International Journal of Business and Economy eISSN: 2682-8359 [Vol. 1 No. 1 June 2019]
http://myjms.mohe.gov.my/index.php/ijbec
SECTORAL EFFECT OF OIL PRICE, NATURAL GAS AND LNG PRICES ON MALAYSIA MANUFACTURING SECTOR’S
GDP
Nur Surayya Mohd Saudi1* and Wong Hock Tsen2
1 Faculty of Defence Studies and Management, Universiti Pertahanan Nasional, Kuala Lumpur, MALAYSIA
2 Faculty of Business, Economics and Accounting, Universiti Malaysia Sabah, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 3 May 2019 Revised date : 10 May 2019 Accepted date : 15 June 2019 Published date : 30 June 2019
To cite this document:
Mohd Saudi, N., & Wong, H. (2019).
SECTORAL EFFECT OF OIL PRICE, NATURAL GAS AND LNG PRICES ON MALAYSIA MANUFACTURING SECTOR’S GDP. International Journal Of Business And Economy, 1(1), 36-47.
Abstract: This study investigates on the impact of GDP on manufacturing sector towards oil and gas prices. As manufacturing sector comprises and is profiled by high oil intensity industry, the study takes account 3 types of energy price, namely Brent Crude, Natural Gas and LNG, and explores the impact of these energies on the manufacturing sector. The study employed an econometrics analysis using ARDL estimates with time series data spanning from 1987 to 2017. The empirical findings depicted that the Brent Crude and Natural Gas prices have a negative relationship with high energy intensity economic sector and the industry that requires high input production of oil and gas.
An increase in the Brent Crude and Natural Gas prices reduces the output for manufacturing of electrical and electronics, and manufacturing of transportation and machinery. It also reported that a strong negative relationship found between petroleum subsector and Natural Gas and LNG prices that shares of GDP is denominated by the export of LNG product.
Keywords: GDP, Economic sector, Manufacturing, Brent Crude, Natural Gas, LNG.
1. Introduction
Extensive studies have been done to explore on the relationship of energy price of macroeconomic variables. This is being a reason, energy; especially oil is the main input of production particularly in industrial production that any changes on the price will affect the production cost of the product.
Oil can be characterised as a moving engine for production, hence giving a significant impact to the economy. This raises a concern to policymakers and investors on the efficiency of oil consumption and resource allocation. Numerous studies have achieved remarkable progress in explaining the dynamics of the impact, especially on developed economies and developing economies respectively. However, there are relatively few studies devoted to small open economies of oil exporter countries like Malaysia. Listed as the third-largest economy in Southeast Asia, Malaysia is ranked as the third highest in terms of energy consumption in the region. The International Energy Agency (IEA) reported that Southeast Asia country’s total primary energy demand doubled over the outlook period from 89 million tonnes of oil equivalent (Mtoe) in 2013 to 160 Mtoe in 2040. It is recorded that Fossil fuels will remain the dominant primary energy sources with their share expected to fall only by 2%, from 95% in 2013 to 93% in 2040.
Notwithstanding the important of Fossil fuel; oil and natural gas on the manufacturing input of production (moving engines) and the role of LNG for export income, Malaysia is exposed to the world price fluctuations that can lead to economic vulnerability.
The oil and gas contributed 20% to Malaysia’s GDP and is identified as a prominent sector in the future. In 2014, oil and gas export for Malaysia is the second highest after export for electrical and electronics (DOSM, 2016). Both export products; oil and gas, and electrical and electronics are categorised under the manufacturing sector portfolio. This paper empirically investigates the impact of oil, natural gas and LNG prices on output of manufacturing subsector in Malaysia by taking the Gross Domestic Products (GDP) as proxy. This study distinguishes from the previous studies, mainly in three aspects. First, this paper is the first of its kind to examine the responses of manufacturing sub-sectors of Malaysia to oil price changes as only few studies have been done before. Second, this paper extends on the impact of price changes on natural gas that is highly consumed by the manufacturing sector (MER, 2017). Third, the present study tests the impact of LNG price on the manufacturing subsector that is less consumed, but highly contributes to petroleum products under the manufacturing sector portfolio (DOSM, 2017).
