Electricity consumption, oil price and economic growth: Global perspective

Renewable and Sustainable Energy Reviews 76 (2017) 9–18

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Electricity consumption, oil price and economic growth: Global perspective

MARK

⁎

Suleman Sarwar , Wei Chen, Rida Waheed School of Economics, Shandong University, Jinan, PR China

A R T I C L E I N F O

A BS T RAC T

Keywords: Electricity consumption Oil price Fixed capital formation Population GDP

Present study use the panel data of 210 countries over the period 1960–2014 to analyze the empirical relationship between economic growth, electricity consumption, oil price, gross fixed capital formation and population. The economic growth of developing countries with industrial infrastructure has higher significant association with electricity consumption as compared to oil price. We use oil price and electricity consumption jointly to study the highly predictive observer for economic growth. Furthermore, the data is categorized into income, OECD and regional level. Pedroni panel cointegration, fully modified OLS and panel vector error correction test apply to analyze the cointegration, short run and long run relationship between the variables. The results of full panel confirm a bidirectional relationship between electricity consumption and GDP, oil price and GDP, fixed capital formation, population and GDP. The results validate that developing countries heavily reliance on electricity consumption despite of oil prices for economic growth. Furthermore, the finding varies across income, OECD and regional level. In short run, growth and feedback hypothesis suggest that more vigorous electricity policies should be implemented to attain high economic growth

1. Introduction The economic progress of developing countries heavily relies on electricity; the production of manufacturing industries decline due to electricity shortage which in turn destabilize the micro and macro economy. Electricity is a key component of economic growth, also direct or indirect complement of labor and capital as factor of production [12]. A large number of studies confirm the impact of electricity price on economic growth [10,2,37,56,57]. Short term electricity consumption is positively related with economic growth [1]. Ferguson et al. [16] analyze 100 countries, the result suggests the relationship between electricity consumption and economic growth is stronger in wealthy countries. Whereas, large number of developing and under developed countries have concerns about electricity shortage because of scarce resources, infrastructure and incompetent policy makers. The relationship between electricity consumption and growth relationship varies across the income level of countries [55]. As one of the key components of energy, the importance of oil price to economic development has been recognized by economists, policy makers, business men, households and researchers. After 1973s oil crises, several studies [18,20,21,34] affirm an inverse association between oil price and economic growth. The economists and researchers have reached a consensus: oil price volatility simultaneously reduces the economic growth (GDP). Although, recent literature show the negative relationship decreasing over time because of discovering ⁎

Corresponding author. E-mail address: [email protected] (S. Sarwar).

http://dx.doi.org/10.1016/j.rser.2017.03.063 Received 6 February 2016; Received in revised form 2 March 2017; Accepted 9 March 2017 1364-0321/ © 2017 Published by Elsevier Ltd.

oil alternatives and governments preemptive measures from sudden oil price shocks [13,24]. The oil importing developing economies are severely affected by the oil price hikes because of lower tax share on oil price. Moreover, the developed economies have higher tax share on oil, such oil price shocks could probable be mitigated to some extent by suspending the tax share as oil price rise. The developing countries with less tax share on oil have less potential to absorb the oil price shock. Consequently, oil price hike appears to have a more adverse impact in developing economies. Present study extends the literature on electricity consumption, oil price, fixed capital formation and labor. Firstly, the study extends the [48] growth model by augmenting electricity consumption and oil price, so the model is more comparable than the prior models which used oil price and electricity consumption separately. Electricity is the basic element for the industrial production; the countries facing electricity shortage issues cannot sustain their economic growth. The economic growth of developing countries with industrial infrastructure has higher significant association with electricity consumption as compared to oil price. We use oil price and electricity consumption jointly to study the highly predictive observer for economic growth. Secondly, the paper examines the relation between energy and growth over 210 countries; furthermore, we categorize the sample into subgroups; regional level, income level, OECD and non-OECD level to mitigate the heterogeneity in the data set. Finally, we apply [41,43,44] panel cointegration test, fully modified OLS (FMOLS) and

