correlation between energy consumption and economic growth, the issue of transforming the country into a middle-income economy in the next two decades. The issue of energy consumption mainly focuses on the relationship between energy and income. Kalyoncu, Gürsoy, and Göcen () recently reviewed the. to most problems facing the poor in the developing world. A lack of access to effect of electricity on income, substituted by household consumption, in Nepal. To account for .. Energy use and energy access in relation to poverty. Econ. Polit.
Different panel unit root tests have been developed to test whether the panel data are stationary over time. The first problem encountered in the unit root testing of panel data is to specify whether the cross-sections of the panel are independent from each other.
The unit roots tests developed for the cases where there is no correlation between the units are called firstgeneration tests, while the tests used in cases where there is a correlation between the units are called second-generation tests. First-generation tests are based on the assumption that cross-sections are independent and all cross-sections are equally affected from the impact of the shocks to any of the panel units.
Consumption of Electricity and Industrial Growth in the Case of Ghana
However, given the relationships of today's international economies, it is more realistic to assume that the cross-section units are affected differently from a shock to one of the units.
In an effort to fill this gap, secondgeneration unit root tests were developed by taking into account the cross-section dependence. The key feature of the second-generation tests is their assumption that there is a correlation between the series of the units.
To decide on which generation root test should be used for testing, first it should be examined whether there is cross-section dependence. The results are affected considerably depending on whether the cross-section dependence between the series is taken into account Breusch and Pagan, ; Pesaran, If the presence of cross-section dependence in the panel data set is rejected, first-generation unit root tests can be used. However, if there is cross-section dependence in the panel data, then the use of second-generation unit root tests will ensure making more consistent, effective and powerful estimations.
The tests show that there is cross-section dependence in the series. This means that there is cross-section dependence between the countries in the panel data. Therefore, in the following sections of the study, we will use panel unit root and panel cointegration tests, both of which take account of the cross-section dependence.
Following the tests to check stationarity, the presence of a long-term relationship between the variables will be determined by using cointegration analyses. While forming a hypothesis for the panel cointegration tests, the stationarity level of the variables changes the type of the test to be used. The series analyzed in this study have both heterogeneity and cross-section dependence.
Therefore, the error-correction-based panel cointegration test developed by Westerlundwhich takes account of cross-section dependence, will be used in this study to test for cointegration between the panel data variables. Westerlund proposes four different panel cointegration statistics to test the presence of cointegration. Two of these statistics are called group mean statistics Ga, Gtwhile the other two are called panel statistics Pa, Pt.
If a long-term relationship is found between the variables as a result of the panel cointegration tests, it is required to estimate the long-term coefficients for all country groups. In this stage, the coefficients in the cointegrating vector are analyzed by using the dynamic ordinary least squares DOLS estimator proposed by Stock and Watson and the fully modified ordinary least squares FMOLS estimator developed by Phillips and Hansen In addition to the long-run parameters, estimation of short-run parameters also gives some valuable information.
In the final stage of the study, PMGE and MGE methods will be used to estimate the short-term and long-term relationship between the variables for each unit. MGE estimates the long-run parameters by using the means of the long-run parameters of the autoregressive distributed lag ARDL models for the estimation of the individual units. Therefore, it allows the long-run parameters to change for the units.
On the other hand, PMGE holds the long-run parameter constant and allows the short-run parameters, and even the variance, to change for the units. Therefore, the series used in the model must first be tested for stationarity by using unit root tests. In this study, it is necessary to first determine the presence of crosssection dependence to determine which generation root test should be applied. This is important when choosing the unit root and cointegration tests to be performed.
Crosssection independence is an important issue for today's markets, as they are becoming increasingly more integrated Herzer, as a result of various common factors, such as countries, global financial crises and fluctuating oil prices, all of which can be affected in such an environment Cavalcanti et al.
These tests examine whether the cross-section units are dependent on each other and whether they are equally affected by a shock to the series. Table II presents the results of the cross-section dependence tests performed on the series.
The results summarized in Table II indicate that there is cross-section dependence in the panel data used to estimate the model at the 1 per cent significance level for all related sub-categories as well as for the whole data set.
This means that any shock to one of the countries affects the other countries. Therefore, while developing energy policies, these countries should take into account the policies adopted by the other countries in the panel, as well as the shocks that affect their energy consumption. For this reason, the stationarity of the series is tested using the CADF test developed by Pesaranwhich is one of the second-generation unit root tests that takes cross-section dependence into consideration.
This method uses the ADF regression augmented by the lagged cross-section means.
As shown in Table IIIthe results of the Pesaran CADF panel unit root test performed on the economic growth and energy consumption variables indicate that the series are not stationary at the 5 per cent significance level, which means that they have a unit root. Therefore, we take the first difference of each series, thus getting a stationary series.
