# Analysis of determinant of the price of palm oil

**Disclaimer:** This work has been submitted by a student. This is not an example of the work written by our professional academic writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Published: *Mon, 5 Dec 2016*

## ABSTRACT

Malaysia involves in trading agriculture sector to other country, especially Malaysia produce oil palm to the world market and become the backbone market in Malaysia. This sector has contributes more than 30 percent of the total income trading to other country. So to maintain the trading of the palm oil, we must know what the factor that influence the price of palm oil. This research help us to determine the factor of determinants the price of palm oil in the Malaysian palm oil market. Nowadays, there are many factor that influence the price of palm oil. After doing this research, founded 4 factor that influence the price of palm oil in Malaysian palm oil market such as, production of palm oil, export of palm oil, consumption of palm oil and plantation of palm oil. To test the relationship between this price and factor determinants the price of palm oil, we are using ordinary least square (OLS) method to check the relationship between this dependent variable and every single independent variable. The data was collected from 1980 to 2009 and still not yet found the data year 2010. As we know, the palm oil is the business that can generate much revenue to our country and has a potential to increase our gross domestic product (GDP).

Keywords: Econometric methods, Malaysian palm oil market. OLS

## INTRODUCTION

The most commercial product that contributed to our national income is palm oil product. Nowadays, palm oil is the one of the major oils and fats that is produced and traded in the world where Malaysia is the second largest exporter to produce the palm oil in this world in terms of production and export and this sector contributes more than 30% of the total income to our country. But in 2003, Malaysia becomes largest producer and Indonesia is a second largest. Because of many land in Indonesia opened to plant the palm oil and exceed the hectares of plantation in Malaysia, Indonesia becomes the largest producer in the world follow by Malaysia. It is all because of the tremendous increase in production and export volume of palm oil to fulfill the world demand of palm oil. The success story of the Malaysian Palm oil industry was due to the synergistic effort of the Malaysian Palm Oil Board (MPOB) and the industry in carrying out R&D and marketing activities. These non-stop efforts have led to higher production and exports, making palm oil always readily available in the world market. But oil palm also facing challenges in enhancing productivity, increasing workflow efficiency and maximizing profits. As the second leader in the palm oil industry, it is worthwhile monitoring the development of its crude palm oil (CPO) production and price.

But the prices of palm oil not pegged by the government, it always fluctuated every year depending on the economic condition. There are many uses of palm oil in our daily life, such as edible oil and soap. Nowadays, the value of palm oil has been increasing quickly because of the decreasing the import tariffs for palm oil and the land for palm oil plantation also increased to expand their business to produce more product of palm oil. Other than that, the Malaysian palm oil also faces some effective competitive strength.

There are many factor that influence the price of palm oil such as hectares of land,

Supply of palm oil, export of palm oil, and consumption of palm oil. But before make this research, we must obtain data from other resources to examine the model to test it with econometric model.

This study will provide data for 29 years annually in terms of prices of palm oil its production, export, consumption and its landarea. Econometric model were developed to analysis the relationship between this prices of palm oil and its economic variable whether the relationship is significant or not. This economic variable test one by one to check the relationship. This study will determine by ordinary least square method to test the independent variable.

## LITERATURE REVIEW

The previous study has been made to understand the impact of palm oil based-biodiesel demand on palm oil prices (Ramli; Roslan and Ayatollah, K 2007). Nowadays biodiesel has become an important fuel to our society. It is because of the growing concern for the environment. Demand for the biodiesel has become higher, and that effect to the amount of palm oil that we can get in the market that putting its prices increased sharply since July 2006. That days, the prices of palm oil influenced by the stock, supply, production. From July 2006 onwards, biofuel become a serious fuel of his renewable production. Biodiesel mainly from palm has increased the demand and altered the economics of palm oil. This study using the autoregressive integrated moving average (ARIMA). This method have been proven to forecast from July 2006 to end 2007 to forecast the prices of palm oil.