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electricity consumption to GDP. Yuan et al. [56] examined the relationship between economic growth and electricity consumption in China over the period 1978–2004. Johansen-Juselius; Hodrick Prescott filter and VDC have confirmed the impact of electricity consumption on economic growth. Narayana and Singh [40] empirically analyzed Fiji Island from 1971–2002, ARDL bound test confirmed the unidirectional relationship from electricity consumption to economic development. Yuan et al. [57] employed Johansen-Juselius; IRF test, the result verified the bidirectional relationship between economic growth and electricity consumption in China. Narayan and Prasad [37] analyzed 30 OECD countries by using bootstrap testing; result reported the strong effect of electricity consumtion on GDP in Australia, Slovak Republic, Italy, Iceland, Czeck Republic, Portugal, Korea and UK. Yoo and Kwak [55] empirically examined the relationship between electricity consumption and economic growth by using seven south American countries data for the period 1975–2006. The result reported univeriate relation from electricity consumption to economic growth in Ecuador, Columbia, Chile, Brazile and Argentina; bivariate casuality in Venezuela; no relationship in case of Peru. Rufael [46] investigated the relationship between electricity consumption and economic growth in 15 transition countries from 1975 to 2010. Bootstarp panel cointegration test found the significant effect of electricity consumption on economic growth only in Bulgaria and Belarus; unidirectional causality from economic growth to energy consumption found in Czech Republic, Lithuania, Latvia and Russian Federation; Ukraine confirmed the bidirectional relation while no association witnessed in Albania, Romania, Macedonia, Moldova, Slovak Republic, Poland, Serbia, and Slovenia. Karanfil and Li [26] verified a long run association between electricity consumption and growth of 160 countries over the period 1980–2012. In short run, economic growth had an impact on energy consumption. Farzanegan and Markwardt [15] studied Iranian economy over the time 1975–2006; higher oil price stimulate the economic growth of oilexporting countries. Imran, Siddiqui [23] examined 3 SAARC countries (Pakistan, India and Bangladesh) to explore a relationship between energy and economy; long run univariate relation verified from energy consumption to economic growth while no evidence found in support of short run relationship. Aydın and Acar [8] elaborated the relation between oil price and macroeconomic variables in Turkey; GDP, Inflation, trade balance, tax, real output in all industries and carbon dioxide emission. Turkey General Equilibrium Model-Dynamic (TurGEM-D) reported the inverse relationship between oil price and GDP. Timilsina [50] investigated 25 countries to investigate the relationship between oil price and GDP; a positive association found in developing countries because of their manufacturing infrastructure which rely on oil supply. On contrary, rise in oil price trigger the economy activities of oil exporting countries. Lee, 2005 observed the panel of developing countries by applying Pedroni panel cointegration; results confirmed a significant relationship between energy usage and real GDP. Al-Iriani [4] also utilized Pedroni panel cointegration test to analyzed the relationship between energy consumption and real GDP in Gulf Cooperation Council (GCC) countries. Narayan and Smyth [39] used Pedroni panel cointegration test for the study of G7 countries for the period 1972–2002; results reported the significant effect of energy consumption on real GDP. Lee and Chang [27] employed Pedroni panel cointegration to examine the role of energy use, real gross fixed capital formation and labor force on real GDP of Asian countries. Lee et al. [29] again applied Pedroni panel cointegration to analyze the relationship between per capita energy consumption and real GDP per capita. Apergis, Payne [6] examined the Central American countries; Costa Rica, Honduras, Guatemala, Nicaragua and Panama, Pedroni panel cointegration confirmed unidirectional relationship from energy consumption to real GDP. Apergis and Payne [5] examine the effect of nuclear energy consumption, real gross fixed capital formation and labor on real GDP in sixteen

vector error correction model (VECM) to scrutinize the short and long run casuality. The existence of feedback hypothesis is verified in full panel, uppermiddle income, high income, OECD, East Asia & Pacific; growth hypothesis is witnessed in nonOECD, Europe & Central Asia and Middle East & North Africa. Whereas, neutrality hypothesis postulated in low income, low-middle income, Latin America & Caribbean, North America, South Asia and Sub-Saharan Africa region. The results of oil price present bidirectional relationship between energy consumption and GDP in full panel, low-middle income and nonOECD countries; unidirectional relationship from oil price to GDP is in low income, East Asia & Pacific and North America; the inverse relationship is in uppermiddle income, high income, OECD, Europe & Central Asia, South Asia and Sub-Saharan countries; whereas, Latin America & Caribbean and Middle East & North Africa find no causal relation. The long run dynamic presents significant negative relationship in low income, upper-middle income, high income, OECD, East Asia & Pacific, Europe & Central Asia and South Asia. The high price of electricity can be a factor for such negative relationship. The results of full panel and Middle East & North Africa are significant negative. In case of oil price, the coefficient of ECT provides the evidence of significant negative in full panel, upper-middle income, high income, OECD, East Asia & Pacific, Latin America & Caribbean and SubSaharan Africa region; significant positive in low-middle income, nonOECD, Middle East & North Africa and South Asia. The positive finding for low-middle and South Asian region is because of the developing industrial infrastructure that has potential to absorb the oil price shocks. For the case of Middle East & North Africa; the rise in oil price leads to increase the growth of oil exporting countries. Shortly, the finding varies across income, OECD and regional level. Rest of the study composites as follows: Section 2 provides a brief literature of energy consumption, electricity consumption, oil price and Pedroni panel cointegration. Section 3 is about the data and methodology used for estimations. Section 4, reports the result and conclusion. Section 5, provides the consluding remarks. 2. Literature Cheng and Lai [11] applied CI and Hsiao's version of Granger causality in case of Taiwan from 1955 to 1993, results reported unidirectional relation from GDP to energy consumption. AsafuAdjaye [7] provided the evidence of unidirectional relationship from energy consumption to economic growth in case of India and Indonesia from 1973–1995, while Thailand and Philippines reported bidirectional relationship over the period 1971–1995. Soytas and Sari [49] used the data of G-7 and ten emerging countries except PR China from 1950–1990. Energy consumption has a significant positive effect on GDP in Argentine; Italy and Korea postulated unidirectional relation from GDP to energy consumption; Turkey, France, Germany and Japan rejected the relationship phenomena. Ghali and El-Sakka [17] evaluated Canadian data from 1961 to 1997, Johansen-Juselius, VDC confirmed bidirectional relationship between energy consumption and economic growth. Mehrara [33] attempted to investigate the relation between energy consumption and economic growth for 11 oil exporting countries over the period 1971–2002, significant positive relationship confirmed from economic growth to energy consumption. Erdal et al. [14] reported bidirectional causality among economic growth and energy consumption in Turkey for the time 1970–2006. Lee et al. [29] studied the 22 OECD countries from 1960–2001, perdroni panel cointegration supported the bidirectional association between energy consumption per capita and real GDP per capita. Bartleet and Gounder [9] supported a effect of economic growth on energy consumption in case of New Zealand over the period 1960– 2004. Chen et al. [10] investigated 10 industrialized countries for the time 1971–2001, the finding confirmed the unidirectional relationship from 10

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obtain more accurate outcomes.

countries; Pedroni panel cointegration and fully modified OLS justified the relation between nuclear energy consumption and GDP.