According to these results, we can say that the variables are integrated at the same level and it is possible to examine a relationship in the long term, which can be done through cointegration tests. For this reason, the panel cointegration test developed by Westerlundwhich takes into account cross-section dependence, is used to analyze the long-term relationship between variables.
Westerlund proposes comparing the test statistics calculated for cross-section dependence with the bootstrap critical values recommended by Chang and Westerlund Table IV presents the results of the panel cointegration analysis. According to the results of the Westerlund panel cointegration test presented in Table IVH0, which states that there is no cointegration between the cross-section units in the panel with respect to either the country groups or the relevant sub-categories, is rejected by each of the four test statistics.
The results further reveal that there is a statistically significant cointegration relationship in the panel data set. Therefore, according to the results of the cointegration analysis of the net energy-importing countries, we conclude that there is a strong relationship between energy consumption and economic growth over the long term. In this study, the coefficients in the cointegrating vector are analyzed using the dynamic OLS estimator and the fully modified OLS estimator.
Table V displays the estimation results of a long-term relationship between energy consumption and economic growth. According to the results presented in the table, t-statistics for the common long-term coefficients are statistically significant at the 1 per cent level for all country groups and for other sub-categories, except for low-income net energy-importing economies.
The results obtained from the DOLS and FMOLS estimation techniques indicate that there is a long-run, positive relationship between energy consumption and economic growth. Thus, a 1 per cent increase in energy consumption in the long term will increase economic growth by 0. However, in addition to the long-run parameters, estimation of short-run parameters also gives some valuable information. Therefore, we will use PMGE and MGE as a panel error correction model to examine both the long-term and short-run relationship between energy consumption and economic growth.
Table VI shows the results of the analysis. According to the PMGE and MGE results, the error correction parameter is negative and significant for all of the net energy-importing countries and subgroups. The error correction parameter represents the adjustment speed of the short-run deviations caused by the nonstationarity of the series to equilibrium in the next period. According to the PMGE method, approximately 69 per cent of the disequilibrium in a period can be corrected in the following period, and thus, long-term equilibrium can be attained.
This rate was 64 per cent for countries with import dependence less than 50 per cent and 74 per cent for countries with import dependence greater than 50 per cent. Furthermore, with respect to the economic subgroups, the rate of disequilibrium was 80 per cent for the low-income economies, 66 per cent for the lower-middle-income economies, 66 per cent for the upper-middle-income economies and 69 per cent for the high-income economies. Additionally, the long-term parameters of the energy consumption variable in the model were significant and positive for all groups, except for the low-income economies, a result that is consistent with a priori expectations.
In other words, a 1 per cent increase in energy consumption increases the GDP per capita by 0. If we categorize the net energy importers by income levels, this increase is 0.
Journal of Energy
The short-run parameter of energy consumption is statistically insignificant, except for the upper-middle-income economies and high-income economies. According to the MGE results shown in Table VI, about 77 per cent of the disequilibrium in a period can be corrected in the following period and the long-term equilibrium can be reached. This rate is found to be 72 per cent for the countries with import dependence less than 50 per cent and 84 per cent for those with import dependence greater than 50 per cent and to be 90 per cent for the low-income economies, 76 per cent for the lower-middle-income economies, 76 per cent for the upper-middle-income economies and 72 per cent for the highincome economies.
Besides, the long-term parameters of the energy consumption variable in the model were found to be significant and positive for all groups, which is a result consistent with a priori expectations.The Multiplier Effect- Macro 3.9B
The ISI strategy resulted in the rapid growth of the manufacturing sector from a 2 percent share of real GDP to 9 percent in andrespectively. The manufacturing sector also experienced growth in employment, about 90 percent in total between and [ 6 ]. As a result of external shocks hikes in oil prices and inappropriate domestic policies during the mids tothe industrial sector and the Ghanaian economy as a whole suffered severe deteriorations in economic and financial performance.
The industrial sector expanded at an annual average of The high cost of credit, which reflects high lending rates, rising fuel prices, and more importantly unreliable electrical power supply compelled many firms, especially those operating in import-dependent manufacturing, to cut production [ 6 ]. The key to unlocking other resources is energy and it also increases the fortune of man by furnishing the modern world with fuel. According to Youngquist [ 10 ], the material standard of living of man is determined directly and indirectly by the availability of energy per capita.
This implies that energy provides fundamental support to all industrial productions in Ghana and the world at large. To this end, the Ghana Poverty Reduction Strategy GPRS outlined wide-ranging policy interventions in the energy subsector to ensure dependable supply of high quality energy services to back the growing agro-industrial and services sector and residential use [ 11 ].
