PETROLEUM CONSUMPTION AND ECONOMIC GROWTH RELATIONSHIP: EVIDENCE FROM THE INDIAN STATES Seema Narayan, Thai-Ha Le, Badri Narayan Rath and Nadia Doytch* This paper reveals that over the period 1985-2013, the wealthier states of India experienced a prevalence of the feedback hypothesis between real gross domestic product growth and petroleum consumption in the short run and the long run. Over the short term, the whole (major) 23 Indian state panels show support for the conservative hypothesis. Regarding the panels comprising low- and middle-income Indian states, although there appeared to be significant bidirectional effects in the long run, none of the results suggest that energy consumption increases economic growth. This implies that growth in energy demand can be controlled without harming economic growth. The results, however, indicate that for the low- and middle-income states, increases in petroleum consumption could adversely affect economic activity in the short and long run. These findings relate to the aggregate data on petroleum. Examining the short-run and long-run energy-growth linkages using disaggregated data on petroleum consumption reveals that only a few types of petroleum products have stable long-run relationships with economic growth. In fact, with disaggregated petroleum data, the vector error correction model (VECM) and cointegration results support the neutral hypothesis for high-incomes states. For the low- and middle-income groups, while the conservation effect is found to prevail in the short run and the long run, higher economic growth appears to reduce consumption of selected types of petroleum products. JEL classification: O13, Q43, C33 Keywords: petroleum consumption, economic growth, feasible generalized least squares (FGLS), cross-sectional dependence, Indian states 21 * Seema Narayan, RMIT University, Australia. Thai-Ha Le, corresponding author, RMIT University, Viet Nam (email: [email protected]). Badri Narayan Rath, Indian Institute of Technology Hyderabad, India. Nadia Doytch, City University of New York, Brooklyn College and Graduate Center.
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PETROLEUM CONSUMPTION AND ECONOMIC GROWTHRELATIONSHIP: EVIDENCE FROM THE INDIAN STATES
Seema Narayan, Thai-Ha Le, Badri Narayan Rath and Nadia Doytch*
This paper reveals that over the period 1985-2013, the wealthier states ofIndia experienced a prevalence of the feedback hypothesis between realgross domestic product growth and petroleum consumption in the shortrun and the long run. Over the short term, the whole (major) 23 Indian statepanels show support for the conservative hypothesis. Regarding the panelscomprising low- and middle-income Indian states, although there appearedto be significant bidirectional effects in the long run, none of the resultssuggest that energy consumption increases economic growth. This impliesthat growth in energy demand can be controlled without harming economicgrowth. The results, however, indicate that for the low- and middle-incomestates, increases in petroleum consumption could adversely affecteconomic activity in the short and long run. These findings relate to theaggregate data on petroleum. Examining the short-run and long-runenergy-growth linkages using disaggregated data on petroleumconsumption reveals that only a few types of petroleum products havestable long-run relationships with economic growth. In fact, withdisaggregated petroleum data, the vector error correction model (VECM)and cointegration results support the neutral hypothesis for high-incomesstates. For the low- and middle-income groups, while the conservationeffect is found to prevail in the short run and the long run, higher economicgrowth appears to reduce consumption of selected types of petroleumproducts.
JEL classification: O13, Q43, C33
Keywords: petroleum consumption, economic growth, feasible generalized least squares
(FGLS), cross-sectional dependence, Indian states
21
* Seema Narayan, RMIT University, Australia. Thai-Ha Le, corresponding author, RMIT University,Viet Nam (email: [email protected]). Badri Narayan Rath, Indian Institute of Technology Hyderabad,India. Nadia Doytch, City University of New York, Brooklyn College and Graduate Center.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
22
I. INTRODUCTION
Energy is an inseparable component of economic development. Among the
different energy sources, such as coal, oil, natural gas, electricity, solar, wind and
nuclear energy, oil continues to play a vital role in a country’s economy, supporting, for
example, transportation, industries, and households. In this regard, India is no
exception, however, oil is the largest energy source of the country, accounting for 31 per
cent of primary energy consumption. In 2018, oil consumption in India was 239.1 million
tons oil equivalent, an increase of 5.3 per cent compared to the previous year, and
represented a 5.1 per cent share of total world oil consumption (British Petroleum, 2019,
p. 21). In terms of barrels per day, the country consumed 5,156,000 barrels per day
(bpd), increased by 5.9 per cent compared to the previous year, and accounted for
5.2 per cent of world oil consumption in 2018, according to British Petroleum (2019).