3.1.1.2. MW unit root test. MW unit root test is a non parametric test having 2nd degree of freedom (d.f.) in chi-square distribution, whereas number of countries is represented by n. The test statistics are given:

3. Data The study use unbalanced panel data of 210 countries over the period 1960–2014, the data of GDP current $US is used as a proxy of economic growth (GDP); electricity consumption (EC) use the data of electric power consumption KWh, gross fixed capital formation (FCF) and total labor (L) are collected from The World Bank database.1 Whereas, the data of crude oil price (OP) is taken from Statistical Review of World Energy 2015.2 To standardize the variables, we calculate the natural logarithm of all variables. Furthermore, special administrative territories and independent regions are treated independently.

n

λ = −2 ∑ loge ( pi ) ∼ χ22n (d . f . ) i =1

Where pi is the probability value of ADF tests for unit i.

3.1.2. Panel cointegration tests Perdroni heterogenous panel cointegration test applied for long run inference which estimates different individual effects by assuming cross sectional interdependence. Models are as follows:

3.1. Estimation strategy

3.1.2.1. Economic growth model.

GDPit = αi + δit + γ1i ECit + γ2i OPit + γ3i FCFit + γ4i POPit + εit

3.1.1. Panel unit root Present study employed [30] (LLC) and [31] (MW, ADF and MW, PP) panel unit root test used to estimate the stationary of the variables. LLC represents a pooled panel unit root estimation, IPS illustrates a heterogeneous panel test while (MW, ADF and MW, PP) is a nonparametric panel unit root test.

Where i=1,2….n refers to each country and t=1,2…..T for time period. Where α represent country specific fixed effect, δ allows deterministic trend, all the variables are natural logritum so γ’s perameter interpret as elasticities. Error term (ε) stand for the variation from the long run relationship. The null hypothesis states no cointegration (ρi=1) and the following unit root test is performed on residuals as given:

3.1.1.1. LLC Unit root test. LLC panel unit root test treats time trend and individual specific intercepts. The test enforces homogeneity on the autoregressive coefficient that specifies unit root presence or absence, whereas intercept and time trend can differ across individual series.

εit = ρi εit −1 + wit Pedroni [41–44] sepertaed cointegration test into two catageries; panel (within dimension) and group (between dimension) which consists on four and three statistics respectively. Panle (within dimension) consists heterogentiy across individual entities into common time factor and have panel v, panel ρ, panel PP and panel ADF3 statistics; group (between dimension) estimates the unit root test on residuals then compute the mean of individual autoregressive cofficients of residual, it contains group ρ, group PP, group ADF statistics.4

H0: each time series contains a unit root

(ρ1 = ρ2 = ........ ρn = ρ = 0) H1. each time series is stationary

(ρ1 = ρ2 = ........ ρn = ρ < 0) The test used augmented dickey fuller (ADF) regression for hypothesis estimation,

3.1.2.2. Estimation of panel cointegration regression. Given the existence of cointegration, estimate the long run cointegration parameters. Kao, Chiang [25] proposed the Dynamic OLS (DOLS) technique based on parametric panel; that grouped the data over the within dimension of panel. Kao and Chiang [25] DOLS estimation ignored the significance of cross sectional heterogeneity. Therefore, [41–44] fully modified OLS (FMOLS) employed for heterogeneous cointegration panel.

pi

△yit = αi + ρiyi, t −1 +

∑ θiL △yit −L + εit L =1

Now it runs two supporting regressions Pi

△yit = λi +

∑ γi,t −L Δyi,t −L +

eit , to obtain eˆit

N ⎛ T ⎞ ⎞−1 ⎛ T ⌢ β = N−1 ∑ ⎜⎜∑ ( yit − y )2 ⎟⎟ ⎜⎜∑ ( yit − y ) ⎟⎟ zit* − T⌢ ηi ⎠ ⎠ ⎝ t =1 i =1 ⎝ t =1

L =1

Pi

yi, t −1 = ∂i +

∑ ℓi,t −L Δyi,t −L + νi,t −1

, to obtain vˆi, t −1

L =1

where

Next step is standardization of eˆit and vˆi, t −1 ∼ eit = eˆit / σˆεi and v∼i, t −1 = vˆit / σˆεi

⌢ ⌢ L L 13i ⌢ ⌢0 ⌢0 ⌢ zit* = (zit − z ) − ⌢13i △yit , ⌢ ηi ≡ Γ13i + Ω13i − ⌢ ( Γ 14i + Ω14i ) L14i L 14i ⌢ ⌢ and L i is the lowest trianguler decomposition of Ωi . t-statistics is given:

Where, σˆεi represent the standard error Finally, pooled OLS regression is estimated e∼ = ρv∼ + ε∼ it

i, t −1

(1)

N

⌢ t⌢ = N−1/2 ∑ tβ *, i β*

it

i =1

LLS panel unit root test has some disadvantages; first, it assumes cross-sectional independence; second, acceptance of null hypothesis indicates all cross sectional contains unit root although some cases don’t have the unit root; if T is very small (N is very large) then panel unit root test is appropriate. So, we also apply further unit root test to

1/2 ⌢ ⌢−1 T , i = ( β i* − β0 ) [Ω13i ∑t =1 ( yit − y )2 ] where t ⌢ β*

3.1.3. Panel VECM causality The existence of cointegration implies that unidirectional or

1

http://data.worldbank.org/ http://www.bp.com/en/global/corporate/about-bp/energy-economics/statisticalreview-of-world-energy.html 2

3 4

11

Panel v, panel ρ, panel PP are non-parametric test, panel ADF is parametric test. Group ρ, group PP are non-parametric, group ADF is parametric test.