For the 13 problems affecting the manufacturing sector in Ghana, unreliable electricity supply is ranked first Owusu, According to Gand [ 13 ] electricity demand in Ghana keeps rising at an average rate of 12 percent per annum from to The domestic electricity consumption in was 6, GWh and was projected to rise by What is not empirically clear is whether the persistent increasing demand for power has any bearing on industrial development.
Aboh [ 14 ] has projected that total electricity use in Ghana will rise from 3, The high projected increase in electrical energy demand requires a significant step-up in both private and public investments towards expanding the operational power generation capacity.
Although a policy framework has been designed for Ghana to attract private investment in power production, very little has been achieved in this area. In addition to these two plants, there are a few numbers of other power plants under construction by the private sector. The public sector has also made a lot of efforts towards providing and expanding the electricity generation in Ghana.
The country relied on diesel generators to supply electricity for use in industrial and health sectors as well as for private consumption [ 16 ]. The amount of electricity power supplied during this period was insufficient relative to the demand. The commissioning of the Akosombo Dam took place inwhiles the Kpong Hydroelectric Plant was completed in The Akosombo Dam alone could produce MW of power as of [ 17 ]. The total installed power generation capacity from Akosombo and Kpong Plant had increased to 1, MW by the end of By the mid-eighties, the demand for electricity power had gone up above the total electricity supply in Ghana [ 16 ].
Efforts have been made in this era to expand the generation of power via the Takoradi Thermal Power and the developing of the West African Gas Pipeline to make available affordable source of fuel for the generation of power [ 16 ].
Currently, there has been huge investment to increase the power generation capacity of the above-mentioned plants. More power plants have also been constructed to increase the supply of power in the country. Notwithstanding the efforts made by successive governments to expand power generation capacity, the country is still far from becoming power sufficient. The government of Ghana is still pursuing policies to improve the shortcomings in the power sector.
The objective is to create a power sector which is financially viable and be able to meet the current needs as well as the future needs for both businesses and households [ 15 ]. Owing to the competing demands on limited government revenues in a developing country like Ghana, there is the need to assess the viability of increasing public and private investments in the power sector to meet the projected demand to propel industrial growth.
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Therefore, the present study is intended to inform public policy and even private investment decisions in the power sector. Despite the above positive developments in the electricity power generation in Ghana, little empirical research efforts have been devoted to studying the causal links between electricity consumption and industrial growth in the specific case of Ghana.
Two earlier studies by Adom [ 19 ] and Enu [ 20 ] can be identified in the extant literature. Adom [ 19 ] focused on the impact of electricity consumption on overall economic growth. Though Enu [ 20 ], which is directly related to this study, reports that electricity promotes manufacturing in Ghana, it has serious time series econometrics deficiencies. This current study therefore seeks to provide new estimates for the relationship between electricity consumption and industrial growth in Ghana.
From the empirical results, we find a long-run relationship between electricity consumption and industrial growth. The results further reveal that electricity consumption has a negative impact on manufacturing sector output in Ghana. In the next section we present a review of related studies on the electricity consumption and industrial growth.
Does more energy consumption support economic growth in net energy-importing countries
Section 3 is a presentation of the data used and the econometric methodology employed. A discussion of the empirical results is done in Section 4 and Section 5 concludes with policy implications. Review of Related Literature Studies on the causal link between energy electricity specifically consumption and economic growth abound in the extant literature [ 121 — 26 ].
From the results of the existing studies, four main types of causal relationships hypotheses emerge and have been summarized by Mawejje and Mawejje [ 1 ] as follows. First is the growth hypothesis where causality is one-way from electricity consumption to output growth see [ 1222427 ]. Second is the conservation hypothesis in which causality rather runs from output growth to electricity consumption see [ 242829 ]. Third, the feedback hypothesis proposes a two-way causality between electricity consumption and output growth see [ 263031 ].
Fourth, the neutrality hypothesis is related to no causality between electricity consumption and output growth see [ 253233 ]. His result suggested that growth drives energy consumption. Thus, the study lends support to energy conservation hypothesis. Adom [ 19 ] applies the Toda and Yomamoto Granger Causality test and time series data spanning the period to investigate the causal link between electricity consumption and economic growth in Ghana. This study brought to light that there is a unidirectional causality running from economic growth to the consumption of electricity.
This study gives credence to the growth-led-energy hypothesis in the case of Ghana. Adom [ 19 ] concludes that electricity conservation measures are required to manage electricity demand and consumption as the economy of Ghana expands. Other studies, which found similar results, include Hu and Lin [ 29 ], Halicioglu [ 34 ], and Mozumder and Marathe [ 28 ] for Taiwan, Turkey, and Bangladesh, respectively.