The sectoral effect in this view is expected to shed light on the impact of energy prices on a specific subsector at the end of the study, policy recommendation can be more specific to the most affected subsectors. The study focuses on the impact of oil, natural gas and LNG prices on the output of five selected manufacturing subsectors that highly contributes on GDP, namely manufacturing of electrical and electronics, transportation and machinery, petroleum products, woods, and foods processing.
2. Literature Review
After the Independence in 1957, Malaysia is dependent on the agriculture sector and natural resources based export. However, since 1985, the industrialisation policy was introduced to focus on the industrialisation sector. Malaysia believes that industrialisation will bring a significant growth to the economy (Kuazam, 2015). The industrialisation instantly becomes Malaysia’s main focus after the manufacturing of electric and electrical subsectors, which leads the shares of GDP contributions to Malaysia’s income. However, as this sector is high in oil intensity sector, it is also exposed to oil and gas fluctuations as these two energies are the main sources of energy. Among the 5 main economic sectors, the manufacturing sector is the second highest Malaysia’s GDP contributor after the services sector. The manufacturing subsectors that contribute to the GDP’s include the manufacturing of electrical and electronics, petroleum products, transportation, foods processing, and woods and furniture (DOSM, 2017). Hence, this study will investigate on the relationship of these highly contributing economic subsectors towards the energy prices. The study on the effect of disaggregate data is utmost important as Wan, (2016) concluded that the manufacturing sector in Malaysia is expected to expand with favourable demand with growth in domestic and export demand. However, the role of other sectors like manufacturing, agriculture and construction is also important for the government to control the oil price fluctuations (Pei, 2013). Additionally, (Taghizadeh-hesary, Rasolinezhad, & Kobayashi, 2015), asserted that the commercial, industrial and transportation sectors are strongly affected by oil price fluctuations compared to some less oil intensity sectors like the residential sector.
The study on the disaggregate data is essential as (Alper and Torul, 2010) concluded that despite the increment in oil price, it did not significantly affect the Turkey manufacturing sector in terms of aggregate although it dampened the progress of several manufacturing subsectors under the manufacturing sector portfolio. As such, the policy maker, government and investor should control the impact of energy prices on output discriminatively by the components embedded within the economic sector. Al Mamun et al. (2017) concluded that the study on the sectoral out is vital to enable policy maker to devise a pro-strategy in specific sectors for concrete policy recommendation. In another study, Yusma et al. (2013) suggested further study on the impact of oil on different sector as each sector has different level of energy use and energy intensity.
Meanwhile, Baffes (2007) explored on the pass-through effect of oil price changes on other commodities and the study concluded that at a disaggregate level, the impact of crude oil prices on other commodities exhibit a greater effect. As such, the study on disaggregate data is important in order identify specific policy implication. Baffes (2007) added that the extension on the finding on disaggregate data should add more explanatory variables like the exchange rate, interest rate and industrial production. Motivated by Baffes (2007), this study has added more control variables that can influence the level of output for the selected subsector. Figure 1 shows the Malaysia’s GDP contribution in percentage for manufacturing subsectors led by the manufacturing of petroleum products and electrical and electronics and Figure 2 on the percentage of GDP contributions from the manufacturing sectors from 1987 to 2017.
Figure 1: Malaysia’s GDP contribution for manufacturing subsectors Source: Department of Statistic Malaysia (DOSM)
3. Methodology
This study employs an econometrics analysis time series technique based on Autoregressive Distribute Lags (ARDL) model. All data were extracted from Department of Statistic Malaysia (DOSM), Index Mundi and Federal Reserved Economic Data (FRED). The study is motivated by Bohi (1997) that emphasised on the impact of energy prices on economic growth by adopting the production function theory. Furthermore, this study enhances the model on the sectoral level as emphasised by (Shaari et al., 2013) and (Ee, 2015). The model developed incorporates macroeconomic fundamentals, including real effective exchange rate (REER) and consumer price index (CPI) as control variables. The model developed is as follows:
LSSt = +LRBCt + LRCPIt + LRER+ µt (1.1)
LSSt = +LRGASt + LRCPIt +LRER + µt (1.2) LSSt = +LRLNGt + LRCPIt +LRER + µt (1.3)
Where LSS is the logarithm of subsectors real value to GDP contribution in USD billion that consists of manufacturing of electrical and electronic, petroleum product, transportation and machinery, woods and foods processing. LRBC, LRGAS and LRLNG are the logarithms of Real Brent Crude, Real Natural Gas and Real Liquefied Natural Gas, and LRCPI is the logarithm of Consumer Price Index and LRER is the logarithm of Real Exchange Rate and µt is the error term.
