., 2013). There are stronger hypotheses in support of the effect of liberalisation of the telecommunication sector on investment in telecommunication (Kim et al., 2013). Pradhan et al. (2014) depict that development in telecommunication infrastructure, economic growth and development are significantly related in G-20 countries for the period of 1981–2012. The study reveals that there is bidirectional relationship between development in telecommunication infrastructure and economic growth in G-20 countries and panel vector autoregressive model is employed to detect the Granger causality. It is established in the study that in the long run, there is bidirectional causality between the development in telecommunication infrastructure and economic growth in both developing and developed countries of the G-20. 2.2. Regional studies Madden and Savage (1998) examine a sample of 27 Central and Eastern European (CEE) countries over the period 1990–1995 and find a positive relationship between investment in telecommunication infrastructure and economic growth. Alleman et al. (2004), opine that Southern African Development Countries (SADC) and the Republic of South Africa (RSA) are among the least developed countries, both economically and in their use of telecommunications. A wide range of studies indicate that expanded telecommunications investment is essential, not only for growth, but also to remain competitive within the increasingly information-oriented global economy. Failure to develop telecommunications systems will only increase the development gap between the SADC and RSA and the industrial countries. The study employed country based analysis of ordinary least square technique for SADC member countries (Botswana, Lesotho, Malawi, Mozambique, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe) to evaluate the importance of telecommunication on economic development. Aker and Mbiti (2010), shows the rate at which telecommunication services have grown in Africa through descriptive analysis. Mobile phone coverage in Africa has grown at staggering rates over the past decade. In 1999, only 10 percent of the African population had mobile phone coverage, primarily in North Africa (Algeria, Egypt, Libya, Morocco, and Tunisia) and South Africa. By 2008, 60 percent of the population (477 million people) could get a signal, and an area of 11.2 million square kilometers had mobile phone coverage—equivalent to the United States and Argentina combined. The study predicted that by 2012, most villages in Africa will have coverage, with only a handful of countries—Guinea Bissau, Ethiopia, Mali, and Somalia—relatively unconnected. Pradhan et al. (2016), evaluate the causal relationship between index of telecommunication development, financial development and economic growth in 21 Asian countries from 1991 to 2012. The study used PVAR to detect the direction of causality, establishes short and long run equilibrium. The causality test reveals that there is Granger-causality in both the short and long run in the Asian countries studied, though exact nature of causalities differs by country in Asian regions. 2.3. Country-specific studies A study was conducted in South Africa by Perkins, Fedderke, and Luiz (2005), Using Pesaran, Shin and Smith's (1996, 2001) Ftests, these authors identified bi-directions of association between economic infrastructure (ICT, water and sanitation, electricity and transportation) and economic growth. They identified long-run forcing relationships from public-sector economic infrastructure investment and fixed capital stock to gross domestic product (GDP), from transportation to GDP and from GDP to a range of other types of infrastructure. They also found that the relationship between economic infrastructure and economic growth run in both directions. Correa (2006), using a novel methodology comprising of econometric modeling and input–output economics, the extent to which telecommunications has contributed to national and sectoral productivity performance is examined. The main findings from this study suggest that most industries have benefited from the incorporation of advances of telecommunications technology, which might have, amongst other things, emanated from encouraging infrastructure investment, in their production processes. The results suggest that telecommunications productivity, over a 34 year period, has outpaced the economy-wide productivity level. Furthermore, the study found that telecommunications was a strong contributor to the performance of the economic system as a whole. This coupled with the telecommunications productivity rate figures suggests that not only has telecommunications contributed its share of total output more efficiently, but it has also contributed to overall economy-wide productivity growth via its influence on other industries. Sridhar (2009) indicates that competition and network effects are the significant factors that positively affect growth of mobile services in India. The traditional factors such as income, population and fixed line penetration do not have any significant impact on the adoption of mobile services after using diffusion technology framework. The study employed panel data analysis spanning 1997–2007 for 23 regions in India. It is widely depicted that telecommunication infrastructure plays a positive and significant role in economic growth and development and also promotes expansion in economic activities in Nigeria (Chiemeke & Longe, 2007; Osotimehin et al., 2010; Posu, 2006). A wide range of studies have indicated that expanded telecommunication investment is essential but not the only determinant of economic growth and development. Infrastructure investments to address these gaps have the potential to alleviate the poverty of 4