Supporting to this journal, the prices of petroleum and vegetable oil down to be moving slowly together. (Anna Awad, Fatimah, 2009). This previous studies have been made to find the long term relationship the prices of crude oil and vegetable oil. They using the Engle-Granger two-stage to do the method. This study use data over the period from January 1983 to March 2008. The two products show the result of the strong evidence of a long relationship. Began in the 1970s until 21 century, the price for all commodities increased between January 2000 and March 2008. The increased of the prices caused by the increased petroleum price more than 300%, while food prices increased 107% during the same period and vegetables oil increased of 192%.

Besides that,(Mohd Nasir, 2003) said that Malaysia is the largest producer and Indonesia is the second largest producer. This two country is the exporter where they contribute 89.6% of palm oil trade in the world and 83.5 % of production. They also export crude palm oil (CPO) and other than that processed palm oil (PPO). The Malaysia exporting quantities of CPO increased from 0.4 million tones to 1.3 million tones in 2000 and 2001, followed by exports of Indonesian CPO increased from 1.8 to 2.0 million tones. Both country also contributed PPO which is the larger share of palm products. CPO and PPO in Malaysian were lower than Indonesia in 2001 and 2002. Both countries products imposed by the tariffs with the objectives initially raising revenue. For example, in Malaysia is to encouraging down stream while for Indonesia it reflected to consumer down. Both country have been imposed taxes on export, and there is a difference way the exports duty payable.

(Ahmad Borhan and Mohd Arif, 2009) said that the prices of palm oil and it comprises crude palm oil and processed palm oil is a strong indicator of the level of palm oil stock. The stock of palm oil has been hovered around 1 million tones. With this amount, the volume has become the psychological which prices tend to be increased and decreased. The end of stock fully depends on the export of palm oil and its production. While local usage and import play smaller roles. The Malaysian palm oil industry has estimated that 1.8 million tones could become the new level of palm oil stock. But it all depends on change in the demand and supply factors.

(James, 2008) said that the prices of crude palm oil (CPO) has a strong relationship with stock according to the conventional economics. The economics of oils and fats had changed in the last two years, that cause both prices and rising in tandem according to traditional economic theory. Because of created the new role played by biodiesel, the strong signs of a linkages also created. The palm oil prices should take into one account of two factors, the petroleum price and the amount of oil stocks. The price band can determines by prices of petroleum. While the stocks can show high or low palm oil prices.

Malaysia’s economic development has indeed been impressive by the contribution of the palm oil industry. (Sabri, Salmiah,Faizah and Nik Abdullah, 2008). It also changing the market trends and rapid development and has continued to pose challenge. The development of oil and fats industry are provides and has undergone in terms of world balance.

In the previous journal, (Mohd Basri, Mohd Arif, and Jamil, 2008) long time ago, the prices of palm oil have been increased, especially to the supply and demand of fats and oil. But since mid 2006, spikes have become more sustained. Besides that, spikes also been attributed to the supply and demand of edible oils and fats and also to the increasing demand for the fuel. Because of the increased of palm oil, the demand curve shifted to the right. The prices of oils have been increased slightly.

(Ayat K Faizah, Ramli Abdullah and Nurul Hufaidah, 2007) study on how to examines volatility spill over. The study focus on between the domestic prices of palm oil and what major factor to the prices volatility. From the research, palm oil has moderate price volatility. And effort should be made to sustained the price of crude palm oil (CPO) to minimize volatility in other prices caused of the prices of (CPO) become a price leader among the other palm oil products. There was a model to developed to forecast prices of palm oil products in domestic.

(Ramli and Mohd Alias, 2006). Malaysia also known the world’s biggest exporter of the palm oil and associated with palm oil. Because of the largest exporter the world, it become important for the country to lead the commodity’s production and its price and can be used to determine the country revenue or in process of decision making. The objective of this paper is to analyses them econometrically and to forecast. The paper forecast that in the future, the production of palm oil can increased. By 2020, the production of palm oil can reach 22 million tones. Prices of palm oil also can fluctuated but in the future, its amount of prices increasing gradually.

