International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 | Vol. 4 No. 4 [December 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
ANALYSING THE EFFECT OF PRODUCTION SECTORS ON ECONOMIC GROWTH IN MALAYSIA: AN ARDL
APPROACH
Nur Alia Farhana Muhammad Harith Fadzillah1, Zuraidah Derasit2*, Nurbaizura Borhan3, Balkish Mohd Osman4 and Naeem Shahzad5
1 2 3 4 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, MALAYSIA
5 College of Statistical and Actuarial Sciences, University of Punjab, Lahore, PAKISTAN
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 7 October 2022 Revised date : 8 November 2022 Accepted date : 27 November 2022 Published date : 6 December 2022
To cite this document:
Muhammad Harith Fadzillah, N. A. F., Derasit, Z., Borhan, N., Mohd Osman, B., & Shahzad, N. (2022).
ANALYSING THE EFFECT OF PRODUCTION SECTORS ON ECONOMIC GROWTH IN MALAYSIA: AN ARDL
APPROACH. International Journal of Business and Economy, 4(4), 1-11.
Abstract: Gross domestic product (GDP) is one of the important indicators of economic performance.
Previous studies focused on univariate modeling although there are various factors affecting the economic growth and production sectors that have been the main impetus of Malaysian economic growth. This study aims to explore the long-run and short-run impact of production sectors, namely agriculture, construction, manufacturing, mining, and services on Malaysia's economic performance, using the ARDL approach. The data ranged from Q1 2005 to Q4 2020 obtained by the Department of Statistics, Malaysia. The result of the ARDL Bound test signified the existence of a long-run cointegration among the variables. Further study on the short-run coefficient revealed only agriculture and mining have a significant positive impact on GDP in the short run. This study concludes that agriculture and mining led to Malaysia’s economic growth. Above all, agriculture, and mining, along with the construction, manufacturing, and service sector should be developed concurrently to achieve sustainable economic growth.
Keywords: ARDL model, economic growth, agriculture, construction, manufacturing, mining, services.
1. Introduction
Economic indicator provides information on the performance of a country’s economy. This information allows us to understand the current and where the economy is headed in the future.
Economists usually measure economic performance in terms of gross domestic product (GDP).
GDP is calculated from a country's national accounts which report annual data on income, expenditure and investment for each sector of the economy. The measure is all of the resources and domestic production, which is the function to determine the economic expansion of a nation (Oecd, 2009).
According to the International Monetary Fund in 2020, Malaysia's economy is the fourth largest in Southeast Asia. It is also the world's 36th largest economy. Malaysia's labour productivity is significantly higher than that of its neighbours Thailand, Indonesia, the Philippines, and Vietnam, due to a growing concentration of knowledge-based sectors and the adoption of cutting-edge manufacturing and processing equipment. Malaysia's economy is ranked as the world's 27th most competitive country (Global Competitiveness Report, 2019).
As reported by Department of Statistics Malaysia data, the economic growth is in declining pattern in year 2020, as shown in Figure 1. This disrupted growth caused by pandemic Covid- 19 as recorded the worst trend with growth of -17.1% in second quarter 2020.
Figure 1: Trend Line of Malaysia’s Economic Growth
There are various sectors that lead to economic expansion. The production sectors have been the major sectors that contribute to this performance. The primary sectors are agriculture and mining and quarrying, while the secondary sectors are construction and manufacturing. The tertiary sector is services. Since the accomplishment of independence in 1957, Malaysia has successfully differentiated its economy from primary sectors such as agriculture and commodity-based, to one that currently plays host to commanding manufacturing and service sectors, which have driven Malaysian as one of the main exporters of electrical machines, electronic parts and components (World Bank, 2020).
