# Rubber Production In Malaysia Commerce Essay

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Rubber can be classified into two main categories; natural rubber and synthetic rubber. The NR is obtained from latex, which is the milky white fluid and easy to find in many plants. However, most of the plants are coming from the Brazilian rubber-tree (Hevea Brasiliensis). In Malaysia, Hevea Brasiliensis covers 2 million hectares areas in Peninsular Malaysia. The main states that are known as the main producers of rubber latex are Johor, Kedah, Perlis and Negeri Sembilan. Other regions include Perak, Sabah, and Sarawak. Unfortunately, SR is the main threat to NR which is due to the price of SR still competitive compare to NR. Meaning, if possible occurs fall in price of fuel in world market will make the SR cheaper than NR. Actually, SR is formed as of unsaturated hydrocarbons.

According to MIDA (in what year?), Malaysia is ranked third behind Indonesia and Thailand in producing NR in the world. It produces approximately 40 percent of NR annually. Furthermore, there are 500 manufacturers in the Malaysian industry producing related rubber-based products such as gloves, tires and shoes. For instance, in 2005, Malaysia was known as the largest producer of the rubber gloves in the world. Meaning, the contribution of rubber industry is very important to the nation, especially to the small holders of rubber plants.

## Problem Statement

In response to the rubber production in Malaysia, there has a certain problem related to the price of rubber over the years. According to Yang Berbahagia Dato' Mohammad Izat Bin Hassan which is the RISDA director in the monthly ceremony assembly speech 2010, rubber has unstable in price. He said that rubber price was starting increase on 19th May 2010 and will be maintain for the short time because it is lowest in production among the country producers. Actually, this price was increase starting January on year 2010 and will be achieved the high level on 16th April 2010, that is RM10.60/kg. Unfortunately, starting 17th April 2010, the price are decreasing from 10.60/kg to RM 8.71 /kg on 18th Mei 2010 with the decreasing in percentage is 17.78% or RM 1.885/kg. This phenomena was impact to many group of people especially RISDA. Furthermore, the main problem related to the unstable price is about NR production competitiveness with SR production. That means, whether the NR production can maintain to sustain threats from SR, especially in tyre manufacturing. The threat from SR production is a most quietly because the quality of both NR and SR are same, the price of SR cheaper than NR and its volume easy to control by market demand. In additional, Malaysia rubber production gets the threats from other rubber producers especially from Thailand and Indonesia.

## Research Objectives

There are several objectives of this paper to achieve the common problem of unstable price of rubber production related to its competitor, SR production and the other country of rubber producer:

To determine whether Malaysia can sustain in their production of rubber by using Log-Log Regression model.

To determine the relationship between each variable in order to show whether Malaysia can achieve its target to sustain in NR production by comparing its production factors to another country, Thailand.

## Scope and Limitation of The Study

This study was focus on Malaysia agriculture of rubber production which is data was covering in time series data from year 1961 until year 2006 that is 46 years. Compare to other commodities such as Cocoa, Oil Palm and Sawn Timber, rubber is the most popular commodity which is stand as the main issue occurs in Malaysia right now. In this paper, Area, Labor and Yield will consider measured to determine the significant of each variable to maintain the NR production to compete with the SR production and other producer of rubber production.

However, while preventing the process of this study, there are several limitations are included.

There is no data founded for labor contribution of rubber industry in Thailand.

Most of the data founded by Thailand is estimate data, especially in production factor of NR.

There is difficult part to find the time series data collection of rubber in both country, Malaysia and Thailand.

Limitation of the internet access because of the line connection.

While get online, there have bureaucracy to find the data, especially in government sector.

## Significance of The Study

This paper is very important for improvement of the economic development, especially in Malaysia.

Provide knowledge to government agencies such as RISDA, RRIM, MARDEC and FELDA and to the researcher, students and officers on how the three factors (labor, area and yield) contribute towards the rubber production.

Very useful to show the impact or effect of labor, area and yield towards the rubber production.

Important to help the policy maker on decision making towards the effectiveness of production factor.

## Chapter Outlines

Chapter 1 discusses briefly about the introduction of NR production in Malaysia compare to Thailand which is the largest producer of NR production in the world. Besides that, we can know which countries are related to NR production and at what rank. The problem of the rubber production can determine while we conduct this study and from the problem the objective developed. Lastly, this chapter discussed the scope and the limitation while conduct this study and who are actually significance of this paper.

Chapter 2 presented about the literature review of this study which supported by other researcher related to this paper. First, theory and model specification are consider to do to know the overall process while conduct this study. Then, this chapter discussed about the efficiency and the role of each variables to rubber production. Furthermore, the matrix form draws to know the summary of the references consist in this paper. Then, conceptual frameworks also draw to shows the relationship between dependent variables and independent variables of the NR production factor.

In Chapter 3, it presented about the data and methodology used while conducts this paper. After that, data, population and sampling methods will be determined. From the data, we can analysis the data and make a hypothesis development to know the relationship between each variables and then test the data using suitable test.