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more enrgy to facilitate their industries which inturn supports the economy. OECD is the only significant negative category in the whole dataset. However, low-income, high-income, nonOECD, Latin America & Caribbean and North America are insgnificant and contradicting with the result of [16]. Oil price confirms significant positive relationship in full panel, low income, lower-middle income, high income, OECD, nonOECD, East Asia & Pacific, Latin America & Caribbean, Middle East & North Africa and Sub-Saharan African regions. The positive relation indicates the energy price saving; lower oil price also mean to curtail the payment for imports (oil). Whereas, North America is only region having adversely affected by the rise in oil price. As these countries are oil exporters as well as oil importers, the rapid decline in oil prices have both negative and positive effects on different sectors. In case of canada, the real GDP increased by 2.4% in last quarter on 2014, on the contrary, real incomes contracted due to the value of export (oil). Fixed capital formation and population help to enhance the economic growth; the results are similar to [5]. The finding implies that concrete electricity generation policies and an efficient measures play a constructive role in economic development. FMOLS results indicate the significance of economic growth in five out of five developing country categories; lower-middle income, uppermiddle income, East Asia & Pacific, Middle East & North Africa and South Asia confirm significant positive effect of electricity consumption on economic growth, whereas oil price is significant only for three out of five developing country categories; upper-middle income and South Asia are shown insignificant. Concisely, the results validate that developing countries heavily reliance on electricity consumption despite of oil prices for economic growth. The proficent and sound fiscal police, monetary policy and industrial infrastructure have the ability to mitigate the effect of oil price shocks on economic growth.

bidirectional causality exists between variables [19]. To infer the causality between variables, [45] panel vector error correction model (VECM) is estimated. The method presents the results of long run and short run adjustments to changes in GDP, speed of correction to disequilibrium and long run coefficients. The following are the VECM models: q

q

ΔGDPit = α1ij + ∑k =1 β11ik ΔGDPit − k + ∑k =1 χ12ik ΔECit − k q

q

+ ∑k =1 δ13ik ΔOPit − k + ∑k =1 ϕ14ik ΔFCFit − k q

+ ∑k =1 φ15ik ΔPOPit − k + γ1i εit − k + ν1it q

q

ΔECit = α2ij + ∑k =1 β21ik ΔGDPit − k + ∑k =1 χ22ik ΔECit − k q

q

+ ∑k =1 δ 23ik ΔOPit − k + ∑k =1 ϕ24ik ΔFCFit − k q

+ ∑k =1 φ25ik ΔPOPit − k + γ 2i εit − k + ν2it q

q

ΔOPit = α3ij + ∑k =1 β31ik ΔGDPit − k + ∑k =1 χ32ik ΔECit − k q

q

+ ∑k =1 δ33ik ΔOPit − k + ∑k =1 ϕ34ik ΔFCFit − k q

+ ∑k =1 φ35ik ΔPOPit − k + γ 3i εit − k + ν3it q

q

ΔFCFit = α4ij + ∑k =1 β41ik ΔGDPit − k + ∑k =1 χ42ik ΔECit − k

ΔPOPit =

q q + ∑k =1 δ43ik ΔOPit − k + ∑k =1 ϕ44ik ΔFCFit − k q + ∑k =1 φ45ik ΔPOPit − k + γ 4i εit − k + ν 4it q q α5ij + ∑k =1 β51ik ΔGDPit − k + ∑k =1 χ52ik ΔECit − k q q + ∑k =1 δ53ik ΔOPit − k + ∑k =1 ϕ54ik ΔFCFit − k q

+ ∑k =1 φ55ik ΔPOPit − k + γ 5i εit − k + ν5it ε is the lagged error term specified in Eq. (1) which measures the long run deviation from long run equilibrium of specified variables; tstatistics is used for the significance of error correction term. Short run Granger casuality is tested by using F-statistics (Table 1).

4.4. Panel causality results

4. Results and discussion

Table 5 present the results of short run and long run causality relationship, the study applies panel VECM to infer the causal relationship. The result of full panel, upper-middle income, high income, OECD, East Asia & Pacific confirm a bidirectional relationship between electricity consumption and GDP; the finding supports the feedback hypothesis and similar to [22,57]. On the other side, nonOECD, Europe & Central Asia and Middle East & North Africa find unidirectional relationship from electricity consumption to GDP; the results certify the growth hypothesis, the finding is consistent with ([3]; [51]; [32]). The bidirectional relationship specify that high economic growth stipulate the industrial development and household living standards which leads to increase the electricity consumption. Whereas, low income, low-middle income, Latin America & Caribbean, North America, South Asia and Sub-Saharan Africa region state no causal relationship and postulate the neutrality hypothesis. In short run, the results of oil price present bidirectional relationship between electricity consumption and GDP in full panel, lowmiddle income and nonOECD countries; unidirectional relationship from oil price to GDP is in low income, East Asia & Pacific and North America; the inverse relationship is in upper-middle income, high income, OECD, Europe & Central Asia, South Asia and Sub-Saharan countries; whereas, Latin America & Caribbean and Middle East & North Africa find no causal relationship. In 2014, the fall in oil prices, Colombia, Venezuela and Ecuador have experienced a decline in their economic growth rate. Nonetheless, Maxico, Brazil and Argentina have experienced prudent economic growth. From a macro perspective, the International Monetary Fund (IMF) has estimated that Latin America reacts mostly neutral, with no net gain from rise/decline of oil price. Gross fixed capital formation and population justify bidirectional relationship with GDP in full sample; subgroup level depicts diverse findings. The long run dynamics are illustrated by the error correction term