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many through the jobs it can create. However, few studies investigated the causal relationship between infrastructure (telecommunication, transport and energy), economic growth, development and the directions of the causality using Granger-causality test. Thus, this study streamlines the infrastructures connection to economic growth and development to telecommunication services in which larger chunk of literature in this study are related to telecommunication infrastructure. 2.4. Literature review summary In summary, studies at global level on telecommunication-growth-development nexus using OECD countries found a bidirectional relationship, while at regional and country-specific levels, mix results exist in term of causality between telecommunication infrastructure, economic growth and development. Thus, from empirical literature, most of the studies on telecommunication infrastructures, economic growth and development were carried out in developed economies where fairly perfect market exist and institutional bottle necks are limited like Europe, North America and part of South East Asia. But, less studies on telecommunication infrastructures, economic growth and development are conducted in underdeveloped economies where there exists high degree of market imperfection due to institutional imbalances. Though, there are some scanty studies on telecommunication infrastructures, economic growth and development in Africa but evaluation and assessment were done on single country (time series) or crosscountries (cross section) analysis. This study bridges the gap between the existing single country (time series) analysis and crosscountries (cross section) analysis by employing panel framework for analysing the causal-relationships and spillover effects of telecommunication infrastructures, economic growth and development on each other in Africa for 16 years (2000–2015). The study measures the telecommunication infrastructures with the spread and penetration of telecommunication use in Africa which considers both the aggregate demand and aggregate supply sides of telecommunication operations but previous studies only consider the supply side through investment. 3. Methodology 3.1. Theoretical framework The studies on the connection of technology (innovation), economic growth and development process dated back to the neoclassical growth theory and became relatively more pronounced, if not explicit, in the modified economic growth and development models and recent studies (Solow & Swan, 1956; Solow, 1964; Romer, 1986; Katz, 2009; Minghetti & Buhalis, 2010). Based on the theory of innovation, emphasis is laid more on technology and new idea in the process of economic growth and development. The importance of innovation, technological and technical progress is well defined and employed to fine-tune stages of socioeconomic prosperity of a nation (Schumpeter, 1942). The pivotal role of technological and technical improvements to nation's economic growth and development is further stressed in the exogenous and endogenous growth models from two convergent distinct angles. The models opined that it is not only labour and capital stock that pathway to economic progress of a nation as argued in the Cobb-Douglas production function but technologies also play a key role to socioeconomic prosperity of a nation (Romer, 1986; Solow, 1964). In order to establish the empirical relationship between telecommunication infrastructures, economic growth and development in Africa, this study built its model on Solow (1956) model using neoclassical approach. Thus, the study anchored this relationship on endogenous growth theory in line with the work Solow and Swan (1956) modeled and later expanded by Romer (1986) by inculcating technical progress which denotes importance of research and development (R&D) in the transition of economy. The model is based on the following assumptions: i. The factors of production; capital and labour, can be substituted for each other; ii. The function exhibits diminishing marginal product to each factor of production, and iii. The production function is homogenous of the first degree. Thus, as long as factors can be substituted freely for one another, the capital-labour ratio will not be constant in the Cobb-Douglas production function. This is clearly proven through the Cobb-Douglas production function: (1)

If Q = AK L1

The theory opined that economic growth is embedded in long-run technical change in gross output, so, capital and labour factors are not sufficient to explain the long run technical change in output. The technical change in output also depends on innovation and technological development. The Cobb-Douglas production function provides framework for determining the contributions to the growth rate in output of technological change. The production function in equation (1) can be decomposed into rate of change by transforming into logarithmic function as:

ln Q = ln A +

ln K + (1

(2)

)ln L

Taking rate of change in output to change in time to determine the economic growth with respect to time (i.e. differentiate with respect to time). Therefore, 5

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Q=A+

K + (1

(3)

)L

From (3), the rate of change in the variables are derived, thus, making A the subject results to technological change

A = Q

K

(1

(4)

)L

The basic assumptions of the conventional models are constant returns to scale, diminishing marginal productivity of capital, exogenously determined by technological progress, and substitutability between capital and labour thus emphasizing the role of savings or investment ratio as crucial driver of short-run economic growth. Technological progress is considered as long-run phenomenon and exogenously determined. However, in the modified Solow model (Lucas, 1988; Romer, 1986), technological progress under the assumption of increasing returns to scale is broadly defined as new knowledge (Grossman & Helpman, 1994; Romer, 1990), innovation (Aghion & Howitt, 1992), public infrastructure (Barro, 1990), among other things (Kumar, 2014; Kumar & Kumar, 2012; Rao, 2010), and are treated as endogenous in the growth model. Notably, the effect of technology is magnified when the latter include technology that supports communication, enhances productivity and improves the wellbeing of the society (Cronin, Colleran, Herbet, & Lewitzky, 1993; Datta & Agarwal, 2004; Kumar et al., 2015; Lam & Shiu, 2010; Shahiduzzaman & Alam, 2014). In this regard, development in technology is expected to lower the cost of production, streamline product distribution chain, provides access to efficient information in decision making and provides perfect market information to the (Buhalis & Law, 2008; Porter, 2001a, 2001b). 3.2. Linking theoretical and empirical frameworks This study draws its philosophy from the work of Solow (1956), Mankiw, Romer and Weil (M-R-W) (1992), Pradhan et al. (2014) and Pradhan et al. (2016) for cross country evaluation of technological progress effects on socioeconomic status of nations. In order to ascertain the cross-country implications of Solow (1964) growth theory proposition, this study adopt with modification the analysis of Mankiw, Romer, and Weil (1992) and later expanded by Pradhan et al. (2016) that aggregate output in country i at time t, Qit, is connected to technology - telecommunication through Cobb-Douglas production function. The aggregate output in country i at time t, Qit is a function of capital input (physical, Kit and human, Hit), man hour input, Lit and level of technology at time t, At. This formed as:

Qit = K it i Hit j (At Lit )1

i

(5)

j

equation (5) is developed on the basis of the following assumptions: 1. 2. 3. 4.

