The “feedback hypothesis” was established by the results of estimation of long run causality for Bangladesh, Indonesia, Iran, Malaysia, Pakistan and Turkey. This finding leads to the conclusion that energy sector is a major player in these economies and it has huge impact on the national income and development of the economies. Both of the variables have dynamic effect on each other. These findings are appropriate for these countries as Iran and Indonesia are major energy exporters and are prominent members of OPEC 1 while Malaysia and Turkey are among the fastest growing energy markets. The economies of these countries are, thus, massively dependent on their energy export revenues and thus there is a bi-directional causality between the real GDP and energy use as more energy production (i.e., a part of energy use) results in more national income with a feedback affect i.e., increased economic prosperity results in increased energy production and use. The economies of Pakistan and Bangladesh are facing energy shortages but are in developing phase where economies rely heavily on the energy use to ensure economic development. Both countries are net importers of energy. Therefore import payments have significant implications for the national income and any change in energy use will lead to a change in GDP and vice versa.
The purpose of this study is to examine the dynamicrelationshipbetweenenergy consumption (BTU of energy) and Purchasing Power Parity (PPP) of GDP (as a proxy for economicgrowth) using a panel for 19 COMESA countries. 1 These include Burundi, Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Swaziland, Sudan, Seychelles, Uganda, Zambia, and Zimbabwe. This will be accomplished by employing panel unit root tests, panel cointegration tests, and finally the dynamic error correction model. The rest of the paper is organized in the following manner: section 2 gives a summary of the economic and energy profile of COMESA countries; section 3 presents the methodology and data sources; section 4 presents the results and the discussion; and section 5 gives the conclusions and policy recommendations.
The ARDL bounds testing cointegration approach by Pesaran, Shin, and Smith (2001) to examine the existence of a long run dynamicrelationshipbetween GDP, energy consumption, financial development, capital formation and population growth. Researchers have applied various approaches to test the presence of cointegration between variables in their studies. Two most common approaches used is the Engle and Granger test (Engle and Granger, 1987) for bivariate data and Johansen test (Johansen and Juselius, 1990) when multivariate. Both tests require that all the series should be integrated at the order of integration I(1). Engle-Granger test for one cointegrating relationship and Johansen test allows for multiple cointegrating relationships. The ARDL bounds testing procedure involves two stages; first to test for the existence of a long- run relationshipbetween the variables and second to estimate the coefficients of the long-run relations and make inferences about their values. The calculated F-statistic is compared against the upper critical bound (UCB) and lower critical bound (LCB) provided by Pesaran et al. (2001) which correspond to the assumptions that the variables are I(0) and I(1) respectively. If the computed F-statistic is greater than the UCB value, then the H0 is rejected (the variables are cointegrated). If the F-statistic is below the LCB value, then the H0 cannot be rejected (there is no cointegration among the variables). If it falls between the LCB and UCB value, the result of the inference is inconclusive.
In other hand, studies such as Keppler (2006) for China, Narayan and Smyth (2008) for G7 Countries, Apergis and Danuletiu (2012) for Romania, Karagöl et al. (2007), for Turkey found unidirectional causality running fromenergy consumption to economicgrowth. Moreover, bidirectional causality was found by Apergis and Payne (2009) for 11 countries of the Commonwealth of Independent States, by Ozturk et al. (2010) for lower-middle income, by Lee and Lee (2010), Bekle et al. (2010) for 25 OECD Countries, by Pao et al. (2014) for Brazil, by Rezitis and Ahammad (2015) for South and Southeast Asian countries, by Vafaeirad et al. (2015) for 7 Asian countries, by Al-mulali and Mohammed (2015) for Emerging countries, Osigwe and Arawomo (2015) for Nigeria and Khobai and Roux (2017) for south Africa. In addition to them, Erdal et al. (2008), Kaplan et al. (2011), Akpolat and Altıntaş (2013), Bayar (2014), Çakmak (2015) for Turkey reached the similar conclusion. In some studies, like Ozturk et al. (2010) for upper-middle income, Kalyoncu et al. (2013) for Georgia and Azerbaijan results indicated no causality betweenenergy consumption and economicgrowth. Similarly, Jobert and Karanfil (2007), Ozturk and Acaravci (2010), Çetin and Seker (2012) for Turkey emphasized the same conclusion in their studies.