## CHAPTER 2

## LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

## 2.1 Introduction

According to a report produced by the Natural Rubber Production: An Outlook of Thailand (2007), Malaysia was declared as the third largest producer of NR after Thailand and Indonesia in 2007. Malaysia produced 1.22 million tons of rubber production compared to Thailand, which was the first largest producer over the world with the volume of 3 million tons, and followed by Indonesia with 2.79 million tons of rubber production.

Thailand has maintained its position as the largest rubber producer in the world due to its consciousness in sustaining and maintaining their position as the main NR producer in the world. According to Rubber Research Institute of Thailand (RRIT), Thailand has taken into consideration two ways in order to sustain its position i.e. up-stream industries and down-stream industries of rubber production. For example, in the up-stream industries of rubber production, the industry does tapping training to their workers and an activity such as farmer group meetings. For down-stream industries, their objective is to increase the domestic consumption of NR within five years from the total number of rubber production worldwide.

## 2.2 Theory and Model Specification

This paper are shows about the statistical technique applied to analyze the data collected, that is the rubber production, area, labor and yield data. The data are covers from year 1946 until year 2006 to examine the correlation between each dependent and independent variables. Furthermore, the Unit Root Test will used to know the significant of each variable in difference level and then Log-Log Regression will be measured to know how strong their correlation . At the end, the statistical test and data analysis were done through Eviews and Microsoft Excel.

## 2.3 Literature Review

The Economic Planning Unit (EPU, in what year?) stated that because of the anticipated yield improvements from 900 kilograms to 1500 kilograms per hectare in 2003 to 2005, the rubber productions were forecasted to increase by 3.8 per cent per annum in the following years. In this case, in order to ensure the sustainable of NR for the long-term period, effort would be continued in producing more the downstream furniture production. Besides that, the EPU asserted that the area of rubber plantation was expected to decline because of the use of land for oil palm plantation and others. According to Carrere (2006), in the case of Cambodia, both rubber and oil palm need larger areas of plantations. Meaning, the decreasing in the area was not a good outlook to sustain in the NR production.

According to Wahid et. al. (2008), in their journal of Review of Malaysian Agricultural Policies with Regards to Sustainability, there was significant result of contribution of smallholders in agriculture sector. But, they was also the most group which is suffer due to uneconomic size of land and also being the lower income group. Furthermore, because of that, the area of agriculture land also decline. In contrast in Menglun Township, Southwest China, the area of rubber plantation is rapidly expanded between 1988 and 2003, Liu et. al. (2006). The change shows that increasing in land was come from forested areas (4150 ha, 42%) and fallow fields (3001 ha, 23%).

Furthermore, in their model, they also found that gender has a significant impact of probability at one percent on the tappers'' efficiency. However, it was estimated statistically significant at one percent that the coefficient for gender variable to be at a negative value. The result found that the female tappers were not efficient compared to the male counterparts. This is because they were only focusing on factors of socio-cultural and the attachment of their times as housewives and taking care of the household activities. This finding also supported by Giroh et. al. (2006) with his findings found out that women were not important part of the most extension activities, but the focus was mostly on the men. In many farm settlements in Nigeria only focused on the men not women for the land allocation.

According to Kaur, A., labour is very important factors that can be effect mostly in the NR production. In his journal, he said that the availability and ability to control, labour would be the important part of production. It is also important in the social relations of transformation. This statement also support by Ke and et.al. which is said that the labour intensive productivity is more quietly close to NR productions. This because of their ability and skill which is can maintain for a long time period. According to Mesike et. al. (2009), smallholder had a responsibility on increasing and sustained the rubber production. This can be explained by their job characteristics which is they provide a highly labor intensive although there was a low level of productions' operating.

By 1925 there were

already thousands of hectares of rubber estates that

were predominantly owned by Europeans in

Southern Nigeria.

It should be noted that Nigeria has a very

vast potential for rubber production especially in

many of the southern States in the country where

the vegetative and climatic conditions are suitable

for its production.

(refer 4..Farmers' Perception of the Factors Militating Against Rubber Production in Edo and)

Other factor (intermediary)

Climate

The west coast of Southern India experiencing humid tropical monsoon climate with high temperature and

rainfall is climatically suitable for rubber. The mean annual rainfall range from 1500 mm to 5000 mm and

mean annual temperature is 27.1 °C in rubber -growing areas. Mean annual temperatures lower than 20 °C

experienced in hill ranges with elevation more than 600 meters above mean sea level was found to seriously

impair the productivity of rubber. Again, the rubber latex production was found to be related to the period of

soil moisture availability (or its deficit) in a year.