(Basri; Mohd Fauzi; Mohd Noor Mamat and Rosli, 2007) analyses the impact of lifting the export tax on Malaysian crude palm oil. Firstly, the equation is developed, especially on processed palm oil (PPO) and crude palm oil(CPO). The study comes out with the conceptual model of the Malaysian palm oil market model, such as the palm oil supply, oil palm area, imports and exports of palm oil products, domestic consumption, domestic price relationship and stocks

According to (Ahmad Borhan, Faizah, Mohd Arif, Norhanani, 2006), said that oilmeals, such as Soyabean meal (SBM) especially an animal feed competes with Malaysian palm kernel expeller (PKE). The competition exists among this two company because of the competitive price. The increasing production also will affect the condition of Malaysian PKE. Future development of the Malaysian PKE depends to world livestock market.

IntermediationConceptual Framework.

Independent variable Dependent var.

Factor/supporting

Price of palm oil Relationship

Production (supply)

Export

Consumption

Land area (Hectares)

Mediating

## Independent variable

There are 4 factor that influences the price of palm oil. There are production (supply), export, consumption and land area in hectares. This independent variable also can support the price of palm oil. Production can influences the prices of palm oil by the supply in the market, the more supply in the market, the more it can influences the prices of palm oil. It mean that, if the supply exceed the demand of the market, many unit of production are waste because of the demand not equal to the unit of production in the market. So to increase the demand in the market, firm can reduces prices of the production in order to attract the demand of the market. Low prices of the production can increased the demand of the production in the market.

Export also can influences the prices of palm oil in the market by having a export more than import in the market can make a country deficit in terms of profit. Same like production, but export trade in other country and make a international business in order to gain profit. If have much demand in palm oil from other country, it means that, we should export more to achieved the demand from other country. From this situation, our country can take advantages to increase the prices of palm oil respectively.

Consumption also can influences the prices of palm oil. It depends on how the consumer fully utilized the use of palm oil. If the consumer already knew the use of the palm oil, easy to them to know how important the palm oil in our society. Because of that, the consumer will ask for the palm oil and directly increased the demand for palm oil.

Other than that, land area of plantation also can influences the prices of palm oil. If we plant more trees of palm oil in land, we are producing more palm oil in the market. If the supply of the palm oil in the market exceed than demand, than many surplus to that product. So to overcome this problem, firm must reduced the prices of palm oil to attract consumer to buy the palm oil. But it is good to the firm if the demand more than supply, then the firm can increased the price of palm oil in order to gain profit.

## Dependent variable

The price of palm oil is depend to this four factor, this four factor can influences the prices of palm oil whether wants to increased or decreased.

## DATA & METHODOLOGY

LEAST SQUARE METHOD (SINGLE REGRESSION) and (MULTIPLE REGRESSION)

The framework developed in this study is drawn both from econometric method (which is based on the economic theory) and the system dynamics approach. This section discusses the econometric model using least square method. Its to correlate and examine the relationships among dependent variable and independent variable. It is a economic theory form with statistical methods.

Factor determinants of prices of palm oil in Ringgit Malaysia (RM). consists of 4 factor that influence the price of palm oil, there are production (supply), export in tones, consumption of palm oil and land area of plantation of palm oil in hectares.

The first step is finding time series data from 1980 to 2009, which influence the price of palm oil to make a research and then describe it according to the econometric model. To regress the econometric model we must use the data which influence the prices of palm oil. So this 4 factor is the important data to make a research. After regress, we can identified relationship between dependent and independent variables.