The empirical evidence that we gathered shows that a pure project organization in the form of a task force separated from the rest of the organization enhances the project team’s ability to absorb knowledge. This emerges when comparing the project team’s proficiency in the initial phases of the knowledge transfer project and in later phases. At the beginning, the project was organized as a matrix structure. We observed that the level of knowledge absorption was poor since members tended to fall back to their routine activities and dedicated little time to the project tasks. Later, it was decided to move to a pure project organization with task force members dedicated 100 % to the knowledge transfer project. According to managers interviewed, AC was improved since team members could fully concentrate on the task and could better coordinate among each other. Stronger interaction between staff with heterogeneous background fostered the incorporation of different competencies: electrical and mechanical engineers were pushed to cooperate in the common understanding of the functioning of different auxiliary systems of the turbines, instead of approaching the system individually. This empirical result can be explained considering the complex and cognitive nature of AC. Knowledge transfer is a one-off activity, which may be felt as overwhelming by team members, whose natural reaction is to prioritize known and routine tasks. A pure project organization can contrast such behavior. Moreover, other benefits from this dedicated organizational structure include more effective coordination and exploitation of each other’s competencies (Henderson, Cockburn, 1994), which is particularly important in the light of the cross-disciplinary nature of AC. Indeed, (Van Den Bosch et al., 1999) find that mixing different competencies in the project is positive for AC. In the light of empirical findings, which only refer to the recognition and assimilation phases of AC, we posit the following proposition:
the same order and cointegrated; it is possible to determine the short-term causality through a VECM. Estimates by VECM for each country, has enabled us to confirm the existence of long- term causal relationships and calculate the adjustment speeds when the system deviates from its long-term path through the significance and the sign of the error correction term. To determine the direction and influence intensity of the series on each other, we applied the FMOLS and DOLS methods of estimation. These approaches are adapted to correct the endogeneity of regressors and the problem of autocorrelation and to have consistent estimators, which is not always the case for OLS. The results show that in the long-run there are two-way causal relationships between GDP and energy consumption for all countries. That is, feedback hypothesis is established in these countries. Bilateral causal relationshipbetween GDP and CO 2
We use annual energy consumption, EC hereafter and GDP per capita data in this study. EC is kg of oil equivalent and GDP data with (LCU) constant. The data are sourced from World Development Indicators (2012). These countries are first on the platform of low income countries and from among them, four African countries are chosen which include Nigeria, Benin, Kenya and Ghana and four non-African (Asian) countries which include Bangladesh, Pakistan, India and Nepal. A Period of 1975- 2010 was considered for the purpose of this study. All variables are employed with their natural logarithms form to reduce or forestall heteroscedasticity. To investigate the relationship and causality issue, panel unit root analysis, panel cointegration analysis, panel causality analysis, panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOL) estimates are employed in this study.
of our results by following the recent study by Lind and Mehlum (2010), which proposes tests for the existence of U or inverted U-shaped relationships. By applying this test, both necessary and sufficient conditions for the existence of an inverted U-shaped pattern can be verified. 7 As regards the second approach, we follow Bick (2010) and Kremer et al. (2013) and estimate a dynamic panel threshold model that accounts for sharp discrete shifts to investigate the potential existence of a threshold level in the linkage between financial development and economicgrowth. To our knowledge, this is the first study that combines these two different approaches to investigate the non-linearity within the finance and growth nexus.Our findings therefore suggest that the relationshipbetween financial development and economicgrowth need not be linear, either in the long or short-run. Rather, the two different techniques used confirm that financial deepening might have a negative effect on growth beyond a certain threshold, which is different from the predominant view that financial development and economicgrowth are positively linked.
