Energy Policy 77 (2015) 70–78

Contents lists available at ScienceDirect

Energy Policy

http://d0301-42

n CorrMarooc

E-mSAnwar

journal homepage: www.elsevier.com/locate/enpol

Electricity consumption, industrial production, and entrepreneurshipin Singapore

Sizhong Sun a, Sajid Anwar b,c,n

a School of Business, James Cook University, Townsville, QLD 4811, Australiab School of Business, University of the Sunshine Coast, Maroochydore DC, QLD 4558 Australiac Shanghai Lixin University of Commerce, 2800 Wenxiang Road, Shanghai, China

H I G H L I G H T S

� Using a unique monthly dataset, we focus on Singapore's manufacturing sector.

� Electricity consumption, output and entrepreneurship are cointegrated.� Electricity consumption adjusts very slowly to shocks to the other variables.� Entrepreneurship causes electricity consumption which causes industrial production.� We find that growth hypothesis governs the electricity consumption and real output.

a r t i c l e i n f o

Article history:Received 18 August 2014Received in revised form22 November 2014Accepted 29 November 2014

Keywords:Electricity consumptionIndustrial productionEntrepreneurshipCointegrationSingapore

x.doi.org/10.1016/j.enpol.2014.11.03615/& 2014 Elsevier Ltd. All rights reserved.

esponding author at: School of Business, Univhydore DC, QLD 4558, Australia.ail addresses: Sizhong.Sun@jcu.edu.au (S. Sun@usc.edu.au (S. Anwar).

a b s t r a c t

Within the context of a tri-variate vector autoregressive framework that includes entrepreneurship, thispaper examines the link between electricity consumption and industrial production in Singapore'smanufacturing sector. Unlike the existing studies, this paper focuses on one sector of the economy andutilises a unique monthly dataset. Empirical analysis based on Johansen's cointegration approach showsthat the three variables are cointegrated – i.e., a stable long-run relationship exists among electricityconsumption, output and entrepreneurship in Singapore's manufacturing sector. Empirical analysisbased on data from January 1983 to February 2014 reveals that electricity consumption adjusts veryslowly to shocks to industrial production and entrepreneurship. Furthermore, entrepreneurship Grangercauses electricity consumption, which causes industrial production. As electricity consumption causesindustrial output, the growth hypothesis concerning energy consumption and economic growth holds inSingapore's manufacturing sector and policies that restrict electricity production, without electricityimports, are likely to lead to a decline in the manufacturing output.

& 2014 Elsevier Ltd. All rights reserved.

1 See Yildirim et al. (2014) and references therein. Yildirim et al. argue thatunidirectional relationship from energy consumption to economic growth reflects

1. Introduction

It is generally believed that technological advancement, amongother things, contributed to a decrease in the cost of transporta-tion and communication, which led to an increase in internationaltrade in goods and services. Growth in trade necessitated an in-crease in the production of goods and services, which is associatedwith increased pollution (especially in the developing countries).There is a direct link between production and demand for re-sources used in the production process. Based on the “growth

ersity of the Sunshine Coast,

),

hypothesis”, energy can be viewed as an additional factor of pro-duction and hence a number of existing studies have attempted toexplore the link between energy consumption and economicgrowth.1 Early studies that consider the link between economicgrowth and energy consumption relied on a bivariate model.However, bivariate models suffer from omitted variables bias,which can render the results of statistical testing invalid. For

the so-called growth hypothesis underlying growth-energy consumption nexus.This hypothesis suggest that energy saving could reduce economic growth. On theother hand, a unidirectional relationship from economic growth to energy con-sumption is described as energy conservation hypothesis. The feedback hypothesisreflects the two-way causation between economic growth and energyconsumption.

Fig. 1. Net formation on companies in Singapore's manufacturing sector.

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–78 71

example, using a bivariate model, Cheng and Lai (1997) conside-red the case of Taiwan. Their empirical analysis, using annualdata from 1954 to 1993, revealed that GDP causes energyconsumption.