India was the third largest consumer of crude oil in the world during the year, only
behind the United States of America (20,456,000 million bpd) and China (13,525,000
million bpd) in terms of consumption (British Petroleum, 2019).
According to Reuters, in 2017, India became the third largest net oil importer in the
world, with imports averaging 4.37 million barrels per day (Verma, 2018). Because of its
fast growing economy, energy demand in India rose rapidly over the years, in terms of
per capita energy consumption and oil consumption. This is attributable to the increased
affordability of oil (on the back of the drop in the price of oil) for a large section of its
population who previously could not afford it, as is evident in the motorization of the
Indian economy (Sen and Sen, 2016).
In per capita terms, however, oil consumption in India remains relatively low in
comparison to the world’s largest consuming economies and to other non-Organization
for Economic Cooperation and Development (OECD) countries (Sen and Sen, 2016).
Interestingly, even though the population of India is 1.3 billion, the country still lags other
emerging market powerhouses in oil consumption per capita, giving it room for rapid
growth. In September 2014, a policy initiative, the “Make in India” programme, was
launched by Prime Minister Narendra Modi.1 The objective of the programme is to put
manufacturing at the heart of the country’s growth model. A government target of
increasing the manufacturing sector’s share of gross domestic product (GDP) from
approximately 15 per cent to 25 per cent by the beginning of the next decade can be
expected to equate to a significant increase in demand for energy, and higher oil
consumption in manufacturing (Sen and Sen, 2016). Also of note, a programme
involving infrastructure construction (roads and national highways), which is being partly
funded through revenue from higher taxation of oil and oil products, is likely to support
oil demand growth in the country.
1 For more information on “Make in India” scheme, see www.makeinindia.com/about.
Petroleum consumption and economic growth relationship: evidence from the Indian states
23
Against this background, for this paper, we use state-wise petroleum consumption
and economic growth data for 23 Indian states. Our study relates to the voluminous
literature that examines the role of the energy consumption (E) and economic growth (Y)
nexus in the cases of a single country and multiple countries (Akarca and Long, 1980;
Asafu-Adjaye, 2000; Fang and Le, forthcoming; Kraft and Kraft, 1978; Le, 2016; Le and
Nguyen, 2019; Le and Quah, 2018; Lee and Chang, 2005; Apergis and Payne, 2009a;
2019b; Narayan, Narayan and Popp, 2010a; 2010b; Narayan, 2016; Oh and Lee, 2004;
Proops, 1984; Rafiq and Salim, 2009; Stern, 1993; and Yang, 2000). The E-Y nexus is
governed by four hypotheses: the growth hypothesis; the conservation hypothesis; the
feedback hypothesis; and the neutrality hypothesis.2
A number of recent studies have analysed the relationship of oil consumption and
economic growth in India. The E-Y literature on India has been based on gas (Akhmat
and Zaman, 2013); oil (Akhmat and Zaman, 2013); nuclear energy (Akhmat and Zaman,
2013; Wolde-Rufael, 2010); coal (Govindaraju and Tang, 2013); electricity (Abbas and
Choudhury, 2013; Akhmat and Zaman, 2013; Cowan and others, 2014; Ghosh, 2002;
Nain, Ahmad and Kamaiah, 2015) and aggregate energy consumption (Pao and Tsai,
2010; Vidyarthi, 2013; Yang and Zhao, 2014) (table 1).
As indicated earlier, we examine the state data for 23 states as a panel and also
divide the states by income in order to account for some heterogeneity that arises as
a result of income (see section II). As explained by the International Energy Agency
(IEA) (2015, p. 21), “(t)he widespread differences between regions and states within
India necessitate looking beyond national figures because of the country’s size and
heterogeneity, in terms of demographics, income levels and resource endowments, and
also because of a federal structure that leaves many important responsibilities for
energy with individual states.” While our study is predominantly based on aggregate
data, we also check the robustness of our findings using disaggregated petroleum data3
and have found the disaggregated data to be informative and useful because of the
importance of each petroleum product tends to vary across states.