Factor inputs and aggregate output are assumed to be continuous in time. There is constant rate of growth in technology level and man hour, g and n respectively. Each of the countries augment its physical and human capital stock at the constant savings rate Sik and Sih . Both physical and human capital stocks depreciate at the same rate, . These assumptions (i.e. 3 and 4) induced capital accumulation equations as:

dKit = Sik Qit dt

Kit

dHit = Sih Qit dt

Hit

(6) (7)

Thus, over any interval T to T+1, output per employed input say man hour

( )

In Q L

iT +

( )

In Q L

iT

= g + (1

e

)

( ) follows: Q

L

it

(8)

Therefore, equation (8) shows the change in the ratio of aggregate output to inputs across countries and time. From equation (5), this study's model is deduced on the basis that it is an expansion of the Solow and Swan (1956) growth model by making capital stock and labour constant in order to capture the exact direction of causality that exist between telecommunication infrastructures, economic growth and development in Africa. Therefore, the relationship between telecommunication infrastructures, economic growth and development is evaluated individually, to ascertain technological progress account for change in economic development as they does in economic growth based on the proposition of the economic a priori. 3.3. Models specification This study adopted Pradhan et al. (2016) models with modification. The study employed estimation techniques follow the order of panel unit root test, panel cointegration test, panel causality test and bivariate regression if feedback causality exist. The panel unit root test establishes the level of stationarity of the series of the data, the cointegration test detect whether there exist a long-run relationship, and Granger-causality test is employed to establish the direction of causality that exist between telecommunication 6

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infrastructures, economic growth and development in the selected African countries. 3.3.1. Panel data unit root test The study conducted the Augmented Dickey-Fuller (ADF) test using the Levine-Lin-Chu (LLC) method (Levine, Lin, & Chu, 2002) and the Im-Pesaran-Shin (IPS) method (Im, Pesaran, & Shin, 2003) to check the order of integration and ascertained stationarity level of each of the series and compared the outcomes with Fisher-ADF, Fisher-PP and Breitung techniques of unit root tests. The LLC method explores the heterogeneity of intercepts, while the IPS method explores the heterogeneity in the intercepts and slopes coefficients of the panel. The test follows the estimation using equation (8)

Yt =

i

+

i Yit 1

pi

+

ij

Yit

j

+

it

+

it

(9)

j=1

where i = 1, 2 … N; t = 1, 2 … T; Δ = first difference operator; Yit is endogenous variables included in the system (i.e. the series for country i in the panel over period t); pi is the number of lags selected for the ADF regression and εit is the normally distributed random error for all i and t. 3.3.2. Panel data cointegration test The determination of long-run relationship between variables show the time of causal-effect adjustments of related variables (Engle & Granger, 1987). If the series are non stationary at levels i.e. integration of ‘order zero’, it is suggested to take the difference of the series and if stationary at integration of ‘order one’, thus the series are cointegrated at ‘order one’. Assuming there is unidirectional relationship between telecommunication operation, economic growth and development based on outcomes of previous studies; that telecommunication infrastructures granger causes economic growth or development. This study employed combined Johansen-Fisher panel cointegration technique to determine the long run equilibrium in economic growth and development models in relation to telecommunication operation. The combined Johansen-Fisher system procedure for long run equilibrium for the series follows the panel vector autoregressive (PVAR) system equations. The system equations are: k

Yit =

Yit

it

j

+

it

it

=

(10)

i=1

where:

HDIit Yit = GDPPCit , CITit

11it

12it

13it

21it

22it

23it

31it

32it

33it

and

it

1it

=

2it

(11)

3it

The reduced form of (18) as set out by Enders (2015) are: (12)

Yit = [HDIit , GDPPCit , CITit ]

Where HDI is human development index, proxy for economic development, GDPPC is real gross domestic product, proxy for economic growth in selected African countries, CIT is composite index of telecommunication measuring telecommunication infrastructures. 3.3.3. Panel data causality test This study follows the pairwise Dumitrescu-Hurlin panel causality tests technique to establish the direction of causality between telecommunication infrastructures, economic growth and development in the selected African countries (Dumitrescu & Hurlin, 2012). The pairwise Dumitrescu-Hurlin tests is dynamic panel test which is more robust and efficient in terms of estimation. The following equations are used:

HDIit =

1j

p1

+

k=1

CITit =

2j

+

3j

+

k

k=1

HDIit

k

k=1

p1

HDIit 3ik

GDPPCit

k

+

k

+

GDPPCit

k

+

HDIit

p2

CITit 3ik

k

+

k

+

1it

(13)

2it

(14)

1ik

p3

CITit

k=1

2ik

k=1

p3 k=1

1ik

p2

+

2ik

k=1

p2

+

1ik

p1 k=1

GDPPCit =

CITit

k

+

2ik

p3

GDPPCit

k=1

3ik

k

+

3it

(15)

where Δ is first difference operator; p1, p2, and p3 are lag lengths; i represents country in the panel (i = 1, 2, … N); t is the year in the panel (t = 1, 2, … T); ξit and it denote normally distributed stochastic term for all country i and at time t. 4. Data and measurements The study considers time series data of 46 African countries from 2000 to 2015 for analysis. The choice of data period is informed 7