Most existing studies examine the relationship between energyconsumptions and economic growth using highly aggregateddata.2 Some studies have found that the relationship betweeneconomic growth and energy consumption is statistically insig-nificant, which is viewed as a confirmation of the “neutrality hy-pothesis” concerning the growth-energy consumption nexus.However support for this neutrality hypothesis does not implythat neutrality also exists at a disaggregated level and hence en-ergy development and energy use polices are irrelevant. At thesame time, support for non-neutrality does not imply that we donot need to worry about separate energy polices for differentsectors. Indeed, not all sectors of any real economy are equallyenergy intensive. It would therefore be worthwhile to consider thelink between energy consumption and economic growth at sec-toral level. Such sectoral analysis could help one to identify in-dustry specific issues that could lead to a more targeted andsharply focused energy policy.

This paper focuses on electricity consumption in Singapore'smanufacturing sector.3 While examining the link between manu-facturing sector growth and electricity consumption, unlike otherstudies, this paper also takes into account the impact of domesticentrepreneurship. Using a unique monthly dataset that spansfrom January 1983 to February 2014, this paper examines the

2 A good review of the existing literature can be found in Shahbaz et al. (2014)and Yildirim et al. (2014). In order to conserve space a separate literature review isnot included in this paper. Some related studies include Abdullah and Morley(2014), Ahamad and Islam (2011), Alam et al. (2012), Ang (2007a, 2008), Chen et al.(2007), Mozumder and Marathe (2007), Narayan et al. (2008), Yoo and Kim (2006).

3 Singapore is used as a case study in this paper because it has not receivedmuch attention in the existing literature. However, monthly data on all relevantvariables is now available from Singapore Department of Statistics. Singapore is asmall open economy that has registered significant economic growth over the pastfew decades The Singapore economy grew from 2007 to 2013 at an average annualof 5.5%. Singapore is now included among the highest per-capita income countries.Manufacturing and services sectors are regarded as twin engines of Singapore'seconomic growth. Contribution of the manufacturing sector to GDP growth aver-aged over the period 1990–2007 was more than 1.63%. With rapid growth in theservices sector in recent years, the share of manufacturing in Singapore's GDP hasdeclined to around 18% in 2013 (Yearbook of Statistics Singapore, 2013).

interrelationship among industrial production, electricity con-sumption and entrepreneurship in Singapore's manufacturingsector.4

This paper makes a number of distinct contributions to theexisting literature. Only a handful of the existing studies have fo-cused on Singapore and none of these studies appear to haveconsidered Singapore's manufacturing sector.5 This is one of thefirst studies that uses relatively high frequency (i.e., monthly) data,which is more appropriate for econometric techniques such ascointegration analysis. Furthermore, while existing studies haveexplored other aspects of entrepreneurship, none of the existingstudies have considered the impact of entrepreneurship in thecontext of growth-energy consumption nexus.6 Conceptually, en-trepreneurship plays an important role in the growth-energyconsumption nexus. As an essential input, energy usage is animportant decision variable, which has a direct bearing on pro-duction. Entrepreneurship captures a firm's decision making ca-pacity. By explicitly including entrepreneurship as a variable in themodel, we are able to statistically measure the impact of man-agerial decision making on growth-energy consumption nexus.

2. Methodology and data

In order to examine the interrelationship among manufactur-ing sector electricity consumption, output and entrepreneurship,this paper makes use of a Vector Autoregressive (VAR) model. VARtype models are particularly useful when sample size is large andthe variables included in the model are not exogenous. The model

4 A number of studies, such as Wennekers and Thurik (1999), Thurik andWennekers (2004), Van Stel et al. (2004), and Wong et al. (2005), have highlightedthe role of entrepreneurship in promoting economic growth.

5 Earlier studies on Singapore include Glasure and Lee (1997) and Chang andWong (2001). Glasure and Lee's work is based on annual data over the period1961–1990. Their empirical analysis based on a bivariate model suggests that GDPcauses energy consumption in Singapore. Based on a bivariate model, Chang andWong found that GDP causes energy consumption, which supports conservationhypothesis. This study utilises annual data from 1975 to 1995, which is a very smallsample.

6 For example, Anwar and Sun (2012) have explored the impact of foreign in-vestment on entry and exit decision of domestic firms in China's manufacturingsector.

Table 1Descriptive statistics.

EM IP NF

Mean 879.5639 48.36179 81.96524Median 931.2000 42.21000 89.50000Maximum 1480.200 115.8300 252.0000Minimum 228.9000 11.29000 �130.0000Standard deviation 361.6324 27.59226 55.62471Skewness �0.145733 0.612883 �0.436538Kurtosis 1.660971 2.325197 3.563661Observations 374 374 374

Table 2Unit root testing.