These 4 factor also known as independent variable and price of palm oil also known as dependent variable which price of palm oil depend on this 4 independent variable whether to increase or decrease. The prices of palm always fluctuate depend on this 4 independent variable. To test this independent variable had a relationship between price or not, the econometric model by using least square method use to test one by one of this independent variable whether this independent variable has a relationship between dependent variable. After test this econometric model, we look to the T-statistics, R-squared, Durbin-Watson stat. if t-stat are less than 2, this means that there is no relationship between independent variable and dependent variable and also known as is not significant. If more than 2, there is a relationship between independent variable and dependent variable. After test one by one, we should regress all dependent variable using multiple regress because of explanatory variable is more than one to check whether all independent variable significant or not.

## Unit root test

Null Hypothesis: D(PRICE) has a unit root

Exogenous: Constant

Lag Length: 4 (Automatic – based on AIC, maxlag=7)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-2.998437

0.0493

Test critical values:

1% level

-3.737853

5% level

-2.991878

10% level

-2.635542

Null Hypothesis: D(PDC) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on AIC, maxlag=5)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-3.998301

0.0047

Test critical values:

1% level

-3.689194

5% level

-2.971853

10% level

-2.625121

Null Hypothesis: D(LAND) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on AIC, maxlag=7)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-4.664706

0.0009

Test critical values:

1% level

-3.689194

5% level

-2.971853

10% level

-2.625121

Null Hypothesis: D(EXP01) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on AIC, maxlag=3)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-3.609207

0.0121

Test critical values:

1% level

-3.689194

5% level

-2.971853

10% level

-2.625121

Null Hypothesis: D(CNSPTN) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on AIC, maxlag=7)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-4.569870

0.0011

Test critical values:

1% level

-3.689194

5% level

-2.971853

10% level

-2.625121

## MODEL LISTING AND FINDING

Least square method-POPt=+bX

Between dependent variable and independent variable

Variable

C

Std-error

T-stat

R-Squared

DW-stat

Prob

D1LNPROD

0.329321

0.177300

1.85

0.113301

1.751273

0.0742

D1LNEXP

0.855506

0.254039

3.36

0.295760

1.816493

0.0023

D1LNCONS

0.171866

0.270477

0.63

0.021939

1.704933

0.5332

D1LNLAREA

0.097365

0.389766

0.24

0.678609

0.571303

0.8046

Analysis of data:

Used e-view to regress and estimation using Ordinary least square based on dependent variable and independent variables from the data collected. Before regress the data, must do the unit root test to make sure the data stationary or not, after that log the data to reduce the problem of autocorrelation and multicollinearity. Then use first difference method because it is already mention in unit root test to check using first difference method.

PPOt=price of palm oil in the market

PROD=production of palm oil(supply)

EXP=export of palm oil to the world demand (tones)

CONS=consumption of palm oil in this market

L.AREA=landarea of palm oil plantation in Malaysia (hectares)

Result of regression between POPt and PROD

Dependent variable: POPt and Independent variable: PROD

POPt=0.054234+0.329321t

The coefficient above shows that the relationship between price and production of palm oil is positive relationship. It means that, 1 unit increased of production of palm will lead to increase price of palm oil by 0.329321.

t-statistic=1.85

Reject Ho, because there is no significant relationship between price and production because t-statistic shows that it is significant and below 2. So production cannot influence the price of palm oil respectively.

R-Squared =0.113301

Explanation. There are 11.33% of the changes in the dependent variables. Only independent variable can explain the dependent variable. 88.67% cannot be explained by the regression analysis due to some omission of independent variables. In other words, this R-squared show the weak relationship between dependent variable (price) and independent variable (production). It’s mean that, when independent variable change 1%, dependent variable will change by 11.33%. So, we can concluded that independent variable and dependent variable has a weak relationship because of the R-Squared is low.

Std-error=0.177300

Explanation. The smaller the value of SEE, the closer the data points/actual points to the regression line.