-20 -15 -10 -5 0 5 10 15
1234123412341234123412341234123412341234123412341234123412341234 2005200620072008200920102011201220132014201520162017201820192020
GDP Rate
Years
According to Department of Statistics, Malaysia in 2019, production sectors contribute about 79.9% toward the economy in year 2019. Nevertheless, the production sectors were affected with the pandemic as they recorded diminishing growth. This is the worst performance since the fourth quarter of 1998 with negative 11.2% growth. Agriculture recorded a 1.0% slightly increases compared to negative 8.7% for first quarter while the other 4 sectors recorded the negative growth with manufacturing and services are -16.2% and -18.3% respectively. Mining and quarrying indicated a decline pattern with -20.0% and the construction sector set the downturn trend with -44.5%.
The government has started to reopen all the economic sectors back in the third quarter of 2020.
This really has an impact, as the economic sectors that are affected by pandemic Covid-19 are slowly gaining momentum and are gradually moving up the growth of those sectors. According to the Department of Statistics, the third quarter of 2020 held a solid increasing as it recorded the less negative growth compared to second quarter 2020 with negative 2.7%. Only the manufacturing sector recorded the positive growth with 3.3% while other sectors such as services, mining and quarrying and construction recorded with -4.0%, -6.8% and -12.4%
respectively. Agriculture slightly dropped 0.7% due to the declining in sub-sector which are fishing and rubber. The forecaster also predicts that the direction of economic movements for the future using Leading index (LI) indicates the that a sign of the economy will continue to recover as the upcoming month.
Nevertheless, the fourth wave of Covid-19 pandemic (August 2021) has ended, and this will be crucial for economy in the coming months as it may execute the challenge of the Leading Index (LI) prediction on the economic pattern in the future. Thus, assessing the effect of production sectors on economic growth is essential for the best policy implications. Empirical analysis is also required to determine which sectors promoted economic growth. The objective of the study was to explore the long-run and short-run impact of production sectors on the economic growth, and to find out which sector drove the Malaysia’s economy.
2. Literature Review
The economic sector that drives the economic growth has been intensely debated within the development literature. Azer et al. (2016) used economic sectors such as agriculture, manufacturing and services to examine the relationship between those sectors and Gross domestic product (GDP) per capita. The results indicated that the manufacturing and service sectors have a relationship with GDP per capita while agriculture is not significant toward GDP per capita. Moreover, Agbugba et al. (2018) included economic sector such as agriculture, foreign exchange, employment status and GDP to compare the contribution of agriculture to other variables. This study was conducted in two countries, Malaysia and Nigeria. The results show that agriculture in Nigeria contributed a mean of 18.30% to total GDP, while agriculture in Malaysia only contributed 8.09% to the GDP of Malaysia.
Upon review of previous studies, it has been found that there are a number of ways to develop economic growth models. For example, Yousuf et al. (2019) adjudicated to explore the connection between the service sector and Bangladesh's economic growth from 1973 to 2017.
By using the unit root test, Granger causality test and ARDL Bound test, the ARDL bound approach shows that service sector and GDP growth are associated both in the short-run and long-run. The result shows that 1% expansion in the service-related sector will result in an increase of 0.64% GDP in the short-run and 0.75% of GDP in the long-run. The Error
correlation model (ECM) and the Cointegration test are used to examine the intensity of the relationship between the service sector and GDP. The Granger causality test indicated the presence of a one-way relationship between the service-related sector and GDP. The error correction term implies that the short-run disequilibrium is adjusted with the long-run at a speed of 17%.
Dilhani et al. (2018) investigated the causal relationship and short run or long-run relationship between the agricultural sector, service sector and industry sectors towards GDP production in Sri Lanka using secondary data from years 1960 to 2016. The result of analysis by the Granger causality test shows that there is a causality between the agricultural, industry and service sectors toward GDP production. The Vector error correction model (VECM) also indicated there are four long-run relationships between the contributions of the agriculture sector, industry sector, service sector and GDP in Sri Lanka.
Furthermore, Zayone, T. I. (2019) studied the impact of mineral exports, manufacturing and agriculture on economic growth in Angola by using GDP time series data and export data from Angola between 1980-2017. By using autoregressive distributed lag (ARDL) model, the study shows that manufacturing exports, mineral exports and non-mineral exports (an aggregate of manufacturing and agricultural exports) positively influenced Angola’s GDP growth in the long-run. Mineral exports also positively affected GDP growth net of exports in the long-run.
In the short run, the lags of agricultural and mineral exports positively affected GDP growth, but the effects of manufacturing exports were negative.
2.1 Problem Statement
The economic sectors are the major elements that keeping the economic development. The current issue of the Covid-19 pandemic has spread across the world and has caused the economy to collapse and slump for the coming year. The pandemic also forced the government to announce the MCO and suspend all the economy movements particularly on production sectors. According to the report from Department of Statistics, Malaysia in 2019, the combination of all production sectors has contributed more than half to the economy performance in Malaysia. This apparent that the production sectors have been the major contributor toward economic growth. Since the prior studies has accumulate this issue, the model that provide to the good prediction value to the economy performance are still uncertain.
The issue arises as previous studies focusing more in univariate modelling compare to the multivariate modelling even though there are various factors that affecting the economic growth. Similarly, this issue become more decisive as many previous studies using Vector autoregression (VAR) approach that indicated only short-term period compare to the methodology for the long-term period. This indicated that only short-term forecasting value will be generated. Therefore, it necessary to identify the long-term multivariate forecasting model that included the production sectors so that the government can provide the long-term solutions to stabilize and making sure that those sectors significantly raise for the time being.
3. Method
This section is divided into several sub-sections as it started with the data description. Next, the methodology used to execute each step will be detailed as checking for data stationarity.
The variables will be extended to model development, model validation and model stability.
3.1 Source and Description of the Data
This study used secondary data, gathered from the Department of Statistics, Malaysia. The data cover the information on Malaysian GDP growth rate as the dependent variable with the set of predictor variables of the Malaysian production sectors, consist of agriculture, manufacturing, construction, mining and quarrying and services. This data set covering 16 years, from first quarter 2005 to fourth quarter 2020. The summary of the data description and the data sources is presented in Table 2.
Table 1: Variable Description
Variable Description
GDP Quarterly GDP percentage growth rate on market prices based on constant local currency. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products.
AGRI Quarterly growth rate for agricultural value added based on constant local currency. Agriculture corresponds to the oil palm, fishing, forestry and logging, rubber as well as cultivation of crops and livestock production.
CONS Quarterly growth rate for construction value added based on constant local currency. It comprises value added of construction sector which are special trades activities, civil engineering, residential buildings and non- residential buildings.
MANU Quarterly growth rate for manufacturing value added based on constant local currency.
Manufacturing refers to industries such as electronics and electrical products, chemical industries, wood and paper products industries, food and beverages industries, non-metallic mineral products and fabricated metal product industries, (leather, textile, footwear and wearing apparel) and transport equipment and other manufactures.
MINING Quarterly growth rate for construction value added based on constant local currency. It comprises value added Mining and quarrying sector which are natural gas and crude oil, and condensate.
SERV Quarterly growth rate for value added in services based on constant local currency. This sector include value added in wholesale and retail trade (including hotels and restaurants), transport, tourism, information and communications technology, Islamic financial services, oil and gas services, and personal services such as education and training, health care and real estate services.
3.2 Determining Stationary of Time Series
Before performing the ARDL model, it is necessary to test the stationarity of the data to avoid spurious effects (Guza et al., 2018). The statistical procedure employed to determine the stationarity of a series is called ‘unit root test’. The presence of unit root in variables means that the series cannot be stationary. In this study, two-unit root test will be used which are Augmented Dickey Fuller (ADF) Uit root test and Phillips-Perron (PP) Unit root test. For ADF and PP test, the null hypothesis is that the time series is stationary around a deterministic trend while the alternative is the presence of a unit root – stochastic trend in time series. This study will use 5% significance level to check whether the series is stationary to be used.