Variables ADF in levels ADF in first differences Phillips–Perron test inlevels

Phillips–Perron test in firstdifferences

KPSS in levels KPSS in first differences

Intercept and no trendEM �1.1517 (0.6960) �6.3746 (0.0000) �1.4614 (0.5523) �30.5021 (0.0000) LM-stat: 2.3954a LM-stat: 0.0274IP 0.7196 (0.9925) �11.2371 (0.0000) �2.1710 (0.2174) �78.5213 (0.00001) LM-stat: 2.3163a LM-stat: 0.1254NF �5.0908 (0.0000) �21.4494 (0.0000) �12.2999 (0.0000) �64.8072 (0.0001) LM-stat: 0.4850b LM-stat: 0.2020

Note: p-Values in parentheses.a Represents significance at the 1% level.b Represents significance at the 5% level.

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–7872

used in this paper is presented in Eq. (1) as follows7:

⎡

⎣

⎢⎢⎢

⎤

⎦

⎥⎥⎥

Y C Y Y Y

Y

.

EM

IP

NF (1)

t t t p t p t

t

t

t

t

1 1 2 2Π Π Π ε= + + + … … … + +

=

− − −

where EMt is the electricity consumption in Singapore's manu-facturing sector in Gigawatts per hour; IPt is the index of manu-facturing sector industrial production, which captures thefluctuations in manufacturing sector output; NFt is a measure ofentrepreneurship in Singapore's manufacturing sector; tε is a ran-dom variable which captures the effect of all omitted variables;and , , .... , p1 2Π Π Π are the unknown population parameters.

Entrepreneurship (NF)t can be measured in various ways (seeOECD, 2014). In this paper, we constructed the entrepreneurshipvariable by subtracting the monthly cessation of companies formmonthly formation. The net effect is the variable that captures thestate of entrepreneurship in Singapore's manufacturing sector.8

Fig. 1 shows the trend in entrepreneurship in Singapore's manu-facturing sector from January 2000 to February 2014.

Fig. 1 shows that during the period of global financial crisis of2008–2009 there was a net decline in the number of firms inSingapore's manufacturing sector. All variables are measured onmonthly basis from January 1983 to February 2014. All data weredownloaded from the website of Singapore Department of Statis-tics (through subscription to STS Online). The basic characteristicsof the data are described in Table 1.

7 Eq. (1) consists of a tri-variate model, which allows one to examine the in-terrelationship among manufacturing sector electricity consumption, industrialproduction and entrepreneurship.

8 Economic growth has created business opportunities in all sectors includingSingapore's manufacturing sector. This had led to formation of new companies. Atthe same time, rapid globalisation has resulted in strong competitive pressures andsome existing firms are forced to exit the domestic manufacturing sector. The netimpact (i.e., the difference between formation of new companies and cessation ofthe existing companies) represents domestic entrepreneurship.

The sample consists of 374 observations. The AugmentedDicky–Fuller (ADF), Phillips–Perron (PP) and Kwiatkowski–Phil-lips–Schmidt–Shin (KPSS) unit root testing procedures were usedto test for stationarity. The results are reported in Table 2.

Table 2 shows that using all three testing procedures, at the 1%level of significance, Singapore's industrial production and elec-tricity consumption are non-stationary in levels but stationary infirst differences. However, the evidence concerning the en-trepreneurship variable is mixed in that the ADF and PP testssuggest stationarity in levels but the KPSS test suggests non-sta-tionarity at the 5% level of significance. Because the first differ-ences of all variables are stationary, it is possible that a long-runcointegrating relationship exists among the three variables.9

3. Results

It is well known that Johansen's methodology, which utilises amultivariate approach, produces the best results, especially inlarge samples. This procedure requires all variables to be sta-tionary in first differences. Based on the KPSS testing procedures itcan be argued that the requirements of Johansen's cointegrationprocedure are satisfied.10 The results of Johansen's cointegrationapproach are shown in Table 3.

The results presented in Table 3 suggest the presence of onlyone cointegrating vector at the 5% level of significance, which has anice interpretation in that all the variables in the system movetogether towards the long-run equilibrium. The implication is that,even though all variables are individually non-stationary, a linear

9 Cointegation analysis also allows one to separate short- and long-run re-lationship among variables and it can also be used to improve long-run forecastaccuracy.