Table 1: Source of Data
Variables Description Sources
GDP Gross Domestic Products for Subsectors DOSM
RER Real Exchange rate in USD FRED
CPI Consumer price index (2010 = 100) FRED
RBC Real Brent Crude Index Mundi
RNG Real Natural Gas Index Mundi
RLNG Real Liquefied Natural Gas FRED
Note: DOSM stands for Department of Statistic Malaysia and FRED stands or Federal Reserved Economic Data
4. Findings and Discussions
Firstly, we test for the stationary of all variables to determine their order of integration. This is to ensure that the variables are not I(2) to avoid spurious results. To test the order of integration of the variables we use the standard tests for unit root, namely the Augmented Dickey- Fuller (ADF) and PP (Phillips-Perron). The unit root results shows that all the variables are stationary at I(0) and I(1). Refer Table 2.
Then, in the step of the ARDL analysis, is to test the existence of a long run relationship among variables, it is important to decide the order of the lag of the ARDL. We have selected lags based on Schwarz Criterion (SC). F-test used for this procedure has a non-standard distribution. Thus, two sets of critical values are computed by (Pesaran et al., 2001) for a given significance level.
Critical values for the I(1) series are referred to as upper bound critical values, while the critical values for I(0) series are referred to as the lower bound critical values. If the computed F-statistic exceeds the upper critical bounds value, we can conclude that there is evidence of a long-run relationship between the variables regardless of the order of integration of the variables. If the F- statistic is below the lower critical bounds value, it implies no co- integration. Lastly, if the F- statistic falls into the bounds, a conclusive inference cannot be made without knowing the order of integration of the underlying repressors. For this study, we have dropped some subsectors that have F-statistic value below the lower bound. Refer Table 3 on ARDL Bound test.
The empirical findings of the impact of oil and gas price change on the manufacturing subsectors reveal that high oil intensive subsectors that consumed high energy of electricity (manufacturing electrical and electronics, manufacturing transport and machinery) have a negative relationship with energy price changes. The same negative magnitude is found on the relationship of manufacturing petroleum products that requires high input production of crude oil and gas.
However, no long-run relationship was detected between the manufacturing of foods and manufacturing of woods on Brent, natural gas and LNG prices. In other words, it can be concluded that the negative relationship appears only for highly energy consuming subsectors either on input source or on industry that requires high energy.
A negative long-run relationship between Brent crude and natural gas prices is observed on highly consumed electricity subsectors, namely the manufacturing of electronic and manufacturing of machine and transportation at 31% to 57% coefficient level. However, manufacturing of petroleum only reacts on LNG and natural gas prices with a negative relationship, while no relationship with Brent crude is found. This pattern is expected from the role of natural gas as the main source for manufacturing LNG, thus making this subsector dependent on these energies. This indicates that export dependent subsectors that highly consume energy are exposed to negative impact of oil price fluctuations. In contrary, no relationship is found at the manufacturing of foods and manufacturing of woods on Brent, natural gas, and LNG prices as these subsectors consume lower and depend more on agriculture resources like timber and foods based industry that have lower reliance on energy. Refer to Table 6. Table 5 displays a diagnostic test that captures a well specified model. The Diagnostic test confirmed that all statistics (P-value) are more than 10%, 5% and 1%
significant level. Based on the critical value of X 2 of one degree of freedom, the null hypothesis of no heteroscedasticity was accepted in all models. In addition, based on the critical values, of X 2 for two degrees of freedom the null hypothesis of no misspecification of the functional form was accepted in all cases. Finally, Figure 2 displays the CUSUM and CUSUMQ test proof that all models are reliable and stable, where all the variables plots are inside the critical bound. The findings are important as they proved that although the manufacturing sector is known to be a high consumer of energy, the subsector inside the GDP’s manufacturing portfolio consist shares of low energy intensive that are not affected by oil and gas price changes. This is supported with Eksi et al., (2011) that depicted that industry that consumes oil or uses oil as direct input of production reacts highly towards price changes.