The objectives of this empirical study are to examine how the variables (i.e., GDP, oil and nuclear energy consumption, and oil prices) are related in the long-run and to assess the long-run causal relationshipbetween these variables. In line with these objectives, cointegration technique is applied to examine the long-run relationship(s) in each country. It is worth noting that early cointegration techniques pioneered by Engle and Granger (1987), Hendry (1986), and Granger (1986) have made a signiﬁcant contribution towards cointegration and long-run relationship(s) analysis and causality testing between time series variables. Thus, these techniques have become popular both as a topic for theo- retical investigation of statistical issues and as a framework within which many empirical propositions can be re-evaluated (Perron and Campbell, 1994). The basic idea the cointe- gration, in general, suggests that two or more variables are said to be cointegrated, that is they exhibit long-run equilibrium relationship(s), if they share common trend(s). More concretely, Engle and Granger (1987) demonstrate that once a number of variables are found to be cointegrated, there always exists a corresponding error-correction represen- tation that denotes that changes in the dependent variable are a function of the level of disequilibrium in the cointegrating relationship (captured by the error-correction term) as well as changes in other explanatory variable(s).
determinant in the demand function for energy”. However, this is not always found when analyzing the data given that the relative importance of energy as a driver of economicgrowth may change. Gales et al. (2007) argue that if energy is crucial for an economy in the sense that growth cannot occur without it, then shortages in its availability could put in danger the growth prospects of the country. On the contrary, in the case that a decoupling between the two variables is found then one can be more optimistic about a country’s economic future. More specifically, after econometrically identifying the mechanisms that determine the interplay betweenenergy consumption and economicgrowth, one can assess the impact of policies that target to decrease environmental degradation. In particular, if for instance a relationshipbetweenenergy and growth is found and runs fromenergy to growth, then this could imply that policies which target to decrease energy consumption, as part of a national environmental strategy, are likely to affect the country’s economicgrowth. As argued by many researchers (Chinatnawat et al. 2006, 2008; Lee, 2006; Masih and Masih, 1998; Stern, 1993, 2000; Zachariadis, 2007), such a finding indicates that the economy under study is highly energy-dependent and consequently it provides strong evidence that the environmental policies which aim at reducing CO 2 emissions and energy consumption could in fact affect negatively the growth potentials of a country. It is an indication that energy efficiency improvements through technological innovations have not been so much developed and consequently economic activity is relatively less energy efficient.
The efficiency of the financial sector in a country is a major determinant of macroeconomic performance. This has been clearly manifested in the aftermath of the Global Financial Crisis (GFC) of 2007-08 and the subsequent Great Recession suffered by many developed and developing economies. At the same time, sustained economicgrowth remains the single most important determinant of societal living standards (Haldane, 2015). Although the empirical research on the finance-growth nexus has grown in the aftermath of this period, the evidence relating to the European and CIS economies has been relatively scarce and with mixed results. The GFC had severe implications for the financial markets and the economicgrowth of these regions, substantiating the argument that the relationshipbetween finance and growth is complex and not necessarily stable over time (Grochowska et al., 2014). Thus, the classic question re-emerges as sclerotic growth remains the overriding economic issue of our time (Cochrane, 2015), especially for a number of the countries examined in this paper. Although the financial sector is crucial for the functioning of the real economy, the exact contribution to growth remains uncertain and varies over the business cycle. In this paper, we explore the possibility that the role of the financial sector in terms of its impact on the economy may have fundamentally changed in recent years, controlling for the effect of changes in investment expenditure, wages, unit labour costs, domestic credit, the money supply, the interest rate margin, government consumption, inflation and trade openness.
`Industrialization brings about structural change in the economy and therefore affects energy demand. As economic activity expands and diversifies, demands for more sophisticated and flexible energy sources arise: while societies that highly depend on agriculture derive a large part of primary energy consumption from traditional biomass coal and liquid fuels such as kerosene and liquid petroleum gas gain in importance with rising income, and electricity, gas and oil dominate at high per capita incomes. From a sectoral perspective, countries at an early stage of development consume the largest part of total primary energy in the residential and to a lesser extent agricultural sector. In emerging economies the manufacturing sector dominates, while in fully industrialized countries services and transport account for steadily increasing shares. Furthermore, several authors have pointed out that electricity which offers higher quality and greater flexibility compared to other forms of energy has been a driving force for the mechanization and automation of production in industrialized countries and a significant contributor to continued increases in productivity. Despite the fact that as a group industrialized countries consume significantly higher amounts of energy per capita than developing ones, a considerable cross-sectional variation of energy use patterns across countries prevails: while some countries (such as, e.g., Japan) display high levels of per capita incomes at comparably low levels of energy use, others are relatively poor despite extensive energy consumption, especially countries abundantly endowed with fossil fuel resources, in which energy is often heavily subsidized.