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by data availability for telecommunication infrastructures. The data of this study are primarily sourced from international organisations (United Nations, World Bank and International Telecommunication Union) that archived countries' raw facts on social and economic indicators. There are few missing data at some point in the data set, but this is corrected through projection by linear trend extrapolation of matching known data points for least squares method and moving average interpolation procedure for missing data in between two data points. The dataset of human development index (economic development), real GDP (economic growth) and telecommunication infrastructures (composite index of telecommunication). 4.1. Measuring telecommunications Studies on telecommunications tend to evaluate the performance of the telecommunication infrastructures indices based on the following indicators; teledensity, connection capacity of local exchange, internet access ratio, total real investment in telecommunication, total telecommunication revenue (fixed and mobile), value added by telecommunication services and total telecommunication employment (Nasab & Aghaei, 2009; Osotimehin et al., 2010; Pradhan et al., 2016). Thus, this study measures Telecommunication infrastructures in Africa by composite index of telecommunication (CIT) which comprises of mobile lines, fixed lines and access to internet to capture the holistic activities of the telecommunications services as ensured by adequate infrastructures and infrastructure. In the process of determining the indices for composite index of telecommunication, the Principal Component Analysis (PCA) is employed to derive the principal index of telecommunication from connected mobile lines, connected fixed lines and access to internet due to correlation that may exist between telecommunication indicators. The PCA is used to achieve single index for connected mobile lines, connected fixed lines and access to internet. Therefore, the first step in constructing the composite index of telecommunication (CIT) is to take the residuals from the regression of a particular composite index of telecommunication. Estimates from the linear regression are satisfactory and similar to that found by (Pradhan et al., 2016). The residual series derived from each regression is aggregated using principal component analysis (PCA). The PCA is a process of taking high dimension sets of indicators and transforming them into new indices that capture information on a different dimension and are mutually uncorrelated (Akanbi, 2014). To derive an aggregated index for infrastructure stocks, the first eigenvectors (loading matrix) from the PCA are used as the required weights and therefore the following linear combination exists:

CIT =

1 mob_line

+

2 fixed_line

+

(16)

3 internet_access

where α1, α2, and α3 are the eigenvectors (weights) from the PCA, and mob_line, fixed_line and internet_access are the three synthetic composite index of telecommunication. 4.2. Measuring economic development The measure of a country's development is one of the most critical and highly debated issues in economic research. Different approaches have been applied and numerous indicators have been employed but the most common ranking of countries is done according to their aggregate output. Nevertheless, due to the fact that this method is unable to capture real inequalities among countries in terms of the different and sometimes contrasting dimensions of the social welfare of their populations (Cracolici, Cuffaro, & Nijkamp, 2010), it is only a partial measure of socio-economic development at best. Furthermore, small and medium enterprises are one of the main factors for national economic development, especially in developing countries where transitional processes are ever the more common (Gveroski, Risteska, & Dimeski, 2011). However, development is much more than economic growth; therefore, non-economic factors must be included in the analysis of a country's welfare. One potential improvement is the human development index (HDI) due to its simplicity. The HDI has been both remarkably successful and much criticized. The actual problem facing the index is its small number of variables (merely three) and the strong correlation among them. Therefore, meaningful inferences about the development of countries are hardly able to be drawn from the variations of this index (Neumayer, 2001). The HDI has been described as “yet another redundant composite development indicator” (McGillivray, 1991) and “conceptually weak and empirically unsound” (Srinivasan, 1994). Attempts at improvement of the HDI have also been made, based on increasing the number of its variables; therein, the 2010 Human Development Report (HDR) introduced several changes in the HDI. Life expectancy remains the indicator used for health, while Gross National Income has replaced GDP as the measure used for living standards. The mean number of years of schooling and expected years of schooling now make up the dimension used for education. Furthermore, these four indicators represent the most basic elements of human development. Thus, empirical studies show that economic development is a multidimensional indicator that reflects the qualitative expansion of countries. There are several indicators of economic development among which are; gross national income per capita, income inequality (poverty rate), level of food security, access to adequate health facilities and literacy level. These indicators are compressed by Haq (1995) to Human Development Index (HDI). The Human Development Index (HDI) measures the average attainment in key dimensions of social and economic development: long and healthy life, being knowledgeable and have a decent standard of living. It is the geometric mean of normalised indices for each of the three dimensions (Haq, 1995; Sen, 1999; United Nations Development Programme [UNDP], 2010). This study used the HDI as an indicator for economic development since it includes both the social and economic dimension of countries. It ranges between 0 and 1, the closer the value to 1, the less poverty ridden such economy and the more developed the economy becomes. Therefore, the closer the value of HDI to 0, the more underdeveloped the economy becomes. 8

Indicator

human development index real gross domestic product connected mobile lines connected fixed lines access to internet bandwidth composite index of telecommunication

Variable

HDI GDPPC mob_line fixed_line internet_access CIT

Table 1 Summary of dataset.

proxy for economic development GDP at market prices (constant 2010 US$) as a proxy for economic growth penetration of connected mobile lines penetration of connected fixed lines percentage of population with access to the internet principal component value of penetration of connected mobile lines, penetration of connected fixed lines and percentage of population with access to the internet as a proxy for telecommunication operation

Variable description Index Billion dollar Percent Percent Percent index

Unit of measurement

United Nations, 2016 World Bank, 2016 International Telecommunication Union (2016) International Telecommunication Union (2016) International Telecommunication Union (2016) Author's computation, 2018 (based on data collected from ITU database, 2016)

Source of data

O.O. David

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Table 2 Selected African countries. 1. Algeria 2. Angola 3. Benin 4. Botswana 5. Burkina Faso 6. Burundi 7. Cameroon 8. Central African Rep. 9. Chad 10. Congo (Dem. Rep.) 11. Congo (Rep.) 12. Côte d'Ivoire

13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.

Djibouti Egypt Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Kenya Lesotho Liberia

25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.

Libya Madagascar Malawi Mali Mauritius Morocco Mozambique Namibia Niger Nigeria Rwanda Senegal

37. 38. 39. 40. 41. 42. 43. 42. 45. 46.