Foreshadowing our key results, in the long run, we find evidence in favour of the
feedback effect for the all states panel in addition to all the subpanels of states at
different income levels. In the short run, we find that while the all states panel shows
support for the conservative hypothesis, all income panels seem to show the presence
of the feedback effect. Regarding the signs of the effects, however, we find that while
petroleum consumption and economic growth are positively related for the high-income
2 The growth hypothesis indicates that E causes Y; the conservation hypothesis indicates that Y causes E;the feedback hypothesis treats both E and Y as leading each other; and the neutrality hypothesis relatesno linkage between E and Y.
3 We are thankful to an anonymous reviewer for the suggestion of introducing disaggregated data in thestudy.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
24
states in the short run and the long run, they can be negatively linked for the middle-
and low-income states. The use of disaggregated petroleum products data in the
analysis reveals that cointegration between petroleum products and income is missing
for the high-income states and only present for selected petroleum products in the case
of low- and middle-income states.
The remainder of the study is organized as follows. Section II includes a review of
the related literature with a focus on India. Section III contains an explanation of the
aggregate petroleum consumption and economic growth patterns for 23 Indian states. In
section IV, the econometric methods and models used to examine the four hypotheses
associated with the petroleum consumption-economic growth nexus are presented.
Section V includes a discussion of the key findings relating to the aggregate data on
petroleum consumption, while section VI presents the results derived using the
disaggregated data on petroleum consumption. Section VII provides a discussion on the
key findings and their implications relating to aggregate and disaggregated data on
petroleum consumption. Section VIII concludes the study with policy implications.
II. LITERATURE REVIEW
A handful of studies have investigated the link between energy consumption and
economic growth in India (Paul and Bhattacharya, 2004; Vidyarthi, 2013; Tiwari,
Shahbaz and Hye, 2013; Shahbaz and others, 2016; Nain, Bharatam and Kamaiah,
2017). Paul and Bhattacharya (2004) find the prevalence of the feedback hypothesis for
the Indian economy over the period 1950-1996, when energy consumption leads to
economic growth in the short run and economic growth leads to higher energy
consumption in the long run. Vidyarthi (2013) shows evidence of the feedback effect for
electricity consumption, although the casual effects in the short run and the long run
were different from Paul and Bhattacharya (2004) (see table 1). Nasreen and Anwar
(2014) find that the feedback effect is prevalent in the short run and long run over the
period 1983-2011. Tiwari, Shahbaz and Hye (2013) examine the Environmental Kuznets
Curve (EKC) hypothesis of India using aggregate coal consumption and economic
growth data along with carbon dioxide (CO2) emissions. They find feedback hypothesis
between economic growth and CO2 emissions. The same interpretation is drawn
between coal consumption and CO2 emissions.
Abbas and Choudhury (2013) concur when looking at electricity consumption in
India and agricultural GDP over the period 1972-2008. Some authors find evidence of
a unidirectional relationship relating to the growth hypothesis, which suggests that
energy consumption drives economic growth in the long run (Pao and Tsai, 2010) and in
the short run (Yang and Zhao, 2014; Nain, Ahmad and Kamaiah, 2015). Akhmat and
Zaman (2013) suggest a unilateral link for electricity and gas consumption in India in
the long run. Wolde-Rufael (2010) shows the same linkage for nuclear energy in the
Petroleum consumption and economic growth relationship: evidence from the Indian states
25
long run. Other studies on India show evidence of the conservative hypothesis, or
a unidirectional link flowing from economic growth to energy consumption, for different
sources of energy: electricity consumption (Ghosh, 2002 (in the short run); Abbas and
Choudhury, 2013 (in the short run and the long run)); nuclear energy in the long run
(Akhmat and Zaman, 2013); and coal consumption in India in the short run (Govindaraju
and Tang, 2013). Similarly, Shahbaz and others (2016) examine the relationship
between globalization and energy consumption in India and have found acceleration of
globalization results in a decline in energy consumption, but economic growth increases
energy demand in the long run.
In the literature, we find that there is also evidence in favour of the neutrality
hypothesis for India. Akhmat and Zaman (2013), for instance, find a relationship
between fuel and oil consumption and economic growth over the period 1975-2009.
Similarly, Govindaraju and Tang (2013) find evidence supporting the neutrality
hypothesis in the case of coal in the long run for the period 1965-2009; and Cowan and
others (2014) find this for electricity consumption over the period 1990-2010.
Almost all these studies come up with short-term and long-term inferences from
Granger causality tests drawing on the vector autoregressive (VAR) model or the vector
error correction model (VECM), depending on whether a cointegration relationship
between non-stationary variables, E and Y, is established. The key variations are in the
datasets in terms of panel or time series (aggregate or disaggregated), and sample
periods; and the techniques (cointegration and causality tests) (see table 1). Naser
(2015) finds that a long-run impact of oil is associated with nuclear energy consumption
on economic growth in India, along with China, the Republic of Korea and the Russian
Federation. Bildirici and Bakirtas (2014) argue that for China and India, this relationship
is bidirectional.
Regarding the cointegration tests, several studies have used the time series
Engle-Granger univariate cointegration approach (see, for instance, Paul and
Bhattacharya, 2004); others have used the time series Johansen multivariate
cointegration method (Paul and Bhattacharya, 2004). Furthermore, to address the issue
of a small sample, some authors use the autoregressive distributed lag (ARDL) bounds
test (such as Nain, Bharatam and Kamaiah, 2017); others have tackled the small
sample problem by including more countries in the study. This gives them the benefit of
taking advantage of a larger dataset and using panel-based cointegration methods, such
as the Pedroni (1999; 2004) cointegration test, the Kao (1999) test, or the Johansen/
Fisher test, to derive results from a larger dataset (Nasreen and Anwar, 2014; Pao and
Tsai, 2010). Instead of applying the standard Granger causality test, Kónya (2006)
employs the bootstrap panel causality approach to allow for cross-section dependence
and heterogeneity within the panel. Yang and Zhao (2014), in place of the usual
in-sample Granger causality tests, apply an out-of-sample Granger causality test to
better gauge the out-of-sample forecasting performance of models. Wolde-Rufael (2010)
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
26
Tab
le 1
. A
su
mm
ary
of
recen
t lite
ratu
re o
n I
nd
ian
en
erg
y c
on
su
mp
tio
n a
nd
eco
no
mic
gro
wth
Stu
dy
Sam
ple
Data
Tech
niq
ue
Va
riab
les
Re
su
lt
Paul and
1950-1
996
Tim
e s
eries
Engle
-Gra
nger
Energ
y c
onsum
ption;
LR
: Y
-> E
; S
R: E
-> Y
Bh
att
ach
ary
aco
inte
gra
tio
n a
nd
GD
P;
gro
ss c
ap
ita
l
(20
04
)G
ran
ge
r ca
usa
lity;
form
ation; popula
tion
Johansen m
ultiv
ariate
coin
tegra
tion
Nasre
en a
nd
1980-2
011
Panel data
:P
edro
ni
Energ
y c
onsum
ption,
LR
and S
R: E
<->
Y
Anw
ar
(2014)
15 A
sia
n c
ountr
ies
conin
tegra
tion
PG
DP
; tr
ade o
penness;
energ
y p
rices
Tiw
ari (
20
11)
19
70
-20
07
Tim
e s
erie
sG
ran
ge
r ca
usa
lity
LR
: Y
->E
(VA
R);
Dola
do a
nd
Lü
tke
po
hl a
pp
roa
ch
En
erg
y c
on
su
mp
tio
n w
ith
carb
on
em
issio
ns a
nd
oth
er
vari
ab
les
Pao a
nd T
sai
1971-2
005
Panel in
clu
din
gK
ao, Johansen/F
isher;
Energ
y c
onsum
ption;
LR
: E
->Y
(20
10
)B
RIC
na
tio
ns
Pe
dro
ni co
inte
gra
tio
n;
real G
DP
; carb
on
(Bra
zil,
Russia
nG
ranger
causalit
yem
issio
ns
Fe
de
ratio
n,
Ind
ia
an
d C
hin
a)
Yang a
nd Z
hao
1970-2
008
Tim
eO
ut-
of-
sam
ple
Gra
nger
Energ
y c
onsum
ption;
SR
: E
-> Y
and C
O2;
(20
14
)se
rie
s/a
gg
reg
ate
ca
usa
lity t
ests
an
dre
al G
DP
; carb
on
trade o
penness->
E
directe
d a
cyclic
gra
phs
em
issio
ns; tr
ade
(DA
G)
openness
Vid
ya
rth
i (2
01
3)
19
71
-20
09
Tim
eJo
ha
nse
n a
pp
roa
ch
;E
nerg
y c
onsum
ption;
LR
:E->
Y; S
R: Y
->E
se
rie
s/a
gg
reg
ate
Gra
ng
er
ca
usa
lity
real G
DP
; carb
on
em
issio
ns
Petroleum consumption and economic growth relationship: evidence from the Indian states
27
Tab
le 1
. (
continued)
Stu
dy
Sam
ple
Data
Tech
niq
ue
Va
riab
les
Re
su
lt
Ahm
ad a
nd
1971-2
014
Tim
eA
RD
LTo
tal energ
y, g
as, oil,
E->
CO
2; Y
<->
CO
2
oth
ers
(2
01
6)
se
rie
s/a
gg
reg
ate
(au
tore
gre
ssiv
ee
lectr
icity a
nd
co
al
dis
trib
ute
d la
g b
ou
nd
s)
co
nsu
mp
tio
n;
RG
DP
;
carb
on e
mis
sio
ns
Ele
ctr
icit
y
Ab
ba
s a
nd
19
72
-20
08
Tim
e s
erie
sJo
ha
nse
n a
pp
roa
ch
Ele
ctr
icity c
onsum
ption
Aggre
gate
: G
DP
- L
R:
Ch
ou
dh
ury
– a
gg
reg
ate
- G
DP
an
d G
DP
; P
GD
P; A
GD
PY
-> E
; S
R: Y
-> E
;
(2013)
and p
er
capita G
DP
PG
DP
- L
R:
E ≠
Y;
(PG
DP
); a
nd
SR
: Y
->E
.
dis
aggre
gate
-D
isaggre
gate
:
agriculture
GD
PA
GD
P -
LR
: Y
<->
E;
(AG
DP
)S
R: Y
<->
E
Akhm
at and
1975-2
010
Tim
e s
eries –
Gra
nger
causalit
yE
lectr
icity,
PG
DP
gro
wth
LR
: E
->Y
Zam
an (
2013)
aggre
gate
- G
DP
(VA
R)
and p
er
capita G
DP
(PG
DP
); a
nd
dis
ag
gre
ga
te -
agriculture
GD
P
(AG
DP
)
Ghosh (
2002)
1950-1
997
Tim
eE
ngle
and G
ranger
Ele
ctr
icity c
onsum
ption
LR
: Y
->E
se
rie
s/a
gg
reg
ate
(1987);
Gra
nger
an
d e
co
no
mic
gro
wth
ca
usa
lity
(pe
r ca
pita
)
Cow
an a
nd
1990-2
010
Panel – B
RIC
S/
Bo
ots
tra
p p
an
el
Ele
ctr
icity,
GD
P g
row
th,
LR
: E
≠Y
oth
ers
(2
01
4)
ag
gre
ga
teca
usa
lity a
pp
roa
ch
;C
O2
Kó
nya
(2
00
6)
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
28
Tab
le 1
. (
continued)
Stu
dy
Sam
ple
Data
Tech
niq
ue
Va
riab
les
Re
su
lt
Nain
, Ahm
ad
1971-2
011
Tim
eA
RD
L b
ounds test;
Secto
ral and a
ggre
gate
Ag
gre
ga
te -
LR
:
an
d K
am
aia
hse
rie
s/a
gg
reg
ate
Tod
a a
nd
Yam
am
oto
ele
ctr
icity c
on
su
mp
tio
n;
E ≠
Y; S
R: E
->Y
;
(20
15
)a
nd
dis
ag
gre
ga
te:
(19
95
)R
GD
Pd
isa
gg
reg
ate
:
secto
ral
agriculture
- E
≠ Y
;
industr
ial -
LR
: E
≠ Y
;
SR
: E
->Y
; dom
estic a
nd
com
merc
ial -
LR
and
SR
: Y
->E
Co
al
Govin
dara
ju a
nd
1965-2
009
Tim
eB
ayer
and H
anck
Coal consum
ption;
LR
: E
≠ Y
; S
R: Y
->E
Tan
g (
20
13
)se
rie
s/a
gg
reg
ate
(2009)
coin
tegra
tion
real G
DP
per
capita
test;
Gra
ng
er
ca
usa
lity
Nu
cle
ar
en
erg
y
Akh
ma
t a
nd
19
75
-20
10
Tim
eG
ran
ge
r ca
usa
lity
Coal consum
ption;
LR
: Y
->E
Zam
an (
2013)
series/a
ggre
gate
real G
DP
per
capita
- P
RG
DP
; a
nd
dis
ag
gre
ga
te -
agriculture
GD
P
(AG
DP
)
Wold
e-R
ufa
el
1969-2
006
Tim
eA
RD
L b
ounds tests
;N
ucle
ar
energ
y; R
GD
PL
R:
E->
Y
(20
10
)se
rie
s/a
gg
reg
ate
Toda a
nd Y
am
am
oto
per
capita; re
al gro
ss
(19
95
)fixe
d c
ap
ita
l fo
rma
tio
n
Petroleum consumption and economic growth relationship: evidence from the Indian states
29
Tab
le 1
. (
continued)
Stu
dy
Sam
ple
Data
Tech
niq
ue
Va
riab
les
Re
su
lt
Oil
Akh
ma
t a
nd
19
75
-20
10
Tim
eG
ran
ge
r ca
usa
lity
Oil
consum
ption
LR
: Y
≠ E
Za
ma
n (
20
13
)se
rie
s/a
gg
reg
ate
-
per
capita G
DP
(PG
DP
); a
nd
dis
ag
gre
ga
te -
agriculture
GD
P
(AG
DP
)
Gas
Akh
ma
t a
nd
19
75
-20
10
Tim
eG
ran
ge
r ca
usa
lity
Gas c
onsum
ption
LR
: E
->Y
Za
ma
n (
20
13
)se
rie
s/a
gg
reg
ate
-
per
capita G
DP
(PG
DP
); a
nd
dis
ag
gre
ga
te -
agriculture
GD
P
(AG
DP
)
Co
mb
inati
on
of
dif
fere
nt
en
erg
y s
ou
rces
Bild
iric
i and
1980-2
011
Tim
eA
RD
L (
auto
regre
ssiv
eC
oa
l, n
atu
ral g
as a
nd
oil
LR
: E
<->
Y (
for
coal and
Ba
kirta
s (
20
14
)se
rie
s/a
gg
reg
ate
dis
trib
ute
d la
g b
ou
nd
s)
consum
ption; R
GD
Poil)
Na
se
r (2
01
5)
19
65
-20
10
Tim
eJo
ha
nse
n c
oin
teg
ratio
nO
il co
nsu
mp
tio
n,
nu
cle
ar
LR
: E
->Y
se
rie
s/a
gg
reg
ate
techniq
ue
consum
ption; R
GD
P
No
tes:
E,
energ
y c
onsum
ption; Y,
econom
ic g
row
th;
GD
P,
gro
ss d
om
estic p
roduct;
PG
DP,
per
capita g
ross d
om
estic p
roduct;
RG
DP,
rea
l gro
ss d
om
estic p
rod
uct;
LR
, lo
ng r
un; S
R, short
run.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
30
apply the multivariate Toda and Yamamoto (1995) approach, which is often employed in
the case of a small sample.
A sectoral perspective on the manufacturing sector of India suggests that the three
dominant and highly energy-intensive manufacturing industries are steel, aluminium and
cement. Dutta and Mukherjee (2010) suggest that unless these sectors innovate in the
way they are using energy, India will lose global competitiveness in related industries.
Innovation in the energy sector of India is also necessary because of the impact of
oil and gas energy consumption on CO2 emissions. Ahmad and others (2016) find that
energy consumption from oil and gas, electricity and coal consumption contributes to
carbon emissions in India. The question of energy consumption in the country as
a determinant of growth is inevitably intertwined with the issue of raising CO2 emissions.
A series of papers that examine various scenarios for future energy consumption
indicate that none of the traditional sources of energy, oil, gas, coal, hydrocarbon,
nuclear, hydrogen, hydro and renewables, will be sufficient to meet the future energy
demands and that India would have to rely on imports for a significant portion of its
energy supply (Parikh and others, 2009; Parikh and Parikh, 2011). At the same time, the
most feasible scenario for CO2 emissions reduction is to cut energy demand and boost
energy efficiency in production and consumption. That would make it possible to meet
environmental conservation goals without compromising on economic development and
future growth (Parikh and Parikh, 2011).
While the overall energy consumption of the country is estimated to rise sharply in
the next decade, energy inequalities in the country are rampant. Saxena and
Bhattacharya (2018) examine the role of caste, tribe, and religion as determinants of
energy inequality in India. Using data at the household level for 2011-2012, the authors
estimate the energy inequalities stemming from differential access to liquid petroleum
gas and electricity, focusing on disadvantaged groups, such as castes, tribes, and
religious denominations, and find that these factors are relevant to energy access. Even
though the above-mentioned social inequalities in energy access exist, residential
energy consumption in India is expected to quadruple in the next decade because of
lifestyle changes related to the county’s recent economic growth (Bhattacharyya, 2015).
Urbanization, a fast-growing middle class and western-style consumerism are factors
behind the expected overbearing residential energy consumption expansion in the near
future. A large part of the energy supply burden on liquefied petroleum gas is expected
to fall (Bhattacharyya, 2015). This makes the unveiling of the link between petroleum
consumption and economic growth in the context of India even more pressing.
The expected rapid growth in energy consumption, in conjunction with the above
described energy inequalities and contribution to carbon emissions, make India a prime
candidate for the development of renewable energy technologies (Singh, 2018). In
addition to coping with the energy deficits, transitioning to renewables would reduce the
exposure of India to variations in the price of crude oil. A recent study by Mallick,
Petroleum consumption and economic growth relationship: evidence from the Indian states
31
Mahalik and Sahoo (2018) finds that crude oil price reduces significantly private
investment, whereas economic growth and globalization tend to boost it. Economic
growth and urbanization are the key factors pushing energy demand higher in the long
run, Shahbaz and others (2016) argue that transitioning to renewables would allow for
supporting raising energy demand without the negative side effects on pollution and of
energy access inequality in India.
III. DATA
Our study covers 23 Indian states,4 which in total encompasses approximately
95 per cent of the national area. We collected the petroleum consumption and its
by-products consumption data for the states from the States of India database,
a comprehensive compilation of state-level statistics published by the Centre for
Monitoring Indian Economy. The only problem with this is related to the state-wise
population data for each year spanning from 1985/86 to 2013/14. The petroleum
product-wise data referred to in each state over the sample period are available in the
absolute value (in thousand tonnes). Therefore, in order to convert the data to per capita
term, we have collected state-wise population data from the Economic and Political
Weekly Research Foundation database for the same period and then divided the
aggregate petroleum consumption and the various by-products by the population for
each state. Furthermore, we note that this is an unbalanced panel data, as there are
missing observations for a number of states. All of the per capita variables (petroleum
products and the by-products consumption) that we converted are in kilograms. For the
by-products of the petroleum data not available for some states for different years, the
per capita term becomes zero for those observations.
State-wise income per capita is defined as real per capita net state domestic
product at factor cost data, with a base year of 2004/05 and is sourced from the Reserve
Bank of India.5 We divided these 23 states into three panels based on their level of
income. For this classification, we calculated the average per capita income of each
state over the study period 1985-2013 and categorized the states by high, middle, and
5 Real gross domestic product (RGDP) data are extracted from Indiastat. Available at Indiastat.com.6 Our classification of the Indian states by income closely follows Narayan, Rath and Narayan (2012) for at
least 15 states.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
32
Table 2. Panels by income
High-income states Middle-income states Low-income states
States Delhi, Gujarat, Haryana, Andhra Pradesh, Assam, Bihar,
Notes: The table covers the Im-Pesaran-Shin (IPS) (Im, Pesaran and Shin, 2003); Levin-Lin-Chu (LLC) (Levin, Lin
and Chu, 2002); and augmented Dickey-Fuller (ADF) (Maddala and Wu, 1999) test results. * suggests
statistical significance at 1 per cent level. PEC is the petroleum consumption in kilogram of oil equivalent per
capita; PGDP is the real per capita net state domestic product at factor cost data with a base year of 2004/05.
As the panels comprise I(1) variables, they all are fit for three panel cointegration
tests: Kao (1999), Pedroni (1999; 2004), and the Fisher type-test from Maddala and Wu
(1999). The test of Pedroni (1999; 2004) is a panel cointegration test that extends the
Engle and Granger method to a system of multivariate independent variables for
homogeneous and heterogeneous properties across individuals for the panel data. The
Kao (1999) test is a residual-based panel test that applies the Dickey-Fuller and
augmented Dickey-Fuller type tests and considers homogeneous properties across
individuals. The Kao (1999) test focuses on both strict endogenous regressors and strict
exogenous regressors.
The Pedroni tests, unlike those of Kao, allow for heterogeneity among individual
units of the panel and no exogeneity requirements are imposed on the regressors in the
cointegrating regressions. The Maddala and Wu (1999) test is a different method that
applies the combination test from Fisher (1932) to derive the test statistics for panel
estimation. The combination statistic is constructed from various individual statistics, this
Petroleum consumption and economic growth relationship: evidence from the Indian states
41
combination statistic follows the Chi-square distribution rule, in which individual test
statistic is computed by Johansen (1988).
Of these tests, the Pedroni (1999; 2004) test allows for cross-sectional
dependence. Such test uses the fully modified ordinary least squares (FMOLS)
estimator that deals with possible autocorrelation and heteroskedasticity of the
residuals, taking into account the presence of nuisance parameters, which is
asymptotically unbiased and deals with potential endogeneity of regressors. As our
panel is burdened by all these three problems, we take this as the superior test of
cointegration.
The results from the three cointegration tests are captured in table 7, panels 1-3.
Pedroni test results (panel 1) suggest at least one cointegrating relationship for all
panels. When compared against the Kao and Fisher test results, we find that the results
for all Indian states and the middle- and low-income states are the same.7
The relationship between petroleum and economic growth within the long-run
models and vector error correction models (VECMs)
Next, we estimate the long-run models and VECMs for the all states and income-
based panels. This approach differs from the literature on the long run and VECM in that
we estimate the long run and VECM nested within the FGLS model relating to petroleum
consumption and economic growth. The long-run results are presented in table 8. The
influence of income on petroleum consumption on per capita is examined in panel 1 and
the impact of petroleum consumption on per capita real income is examined in panel 2.
In the long run, we see signs of a feedback effect for the Indian states at the higher end
of the income spectrum. In this regard, our findings are consistent with only two out of
16 studies on energy-economic growth that support the feedback hypothesis.
Per capita real income is found to have a positive and significant influence on
petroleum consumption for all the states in the long run (table 8, panel 1). Petroleum
consumption positively affects per capita income of the high-income states (table 8,
panel 2). However, for the all states panel, and the middle- and low-income Indian
states, we find that petroleum consumption reduces per capita real income in the long
run. Hence, while the bilateral link exists between the two variables, it is clear that we
fail to find evidence on the feedback hypothesis in its true form.
7 Before the estimation, we conduct the Di Iorio and Fachin (2007) test for breaks in cointegrated panelsto examine the stability of the relationship between our variables of interest. The results support theacceptance of the null hypothesis of no break. That is, the relationship among the investigated variablesis stable and not subject to structural breaks during the investigation period. The results are notpresented here to conserve space, but they are available upon request.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
42
Table 7. Cointegration results
All statesHigh-income Middle-income Low-income
states states states
Panel 1: Pedroni residual
cointegration test Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob.
Panel v 4.242* 0.000 2.665* 0.004 2.347* 0.010 2.431* 0.008
heavy stock hot heavy stock, lubes and greases, itumen, and others. ***, ** and * indicate rejection of the null
hypothesis at 1 per cent, 5 per cent and 10 per cent significance levels. Standard errors are reported in
parentheses.
Asia-Pacific Sustainable Development Journal Vol. 26, No. 1
62
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