The relationshipbetweenenergy input and economic development has strong implications for energy and growth policy. Theoretically, energy input can boost economicgrowth, while economicgrowth can drive up energy input. In other words, there is a possible causal relationshipbetweenenergy input and economic development. Scholars tried to provide empirical evidences with data from different countries and various methods. For example, Yoo and Kwak (2010) studied the causal relationshipbetween electricity consumption and economicgrowth using data from seven South American countries(Argentina, Brazil, Chile, Columbia, Ecuador, Peru, and Venezuela) during 1975 to 2006. They found that the causal nexus between electricity consumption and economicgrowth varied across countries: a unidirectional causality from electricity consumption to real GDP for Argentina, Brazil, Chile, Columbia, and Ecuador; a bidirectional causality in Venezuela and no causal relationships in Peru. Chinese scholars Wang, Tian and Jin (2006) evaluated the relationshipbetween China's economicgrowth and energy consumption by applying the state space model to China’s data from 1953 to 2002. Their conclusion was that a long-run equilibrium relationship existed between China's economicgrowth and energy consumption. However, the relationship is not constant. Instead, it evolved over time. Wang and Liu (2007) proposed a unidirectional relationshipfromenergy consumption to economicgrowth in China during 1978 to 2005; on the contrary, Lin, Wei and Li (2007) discovered a unidirectional relationshipfromeconomicgrowth towards the major energy resource i.e. coal consumption for the almost overlapping period from year 1980 to 2004.
seek to slow consumption growth of oil, it is still the dominant fuel particularly in the transport sector. According to the EIA, since developed countries tend to have higher vehicle ownership per capita, oil consumption within the OECD transportation sector usually accounts for a larger share of total oil consumption than in non-OECD countries. In addition, oil is used in many ways, from the manufacture of goods, to transport of goods and people, to food production, to operating construction equipment, to mining. Therefore, it should not be surprising to find that there is a close relationshipbetween GDP growth and oil consumption. A positive and significant impact of natural gas on real GDP denotes that natural gas as a non- renewable energy source has a substantial role in economicgrowth in OECD countries. Natural gas, which seems to be of secondary importance behind oil, has an important feature in that it generates less carbon emissions compared with the other fossil fuels. Thus, fuel transformation at least from coal and/or oil to natural gas should be taken into account by policymakers. Finally, the long-run results suggest that there is a positive and statistically significant relationshipbetween coal, oil, and natural gas consumption and industrial output. It appears that industrial sectors in developed countries still benefit from the use of coal. The reason may be that coal is an abundant and affordable source of energy and also it is easy to transport and store.
Although economic theories do not explicitly state a relationshipbetween these variables, overall findings are that there exists a relationshipbetweenenergy consumption and economicgrowth. Yang and Zhao (2014) found that energy consumption causes economicgrowth. Gupta and Sahu (2009) reported that energy consumption Granger-caused economicgrowth over the 1960 – 2009 period. Cheng (1999) showed that economicgrowth Granger- causes energy consumption over the short term and the long term. Asafu Adjaya (2000) viewed that energy caused GDP and relationship exists betweenenergy consumption and economicgrowth. Ahmad et al. (2016) found a feedback relationshipbetweenenergy consumption and economicgrowth in India over the 1971–2014 period. Ouedraogo (2013) found causality running from GDP to energy consumption in the short run and the reverse in the long run for the Economic Community of West African States. Morimoto and Hope (2001) revealed that energy supply have significant impact on a change in real GDP in Sri Lanka. Aqueel and Butt (2001) investigate the relationship in Pakistan and results inferred that energy consumption leads to economicgrowth. Fang and Chang (2016) found that economicgrowth caused energy use in the Asia Pacific region (16 countries) from 1970 to 2011, but the relationship may have varied for individual countries.