4.1. Panel unit root results Table 2 presents the panel unit root estimations of variables at level and first difference, we apply three unit root tests: LLC, MW (ADF) and MW(PP). From these tests, it is pointed that at level all the variables are non-stationary but stationary in first difference at 1% level of significance. Thus, null hypothesis of non-stationary affirmed that series are integrated of order one I(1) in full panel. 4.2. Pedroni panel cointegration We then proceed for Pedroni panel cointegration approach based on Eq. (1). The results are reported in Table 3. The finding of panel and group statistics reject the null hypothesis of no cointegration in full panel, income level, OECD level and regional level. Therefore, Pedroni panel cointegration results supported a long run relationship between GDP, electricity consumption, oil price, gross fixed capital formation and population. 4.3. Fully Modified OLS (FMOLS) The empirical results of FMOLS are shown in Table 4. The reults indicate the significant positive impact of electricity consumption on GDP in case of full panel, lower-middle income, upper-middle income, East Asia & Pacific, Europe & Central Asia, Middle East & North Africa, South Asia and Sub-Saharan African region. The positive significant relation of lower-middle income and upper-middle income, East Asia & Pacific, Middle East & North Africa and South Asia countries support the feedback hypothesis. Furthermore, the countries belong to these categories are developing economies thus they need 12

13 1971–2002 2004projection 1990–2010

Fiji Island

Turkey

1990–2012

High Renewable energy consumption countries

Developing and Developed Countries

High, Middle and Low income countries

2009projection At least 30 years 1991–2012

25 countries

Oil exporting countries

1966–2002 1971–1999

1971–2001

1971–2002

FMOLS - Heterogeneous panel causality test GMM

REC → GDP (+)Role of RECDeveloped > DevelopingMajor Developed > other DevelopedUpper-income countries > Lower-income countries

18 countries (EC↔GDP)25 countries (EC→GDP)40 countries (GDP→EC)36 countries (EC≠GDP) REC → GDP (+)

Developing countries (OP →GDP) (-)Oil exporting countries (OP →GDP) (+)

Simulation Granger causality test

(EC →GDP)

(OP →GDP) (-)

EC→GDP

PMG cointegration

Simulation

ARDL bounds test

Johansen-Juselius; Pedroni Panel Cointegration Johansen-Juselius Johansen-Juselius

Johansen-Juselius

EC↔GDP (Malaysia, South Korea)GDP→EC (Colombia, El Salvador, Indonesia, Kenya, Mexico)EC→GDP (Canada, Pakistan, Singapore, Turkey)EC≠GDP (France, Germany, India, Israel, Luxembourg, Norway, Philippines, Portugal, UK, USA, Zambia) EC→GDP EC→GDP GDP→EC EC↔GDP EC↔GDP (Egypt, Morocco)GDP→EC (Cameroon, Gabon, Ghana, Nigeria, Senegal, Zambia, Zimbabwe)EC→GDP (Benin, Congo DR, Gabon, Tunisia) EC≠GDP (Algeria, Congo Rep, Kenya, Sudan, South Africa) EC↔GDP (Malaysia, Singapore) GDP→EC (Thailand, Indonesia) EC↔GDP (Hong Kong)GDP→EC (India, Korea, Malaysia, Philippines, Singapore)EC→GDP (Indonesia)EC≠GDP (China, Taiwan, Thailand) EC↔GDP GDP→EC

Conclusion(s)

EC represents energy consumption, REC presents renewable energy consumption, OP stands for oil price and GDP is gross domestic product; the sign (→) shows unidirectional relationship, (↔) is about bidirectional relationship and (≠) represents no relationship between variables.

Ahmed and Azam (2016) Bhattacharya et al. (2016) Amri and Fethi (2017)

Damette and Seghir, (2013) Timilsina, [50]

Ho and Siu [22] Mozumder and Marathe [35] Narayan and Singh (2007) Aydın and Acar, [8]

Chen et al. [10]

Yoo [54]

Johansen-Juselius Johansen-Juselius ARDL bounds test Johansen-Juselius Toda-Yamamoto causality

Granger Causality

1970–1990

1971–2000 1954–2003 1966–1999 1970–2002 1971–2001

Methodology

Time Period

China, Hong Kong, Indonesia, India, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand Hong Kong Bangladesh

Canada, Colombia, El Salvador, France, Germany, Hong Kong, India, Indonesia, Israel, Kenya, Luxembourg, Malaysia, Mexico, Norway, Pakistan, Philippines, Portugal, Singapore, South Korea, Turkey, UK, USA, Zambia China Taiwan Australia Korea Algeria, Benin, Cameroon, Congo DR, Congo Rep, Egypt, Gabon, Ghana, Kenya, Morocco, Nigeria, Senegal, South Africa, Sudan, Tunisia, Zambia, Zimbabwe Indonesia, Malaysia, Singapore, Thailand

Murray and Nan [36]

Shiu and Lam [47] Lee and Chang [28] Narayan and Smyth [38] Yoo [53] Wolde-Rufael [52]

Countries

Author(s)

Table 1 Literature highlights.

S. Sarwar et al.

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Table 2 Panel Unit Root tests.

Table 4 FMOLS.

Variables

At Level

LLC test GDP EC OP FCF POP MW (ADF) test GDP EC OP FCF POP MW (PP) test GDP EC OP FCF POP **,*

Coefficient

Coefficient

3.211 36.935 5.926 11.534 78.456

−59.588** −38.042** −87.655** −47.404** −91.968**

447.747 64.664 125.090 379.971 0.00084

3409.970** 2437.750** 5535.340** 2413.910** 8590.780**

501.170 60.493 163.965 433.705 0.0001

4467.410** 2663.550** 5528.020** 3494.840** 8624.720**

indicates the significance at 1% and 5% respectively.