Meanwhile, no relationship was detected in low-intensity subsectors. The findings also reveal that every subsector has a different level of magnitude on oil and gas, which is also in line with (Alper and Torul, 2010). In contrary, Korsakienė et al., (2014), asserted, energy prices like natural gas, crude oil and electricity do not influence the GDP and the export growth for Baltic State, which is profiled as energy dependent. However, the limitation of their study is the limited data of seven years, (2003 to 2010), which might influence the outcome of the study. The longer data and more variables added might produce different results. Finally, this study focuses on the impact of GDP on the manufacturing subsector because regardless, the oil and gas is indirectly consumed as input of production and it does affect the production cost for certain economic sectors (Eksi et al., 2012).
Therefore, it is deemed important for the government, investors and policy makers to control the impact of energy price on output discriminatively by the components embedded within the economic sector. In addition, this study contributes to policy makers on a specific subsector that highly benefits on oil and gas subsidy. As such, a subsidy allocation can be more focused on affected industries. In addition, Malaysia’s can emphasize on expansion fiscal policy, particularly during oil price shock, and providing fuel subsidies policy and environmental policy, that can assist and boost Malaysia’s economic growth that also mentioned by Bekhet and Yusoff (2013).
Figure 2: Malaysia’s GDP contribution for manufacturing sector.
Source: Department of Statistic Malaysia (DOSM).
Empirical Findings Table 2 Unit Root Test
Note: ***, ** and * are 1%, 5% and 10% of significant levels, respectively. The optimal lag length is selected automatically using the Schwarz information criteria for ADF test and the bandwidth has been selected by using the Newey–West method for the PP test.
ADF PP KPSS
Variables Trend and
Intercept
Trend and intercept
Trend and intercept Manufacturing Subsectors
LELECTRO I(0) -3.1313 -2.9934 0.1449***
LELECTRO I(1) -4.3363** -4.3367*** 0.1656**
LPETRO I(0) -1.336068 -1.789316 0.1644***
LPETRO I(1) -4.312237** -5.5140*** 0.5000
LTRANS I(0) -3.68877** -10.412*** 0.1868***
LTRANS I(1) -7.069*** -3.6955** 0.1836
LFOODS I(0) -2.220351 -2.359974 0.072269***
LFOODS I(1) -5.1072*** -5.067592*** 0.083869
LRBC I(0) -1.538 -1.7253 0.101***
LRBC I(1) -4.703*** -4.717*** 0.136
LRGAS I(0) -1.457 -1.610 0.100***
LRGAS I(1) -5.200*** -5.200*** 0.120
LRLNG I(0) -2.418 -0.928 0.103***
LRLNG I(1) -3.964** -3.677** 0.136
LRGDP I(0) -2.523 -2.513 0.1142***
LRGDP I(1) -5.734*** -5.727*** 0.1387**
LCPI I(0) -1.604 -1.637 0.157***
LCPI I(1) -4.804*** -4.835*** 0.190***
LRER I(0) -0.0261 -2.691 0.1372***
LRER I(1) -0.4795*** -7.532*** 0.1758
Table 3 Bound Test
Note: ***, ** and * are significant at the 1%, 5% and 10% levels, respectively.
Table 4 Short-run elasticities
Note:
***,
** and
* are
significant at the 1%, 5% and 10% levels, respectively.
Model AIC (Lag order) F-statistics
Manufacturing Subsectors LELECTRO
LRBC (1, 0, 0, 0) 5.220***
LRGAS (1,0,0,0,) 5.036***
LRLNG (1, 0, 0, 0) 3.196
LTRANS
LRBC (1, 0, 1, 1) 6.443***
LRGAS (1, 0, 1, 1) 6.659***
LRLNG (1, 0, 1, 1) 5.768***
LPETRO
LRBC (1, 1, 1, 1) 6.135***
LRGAS (1, 2, 1, 1) 11.278***
LRLNG (1, 1, 1, 1) 8.026***
LFOODS
LRBC (1 ,0, 0, 0) 9.319***
LRGAS (1, 1, 0, 0) 4.491***
LRLNG (1, 0, 0, 0) 3.664
LWOODS
LRBC (1, 0, 0, 0) 1.984
LRGAS (1, 0, 0, 0) 1.891
LRLNG (1, 1, 0, 0) 2.645
Subsectors IV Coefficient Error Correction Term (ECT)
LELEC Brent -0.1507** -0.340***
Gas -0.1739** -0.3024***
LNG - -
LTRANS Brent -0.1636* -0.533***
Gas -0.1751* -0.48673***
LNG -0.174539 -0.5009***
LPETRO Brent 0.056025 -0.29181***
Gas 0.10113** -0.35623***
LNG 0.012611 -0.34186***
LWOODS Brent - -
Gas - -
LNG - -
LFOOD Brent -0.128695 -0.86500***
Gas -0.192176* -0.248192*
LNG - -
Table 5 Diagnostic Test
Note. 1. ** represents the 5% significance level. 2. The diagnostic test is performed as follows A. Lagrange multiplier test for residual serial correlation; B. Ramsey’s RESET test using the square of the fitted values; C.
Based on a test of skewness kurtosis of residuals; D. Based on the regression of squared fitted values.
Subsectors A. Serial correlation X 2 (1) [p-value]
B. Heteroscedasticity X2 (2) [p-value]
C. Normality X2 (3) [p-value]
LELECTRO
Brent 0.0873
[0.7416]
0.6509 [0.5904]
2.1083 [0.34387]
Gas 0.2575
[0.5725]
0.7925 [0.4544]
4.1331 [0.12262]
LNG - - -
LTRANS
Brent 3.15
[0.9948]
1.1816 [0.7871]
0.8590 [0.6508]
Gas 0.0631
[0.7695]
1.525 [0.6704]
0.8648 [0.6459]
LNG 0.0221
[0.8620]
0.4542 [0.9727]
0.5250 [0.7691]
LPETRO
Brent 1.0995
[0.2218]
4.2809 [0.6440]
1.4725 [0.47789]
Gas 0.1965
[0.5858]
1.2611 [0.5594]
3.244 [0.1974]
LNG 0.01376
[0.8885]
2.5263 [0.7932]
1.7032 [0.4267]
LFOODS
Brent 0.636588
[0.2868]
1.457437 [0.9012]
0.9656 [0.6170]
Gas 0.558768
[0.4843]
0.2664 [0.2639]
1.952007 [0.3428]
LNG - - -
Table 6 Long -run elasticities Stability Test
CUSUM Test
-15 -10 -5 0 5 10 15
2000 2002 2004 2006 2008 2010 2012 2014 2016 CUSUM 5% Significance
CUSUMQ Test
Note: ***,
** and * are
significant at the 1%, 5% and 10% levels, respectively.
Figure 2Stability Test
5. Recommendations
The outcome of the study is deemed important for the policymakers in planning budget allocation on specific manufacturing subsectors that highly contribute to GDP. An empirical finding that a negative relationship on the energy prices changes, especially on the subsectors like electrical and electronics, petroleum products and transportation could be assisted by providing oil subsidy and tax exemption on affected industries. This is to ensure such external impact won’t affect the production performance and provides betterment in aiding policymakers and safeguard these important sectors. The findings also detected that the impact of LNG prices only occurs in the manufacturing petroleum subsectors; this is an indication that export dependency also influences the impact of energy price changes in Malaysia. The impact of energy price changes creates a transmission effect on the income received from oil exporting to oil importing countries. Hence, a constant increase in energy prices will reduced the purchasing power for oil and gas importing countries that translates to lower import demand that in a long-run. Finally, the manufacturing sector in Malaysia can replace the oil and gas as production input with biodiesel, solar, hydroelectric, and nuclear power generation in large-scale. This is an effective strategy to keep the economy intact and to be independent from world oil and gas price fluctuation, as it has been proven in this study that hike in energy price can retard Malaysia’s economy.
DV IV Coefficient
Subsectors
LELEC Brent -0.442996**
Gas -0.575171**
LNG -
LTRANS Brent -0.306923*
Gas -0.359749*
LNG -0.348444
LPETRO Brent -0.280318
Gas -0.46812***
LNG -0.455822**
LFOOD Brent -0.140152
Gas 0.589703
LNG -
LWOODS Brent -
Gas -
LNG - -0.4
0.0 0.4 0.8 1.2 1.6
2000 2002 2004 2006 2008 2010 2012 2014 2016 CUSUM of Squares 5% Significance
Acknowledgement
Acknowledgment goes to my PhD Supervisor, Associate Professor Dr.Wong Hock Tsen, for constructive comments and suggestions, and financial support from the Universiti Pertahanan Nasional Malaysia (UPNM).
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