In time series econometrics most recent studies have tended to focus on VAR and VEC models and cointegration approach. For example Asafu and Adjaye (2000) investigated the causal relationshipbetweenenergy use and income in four Asian countries using cointegration and error correction mechanism. They found that causality runs fromenergy use to income in India and Indonesia and bi-directional causality in Thailand and Philippines. Yang (2000), found bi-directional causality betweenenergy consumption and GDP in Taiwan and this results contradicts with Cheng and Lai (1997) results. Soytas and Sari (2003) found bidirectional causality in Argentina and unidirectional causality from GDP to energy consumption in Italy and South Korea, and fromenergy consumption to GDP in Turkey, France, Germany and Japan. Paul and Bhattacharya (2004) found bidirectional causality betweenenergy consumption and economicgrowth in India. Using cointegration analysis Wietze and and Van (2007) found that unidirectional causality from GDP to energy consumption in Turkey. Dirck (2008) used the cointegartion approach to study the causal relationshipbetween electricity consumption and economicgrowth for the panel of 15 European countries. He found the unidirectional causality from electricity consumption to economicgrowth for Greece, Italy, and Belgium, and fromeconomicgrowth to electricity consumption for Great Britain, Ireland, Netherland, Spain and Portugal, no causality is found in Austria, Germany, Denmark, Finland, France, Luxembourg, and Switzerland. Narayan, Narayan and Popp (2010) used the cointegartion approach to study the causal relationshipbetween electricity consumption and economicgrowth for six different panels of 93 countries. They found bidirectional causality relationshipbetween
The 1973 oil crisis that led to increased inflation, high unemployment rates and decreasing growth rates revealed that energy consumption had a considerable influence on economicgrowth. Countries trying to reduce their oil dependency began to seek new energy sources. Due to global warming and increased air pollution since the 20th century, sustainable economicgrowth and development became economically important. Due to both reasons, today, developed countries encourage the use of renewable sources of energy such as solar, wind, biomass and hydropower to reduce greenhouse gas emissions (GHG) instead of the use of non- renewable energy sources that pollute the air such as oil and coal.
These are views which believe that financial development causes economicgrowth. One of the earliest scholars to write on this issue was Bagehot (1873) in his book Lombard Street: A Description of the Money Market. At the height of British industrial power, he asserted that what separated England from all countries was the ability of its financial markets to mobilize savings to finance immense works. Bagehot was the first to define the two primary roles of financial markets: (1) facilitating the accumulation of capital, and (2) managing risk inherent in particular investment projects and industries. Later, other scholars like Schumpeter (1911), McKinnon (1973); Shaw (1973); and King and Levine (1993) to name just a few, followed the same path. They argue that financial development affects economicgrowth through technological changes (Schumpeter, 1911); through mobilizing savings and channeling them into profitable large-scale investments (McKinnon and Shaw, 1973); and through diverisfying portfolios, reducing information costs, monitoring borrowers, and encouraging specialization (King and Levine, 1993).
Renewable energy consumption minimizes environmental impact of energy consumption, improves stability and reliability of energy supply and enhances energy security (Voivontas et al 1998). It also helps countries meet emissions targets such as the one set up by the Kyoto Protocol and European Union. The World Bank (1999) has also indicated that renewable energy consumption improves access to clean and modern energy in rural areas which are connected to the national electricity grid. Despite these advantages, the consumption of renewable has not grown as compared other sources of energy. Painuly (2001) argued that the reasons for relative low growth in renewable energy are economic barriers such as high capital cost, market barriers and technological barriers. On cost, Stern (2007) has estimated that the economic impact of global warming could reduce global GDP by 25% whilst the mitigation of global warming through the use of renewables and efficiency cost 1% of global GDP. This 1% cost even represents initial investment.
According to the results of the short run causality, there is evidence to support the growth hypothesis in OECD countries with high income. There is evidence to support the conservation hypothesis for Brazil, France, Mexico Turkey and countries with high growth. The conservation hypothesis suggests that the policy of conserving hydropower energy may be implemented with little or no adverse effects on economicgrowth, such as in a less energy-dependent economy. Therefore this is not the theoretically expected outcome for developing countries.