Groups

EC

OP

FCF

POP

Full Panel Income Level Low Lower-Middle Upper-Middle High OECD OECD non-OECD Region East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa North America South Asia Sub-Saharan Africa Renewable Energy Low Middle High Oil Import / Export Import Export

0.216**

0.112**

0.509**

1.298**

0.043 0.355** 0.498** −0.049

0.213** 0.191** 0.0101 0.117**

0.372** 0.441** 0.494** 0.587**

2.295** 0.587** 0.902 1.895**

−0.130** 0.0811

0.040** 0.243**

0.684** 0.429**

1.722** 2.176**

0.156** 0.322** 0.109 0.180** −0.214 0.458** 0.161*

0.119** 0.031 0.113** 0.224** −0.159** 0.072 0.215**

0.462** 0.586** 0.565** 0.404** 0.264** 0.290** 0.478**

1.149** 1.176 1.206** 2.176** −0.049 0.159 1.312**

−1.087** −0.017 0.141**

0.190 −0.070 0.109**

0.861** 0.967** 0.526**

0.763** 0.164** 1.599**

−0.332** 0013

0.223 −0.008

0.764** 0.777**

0.503** 1.146**

**, *

indicates the significance at 1% and 5% respectively.

Table 3 Panel cointegration tests. Test

Panel ν -statistics Panel ρ -statistics Panel PPstatistics Panel ADFstatistics Group ρ -statistics Group PPstatistics Group ADFstatistics

Panel ν -statistics Panel ρ -statistics Panel PPstatistics Panel ADFstatistics Group ρ -statistics Group PPstatistics Group ADFstatistics

Panel ν -statistics Panel ρ -statistics Panel PPstatistics Panel ADFstatistics Group ρ -statistics Group PPstatistics Group ADFstatistics

Full panel

Income level

OECD level

Low

Lower-Middle

Upper-Middle

High

OECD

nonOECD

12.938** −7.826** −16.515**

−0.690 −0.703 −2.460**

3.156** −3.040** −8.762**

8.694** −0.052 −5.823**

9.902** −10.051** −16.380**

9.826** −10.294** −15.695**

0.685 0.518 −1.203

−4.537**

−0.164

−4.304**

−0.158

−3.034**

−3.132**

−0.982

4.410 −8.199**

1.109 −2.252*

3.171 −1.428

3.629 −2.570**

0.963 −8.770**

−0.035 −6.078**

1.607 −6.463**

−6.407**

2.794

−2.229*

−3.346**

−6.692**

−5.901**

−3.302**

Regional level East Asia & Pacific 1.610 −9.275** −14.514**

Europe & Central Asia 10.975** −0.027 −6.640**

Latin America & Caribbean 1.571 −0.564 −1.096

Middle East & North Africa 0.503 1.819 1.000

North America

South Asia

3.781** −3.281** −6.725**

2.663** −3.090** −7.187**

Sub-Saharan Africa 0.973 −0.545 −3.376**

−5.182**

2.451

−1.546

0.358

−3.130**

−2.369**

−1.984*

1.070 −2.059*

3.620 −3.918**

1.338 −2.127*

2.487 −3.725**

−3.544** −7.918**

−0.350 −3.304**

2.198 −3.094**

−3.457**

−3.746**

−2.415**

−2.750**

−1.517

−0.729

−1.578

Renewable energy consumption level Low Middle High 11.1568** 0.005 −0.990 −10.3088** 2.912 2.449 ** −19.5558 −1.373 −2.5558**

Oil import/export Import 3.9938** −7.4668** −15.0968**

−7.0428**

−0.424

−2.4128**

−6.6598**

0.229

5.830 −7.5258**

5.6136 −3.1218**

7.125 −6.311**

2.7228 −4.9798**

3.476 −6.5208**

−6.2698**

−1.8118*

−4.0778**

−4.6388**

−4.4798**

** *

, indicates the significance at 1% and 5% respectively.

14

level Export 11.8748** −3.2368** −9.5338**

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Table 5 VECM based Granger causality results. Dependent Variables

Sources of causation (independent variables) Short-run ΔGDP

Full panel ΔGDP ΔEC ΔOP ΔFCF ΔPOP Low income ΔGDP ΔEC ΔOP ΔFCF ΔPOP Low-Middle income ΔGDP ΔEC ΔOP ΔFCF ΔPOP Upper-Middle income ΔGDP ΔEC ΔOP ΔFCF ΔPOP High Income ΔGDP ΔEC ΔOP ΔFCF ΔPOP OECD ΔGDP ΔEC ΔOP ΔFCF ΔPOP non-OECD ΔGDP ΔEC ΔOP ΔFCF ΔPOP East Asia & Pacific ΔGDP ΔEC ΔOP ΔFCF ΔPOP Europe & Central Asia ΔGDP ΔEC ΔOP ΔFCF ΔPOP Dependent Variables