3.3 ARDL Model Specification
The ARDL model does not only include a current value, but it also involves the previous value (lag). This approach can see a long-run response from the dependent variable to changes in the independent variables (Gujarati, 2003). ARDL model can be used for both stationary and non- stationary time series as well with the times series that have mixed order of integration. When employing the ARDL method, it is possible to have different variables that have different optimal lags. The order of lag for the ARDL model can be selected using Akaike information criteria (AIC). The study involves constructing the following case: Dependent variable: GDP;
Independent variable: Agriculture, Conctruction, Manufacturing, Mining and Service. The ARDL model specification for this study is as follow:
𝛥𝐺𝐷𝑃𝑡 = α + 𝛽1𝐺𝐷𝑃𝑡−1+ 𝛽2𝐴𝐺𝑅𝐼𝑡−1+ 𝛽3𝐶𝑂𝑁𝑆𝑡−1+ 𝛽4𝑀𝐴𝑁𝑈𝑡−1+ 𝛽5𝑀𝐼𝑁𝐼𝑁𝐺𝑡−1 + 𝛽6𝑆𝐸𝑅𝑉𝑡−1+ ∑ 𝛿1i
𝑝
𝑖=1
𝛥𝐺𝐷𝑃𝑡−𝑖+ ∑ 𝛿2i
𝑞1
𝑖=0
𝛥𝐴𝐺𝑅𝐼𝑡−𝑖+ ∑ 𝛿3i
𝑞2
𝑖=0
𝛥𝐶𝑂𝑁𝑆𝑡−𝑖
+ ∑ 𝛿4i
𝑞3
𝑖=0
𝛥𝑀𝐴𝑁𝑈𝑡−𝑖∑ 𝛿5i
𝑞4
𝑖=0
𝛥𝑀𝐼𝑁𝐼𝑁𝐺𝑡−𝑖+ ∑ 𝛿6i
𝑞5
𝑖=0
𝛥𝑆𝐸𝑅𝑉𝑡−𝑖+ 𝑒𝑡
(1) Where,
α = intercept term
𝛽𝑖 = long-run coefficients 𝛿𝑖 = short-run coefficients
p = lag length for the differenced predictor variables
𝑒𝑡 = error term assumed to be independent and a constant variance
Following the ARDL model, this study will be continuing to investigate the cointegration or long-run association between the variables using F-bounds test. This cointegration bound test is proposed by Pesaran, Shin and Smith (2001) and the hypothesis is as follows:
H0= 𝛽1 = 𝛽2= 𝛽3 = 𝛽4 = 𝛽5 = 𝛽6 = 0 (There is no cointegration exists) H1 = 𝛽1 ≠ 𝛽2≠ 𝛽3 ≠ 𝛽4 ≠ 𝛽5 ≠ 𝛽6 ≠ 0 (There is cointegration exists)
For this study, the 5% significant level is being used. If the calculated F-statistic is greater than critical value for upper bound I (1), then it can be concluded that there is cointegration or long- run relationship.
If the variables are co-integrated, then an error correction model, which includes a lagged error correction term, and differenced variables at a selected lag length (𝑖) can be used to determine the long-run and short-run effects without losing long-run evidence. If testing gets to the ECM analysis (short-term relationship), it will meet with the term Error Correction Term (ECT). This is used as an adjustment of the state of the equilibrium (speed of adjustment) is expected to be negative (convergent). The equation of error correction model from this study is indicated as follows:
𝛥𝐺𝐷𝑃𝑡 = α + ∑ 𝛿1i
𝑝
𝑖=1
𝛥𝐺𝐷𝑃𝑡−𝑖+ ∑ 𝛿2i
𝑞1
𝑖=0
𝛥𝐴𝐺𝑅𝐼𝑡−𝑖+ ∑ 𝛿3i
𝑞2
𝑖=0
𝛥𝐶𝑂𝑁𝑆𝑡−𝑖
+ ∑ 𝛿4i
𝑞3
𝑖=0
𝛥𝑀𝐴𝑁𝑈𝑡−𝑖∑ 𝛿5i
𝑞4
𝑖=0
𝛥𝑀𝐼𝑁𝐼𝑁𝐺𝑡−𝑖
+ ∑ 𝛿6i
𝑞5
𝑖=0
𝛥𝑆𝐸𝑅𝑉𝑡−𝑖+𝜑𝐸𝐶𝑇𝑡−𝑖+ 𝑒𝑡𝑡
Where all the variables are defined on the equation above,
ECT(t-1) = the lagged error correction term
𝜑 = speed of adjustment for variables to return to long run equilibrium after a shock.