10 An alternative to Johansen's methodology is Autoregessive Distributed Lag(ARDL) based bounds testing approach, which is particularly suitable in the case ofsmall samples. Furthermore, the bounds testing approach developed by Pesaranet al. (2001) does not require all variables to be non-stationary in levels. We alsoreport the results of ARDL-based bounds testing approach.

Table 3Johansen's cointegration test.

Number of cointegrating vectors Eigenvalue test Trace test

Max-Eigenvalue statistic 0.05 Critical value p-Valuesb Trace statistic 0.05 Critical value p-Valuesb

Nonea 32.69 22.30 0.0013 45.23 35.19 0.0030At most 1 8.08 15.89 0.5381 12.54 20.26 0.4015At most 2 4.46 9,16 0.3478 4.46 9.16 0.3478

a Denotes rejection of the hypothesis at the 0.05 level.b MacKinnon–Haug–Michelis p-values.

Fig. 2. Cointegration relationship (at the 5% level of significance).

Table 4Estimated long-run coefficients and error correction.

Estimated long-run coefficients (p-values inparentheses)

Error correctionterm

Max laglength

IPt NFt

EMt 19.0969(0.0000)

�21.5633(0.0000)

�0.02552(0.0000)

3

Table 5Lagrange-multiplier test.

Lag Chi-square value Degrees of freedom Prob4chi-square

1 8.3693 9 0.497402 10.7683 9 0.291923 15.7689 9 0.07187

H0: No autocorrelation at lag order

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–78 73

combination of the three variables is stationary (i.e., the threeoriginal non-stationary variables are cointegrated). Based on theempirical results reported in Table 3, it can be argued that there isa stable long-run relationship among electricity consumption, in-dustrial production and entrepreneurship in Singapore's manu-facturing sector.11 The graph of this cointegrating relationship isshown in Fig. 2.

The existence of cointegration implies that the relationshipamong electricity consumption, industrial production and en-trepreneurship in Singapore's manufacturing sector can be mostefficiently represented by an error correction model. The VectorError Correction Model (VECM) corresponding to Eq. (1) is asfollows:

EM (EC) IP NF(2)

t ti

t ii

t i t0 1 11

211

22∑ ∑ϕ ϕ ϕ ϕ υΔ = + + Δ + Δ +−=

−=

−

11 This conclusion also holds when a linear trend is included in the model.

where ECt 1− is the lagged value of the corresponding error cor-rection term; sϕ′ are the population regression coefficients and tυis a random variable.

The estimated results based on Eq. (2) are reported in Table 4,which shows that both industrial output and entrepreneurshiphave a strong and statistically significant impact on electricityconsumption in Singapore's manufacturing sector. An increase inindustrial production is positively related to electricity consump-tion but the relationship between entrepreneurship and electricityconsumption is negative, which could be attributed to the use ofrelatively more energy efficient technologies by new firms and theexit of relatively less energy efficient firms. The negative re-lationship also implies substitution away from the use of elec-tricity by the incumbent firms. The estimated error correctionterm indicates that energy consumption in Singapore's manu-facturing sector adjusts to its long-run value at a fairly slow rate(approximately 2.9% per year). The estimated model was tested forthe presence of autocorrelation in the residuals. The results of theLagrange Multiplier (LM) test, presented in Table 5, suggest theabsence of significant autocorrelation in residuals.

-1

0

1

2

3

0 2 4 6 8step

95% CI impulse-response function (irf)

Resposne of Energy Consumption to Industrial Production

-1.5

-1

-.5

0

0 2 4 6 8step

95% CI impulse-response function (irf)

Response of Enegry Consumption to Enterpreneurship

Fig. 3. Impulse response functions.

12 The ARDL technique has been used by a number of studies in differentcontexts. For example see Ang (2007b).

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–7874

The estimated error correction model was found to be stable.The impulse response functions trace the time path of the effect ofstructural shocks to electricity consumption in response to a unitchange in shock to industrial production and entrepreneurship.Fig. 3 shows that a positive unit shock to industrial productioncontributes to an almost permanent increase in electricity con-sumption but a positive unit shock to entrepreneurship leads to asteady decline in electricity consumption, which appears to sug-gest increasing reliance on non-electricity energy sources.