11.814** 56.254** 146.784** 13.814*

ΔFCF

ΔPOP

29.376**

12.156** 14.406**

10.430** 0.349 0.009

16.665** 4.914 88.443** 12.270**

0.00003 0.00005* −0.00048** −0.00006 0.00004**

0.507 0.846 26.149** 0.326

−0.007413 −0.011798* −0.018017 0.009334 0.002111**

16.330** 5.019 103.284** 21.707**

0.0000012 0.0000067 0.000063** 0.0000012 0.000006**

37.950** 2.989 106.122** 34.021**

−0.000694 −0.00596**. −0.03811** 0.017952** −0.00276**

6.653* 3.106 20.204** 1.956

−0.00759 −0.0483** −0.0667** 0.01372 0.00108*

10.542** 1.487 5.188 3.165

0.00165 −0.04073** −0.04286** 0.00876 0.00126*

57.365** 5.665 69.183** 53.273**

0.00842 0.00719 0.02545** 0.02265** 0.00226**

6.582* 0.346 10.526** 0.379

0.00394** −0.00873** −0.01086** 0.002344 0.000575**

7.156* 0.515 25.940** 9.193*

0.12632** −0.01403* 0.02504 0.30561** −0.00542**

1.543 7.333* 29.339**

0.428 0.736 3.267 0.946

2.468 10.460** 22.487** 0.369

0.384 6.547* 0.729 15.872**

15.652** 17.135** 19.818** 3.399

1.983 39.547** 2.147 48.821**

53.403** 32.913** 108.194** 7.889*

5.82 3.868 49.650** 59.774**

44.452** 27.304** 66.564** 6.202*

4.779 4.641 45.104** 10.739**

0.138 6.472* 7.976* 1.1

2.641 0.263 1.603 15.941**

35.599** 4.41 86.131** 2.106

1.237 9.636** 91.207** 62.161**

0.866 41.866** 26.985** 6.809*

2.663 63.554** 7.628*

15.293** 9.815** 6.671* 0.628 1.858 1.674 12.264** 0.902 17.950** 8.433* 0.986 2.949 4.124 5.13 5.022 9.380** 1.864 4.69 2.859 10.410** 2.407 2.966 11.443** 2.098 8.609* 2.177 6.369* 0.617 1.22 0.325 4.499 37.705**

8.213** 3.051 0.576 0.313 1.612 14.923** 18.538** 0.117 1.709 8.293* 2.068 0.154 2.288 1.224 41.539** 1.928 5.612 2.342 36.697** 0.198 4.94 2.95 3.218 2.046 1.945 0.651 8.085* 2.035 0.472 8.332* 1.334 3.063

3.315 7.860*

4.143

ΔEC

ΔOP

ΔFCF

ΔPOP

0.629

3.776 0.705

5.756 3.603 0.254

12.858** 8.575* 68.358** 17.004**

Sources of causation (independent variables) Short-run ΔGDP

Latin America & Caribbean ΔGDP ΔEC ΔOP ΔFCF ΔPOP Middle East & North Africa ΔGDP ΔEC ΔOP

ΔOP

1.558 1.912 0.734 0.718 1.496

Long-run ECT

ΔEC

2.746 4.915 9.321** 0.54

1.662 2.268 0.276 19.851**

1.46 1.847

4.201 2.854 5.561 2.868

0.895

15

0.911 6.046* 1.851 1.489

44.185** 0.672 53.923**

Long-run ECT

−0.00445 −0.00331 −0.04847** 0.00169 −0.00197** 0.00397 0.00529** 0.03495** (continued on next page)

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Table 5 (continued) Dependent Variables

ΔFCF ΔPOP North America ΔGDP ΔEC ΔOP ΔFCF ΔPOP South Asia ΔGDP ΔEC ΔOP ΔFCF ΔPOP Sub-Saharan Africa ΔGDP ΔEC ΔOP ΔFCF ΔPOP Low REC ΔGDP ΔEC ΔOP ΔFCF ΔPOP Middle REC ΔGDP ΔEC ΔOP ΔFCF ΔPOP High REC ΔGDP ΔEC ΔOP ΔFCF ΔPOP Oil Import ΔGDP ΔEC ΔOP ΔFCF ΔPOP Oil Export ΔGDP ΔEC ΔOP ΔFCF ΔPOP **, *

Sources of causation (independent variables) Short-run ΔGDP

ΔEC

ΔOP

ΔFCF

20.890** 2.051

8.026* 1.179

12.876** 1.744

3.613

2.218

6.337* 2.978

4.878 0.595 3.681 0.184

0.806 3.511 1.085 0.771

2.3 6.352* 0.783 6.514*

2.87 1.263 1.794 0.81

4.191 13.763* 13.094** 1.906

0.795 2.758 2.268 8.366*

20.015** 3.403 43.596** 8.038*

3.726 0.199 19.786** 5.118

4.352 28.843** 40.584** 0.896

4.402 0.298 0.167 2.719

6.325* 28.929** 45.183** 0.228

0.208 4.366 0.846 11.065**

16.948** 25.239** 94.641** 4.103

1.251 5.386 77.713** 19.256**

20.277** 36.394** 46.864** 13.781**

0.152 26.760** 3.623

6.397* 7.133* 0.917

0.395 0.09

2.398 18.625** 0.488

1.014 0.241

0.00934 0.00245**

2.587 1.628 1.104 2.096

−0.34985** 0.00002 0.06415 −1.4119** 0.00544

16.625** 4.313 10.527** 6.134*

−0.01592 −0.01896** 0.03032* 0.02611* 0.0002

12.778** 1.883 65.922** 0.842

0.0000854 0.0000003 −0.002608** 0.001738 −0.000316**

38.983 10.443** 351.378** 20.869**

0.001787** −0.00648** 0.006457 0.003165 −0.000351**

24.399** 9.499** 143.520** 26.274**

−0.005597* −0.003010 −0.052190** 0.002895 0.002058**

0.870 4.788 1.476 4.476

0.000455** 0.000110** 0.002300** 0.000601** 0.000003

2.545 3.035 44.343** 5.336

0.000052 −0.000590** −0.004300** −0.000880 0.000196**

13.441** 3.503 67.656** 3.722

0.017018 −0.029750** −0.048910** 0.117491** −0.004528**

0.049

2.624 1.786

9.756** 3.414 0.405

2.9 3.881

1.259

0.804 5.262

8.341* 7.341* 1.599

5.317 23.311** 0.703 6.868*

4.128 9.389** 0.864 4.952

2.210 2.166

3.870

11.652** 7.547* 12.763** 0.970 6.284* 3.398

15.124** 7.351* 0.778 1.308 12.017** 11.682** 0.852

12.885** 5.960 6.282* 13.114**

5.415 2.715 18.890** 1.864 0.936

Long-run ECT

46.346**

0.562

0.102 0.481

6.5466* 6.791*

ΔPOP

indicates the significance at 1% and 5% respectively.