The estimated coefficients of GDP, agriculture, construction, manufacturing, mining and services can be interpreted as elasticises since all variables in the model is using the original data.
3.4 Validity and Reliability
To ensure our model is good, diagnostic and stability tests are conducted. The tests consist of Jarque-Bera (normal distribution), Breusch-Pagan-Godfrey (heteroscedasticity), and Breusch- Godfrey LM Test (autocorrelation. Then, a structural stability test is conducted to determine the stability of the model by employing the CUSUM. If the CUSUM line falls within the 5%
significance line, it suggests that the model is stable.
4. Results and Discussion
Based on the Table 2, the ADF and PP unit root test shows the result for stationary of the variables. As it can be seen, all the variables are stationary at the level phase except one variable in ADF which is Mining and PP which is Construction. The result shows that the probability of mining is 0.3932 while construction is 0.0564 which are higher that p-value with 0.05 significant level. Hence, this will proceed to first differencing. The result of the first differencing shows that the mining and construction variables has stationary at first differencing. Hence, by comparing those two-unit root test, the result shows that all variables are not stationary at same phase or on the other word, the variables are mixed stationary.
Therefore, the ARDL approach can be conducted as the variables under consideration are stationary at different phase.
Table 2: ADF and PP Unit Root Results Variables
ADF Unit root Test PP Unit root Test Stationary phase (prob*)
I (0) I (1) I (0) I (1)
GDP 0.0102 0.000 0.0102 0.000
AGRI 0.0000 0.000 0.0010 0.001
CONS 0.0357 0.000 0.0564 0.000
MANU 0.0001 0.000 0.0025 0.000
MINING 0.3932 0.000 0.0014 0.000
SERV 0.0305 0.000 0.0344 0.000
To examine whether there is a co-integration relationship, the bound test is conducted. If the F-statistics is higher than the upper bound, it can be inferred that there is a long-run co- integration. If the F-statistics is lower than the lower bound, it suggests that there is no co- integration. If the F-statistic falls within the upper bound and lower bound, it is inconclusive.
Table 3 reveals the outcome of F-bound test. The F-statistic value is 1808.449 appear to be higher than 5% significant level with 2.62 for I (0) and 3.79 I (1). This signifies that the production sectors have a long-run association with the economic growth. Since, these variables are cointegrate and has the relationship toward the economic growth, the analysis will be proceeded to the long-run and short-run dynamics (error correction model).
Table 3: ARDL Co-integration Bounds Test Test statistic Value Significant
level
Lower bound I (0)
Upper bound I (1)
Long-run relationship F-statistic
k
1808.449 5
10%
5%
2.5%
1%
2.26 2.62 2.96 3.41
3.75 3.79 4.18 4.68
Present
Table 4 shows the results of estimated long-run coefficients using ARDL approach. As it can be observed from the table, probability of t-statistic of all variables are significant as the value are 0.000, less than 5% significant level except for GDP at lag 1 and Mining at lag 2. The r- squared and adjusted shows the higher value with 0.99.
Table 4: Long-Run Coefficient of ARDL Model
Variables Coefficient Standard error T-statistic Prob*
C GDP (-1) AGRI AGRI (-1) CONS MANU MINING MINING (-1) MINING (-2) SERV
-0.096389 0.020743 0.074138 0.017274 0.043579 0.294953 0.064833 0.011849 0.015175 0.496431
0.059212 0.011199 0.006728 0.007380 0.004428 0.005587 0.008816 0.009264 0.006637 0.012762
-1.627859 11.01971 2.340592 9.842204 52.79441 7.353924 1.279077 2.286474 38.89772 -1.627859
0.1096 0.0697 0.0000 0.0231 0.0000 0.0000 0.0000 0.2065 0.0263 0.0000
R Squared
Adjusted R Squared Durbin Watson
0.997996 0.997649 1.855882
F - statistic
Probability F - statistic
2877.201 0.000000
Result of estimated short-run coefficient is displayed in Table 5. Based on the probability of T-statistic, only agriculture and mining are significant and has a short-run relationship with economic growth. As expected, the ECTt-1 term that represented as CointEq (-1), is negative with an associated coefficient estimate of −0.979257. This implies that about 97.92% of any movements into disequilibrium are corrected for within one period.
Table 5: Error Correction Estimation Result
Variables Coefficient Standard Error T-statistic Prob*
C
D (AGRI) D (MINING) D (MINING (-1)) CointEq (-1) *
-0.096389 0.074138 0.064833 -0.015175 -0.979257
0.023292 0.005780 0.007418 0.005816 0.008979
-4.138299 13.52807 8.740298 -2.609109 -109.0598
0.0001 0.0000 0.0000 0.0118 0.0000
R Squared
Adjusted R Squared Durbin Watson
0.997324 0.997136 1.855882
F - statistic
Probability F - statistic
5310.531 0.000000
The overall validation of the models was examined through a few diagnostic tests of the ARDL model as it shows in Table 6. The Breusch-Godfrey LM test suggest that the model does not have autocorrelations. For the Jarque–Bera and Breush-Pagan-Godfrey test, the null hypothesis is not rejected as all the p-value are not significant. Therefore, there is no heteroscedasticity present in the model. Next, the results illustrated in Figure 2 shows that the model is stable because the plot of the tests fall within the critical limits. This indicates results of ARDL are consistent and can be used to predict the economic growth.
Table 6: Test for Model Validation for ARDL Residuals of ARDL model (𝜶 = 𝟎. 𝟎𝟓)
Test Jarque – Bera Breusch-Pagan-Godfrey Breusch-Godfrey LM Test
H0 Residuals are
normally distributed Residuals are homoscedasticity Residuals have no autocorrelation.
Test
statistic 2.211328 2.046363 1.189452
P-value 0.330991 0.0552 0.3129
Decision Failed to Reject H0 Failed to Reject H0 Failed to Reject H0
Conclusion Residuals are normally distributed
Variance of residuals is assumed to be constant for all period of time.
Residuals are not correlated to each other.
Figure 2: CUSUM Plot for ARDL
5. Conclusion
The central theme of this study is based on economic development focusing on production sectors and how a change in one sector influences the behaviour of economic growth. The empirical results of ARDL model indicated that all production sectors are significantly affected by economic growth. In other words, all sectors must have flexibility and adaptability in case of output in the shortest time possible, as this study only used a small sample.
Policymakers, industrialists, stakeholders, and investors, not only in Malaysia but throughout the world, can benefit from the findings of this study. Malaysian policymakers can learn more about the regulatory measures imposed on the production sectors to determine their contribution to the country's GDP. Investors and stakeholders can establish strategies, monetize, and take decisive steps to improve their business, which will lead to a sustainable sector, to prevent themselves from being caught in unexpected economic situations such as inflation or a country's economic crisis. In Malaysia, joint venture initiatives involving multinational corporations and local governments must be expanded in order to enhance human capital, increase skilled labour, complete essential work in less time, and increase personnel trained in modern techniques. Knowledge exchange should be promoted to build confidence among local and international investors, resulting in more foreign direct investment in the production sectors and, as a result, increased Malaysian GDP.
Therefore, further studies can purpose other techniques that are appropriate and significantly contribute to the growth of the economy and develop a new model that is more accurate and efficient for predicting the upcoming economic growth in Malaysia.
6. Acknowledgement
The authors gratefully acknowledged the help of supervisor and lecturers in Universiti Teknologi MARA Shah Alam, Malaysia for given the advice for completing and involving in this research.
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