Sierra Leone South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Zimbabwe

4.3. Measuring economic growth The economic barometer of a country lies in the strength of its gross domestic product (GDP). The gross domestic product, is the market value of all final goods and services produced in a country in a given time period usually one calendar year. Economists use many different methods to measure how fast the economy is growing. Barro and Sala-i-Martin (1995) measures economic growth through rate of real per capita GDP with the perception of inclusive growth that better maintenance of the rule of law, smaller government consumption, longer life expectancy, more male secondary schooling and higher levels of schooling, lower fertility rates, and improvements in the terms of trade are the determinants of economic growth. This approach is comprehensive and sometimes cumbersome for most especially developing countries due to inclusion of social indicators in the determinants of economic growth which defines the inclusiveness of the economic growth. The most common way to measure the economy is real gross domestic product, or real GDP. This includes the value of the total output with inclusion of the change in price level. The real GDP approach forms the rationale for this study due to its simplicity in the application. Thus, the GDP at market prices (constant 2010 US$) from the World Bank indicators is used in this study. Therefore, summary of the dataset employed in this research work, unit of measurements, description and sources of the data are presented in Table 1. Table 2 shows the African countries selected for this research work with five (5) non sub-Saharan African countries and forty one (41) sub-Sahara African countries making a total of 46 African countries. The non-sub-Saharan African countries are important in the study due to high level of telecommunication development in the countries before 2000 and it has spillover effects on sub-Saharan African countries. The selected African countries are chosen on the basis of availability of data and the countries are clearly specified in Table 2. 5. Empirical results and analysis 5.1. Panel data stationarity results analysis Table 3 shows the unit root test results of the panel series for this study. The study employed Levin, Lin and Chu (LLC), Im, Pesaran and Shin (IPS), Fisher-ADF, Fisher-PP and Breitung stationarity test techniques to establish existence of unit root in the panel series. The tests were conducted at none, only drift and drift and trend at levels for economic development (HDI), economic growth (GDPPC) and composite index of telecommunication (CIT). The empirical results of stationarity tests which has null hypothesis as unit root, reveal that most of the series are not stationary at levels due to the null hypotheses not been rejected. This implies that most of the panel series; economic development, economic growth and composite index of telecommunication have presence of unit roots at levels. The non-stationary panel series are mostly revealed when the estimations are conducted with constant and trend. This suggested that there is need to conduct panel cointegration tests to establish the existence of long run equilibrium in the series. 5.2. Panel cointegration results analysis Based on the model specification of the cointegration tests for lon grun equilibrium in model specification section, this study conducted combined Johansen-Fisher cointegration test in which the null hypothesis is no cointegration. The estimation follows vector autoregressive (VAR) process for the combination of the panel series using Fisher-Trace and Fisher-Maximum eigenvalue tests. The empirical results revealed that at least two of the cointegrating vectors of telecommunication infrastructures (composite index of telecommunication), economic growth (real gross domestic product) and development (human development index) have presence of panel cointegration. It is clear that there is long run equilibrium between telecommunication infrastructures, economic growth and economic development in Africa. This implies that there is existence of long run relationships between the combined panel series of telecommunication infrastructures, economic growth and economic development. 10

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5.3. Panel causality results analysis Since the cointegration test identified the existence of long run relationship among the panel series but magnitude of relationship was not revealed. Thus, this study conducted bivariate panel causality tests using Dumitrescu–Hurlin process. The study simplified and reduced the panel causality test to the three key panel series of the study; telecommunication infrastructures (composite index of telecommunication), economic growth (real gross domestic product) and development (human development index) for easier trace of bivariate relationships between them. The null hypotheses are “not homogenously cause” and if otherwise, the study rejected the null hypotheses. The empirical results from table 5 show that all the panel series rejected the null hypotheses at 1 percent significance level and there is higher bi-directional relationships between economic development, economic growth and telecommunication infrastructures in Africa. These results implied that there are feedback causal relationship between telecommunication infrastructures, economic growth and development in Africa. 5.4. Long-run estimates analysis To further substantiate these higher bi-directional relationships between economic development, economic growth and telecommunication infrastructures (composite index of telecommunication) and the study conducted long run bivariate regression analysis using fixed effect methods after controlling for endogeneity and the results are shown in table 6. The trivariate regression estimates for economic development, economic growth and telecommunication infrastructures are directly related to each other at all levels in the three equations. The empirical result for the economic development (HDI) equation revealed that economic growth and composite index of telecommunication are positively significant to economic development (HDI) in Africa at 1 percent significance level. The implication of these results are; if economic growth and composite index of telecommunication increase by 1 percent, economic development (HDI) will rise by 0.113 percent due to economic growth increase by 1 percent and 0.062 percent in relative to composite index of telecommunication increase by 1 percent. The policy implications of these results are; to improve economic development by 0.062 percent, composite index of telecommunication must increase by 1 percent. The economic growth need to be adjusted by 1 percent if economic development is targeted to increase by 0.113 percent in Africa. The economic growth equation shows that economic development and composite index of telecommunication are positively significant at 1 percent significance level to economic growth in Africa. The policy implication of these results are; if economic development and composite index of telecommunication rise by 1 percent, economic growth will increase by 0.103 percent and 0.167 percent respectively in Africa. The telecommunication infrastructures equation as proxied by composite index of telecommunication shows that economic development and economic growth are positively significant to composite index of telecommunication in Africa. The magnitude of causation shows that if economic development and economic growth increase by 1 percent, composite index of telecommunication will move in the same direction by 0.179 percent and 0.525 percent respectively in Africa. These empirical results of trivariate regression further validate the outcome of the Dumitrescu-Hurlin homogenous causality tests that there is meaningful statistical feedback relationships between telecommunication infrastructures, economic growth and development in Africa. 5.5. Variance decomposition analysis The magnitude of causation is further substantiated by variance decomposition and impulse response analysis of unrestricted VAR estimation process using orthogonalised Cholesky ordering technique. table 7 shows the variance decomposition of economic development, economic growth and composite index of telecommunication for 16 periods in which one fourth of the periods (i.e. period 4) is assumed to be the short run and period 16 is the long run. In panel A of table 7, the response of economic development to shocks in itself shows that at period 4, in the short run, own shocks will cause 95.212 percent fluctuations but 94.764 percent fluctuations in the long run to economic development in Africa. In the short run, shocks in economic growth causes 0.634 percent fluctuations to economic development while in the long run, shocks in economic growth causes 0.679 percent variations in economic development. In the short run, shocks in composite index of telecommunication contributes 4.154 percent fluctuations to economic development, but in the long run, innovations in composite index of telecommunication contributes 4.557 percent variations in economic development in Africa. In panel B, own shocks of economic growth accounted for 92.788 percent fluctuations in economic growth in the short run but causes 92.687 percent variations in the long run. Shocks in economic development cause 3.121 percent variations in economic growth in the short run but in long run causes 3.122 percent fluctuations in economic growth. Innovations in composite index of telecommunication cause 4.090 percent fluctuations in economic growth in the short run but cause 4.191 percent fluctuations in economic growth in the long run. Panel C of table 7 shows the response of composite index of telecommunication to own shocks and shocks in economic development and growth in Africa. The empirical results identified that own shocks of composite index of telecommunication causes 94.293 percent variations to composite index of telecommunication in the short run but 94.242 percent fluctuations in the long run. The shocks in economic development in the short run cause 1.505 percent fluctuations to composite index of telecommunication but 1.521 percent in the long run. Shocks in economic growth causes 4.202 percent variations to composite index of telecommunication in the short run but accounted for 4.237 percent fluctuations in composite index of telecommunication in the long run in Africa. The shocks-fluctuation/variations in these panel series form the impulse response analysis. 11

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(b): Response of LN_HDI to LN_GDPPC

(a): Response of LN_HDI to LN_HDI

(c): Response of LN_HDI to LN_CIT

.0012 .0025

.0010

.015

.010

.0008

.0020

.0006

.0015

.0004

.0010

.0002

.0005

.005

.0000 2

4

6

8

10

12

14

2

16

(d): Response of LN_GDPPC to LN_HDI

4

6

8

10

12

14

16

.0000 2

(e): Response of LN_GDPPC to LN_GDPPC

.024

4

6

8

10

12

14

16

(f): Response of LN_GDPPC to LN_CIT .024

.125

.020

.020 .100

.016

.016 .075

.012

.012

.050

.008

.008

.025

.004

.004 .000

2

4

6

8

10

12

14

16

2

(g): Response of LN_CIT to LN_HDI

4

6

8

10

12

14

16

2

(h): Response of LN_CIT to LN_GDPPC

.016

6

8

10

12

14

16

(i): Response of LN_CIT to LN_CIT .16

.024 .020

.012

4

.12

.016 .008

.08

.012 .008

.004

.04

.004

2

4

6

8

10

12

14

16

2

4

6

8

10

12

14

16

2

4

6

8

10

12

14

16

Fig. 1. Impulse response (line graphs) Source: Author's calculations and analysis of data, 2017.

5.6. Impulse response analysis The impulse response measures the unit shock applied to each series and its effect on the VAR system. This identifies the degree of reaction of the endogenous variables in the VAR system to shocks/innovations (i.e. stochastic components). The essence of this is to detect time path of various shocks and how VAR system reacted to the shocks. table 8 and Fig. 1 show the reactions of the VAR system to standard deviation shocks and innovations in this study. Pane A of table 8 revealed that economic development reacted to own one standard shock positively both in the short run and long run (i.e. all through to the 16 periods), though declined steadily from period 1 to period 16 but positive all through as shown in panel A of Fig. 1 and fig1. The economic development reacted positively to one standard deviation shock in economic growth from period 1 to period 16 and reaches peak at period 2 which is the short run period. One standard deviation shock in composite index of telecommunication causes positive reactions to economic development in the short run and long run. The panel B of table 8 shows the reaction of economic growth in Africa to one standard deviation shock in own shocks, economic development and composite index of telecommunication from 2000 to 2015. The empirical finding revealed that economic growth reacted positively to one standard deviation shock in own shock in both the short run and long run period. The one standard shock in economic development in the short run and long run causes positive reactions to economic growth and also reacted positively to composite index of telecommunication in the short run and long run as shown in panel e and panel f of Fig. 1. 12

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Panel C of table 8 shows that composite index of telecommunication reacted to one standard deviation shock in own shock, economic development and economic growth positively from 2000 to 2015 in Africa. 6. Conclusion and policy recommendations Trivariate framework of growth-development-technology (telecommunication) nexus argument is inconclusive due to mix empirical results as period (time) and place (region) are primal to possible outcomes of the causality existence. Existing literature conclude that technology (telecommunication)-growth-development led long run causality dominates their findings. This study further contributes to the argument by considering forty six African countries as study region for sixteen years. The empirical results of the nexus between telecommunication infrastructures, economic growth and development using cointegration and causality analysis for African countries from 2000 to 2015 show that there is feedback (bidirectional) long run relationships between telecommunication infrastructures, economic growth and development in Africa. Presence of feedback long run causality suggests that telecommunication infrastructures, economic growth and development are interdependent in Africa. These indicators are important to the improvement of each other in the long run as revealed by the PVAR results. The reasons for the interdependence of long run relationships may be due to low economic status, poor infrastructural development, cultural similarity, high poverty and inefficient institutions in Africa. Since most African countries are still developing, it is expected to have feedback causality in socioeconomic indicators. From the conclusions, the study first, recommends that telecommunication sector, economic growth and development needs overhauling concurrently since mutual causality exist between them to enhance simultaneous sustainable development in Africa. With improved telecommunication sector, digital provide for institutional efficiency is attainable in Africa. Secondly, to develop and promote policies in the telecommunication sector that will enhance improvement in the spread of fixed lines penetration and internet access penetration since accessibility is still low in Africa and that will enhance economic performance. Further development and spread of fixed telecommunication networks will have strong spillover impact on other medium of electronic communication (mobile and fixed broadband internet services) since interconnectivity of telecommunication services still relied on efficient fixed telecommunication networks. Thirdly, the empirical results revealed that index of telecommunication infrastructures (CIT) are strongly important to economic performance - growth and development and vice versa in Africa, therefore, African countries should enact more policies that enhance efficient operation of telecommunication services that will stimulate inclusive economic growth and development. There should be holistic policies in term of technology transfer, training and investment to boost domestic production of smart phones since there is rapid migration from features phones. These will further create more economic gains to African region. 7. Compliance with ethical standards This research work is an extract from my doctoral thesis submitted and passed at the University of South Africa in 2017. It has never been submitted to any journal. The doctoral thesis was funded through Master and Doctoral Bursary of the University. There was no conflict of interest during the course of conducting the study. Annex. List of tables Table 3

Panel unit root test results. Series

Model

LLC

HDI

None Constant Constant and trend None Constant Constant and trend None Constant Constant and trend

−4.33590 (0.0000)*** −8.31532 (0.0000)*** 11.8826 (1.0000) 7.23626 (1.0000) −8.21965 (0.0000)*** 2.69894 (0.9965) 3.05404 (0.9989) −14.6271 (0.0000)*** 1.13130 (0.8710)

GDPPC CIT

IPS −3.40094 (0.0003)*** 12.6225 (1.0000) −1.16803 (0.1214) 5.10664 (1.0000) −5.58306 (0.0000)*** 7.48586 (1.0000)

Fisher-ADF

Fisher-PP

122.792 146.528 51.9665 21.8493 105.511 55.0338 30.5375 187.113 47.7607

874.397 313.958 78.1232 12.0791 124.865 68.8432 10.4693 378.460 65.6979

(0.0176)** (0.0003)*** (0.9998) (1.0000) (0.1587) (0.9992) (1.0000) (0.0000)*** (1.0000)

(0.0000)*** (0.0000)*** (0.8484) (1.0000) (0.0129)** (0.9661) (1.0000) (0.0000)*** (0.9827)

Breitung

11.6174 (1.0000) 4.97487 (1.0000) 6.22663 (1.0000)

Notes: Null: Unit root (assumes common unit root process): Levin, Lin & Chu (t*) and Breitung (t-stat). Null: Unit root (assumes individual unit root process): Im, Pesaran and Shin (W-stat), ADF - Fisher (Chi-square) and PP - Fisher (Chi-square). ***, ** and * are 1%, 5% and 10% significance level respectively. Source: Author's computations, 2018.

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Table 4

Panel cointegration test results. Trace test H0

Maximum eigen value test H1

HDI, GDPPC and CIT r=0 r r r 1 r 2 r

1 2 3

λ-trace statistic

p-value

H0

H1

725.0 377.5 349.6

0.0000* 0.0000* 0.0000*

r=0 r 1 r 2

r r r

1 2 3

λ-max statistic

p-value

486.1 244.1 349.6

0.0000* 0.0000* 0.0000*

Notes: *Rejection of the null hypothesis of no cointegration at least at the 10% level of significance. Probabilities are computed using asymptotic Chi-square distribution. Source: Author's computations, 2018.

Table 5

Panel causality test results. Model

Null hypothesis

w-statistic

zbar-statistic

p-value

Direction of relationship observed

A

GDPPC does not homogeneously cause HDI HDI does not homogeneously cause GDPPC CIT does not homogeneously cause HDI HDI does not homogeneously cause CIT CIT does not homogeneously cause GDPPC GDPPC does not homogeneously cause CIT

4.30320 9.29782 8.26463 3.81026 9.76125 3.25392

10.5765 27.5994 24.0780 8.89640 29.1789 7.00028

0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000***

GDPPC

B C

CIT CIT

HDI

HDI GDPPC

Notes: ***, ** and * are 1%, 5% and 10% significance level respectively. Source: Author's computations, 2018.

Table 6

Long-run trivariate regression results. Regressor

Constant HDI GDPPC CIT

Regressand HDI

GDPPC

CIT

−1.201190 [-13.05942] (0.0000) – 0.112779 [2.807684] (0.0051)*** 0.062207 [2.749720] (0.0061)***

1.935170 [30.26683] (0.0000) 0.102658 [2.807684] (0.0051)*** – 0.166519 [8.031201] (0.0000)***

1.476806 [8.953819] (0.0000) 0.178597 [2.749720] (0.0061)*** 0.525214 [8.031201] (0.0000)*** –

Notes: Values in parentheses [ ] and ( ) are t-statistics and p-value. ***, ** and * are 1%, 5% and 10% significance level respectively. Source: Author's computations, 2018

Table 7

Variance decomposition Period

Variables SE

Panel A: Variance Decomposition of HDI 1 0.018999 2 0.020653 3 0.021063 4 0.021190 5 0.021231 6 0.021244 10 0.021250 11 0.021250 12 0.021250 13 0.021250 14 0.021250

HDI

GDPPC

CIT

100.0000 97.69820 95.99516 95.21176 94.91448 94.81226 94.76440 94.76410 94.76402 94.76399 94.76398

0.000000 0.352364 0.551010 0.633944 0.663989 0.674061 0.678691 0.678719 0.678728 0.678730 0.678731

0.000000 1.949438 3.453828 4.154298 4.421531 4.513682 4.556912 4.557177 4.557257 4.557281 4.557288

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Table 7 (continued) Period

Variables SE

15 0.021250 16 0.021250 Panel B: Variance Decomposition of GDPPC 1 0.152128 2 0.154919 3 0.155710 4 0.155939 5 0.156006 6 0.156025 10 0.156033 11 0.156034 12 0.156034 13 0.156034 14 0.156034 15 0.156034 16 0.156034 Panel C: Variance Decomposition of CIT 1 0.174731 2 0.197139 3 0.203134 4 0.204846 5 0.205344 6 0.205491 10 0.205552 11 0.205552 12 0.205552 13 0.205552 14 0.205552 15 0.205552 16 0.205552

HDI

GDPPC

CIT

94.76398 94.76398

0.678731 0.678731

4.557290 4.557291

3.146064 3.123051 3.120491 3.121210 3.121725 3.121938 3.122048 3.122049 3.122049 3.122049 3.122049 3.122049 3.122049

96.85394 93.89035 93.03409 92.78854 92.71702 92.69603 92.68732 92.68728 92.68726 92.68726 92.68726 92.68726 92.68726

0.000000 2.986601 3.845419 4.090248 4.161251 4.182027 4.190628 4.190674 4.190688 4.190692 4.190693 4.190694 4.190694

1.084313 1.372559 1.470873 1.504830 1.516081 1.519674 1.521268 1.521277 1.521280 1.521281 1.521281 1.521281 1.521281

2.371155 3.799020 4.116807 4.202368 4.226848 4.233993 4.236953 4.236969 4.236974 4.236975 4.236976 4.236976 4.236976

96.54453 94.82842 94.41232 94.29280 94.25707 94.24633 94.24178 94.24175 94.24175 94.24174 94.24174 94.24174 94.24174

Note: Orthogonalised Cholesky ordering used. Source: Author's computations, 2018.

Table 8

Impulse response. Period

Panel A: Response of HDI 1 2 3 4 5 6 10 11 12 13 14 15 16 Panel B: Response of GDPPC 1 2 3 4 5 6 10 11 12

Variables HDI

GDPPC

CIT

0.018999 0.007469 0.003024 0.001276 0.000563 0.000260 1.65E-05 8.71E-06 4.65E-06 2.50E-06 1.35E-06 7.30E-07 3.96E-07

0.000000 0.001226 0.000970 0.000634 0.000383 0.000222 2.13E-05 1.17E-05 6.38E-06 3.48E-06 1.90E-06 1.04E-06 5.64E-07

0.000000 0.002884 0.002647 0.001825 0.001130 0.000664 6.49E-05 3.56E-05 1.95E-05 1.06E-05 5.81E-06 3.17E-06 1.73E-06

0.026983 0.004630 0.002656 0.001549 0.000881 0.000493 4.51E-05 2.46E-05 1.34E-05

0.149716 0.010899 0.004803 0.002570 0.001388 0.000752 6.56E-05 3.57E-05 1.94E-05

0.000000 0.026773 0.014682 0.007892 0.004258 0.002305 0.000201 0.000109 5.94E-05

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Table 8 (continued) Period

13 14 15 16 Panel C: Response of CIT 1 2 3 4 5 6 10 11 12 13 14 15 16

Variables HDI

GDPPC

CIT

7.33E-06 4.00E-06 2.18E-06 1.19E-06

1.06E-05 5.76E-06 3.14E-06 1.71E-06

3.24E-05 1.76E-05 9.59E-06 5.22E-06

0.018195 0.014226 0.008573 0.004952 0.002797 0.001559 0.000142 7.74E-05 4.22E-05 2.30E-05 1.25E-05 6.83E-06 3.72E-06

0.026906 0.027432 0.014909 0.008041 0.004350 0.002358 0.000206 0.000112 6.10E-05 3.32E-05 1.81E-05 9.84E-06 5.36E-06

0.171686 0.085895 0.045868 0.024681 0.013335 0.007224 0.000630 0.000343 0.000187 0.000102 5.53E-05 3.01E-05 1.64E-05

Note: Orthogonalised Cholesky ordering used. Source: Author's computations, 2018.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.telpol.2019.03.005.

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