(refer Land and soil controls over the spatial distribution and productivity of rubber)

Land

Tropical lands experiencing hot humid climate are eminently suitable for rubber production. The land related

controls over rubber are elevation, climate and susceptibility of land to flooding. Elevation induced lower

temperatures limit the suitability of land for rubber in high altitude regions of tropics. Well distributed

rainfall, even when the total rainfall is only around 1500 mm, is adequate for the crop. Highly weathered,

well drained tropical soils, though strongly acidic, low in bases and cation exchange capacity, were not found

to limit rubber production. Soils with drainage limitations were unsuitable for rubber. Available water

capacity of soils, as determined by the effective soil volume, coupled with amount and distribution of

precipitation had a significant influence on productivity of the crop. Soil classification following USDA Soil

© 2010 19th World Congress of Soil Science, Soil Solutions for a Changing World

1 - 6 August 2010, Brisbane, Australia. Published on DVD.

71

Taxonomy can be used as a primary guide for selecting areas for natural rubber production. Iso-hyperthermic

temperature regime employed as a criterion for differentiating soil families according to Soil Taxonomy may

be used with caution for assessing climatic suitability for rubber since the lower limit of the class (mean

annual temperature of 22 °C) do not ensure year long temperature above 22 °C. Yield performance of rubber

declines rapidly as the temperature goes below the value.

(refer Land and soil controls over the spatial distribution and productivity of rubber)

## 2.4 Conceptual Framework

## Dependent Variable Independent Variable

## Rubber

## Production

## Yield

## Area

## Labour

## Dependent Variable

Production- It is covers the NR production produced in Malaysia and Thailand and it is includes total estates and smallholdings, (' 000) Tonnes in both country.

## Independent Variables

Area- includes total area of planted hectareage of estates and smallholdings, (' 000) Hectares in Malaysia and Thailand.

Labour- Total number of workers employed during the last pay period. The data refer to Peninsular Malaysia (1946-1965) and the coverage was expanding to Sabah (1966-1973) and the other data refer to Malaysia.

For Thailand, there was no labour data provided.

Yield- Yield per hectare can be defined as a harvest or return of the NR production per hectare. The company or smallholders can gain the benefit and generate income from these activities.

## 2.6 Conclusion

## CHAPTER 3

## DATA AND METHODOLOGY

## 3.1 Introduction

The determination of rubber production in Malaysia and Thailand was analysed by using the Ordinary Least Square (OLS) method. However, before we could test all data by using Log-Log regression, we must first identify its coefficient using the Unit of Root test, which is a test to determine their characteristics by using ADF tests. After that by useing the regression analysis model, we can detected the problem occurs in this study. By using the appropriate model which is Log-Log regression model, the problem can be solved. The analysis was presented by the time series of data covering the year of 1961 until 2006.

## 3.2 Data, Population, Sampling Methods

In this paper, data was covering in time series data from year 1961 until year 2006 that is 46 years. For the population, this paper focuses on Malaysia and Thailand rubber industry. In Malaysia, census of rubber area was conducted on the rubber processor which is starting from year 2004 and the year of rubber dealers is conducted before.

## 3.3 Analysis of Data

Regression Analysis concerns with a study of relationship between one dependent variable and another or more independent variables.

## Y= Î²â‚€ + Î²â‚X1 + Î²â‚‚ X2+ Î²â‚ƒ X3 +µ (3.0)

Source: Gujarati and Porter (2010), the book of Essential of Econometrics (4th Edition).

The model of Regression sometimes shows the spurious results, or of dubious value which is involving the time series data. Means that at the condition the result looks better but opposite of further investigation there have not good results.

Ordinary Least Square (OLS) is used most frequently in the regression analysis. It states that b1 and b2 should be choosen in such a way of Residual Sum of Square (RSS) and the µ is as small as possible.

1. Log-log Regression

Single: lnY= Î²â‚€ + Î²â‚ lnX1 +µ (3.1)

Source: Gujarati and Porter (2010), the book of Essential of Econometrics (4th Edition).

Which is there is a statistical analysis which indicates that the changes in dependent variables associated with the changes in one independent variables.

Where in this paper;

lnY = lnProduction = Production of rubber

lnX1= lnArea = Total area of planted hectareage

lnX2= lnLabour = Total number of workers employed during the last pay period

lnX3= lnYield = Yield per hectare

Multiple: lnY= Î²â‚€ + Î²â‚ lnX1 + Î²â‚‚ lnX2 + Î²â‚ƒ lnX3 +µ (3.2)

Source: Gujarati and Porter (2010), the book of Essential of Econometrics (4th Edition).

Which is there is a statistical analysis which indicates that the changes in dependent variables associated with the changes in one or more independent variables.

Where in this paper;

lnY = lnProduction = Production of rubber

lnX1= lnArea = Total area of planted hectareage

lnX2= lnLabour = Total number of workers employed during the last pay period

lnX3= lnYield = Yield per hectare

According to Heij, C., et. al., (2004), the OLS can be computed as following steps:

Step 1: Variables choosed.

X1, X3, X4...........Xk where the was constant in X which is usually take the value one.

Step 2: Data collection

The related value of X and the n observation of Y are collected. Than the data of Y would be store in an n x 1 vector while for data in an expalnatory variables would be store in the n x k matrix X.

Step 3: Estimated computed

It was computed by using regression package by b = (X'X)-1 x 'Y (3.3)

Means, rank k shoul be had by matrix X. The X is an n x k matrix requires n > k is the number of parameters which is there was than or equal to number of obsevations. k is considerably smaller than n are almost required by human in a real situation.

## Augmented Dickey-Fuller (ADF)

According to Gujarati and Porter (2010), to test the stationarity, the Unit Root Test should be considered. The test could be obtained as follows:

1. Regression is estimated as follows:

## Î”Yt = A1 + A2t + A3Yt-1 + µt (3.4)

Letting Yt = represent the stochastic time series (Rubber Production)

Where:

Î”Yt = First difference of Yt

t = Trend variable

Yt-1 = One period lagged value of the variable Y

µt = White noise

Source: Gujarati and Porter (2010), the book of Essential of Econometrics (4th Edition).

2. Unit Root Hypothesis:

A3 is the null hypothesis, where the Yt-1 coefficient is zero or in other word there is

nonstationary of time series.

3. Dickey-Fuller (DF) test:

To test the a3 which is using the estimated value A3, is zero. If the value of estimated value A3

is more than the critical DF values, unit root hypothesis will rejected. In that case, we

conclude that there is stationary in time series. In contrast if the value of estimated value A3 is

less than the critical DF values, unit root test cannot be rejected. In this case, we conclude

that there is nonstationary in time series.

After done all of the process, we can look whether there had the problem or not by look at the durbin watson values.

Durbin-watson statistic shows that there is an autocorrelation problem if the result shows less than one or get near to zero, meaning the value is far from two. In contrast, there is no autocorrelation occur when the result shows near to two.

It can be used to solve the problem of autocorrelation.

Autocorrelation shows that there is an independent of each other of error terms. This can be detected by looking at the Durbin Watson statistic. When this problem occurs, there is an underestimation of the regression coefficient of the standard errors.

According to Anuar, M.H.M.A., (1998) and supported by Gujarati D.N., (1995), it also occurs in the time series which is related with the correlation the same variables and not for different variable. The relationship between two variable can be expalained as follows:

ut = Ïut -1 + Î½t , -1 < Ï < 1 (3.5)

Where:

Ï = Coefficient of autocovariance

Î½t = Stochastic ditribution

In the classical model, it assumes that the disturbances ui in such correlation does not exist. Means, that one disturbances term does not influenced with the other disturbances term relating to other observation. It can be expressed as follows:

## E (uiuj) = 0, i â‰ j or,

## E (u)= 0, E (Î½2) = Ïƒu2 and E (uiuj) = 0 (3.6)

Source: Gujarati D.N, (1995), the book of Basic Econometrics (3rd Edition) and Gujarati and Porter (2010), the

book of Essential of Econometrics (4th Edition).

Diagnostics problem by using the lag time for each variable to make the entire variable

near to value 2 of Durbin Watson. This can be done by looking at the ADF testing model.

Multicollinearity shows when there are highly correlated of each independent variable and occurs when there is highly value of R2 and only few variables are significant to explain the dependent variable. This problem can be test by using Variance Inflation Factors (VIF) test. When the data had equal or more than 10 value of VIF centered, means the data had a multicollinearity problem. In contrast, when the data had less than 10 value of VIF centered, means there is no multicollinearity problem occurs.

Diagnostics problem by using the Generalized Linear Model (GLM) to make all the

variables are significant to explain the dependent variable.

## 3.4 Hypothesis Development

Hâ‚€: There is no relationship between production of rubber and total are of planted hectareage

H1: There is a relationship between production of rubber and total are of planted hectareage

H0: There is no relationship between production of rubber and Total number of workers employed during the last pay period

H2: There is a relationship between production of rubber and Total number of workers employed during the last pay period

H0: There is no relationship between production of rubber and Yield per hectare

H3: There is a relationship between production of rubber and Yield per hectare

H0: There is no relationship between production of rubber with total area of planted hectareage, total number of workers employed during the last pay period and Yield per hectare

H4: There is a relationship between production of rubber with total area of planted hectareage, total number of workers employed during the last pay period and Yield per hectare

## 3.5 Conclusion

From data in Table II, 89% of the variability in rubber

output was explained by the variables in the model. The Fratio

revealed that the model has a good fit to the data. All

the five inputs were significant. A 100% increase in land,

hired labour, family labour, capital and planting material

will, respectively increase the output of rubber by 42.1, 6.5,

13.4, 80.9 and 7.4%. Hence, the degree of responsiveness of

rubber output to an increase in each of the input is inelastic.

The scale coefficient was 1.503 and was significantly

different (p < 0.05). This has implications for rubber output,

because rubber production in the study area is within the

domain of increasing return - to - scale. That is, if all inputs

can be increased by 100%, the output of rubber will rise by

150.3%.

Land in the study area

is underutilized either because of the restrictive land tenure

system operating in the study area or probably, because

some of the rubber farmers do not intercrop with food crop.

Labour (family & hired) is very scarce in the area probably,

because people now prefer to train their children in school

or on trade that will eventually pull them away from

agriculture. As for capital, most of the rubber smallholders

do not have enough capital to expand their production.

(refer 5..Resource-Use Efficiency among Rubber)

The R2 of 0.63 is high, thus this explained 63%

of the variations in the value of rubber output in

the study area. Moreover, land variable was

significant at 1% and this explained that land

availability determined the value of rubber output

in the study area

(refer 2..Resource-Use Efficiency and Return of rubber)

Results revealed that land, hired

labour, family labour, capital and planting materials were significant factors influencing the output of rubber.

(refer 5..Resource-Use Efficiency among Rubber)

## CHAPTER 4

## RESULT AND FINDING

In this chapter, the findings of the study will be analysed. This includes calculating the significance between variables, the relationships, the interpretations and also the regression problems. The data will be analysed by using the E-view program to run a regression. The finding could be obtained as follows:

## 4.1 Findings

## 4.1.0 Stationary test

Both table 1 and table 2 show the results of the Augmented Dickey-Fuller (ADF), which classified the unit root tests for Malaysia and Thailand. The tests were applied to each variable over the period of 1961-2006 with a time trend at the variables level and at their stationary of different.

The regression analysis is done to examine the correlation between the dependent variable, which is production of the rubber and the independent variable, which is area, labour and yield. From this regression, it will be determined whether there is stationary or nonstationary in data which is to detect the spurious results, or of dubious value which is involving the time series data. We can reject or accept the Unit Root hypothesis using the DF test.

## Table 1: ADF Unit Root Test result of time series variables for Rubber Production, Area,

## Labour and Yield for Malaysia.

Variables

Coefficient

Critical values

t-statistic

Order of Integration

PRODUCTION

CONSTANT

TREND

-1.336774*

66.07680

-2.149168

1% = -4.180911

5% = -3.515523

10%=-3.188259

-8.936071

1.409762

-1.226468

Stationary at 1st difference

AREA

CONSTANT

TREND

-1.002415*

39.24341

-2.073036

1% = -4.180911

5% = -3.515523

10%= -3.188259

-6.376800

2.318932

-3.063088

Stationary at 1st difference

LABOUR

CONSTANT

TREND

-1.217074*

-1.645362

0.058577

1% = -4.211868

5% = -3.529758

10% = -3.196411

-7.375356

-0.871814

0.875714

Stationary at 2nd difference

YIELD

CONSTANT

TREND

-1.352023*

48.71047

-1.209431

1% = -4.180911

5% = -3.515523

10% = -3.188259

-8.592401

2.289339

-1.547198

Stationary at 1st difference

Notes:*Significant at 1% , critical value of ADF tests are based on one sided p-values. There was automatic lag

length selection based on E-views 7.0 Schwarz information.

Î”PRODUCTIONt = 66.07680 - 2.149168t - 1.336774PRODUCTIONt-1

t-stat (1.409762) (-1.226468) (-8.936071) R2 = 0.660868

d = 2.014523

Î”AREAt = 39.24341 - 2.073036t - 1.002415AREAt-1

t-stat (2.318932) (-3.063088) (-6.376800) R2 = 0.499105

d = 1.982663

Î”LABOURt = -1.645362 + 0.058577t - 1.2170745LABOURt-1

t-stat (-0.871814) (0.875714) (-7.375356) R2 = 0.602176

d = 1.511077

Î” YIELDt = 48.71047 - 1.209431t - 1.352023YIELDt-1

t-stat (2.289339) (-1.547198) (-8.592401) R2 = 0.643984

d = 1.885042

Based on the ADF test, the DF value of variable (Production, Area, Labour, Yield) are less or much smaller than any of critical values of proceeding DF values. So, the overall variables time series are nonstationary and the Unit Root Test cannot be rejected. Means, there shows the spurious regression in time series. Furthermore the result shows that only Labour stationary significance at 2nd difference, while the other variable such as Production, Area and Yield stationary significance at 1st difference.

## Table 2: ADF Unit Root Test result of time series variables for Rubber Production, Area,

## and Yield for Thailand.

Variables

Coefficient

Critical values

t-statistic

Order of Integration

PRODUCTION

CONSTANT

TREND

-0.800987*

-3043.059

2372.852

1% = -4.180911

5% = -3.515523

10% = -3.188259

-5.209653

-0.162732

2.816844

Stationary at 1st difference

AREA

CONSTANT

TREND

-1.737177*

75096.82

-1122.771

1% = -4.186481

5% = -3.518090

10% = -3.189732

-6.831533

3.079082

-1.378975

Stationary at 1st difference

YIELD

CONSTANT

TREND

-1.861778*

152.6856

-6.000705

1% = -4.192337

5% = -3.520787

10%= -3.191277

-7.691344

2.567888

-0.917347

Stationary at 2nd difference

Notes:*Significant at 1% , critical value of ADF tests are based on one sided p-values. There was automatic lag length selection based on E-views 7.0 Schwarz information.

Î”PRODUCTIONt = -3043.059 + 2372.852t - 0.800987t-1

t-stat (-0.162732) (2.816844) (-5.209653) R2 = 0.398422

d = 2.014523

Î”AREAt = 75096.82 - 1122.771t - 1.737177t-1

t-stat (3.079082) (-1.378975) (-6.831533) R2 = 0.706787

d = 1.851611

Î” YIELDt = 152.6856 - 6.000705t - 1.861778t-1

t-stat (2.567888) (-0.917347) (-7.691344) R2 = 0.720485

d = 2.020322

Based on the ADF test, the DF value of variable (Production, Area, Yield) also same with the Malaysia ADF test which is there are also less or much smaller than any of critical values of proceeding DF values. So, the overall variables time series also are nonstationary and the Unit Root Test cannot be rejected. Means, there shows the spurious regression in time series. Furthermore the result shows that only Yield stationary significance at 2nd difference, while the other variable such as Production and Area stationary significance at 1st difference.

## 4.2 Log-Log Regression test

## MALAYSIA

## Table 3: The Simple Log-Log estimated of Area, Labour and Yield equation over the

## period 1961-2006.

lnProduction= Î²â‚€ + Î²â‚ lnArea +µ

lnProduction= Î²â‚€ + Î²â‚ lnLabour + µ

lnProduction= Î²â‚€ + Î²â‚ lnYield +µ

Constant

1.334565

6.902260

-0.512884

Coefficient

0.768991***

0.041085

1.062754*

R-squared

0.209560

0.024284

0.473431

Adjusted R-squared

0.191596

0.001593

0.461464

F-statistic

11.66522

1.070223

39.55984

P-value

0.001380

0.306678

0.000001

Akaike info criterion

-0.092194

0.139744

-0.498401

Durbin-Watson stat

0.283515

0.223427

0.430912

Notes: Coefficients of variable is significant * at 1%, ** at 5% and *** at 10%

## lnProduction= Î²â‚€ + Î²â‚ lnArea +µ

lnProduction = 1.334565+ 0.768991Area

Se = (1.684795) (0.225151)

t-Stat = (0.792123) (3.415439)

p Value = (0.4325) (0.0014)

R2 = 0.209560

F = 11.66522 (0.001380)

## Interpretations:

R2 = 0.209560

It is means that about 21% of the variation in the (log) of production is explained by the (log) of area. There is 21% of dependent variable can be explained by independent variable. In contrast, there is 79% of dependent variable cannot be explained by independent variable.

The low degree of explanation means it is suggesting that the model fits the data are not very well.

The partial slope coefficient of 0.768991 measures the elasticity of rubber production with respect to the area. Specifically, this number states at 1% increase in area leads to a 0.77% increases in rubber production.

## lnProduction= Î²â‚€ + Î²â‚ lnLabour + µ

lnProduction = 6.902260 + 0.041085Labour

Se = (0.185027) (0.039715)

t-Stat = (37.30403) (1.034516)

p Value = (0.0001) (0.3067)

R2 = 0.024284

F = 1.070223 (0.306678)

## Interpretations:

R2 = 0.024284

It is means that about 2.4% of the variation in the (log) of production is explained by the (log) of labour. There is 2.4% of dependent variable can be explained by independent variable. In contrast, there is 97.4% of dependent variable cannot be explained by independent variable.

The low degree of explanation means it is suggesting that the model fits the data are not very well.

The partial slope coefficient of 0.041085 measures the elasticity of rubber production with respect to the labour. Specifically, this number states that, at 1% increase in labour leads to a 0.04% increases in rubber production.

## lnProduction= Î²â‚€ + Î²â‚ lnYield +µ

lnProduction = -0.512884 + 1.062754Yield

Se = (1.208740) (0.168968)

t-Stat = (-0.424313) (6.289661)

p Value = (0.6734) (0.0001)

R2 = 0.473431

F = 39.55984 (0.000001)

## Interpretations:

R2 = 0.473431

It is means that about 47% of the variation in the (log) of production is explained by the (log) of yield. There is 47% of dependent variable can be explained by independent variable. In contrast, there is 53% of dependent variable cannot be explained by independent variable.

The low degree of explanation means it is suggesting that the model fits the data are not very well.

The partial slope coefficient of 1.062754 measures the elasticity of rubber production with respect to the yield. Specifically, this number states at 1% increase in area leads to a 1.06% increases in rubber production.

## Table 4: The Multiple Log-Log estimated of Area, Labour and Yield equation over the

## period 1961-2006.

Particulars

Values

Variables:

lnArea

lnLabour

lnYield

## Coefficient

0.257312

0.111938

1.389763

Constant

-5.289405

R-squared

0.751515

Adjusted R-squared

0.733333

F-statistic

41.33327

P-value

0.000001

Akaike info criterion

-1.139155

Durbin-Watson stat

1.016990

lnProduction = -5.289405+ 0.257312 Area + 0.111938 Labour + 1.389763 Yield

Se = (2.169469) (0.485409) (0.084622) (0.232363)

t-Stat = (-2.438110) (0.530093) (1.322804) (5.980991)

p Value = (0.0192) (0.5989) (0.1932) (0.0001)

R2 = 0.751515

F = 41.33327 (0.000001)

## Interpretations:

## R2 = 0.751515

It is means that about 0.75% of the variation in the (log) of production is explained by the (log) of area, labour and yield. There is 75% of dependent variable can be explained by independent variable. In contrast, there is 25% of dependent variable cannot be explained by independent variable.

The high degree of explanation means it is suggesting that the model fits the data very well.

The partial slope coefficient of 0.257312 measures the elasticity of rubber production with respect to the area. Specifically, this number states that, holding the labour and yield constant, at 1% increase in area leads to a 0.25% increases in rubber production.

The partial slope coefficient of 0.111938 measures the elasticity of rubber production with respect to the labour. Specifically, this number states that, holding the area and yield constant, at 1% increase in labour leads to a 0.11% increases in rubber production.

The partial slope coefficient of 1.389763 measures the elasticity of rubber production with respect to the yield. Specifically, this number states that, holding the area and labour constant, at 1% increase in yield leads to a 1.39% increases in rubber production.

## Intercept = -5.289405

Means, this number states an average value of lnProduction when lnArea, lnLabour and lnYield are zero.

## THAILAND

## Table 5: The Simple Log-Log estimated of Area and Yield equation over the period

## 1961- 2006.

lnProduction= Î²â‚€ + Î²â‚ lnArea +µ

lnProduction= Î²â‚€ + Î²2 lnYield +µ

Constant

-16.84112

0.989125

Coefficient

2.180200*

0.989125*

R-squared

0.773542

0.918668

Adjusted R-squared

0.768396

0.916820

F-statistic

150.2969

496.9954

P-value

0.000001

0.000001

Akaike info criterion

1.319162

0.295139

Durbin-Watson stat

0.122048

0.147062

Notes: Coefficients of variable is significant * at 1%, ** at 5% and *** at 10%

## lnProduction= Î²â‚€ + Î²â‚ lnArea +µ

lnProduction = -16.84112 + 2.180200Area

Se = (2.479137) (0.177837)

t-Stat = (-6.793136) (12.25956)

p Value = (0.0001) (0.0001)

R2 = 0.773542

F = 150.2969 (0.000001)

## Interpretations:

R2 = 0.773542

It is means that about 77% of the variation in the (log) of production is explained by the (log) of area. There is 77% of dependent variable can be explained by independent variable. In contrast, there is 23% of dependent variable cannot be explained by independent variable.

The high degree of explanation means it is suggesting that the model fits the data very well.

The partial slope coefficient of 2.180200 measures the elasticity of rubber production with respect to the area. Specifically, this number states at 1% increase in area leads to a 2.18% increases in rubber production.

## lnProduction= Î²â‚€ + Î²â‚ lnYield +µ

lnProduction = 0.989125 + 1.423787Yield

Se = (0.564473) (0.063866)

t-Stat = (1.752299) (22.29339)

p Value = (0.0867) (0.0001)

R2 = 0.918668

F = 496.9954 (0.000001)

## Interpretations:

R2 = 0.918668

It is means that about 92% of the variation in the (log) of production is explained by the (log) of yield. There is 92% of dependent variable can be explained by independent variable. In contrast, there is 8% of dependent variable cannot be explained by independent variable.

The high degree of explanation means it is suggesting that the model fits the data very well.

The partial slope coefficient of 1.423787 measures the elasticity of rubber production with respect to the yield. Specifically, this number states at 1% increase in area leads to a 1.42% increases in rubber production.

## Table 6: The Multiple Log-Log-Log estimated of Area and Yield equation over the

## period 1961-2006.

Particulars

Values

Variables:

lnlnArea

lnlnYield

## Coefficient

1.070982*

0.639566*

Constant

-1.608141

R-squared

0.999989

Adjusted R-squared

0.999988

F-statistic

1891232

P-value

0.000001

Akaike info criterion

-13.74614

Durbin-Watson stat

1.030239

Notes: Coefficients of variable is significant * at 1%, ** at 5% and *** at 10%

lnProduction = -1.608141 + 1.070982Area + 0.639566Yield

Se = (0.003868) (0.001819) (0.000707)

t-Stat = (-415.7971) (588.8488) (904.1659)

p Value = (0.0001) (0.0001) (0.0001)

R2 = 0.999989

F = 1891232 (0.000001)

## Interpretations:

R2 = 0.999989

It is means that about 99.9% of the variation in the (log) of production is explained by the (log) of area, labour and yield. There is 99.9% of dependent variable can be explained by independent variable. In contrast, there is 0.1% of dependent variable cannot be explained by independent variable.

The high degree of explanation means it is suggesting that the model fits the data very well.

The partial slope coefficient of 1.070982 measures the elasticity of rubber production with respect to the area. Specifically, this number states that, holding the labour and yield constant, at 1% increase in area leads to a 1.07% increases in rubber production.

The partial slope coefficient of 0.639566 measures the elasticity of rubber production with respect to the yield. Specifically, this number states that, holding the area and labour constant, at 1% increase in yield leads to a 0.64% increases in rubber production.

Intercept = -1.608141

Means, this number states an average value of lnProduction when lnArea and lnYield are zero.

## 4.3 Variance Inflation Factors (VIF) Test

## Table7: VIF test of Area, Labour and Yield in Malaysia

Variable

Centered VIF

lnArea

13.57438*

lnLabour

16.99814*

lnYield

3.663947

Notes: * shows there was equal or more than 10 value of centered VIF

This table shows that there is a multicollinearity problem occurs for Area, Labour and Yield. This is because the value of data is more than 1o value of centered VIF.

## Table8: VIF test of Area and Yield in Malaysia

Variable

Centered VIF

lnlnArea

1.977359

lnlnYield

1.977359

Notes: * shows there was equal or more than 10 value of centered VIF

For this table, there is no multicollinearity problem occurs for both Area and Yield. This because the value of data is less than 10 value of centered VIF.

## 4.4 Regression Analysis

Figure 1: Regression Analysis for Figure 2: Regression Analysis for

Malaysia Thailand

Figure 1 and Figure 2 shows the line of best of fit for area, labour and yield related to rubber production of both countries. From this figure, we can detected whether there is problem occurs. In this case, both Malaysia and Thailand regression analysis has the autocorrelation problem occurs.

## 4.5 Diagnostics problem

## CONCLUSION

According to Mesike and Ubani (2008),

Based on the findings from this study, it is

recommended that rubber production should be

based on the technique that will utilize more land,

labour and capital. Also, any bids that may

constrain the increased use of these resources

should be guarded against

(refer 2..Resource-Use Efficiency and Return of rubber)

Based on the findings of the study, it is recommended

that rubber production should be based on any technique

that will use more of the production inputs examined in the

study area. Also, efforts should be made to alleviate the

poverty level of the people, so that they can invest in yield

increasing and soil conservation technologies that will

ensure increased supply of agricultural output in addition to

conserving the environment.

(refer 5..Resource-Use Efficiency among Rubber)

Thus, there has been a

significant increase in the area under industrial/commercial crops, such as coffee,

rubber, cashew nut, pepper, fruits, etc from 1,135,300 ha in 1993 to 2,632,500 ha in

2007 (131.88%).

(refer THE LINKAGES BETWEEN LAND REFORM AND LAND USE CHANGES)

The ADF test done with both of Malaysia and Thailand had nonstationary in their result which is shows the spurious problem. This also support by Mesike et. al., (2010) which is reveals that the variables had the nonstationary in their data.

For the Malaysia of simple log-log estimated of Area, Labour and Yield shows that labour not significant to explain its relationship with rubber production. All of the data also shows there is autocorrelation problem occurs because of their value of data is far from value 2 of Durbin Watson. In contrast for the Thailand of simple log-log estimated of Area and Yield shows that the entire variable is significant to explain their relationship with rubber production. All of the data also shows there is autocorrelation problem occurs because of their value of data is far from value 2 of Durbin Watson.

For the Malaysia of Multiple Log-log estimated of Area, Labour and Yield shows that area and labour is not significant to explain their relationship with rubber production. In contrast for the Thailand of Multiple Log-log- estimated of Area and Yield shows that all of variables is significant to explain its relationship with rubber production. Both of Malaysia and Thailand data shows that there is autocorrelation problem occurs in the multiple regression models.

Based on the finding on single regression model in Malaysia, the result shows only labour not significant variable to explained the relationship of sustainable to the NR production. This is because diseconomic of scale will occur in the long run. At that situation, the more the labour will lead to inefficiency in their production because some of labour will not use their all ability in the production process. So, in this, it will lead to affect the low of NR production. It also occur a same problem in multiple regressions for Malaysia; the result also found that labour and area is not significant to explain the relationship of sustainable to the NR production. The reasons for area because of most of the land are not suitable to plant the rubber such as their temperature and the condition of land.

In order to sustain in NR production, Malaysia was encourage their Rubber Industry to increase their competency, expertise and knowledge of rubber production, Seminar On Sustainability of Rubber Industry (2009). In contrast of Thailand, the country will get the benefit if there was the optimum lead from both sides if there were well cooperates between supply and demand (RRIT). Beside that Malaysia need to train their worker how to work well by provided them one expertise guide to follow up them. The labour also should provide the motivated intensive such as bonuses and reward for the hardworking workers. For the area, they should to find the right area consider to its temperature and the condition of land. All of this effort can be used to make the sustainable in NR production in order to compete with other producer of NR production especially Thailand.