The reliability of the estimated model can also be evaluated bymeans of its forecasting accuracy. Based on the estimated model,the forecasted values of all three variables are plotted in AppendixA1–A3. The estimated values are close to the actual values of eachof the three variables, which appear to confirm the reliability ofthe estimated model.

The test for cointegration developed by Johansen requires allvariables to be integrated of order one. However, not all timesseries variables satisfy this condition. The results of ADF and PPtesting procedures reported in Table 2 suggest that en-trepreneurship is integrated of order zero whereas the electricityconsumption and industrial production are integrated of order 1. Itcan therefore be argued that ARDL-based bounds testing approachto cointegration may be a good alternative to Johansen'sapproach.12 The bounds testing approach, developed by Pesaranet al. (2001), does not require all variables to be integrated of orderone. In the following, we test for cointegration using the boundstesting approach.

Table 6ARDL bounds testing approach (dependent variable is ΔEM).

Variable Coefficient Std. error p-Value

EMt�1 �0.043375 0.021404 0.0435IPt�1 0.626289 0.291095 0.0321NFt�1 �0.536280 0.120798 0.0000ΔEMt�1 �0.479718 0.060640 0.0000ΔEMt�2 �0.055313 0.059817 0.3557ΔIPt�1 0.744167 0.650074 0.2531ΔIPt�2 �0.006264 0.702360 0.9929ΔIPt�3 0.673584 0.568765 0.2371ΔNFt�1 0.288444 0.143688 0.0455ΔNFt�2 0.174676 0.123647 0.1586Constant 26.73129 8.489852 0.0018

R-squared 0.242765 Mean dependent var 2.710000Adjusted R-squared 0.221672 S.D. dependent var 58.68471S.E. of regression 51.77336 Akaike info criterion 10.76091Sum squared resid 962292.5 Schwarz criterion 10.87725Log likelihood �1979.768 Hannan–Quinn criter. 10.80712F-statistic 11.50932 Durbin–Watson stat 1.991452Prob(F-statistic) 0.000000 n¼370

Table 7Error correction representation for the selected ARDL model.

ARDL(2,3,2) selected based on SBC

Regressor Coefficient Standard error p-Value

dEM1 �0.39 0.05 0.000dIP 4.48 0.48 0.000dIP1 3.18 0.58 0.000dIP2 1.48 0.49 0.003dNF 0.45 0.10 0.000dNF1 0.34 0.11 0.002Constant 24.2 7.38 0.001EM(�1) �0.054 0.02 0.004

Note: dEM is the first difference of EM; dEM1 is the first difference of one period lagged values of EM; dIP2 is the first difference of two period lagged values of IP; EM(�1) isthe error correction term. Durbin–Watson¼2.05.

Table 8Estimated long run coefficients using the ARDL Approach.

Dependent variable: EM (371 observations used for estimation from 1983M4 to 2014M2); ARDL(2,3,2); Lag length selected based on Schwarz Bayesian Criterion

Regressors Estimated coefficient Standard error p-Value

IP 13.08 1.78 0.000NF �6.12 2.71 0.024Constant 450.14 106.87 0.000

Fig. 4. Model stability (ARDL approach).

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–78 75

Table 9Granger causality tests (3 Lags).

Null hypothesis Observations F-statistic p-Value

IP does not Granger Cause EM 371 1.64863 0.1778EM does not Granger Cause IP 371 3.39887 0.0180NF does not Granger Cause EM 371 5.32554 0.0013EM does not Granger Cause NF 371 1.86532 0.1350NF does not Granger Cause IP 371 2.80183 0.0398IP does not Granger Cause NF 371 2.73700 0.0434

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–7876

3.1. ARDL-based cointegration analysis

ARDL procedure is based on the Wald or F-statistic in a gen-eralised Dickey Fuller type regression used to test the significanceof the lagged levels of relevant variables in a conditional unrest-ricted error correction model (ECM). Inferences are made bymaking use of two sets of asymptotic critical values correspondingto two extreme cases one assuming purely I(0) and the other as-suming purely I(1), without the need to know the regressors'underlying order of integration. ARDL approach is based on theapplication of an F-test to establish the joint significance of thelagged level variables. This procedure involves estimating Eq. (3)as follows:

a b c

d

EM EM IP

NF

EM IP NF (3)

tk

n

k i t kk

n

k i t k

k

n

k i t k

i t i t i t i t

1,

0,

0,

1 , 1 2 , 1 3 , 1 ,

∑ ∑

∑

λ λ λ ε

Δ = + Δ + Δ

+ Δ

+ + + +

=−

=−

=−

− − −

In terms of Eq. (3), cointegration exists as long as01 2 3λ λ λ= = = . Pesaran et al. (2001) also proposed a t-test that is

used to test the significance of the coefficient of EMi t, 1− . In otherwords, cointegration exists if λ1¼0. The ARDL-based bound testingapproach allows each variable to have different optimal lag. UsingSchwarz Bayesian Criteria (SBC), we found the optimal lag lengthof (2,3,2). Using the optimal lag length, Eq. (3) was estimated. Theestimated results are reported in Table 6. Use linear restrictionscommand, the null hypothesis of 01 2 3λ λ λ= = = was tested. Theestimated value of the F-statistic (3, 359) was found to be 7.554,which is well above the upper bound of 4.73 provided in Pesaranet al. (2001). This allows us to conclude electricity consumption,industrial production and entrepreneurship in Singapore's manu-facturing sector are cointegrated.13 This confirms our earlier re-sults based on Johansen's approach. It can therefore be argued thatour results are robust. It is well-known that an ARDL model can bere-parameterized to yield the ECM. The estimated results of thismodel are reported in Table 7.

The main difference is that the coefficient of the error correc-tion term is relatively large but still quite small in absolute terms.The ARDL-based estimated long-run coefficients are presented inTable 8.

The estimated results presented in Table 8 are qualitativelysimilar to those reported in Table 4. The signs and the level ofsignificance are consistent with Johansen's cointegration results.LM test for autocorrelation indicates the absence of significantautocorrelation – the estimated F-value of the test statistic is 5.41with a p-value of 0.000). The estimated results are free from sig-nificant heteroscedasticity as indicated by the Chi-square teststatistic value of 22.81 (with a p-value of 0.000). The functionalform based on Ramsey's RESET test using the square of the fittedvalue is also appropriate (the estimated F-value is 9.60 with a p-value of 0.002).

The stability of the estimated model can be examined by meansof Cumulative Sum of Recursive Residuals (CUSUM) test. The re-sults of CUSUM test are shown in Fig. 4. These results show thatthe estimated model is stable.

13 The results of the t-test also support this conclusion. The estimated p-valueof 1λ is zero in four decimal points, which also supports the view that the threevariables are cointegrated.

4. Discussion

The existing studies on Singapore (Glasure and Lee, 1997 andChang and Wong, 2001), using aggregate data, suggest that economicgrowth (as measured by GDP) causes energy consumption. Using a tri-variate model, where entrepreneurship has been taken into account,this paper finds that industrial production does not cause electricityconsumption but electricity consumption in Singapore's manufactur-ing sector causes industrial production. This result suggests that thegrowth hypothesis is relevant in the case of Singapore's manufacturingsector. In other words, a decrease in the use of electricity, which couldreduce environmental pollution, would lead to a decline in Singapore'smanufacturing sector's industrial production. As shown in Table 9, atthe 5% level of significance, there is a two-way causation betweenentrepreneurship and industrial production. However, the estimatedresults presented in Table 9 suggest a unidirectional causality betweenelectricity consumption and entrepreneurship in Singapore's manu-facturing sector. At the 1% level of significance, entrepreneurshipcauses electricity consumption. It can therefore be concluded that inthe case of Singapore's manufacturing sector, entrepreneurship causeselectricity consumption, which causes industrial production.

In summary, the empirical results presented in this paper suggestthat electricity consumption Granger causes industrial production. Butelectricity consumption does not cause industrial production. In otherwords, growth hypothesis is relevant in the case of Singapore butenergy conservation hypothesis is not supported by data. This impliesthat the feedback hypothesis that envisages a two-way causation be-tween energy consumption and output growth is also not relevant inthe case of Singapore's manufacturing sector. It is interesting to notethat domestic entrepreneurship does not Granger cause electricityconsumption. But domestic entrepreneurship does Ganger cause in-dustrial production.