DW-statistics=1.751273

Explanation. There is a less problem in auto-correlation because DW shows value almost than value 2. If the value of DW shows above 2,means that there is no problem in autocorrelation

Probability=0.0742

Explanation. The variable is significant at 0.05 (5%) significant level or 95% confidence level. If the probability is less than 0.05, it means that 95% confidence interval accepted and there is a relationship between dependent variable and independent variable. The independent variable for the production is 0.0742. This production of palm oil (supply) cannot influence the price of palm oil. It’s because if the production is too many in the market, the price of palm oil will not effect because society only concern for the basic needs such as shelter, food and society not concern for the production of palm oil. Same like if the production of palm oil is low, it will not influence the price of palm oil because society not really wants the uses of palm oil.

Result of regression between POPt and EXP

Dependent variable: POPt and Independent variable: EXP

POPt=0.063604+0.855506t

The coefficient above show the positive relationship between price and export. It means that, 1 unit increased export of palm oil will lead to increase price of palm oil by 0.855506

t-statistic=3.36

Also accept Ho, because there also a relationship between price and export because t-statistic shows that it is significant and above 2. So export also can influence the price of palm oil respectively.

R-Squared=0.295790

Explanation. There are 29.57% of the changes in the dependent variables. Only the independent variable can explained the dependent variable. 70.43% cannot be explained by the regression analysis due to some omission of independent variables. R-squared show the weak relationship between dependent variable (price) and independent variable (export). It can be explained when independent variable change 1%, dependent variable also will change by 29.57%. we can concluded that independent variable and dependent variable has a weak relationship because of the value of R-squared is low.

Std-error=0.254039

Explanation. The smaller the value of SEE, the closer the data points/actual points to the regression line.

DW-statistics=1.816493

Explanation. There is a little bit problems in auto correlation because DW shows value almost 2.

Probability=0.0023

Explanation. The variable is significant at 0.05 (5%) significant level or 95% confidence level. If the probability less than 0.05, it means that 95% confidence interval accepted and there is a relationship among dependent variable and independent variable. The independent variable for the export is 0.0023 and this export of palm oil (supply) can influence the price of palm oil. The price of palm oil will influence if the export more than demand from other country. This kind of scenario happened because it’s good to decrease price of palm oil to the trading country, so that the trading country can buy with cheapest price. But if the export less than demand from other country, it will influence the price of palm oil, because our country can sell with high price in order to get more profit from other country. It happen when the shortage of the production and demand will keep increasing every year and the export are limited.

Result of regression between POPt and CONS

Dependent variable: POPt and Independent variable: CONS

POPt=0.010169+0.171866t

The coefficient above also shows the positive relationship between price of palm oil and consumption of palm oil in the market. It can explained when 1 unit increased consumption of palm oil will lead to increase price of palm oil by 0.171866

t-statistic=0.635418

Reject Ho, because there is no relationship between price and export because t-statistic shows that it is no significant and below 2. So consumption cannot influence the price of palm oil respectively.

R-Squared=0.021939

Explanation. There are 2.19% of the changes in the dependent variables. Only the independent variable can be explained the dependent variables. 97.81% cannot be explained by the regression analysis due to some omission of independent variables. This R-squared also show the weak relationship between dependent variable (price) and independent variable (consumption). It means that, when independent variable change 1%, it will effect to the dependent variable and also will change by 2.19%. Because of the value of R-squared is low, independent variable and dependent variable has a weak relationship.

Std-error=0.270477

Explanation. The smaller the value of SEE, the closer the data points/actual points to the regression line.

DW-statistics=1.704933

Explanation. There is fewer problems in auto correlation because DW show value almost 2. If this value high than 2,it means that there is no problem in auotocorrelation.