low-middle and South Asian region is because of the developing industrial infrastructure that has potential to absorb the oil price shocks. Oil price shocks increase the economic risk which in turn yields higher economic growth [21]. For the case of Middle East & North Africa; the rise in oil price leads to increase the growth of oil exporting countries; the finding is consistent to [50]. The result of gross fixed capital formation is significant positive in full panel, low-middle income, upper-middle income, Europe & Central Asia, Middle East & North Africa, North America and Sub-Saharan African countries. Population is significant positive in full panel and all sub groups (except low income and North American countries). Shortly, the finding varies across income, OECD and regional level.

(ECT) in Table 5. The coefficient of error correction term is significant negative in low income, upper-middle income, high income, OECD, East Asia & Pacific, Europe & Central Asia and South Asia; in long run, electricity consumption undermine the process of economic developmet. May be the excess use of energy by inefficient industrial sector is main cause for negative ralationship between energy consumption and economic growth [51]. The results of full panel and Middle East & North Africa are significant negative; these regions should have to invest more in electricity sector and infrastructure to enhance the GDP [51]. Oil price hike reacts differently across countries [50]. In case of oil price, the coefficient of ECT provides the evidence of significant negative in full panel, upper-middle income, high income, OECD, East Asia & Pacific, Latin America & Caribbean and Sub-Saharan Africa region; significant positive in low-middle income, nonOECD, Middle East & North Africa and South Asia. The positive finding for

5. Summary and conclusion Present study use the panel data of 210 countries over the period 16

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For future directions, the countries with several years’ data and few years’ data should be scrutinized and treated separately to attain more reliable results. Further subgroups should be analyzed on the basis of underdeveloped, developing and developed countries. The oil crises period should be treated separately to examine the structural shifts in the relationship. Another direction that upcoming researchers, electricity price directly hit the firms earning, industrial growth and economic indicators. So, oil price can be replaced by electricity price.

1960–2014 to analyze the empirical relationship between economic growth, electricity consumption, oil price, gross fixed capital formation and population. Furthermore, the data is categorized into income, OECD and regional level. To find the cointegration among variables, we have employed Pedroni panel cointegration test. The panel vector error correction model use to study short run and long run relationship between variables. The existence of feedback hypothesis is verified in full panel, uppermiddle income, high income, OECD, East Asia & Pacific; growth hypothesis is witnessed in nonOECD, Europe & Central Asia and Middle East & North Africa. The bidirectional relationship specify that high economic growth stipulate the industrial development and household living standards which leads to increase the electricity consumption. Whereas, neutrality hypothesis postulated in low income, lowmiddle income, Latin America & Caribbean, North America, South Asia and Sub-Saharan Africa region. The results of oil price present bidirectional relationship between energy consumption and GDP in full panel, low-middle income and nonOECD countries; unidirectional relationship from oil price to GDP finds in low income, East Asia & Pacific and North America; the inverse relationship is in upper-middle income, high income, OECD, Europe & Central Asia, South Asia and Sub-Saharan countries; whereas, Latin America & Caribbean and Middle East & North Africa region find no causal relationship. The findings indicate the significance of economic growth in five out of five developing country categories; lower-middle income, uppermiddle income, East Asia & Pacific, Middle East & North Africa and South Asia confirm significant positive effect of electricity consumption on economic growth, whereas oil price is significant only for three out of five developing countiry categories; upper-middle income and South Asia are shown insignificant. Concisely, the results validate that developing countries heavily reliance on electricity consumption despite of oil prices for economic growth. The proficent and sound fiscal police, monetary policy and industrial infrastructure have the ability to mitigate the effect of oil price shocks on economic growth. The long run dynamic presents significant negative relationship in low income, upper-middle income, high income, OECD, East Asia & Pacific, Europe & Central Asia and South Asia. May be the excess use of energy by inefficient industrial sector is main cause for negative ralationship between energy consumption and economic growth [51]. The results of full panel and Middle East & North Africa are significant negative. In case of oil price, the coefficient of ECT provides the evidence of significant negative in full panel, upper-middle income, high income, OECD, East Asia & Pacific, Latin America & Caribbean and Sub-Saharan Africa region; significant positive in low-middle income, nonOECD, Middle East & North Africa and South Asia. The positive finding for low-middle and South Asian region is because of the developing industrial infrastructure that has potential to absorb the oil price shocks. For the case of Middle East & North Africa; the rise in oil price leads to increase the growth of oil exporting countries. Shortly, the finding varies across income, OECD and regional level. In short run, growth and feedback hypothesis suggest that more vigorous electricity policies should be implemented to attain high economic growth. Whereas, government should allocate more funds to discover cheap alternates of electricity generation and reduce the electricity power distribution and transmission losses for long term steady growth. Likewise, the preemptive measures should be taken to rescue the economy from oil price shocks; implement high tax share on oil price and increase the oil reserves during the time of low oil price. For future directions, the countries with several years’ data and few years’ data should be scrutinized and treated separately to attain more reliable results. Further subgroups should be analyzed on the basis of underdeveloped, developing and developed countries. The oil crises period should be treated separately to examine the structural shifts in the relationship. Another direction that upcoming researchers, electricity price directly hit the firms earning, industrial growth and economic indicators. So, oil price can be replaced by electricity price.

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