While the empirical analysis based on Johansen's methodologyconfirms the presence of cointegration, in order to evaluate therobustness of our empirical results, we also tested for cointegra-tion using the ARDL-based bounds testing procedure. The resultsof the bounds testing approach confirm the presence of a commonstochastic trend among the three variables. In other words, theempirical results presented in this paper are robust.

5. Concluding remarks and policy implications

A number of existing studies have considered the link betweenenergy consumption and economic growth. Early studies relied ona bivariate approach. While recognising the omitted variables biasissue, more recent studies have used a tri-variate approach.However, almost all existing studies use annual aggregated data.We argue that the results based on aggregated data cannot bedirectly applied to all sectors of every economy and all sectors arenot equally energy intensive. Furthermore, globalisation has re-sulted in new business opportunities and hence it is imperativethat the impact of entrepreneurship is also taken into account.

0

200

400

600

800

1,000

1,200

1,400

1,600

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

EM EM_forecastedLower bound Upper bound

Fig. A1. Forecasted values of Singapore's manufacturing sector energyconsumption.

0

20

40

60

80

100

120

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

IP IP_forecastedLower bound Upper bound

Fig. A2. Forecasted values of Singapore's manufacturing sector industrialproduction.

-50

0

50

100

150

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–78 77

Due to unavailability of data, a large number of studies useelectricity use as a proxy for energy consumption. Some studiesfound that electricity consumption causes economic growth,which is known as the growth hypothesis. The growth hypothesisimplies that a decrease in energy generation and henceconsumption would reduce economic growth and hence en-vironmental pollution can be reduced only if we sacrifice someeconomic growth. Another group of studies found that economicgrowth causes energy consumption, which is known as the con-servation hypothesis. This hypothesis suggests that by controllingthe economic growth rate and thereby conserving energy, en-vironmental pollution can be reduced without too much sacrifice.Some studies found no causation between economic growth andenergy consumption, which is known as the neutrality hypothesis.The neutrality hypothesis suggests that environmental policies donot have economic consequences. Finally, some studies found atwo-way causation between economic growth and energy con-sumption, which is known as the feedback hypothesis.

Using a unique monthly dataset, from January 1983 to February2014, this paper focuses on Singapore's manufacturing sector. Statis-tical analysis using Johansen's cointegration methodology suggeststhat a statistically significant long-run relationship exists amongelectricity consumption, industrial production and entrepreneurship inSingapore's manufacturing sector. The estimated error correctionmodel suggests that electricity consumption in Singapore's manu-facturing sector adjusts very slowly to its long-run value in response toshocks to industrial production and entrepreneurship. While the ex-isting studies, using aggregate annual data, have shown that economicgrowth, as measured by increase in gross domestic product (GDP)causes energy consumption, using monthly data from Singapore'smanufacturing sector, this paper found a unidirectional causation fromelectricity consumption to industrial production. Furthermore, en-trepreneurship is found to cause electricity consumption, which cau-ses industrial production. The empirical results based on Johansen'smethodology are confirmed by the autoregressive distributed lag(ARDL) based bounds testing approach.

The results presented in this paper have important policy im-plications. First, studies using aggregated data support conserva-tion hypothesis concerning the growth-energy consumption nexusin Singapore. However, analysis of the disaggregated data revealsthat growth hypothesis is relevant in the case of Singapore'smanufacturing sector. This suggests that there is a need for man-ufacturing sector specific energy policy. Second, as growth in en-trepreneurship is found to be an important determinant of elec-tricity consumption in Singapore, there is a need for increasedengagement with the industry. This will also allow relatively moreaccurate forecasting of energy demand in Singapore. Singaporecannot afford a decrease in its economic growth rate as it will hurtits manufacturing sector, an engine of its economic growth. Carefulinvestment in renewable energy, including solar power, can in-crease electricity production without creating significant addi-tional pollution. A recent report suggests long delays in approval ofnew renewable energy investment projects in Singapore (Finenko,2014).

-150

-100

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

NF NF_forecastedLower bound Upper bound

Fig. A3. Forecasted values of entrepreneurship in Singapore's manufacturingsector.

Acknowledgements

This paper has greatly benefited from very helpful commentsand suggestions received from two reviewers. We are also gratefulto several colleagues for helpful comments and suggestions on anearlier draft. However, the authors are solely responsible for allremaining errors and imperfections.

Appendix A

See Figs. A1–A3.

S. Sun, S. Anwar / Energy Policy 77 (2015) 70–7878

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