Probability=0.5332

Explanation. This variable also significant 0.05 (5%) significant level 95% confidence level. If probability less than 0.05, it means that 95% confidence interval accepted and there is a relationship among dependent variable and independent variable. The probability of this independent variable is 0.5332 and this consumption cannot influence the price of palm oil because nowadays, society in Malaysia don’t know what is the uses of palm oil, they just ignore the uses of palm oil, they don’t want to create a new things from uses of palm oil. So this uses of palm oil will not effect to the society and then directly the consumption of palm oil will not influence the price of palm oil in the market

Result of regression between POPt and LANDAREA

Dependent variable: POPt and independent variable: L.AREA

POPt=0.021867+0.097365t

Only this coefficient above shows the positive relationship between price of palm oil and hectares of landarea. It is mean that, 1 unit increased landarea will lead to increased price of palm oil by 0.097365

t-statistic=0.249805

Reject Ho, because there is no relationship between prices and landarea because t-statistic shows that it is significant and below 2. So landarea cannot influence the price of palm oil respectively.

R-Squared=0.002306

Explanation. There are 0.23% of the changes in the dependent variables. Only the independent variable can explained the dependent variables. 99.77% cannot be explained by the regression analysis due to some omission of independent variables. R-squared above show the weak relationship between dependent variable (price) and independent variable (landarea). It means that, if the independent variable change 1%, the dependent variable also will change by 0.23%. The dependent variable and independent variable has a weak relationship because of the R-squared is low.

Std-error=1.86E-05

DW-statistics=1.47897 0

Explanation. Also have a problem in auto correlation because DW show value less than 2

Probability=0.8046

Explanation. The variable is significant at 0.05 (5%) significant level or 95% confidence level. If the probability less than 0.05, it means that 95% confidence interval accepted and there is a relationship between dependent variable and independent variable. The probability of this independent variable is 0.8046 and these hectares of landarea also cannot influence the price of palm oil. These hectares of landarea are the factor that cannot influence the price of palm oil, because it involved many stages to

the more hectares of palm oil, the more production of palm oil. If production is much and exceed the total demand in the market, the price of palm oil will decrease because to attract from society to buy that product. But if the hectares of landarea of palm oil is small, the production also less and if the demand over than production, the price increase to gain the big profit,

Multiple Regression

Test using first difference

Dependent variable=POPt

Coefficient

Probability

Dlnpdc

-2.234175

0.0724

Dlnland

0.417220

0.1939

Dlnexp

1.778560

0.0036

Dlncnsptn

0.794493

0.1748

C

-0.041683

0.4468

R-squared=0.520161

Durbin Watson=1.511417

Dlnprice=-0.041683+0.794493+1.778560+0.417220-2.234175

We using the first difference method because we want to make the data significant. Before regress the data using first difference, we must log all the data to make the probability below than 0.05, in order to make the data significant. After regress, we found that only probability for Dlnexp less than 0.05. Means that only dlnexp significant and can influence the price of palm oil in the market, other independent variable not significant and cannot influence the price of palm oil in the market because the probability shows over than 0.05. R-squared show strong relationship between all independent variable and dependent variable. it means that, 52.01% changes in the dependent variables and 47.99% cannot be explained by the regression analysis due to some omission of independent variables. In other words said that, if the independent variable changes 1%, the dependent variable also will change by 52.01%. DW shows that, there is fewer problems in autocorrelation because DW show less than 2. Other independent variable not significant because of multicollinearity and autocorrelation problem. Multicollinearity defined that, there is no linear relationships among the explanatory variables. It is also can happen when two or more independent variable are related to dependent variable. Autocorrelation can be defined as ‘correlation between members of observations ordered in time (as in time series data)

HYPOTHESIS TESTING

It was established to test the relationship whether the relationship exists or not between the dependent variable and independent variable. Besides that, single independent variable which represented by t-test also can check by hypothesis testing.

H0: there is a relationship between dependent variable and independent variable

H1: There are no relationship between dependent variable and independent variable

Hypothesis testing for POPt and PROD

Acceptance region

Critical region Critical region

t = 2 t = 2

T-statistic=1.85 (based on result from regress with E-views)

T-Value=2

Reject H0 if T-statistic is less than T-value. The result show that the T-statistic = 1.85 and less than T-value = 2. and we accept H1 the alternative hypotheses because there is no relationship between this

### Cite This Work

To export a reference to this article please select a referencing stye below: