Proton and demand of automotive industry in Malaysia

1.1 BACKGROUND OF THE RESEARCH

Automotive industry in Malaysia was started in 1960 when The Malaysian Automotive Association (MAA), formally known as Malaysian Motor Traders Association (MMTA) was established in November 1960. MAA is responsible to encourage, promote and protect the interest of automotive industry in Malaysia. In 1983, Perusahaan Otomobil Nasional Berhad (PROTON) was established as a Malaysia first car manufacturer to prove that automotive industry in Malaysia is rapidly growing. Proton Saga became Malaysian first national car when it was produced in 1985. In 1993, Malaysia moves forward in automotive industry when Perusahaan Otomobil Kedua Sdn.Bhd (PERODUA) was established. It is a joint venture company between Malaysian and Japanese partner.

The automotive industry is considered the single largest manufacturing sector in the world (Turnbull, 1992) and specially Malaysia. Since National Economic Policy was implemented in 1971, government played main roles in automotive industry development. In early 1980s, government started another phase in NEP policies by promoting heavy industry such as national car project (Proton) and steel plant (Perwaja). Investments in these projects were supported by increases in import duties on both automobiles and steel. To strengthen automotive industry in Malaysia, government introduced National Automotive Policy (NAP) in 2006 to facilitate the required transformation and optimal integration of local automotive industry.

Since Proton and Perodua take place in automotive industry in Malaysia, demand for cars is increasing for each year. In 2009 Proton sold 146,760 unit of cars compared in 2006, Proton only sold 115,538 unit of cars. Meanwhile Perodua sold 168,671 unit of cars in 2009 compared to 152,733 unit of cars in 2006.

Proton or ‘Perusahaan Otomobil Nasional Berhad’ was incorporated in 1983 is the company that entrusted by government to manufacture Malaysia first national car which is Proton Saga. The idea came from our former prime minister, Tun Dr.Mahathir Bin Mohamad when President of Mitsubishi Corporation visits Malaysia in 1981. It also his dream to turn Malaysia into Southeast Asia’s new auto-making powerhouse.

Based on technology and parts from Mitsubishi Motors, our first national car, Proton Saga was released on 9th July 1985. It received encouraging response from Malaysian with 100,000 unit of car sold. With this successful, Proton introduced Proton Saga 1.5 litre and Aeroback model. More than 50,000 units were sold in the country. At this stage, Proton has penetrated various international markets such as Brunei, New Zealand and Sri Lanka.

Proton export their cars to many other countries and 21,261 units of cars were exported in 2008 for instance United Kingdom, South Africa and Australia. This company also markets their cars into Middle East countries. Proton also export small amount of cars to Singapore, Brunei, Thailand, Indonesia and Nepal.

For the past 3 years, we can see that the total export of Proton cars is unstable. In 2007, total export of Proton cars are 15,567 units. It increased into 21,261 units in 2008 and drop in 2009 with 18,757 units. The main reason is Proton has lost access to new technology, including net platform and engines as well as an opportunity to improve performance and increase export (Mack Chrysler, 2010). It gets worst when our country faced with global economy crisis.

To increase back the total export, again with technology from Mitsubishi, Proton introduced new models which is Proton Saga FL and Proton Inspira. Until 19 December 2010, 4000 units of Proton Inspira were sold since its launch November 2010.

In this study, I will discuss the variables that affect the demand for Proton car sales. I will investigate the impact of inflation rate on demand of Proton cars, impact of GDP by manufacturing sector on demand of Proton cars, and impact of fuel price on demand of Proton cars.

1.2 PROBLEM STATEMENT

What is the effect of inflation rate towards demand of Proton cars?

What is the effect of fuel price on demand of Proton cars?

What is the effect of Gross Domestic Product (GDP) by manufacturing sector on demand of Proton cars?

1.3 RESEARCH OBJECTIVE

The objective of this research is to investigate which variable gives the greatest impact on demand of Proton cars. Specifically, the objectives are:

To identify whether there is a positive relationship between inflation rate and demand of Proton cars.

To determine whether there is a positive relationship between Gross Domestic Product (GDP) and demand of Proton cars.

To analyze whether there is a positive relationship between fuel price and demand of Proton cars.

1.4 SCOPE AND LIMITATION OF RESEARCH

In this research, among car manufacturers in Malaysia, this research only focuses on Perusahaan Otomobil Nasional Berhad (PROTON). This research is covering 20 years period starting year 1991 until year 2010. This research only involves secondary data and gathers data from Bursa Malaysia and Bank Negara Malaysia (BNM). However, some data cannot be assessed. For example, this research should have Gross Domestic Product (GDP) per capita Purchasing Power Parity as one of the its variable. Because of Bank Negara Malaysia (BNM) cannot provide the information, this research replace the variable with GDP in manufacturing sector.

1.5 SIGNIFICANCE OF THE STUDY

To the buyers

From this research, it will provide some information about demand of Proton cars. As a result, the buyers will plan before they buy the car.

To the Proton

At the end of this research, it will help Proton to know the factors that influence their demand of cars. So, Proton will considered these factor when they planning and making decision.

To the government

From this research, it will help government to make decision regarding Proton such as budget for automotive industry in order to increase the demand.

CHAPTER 2: LITERATURE REVIEW

2.0.1 FUEL PRICE

Pankaj Doval (2011), in his study indicates that hike in fuel prices will reduce sales in short term but not in a long term. In short term, sales of small prices may jump more while sales of big car may reduce. If this happened, it will be a temporary phenomenon. It is because automotive industry will maintain its growth momentum.

Other study made by Cheng and Tan (2002) mentioned that fuel price has significant influence on Malaysian inflation in 1973 and it will effect demand for cars. The fuel price will affect the demand for cars in the car market in countries. Higher price of fuel, lower the demand of cars in the markets. People will prefer using public transport rather than using their own cars. On the other way, countries with high fuel price will reduce people intention to buy a car.

In the study made by McManus (2007) shows that there are direct link between fuel prices and vehicles sales. Same as homes needs repair, computer use electricity, vehicles also need ongoing input of fuel which consumer should consider before buy it. When the fuel price increase, vehicle’s owner have to use spend more on fuel and it will give negative impact on their income. Consumers tend to less spend and it will affect vehicles sales.

Harris and Kelly (2009), in their study indicates that 17 percent of car buyers have already changed their mind about what vehicle they plan to buy due to high fuel cost. 21 percent are strongly considering vehicles that they had not considered before and additionally 15 percent of car buyers indicated that they would strongly consider more fuel efficient vehicles if the price increased by as little as 25 cents.

2.0.2 INFLATION RATE

Research entitled “Economic Environment for Business” made by Prof. Sanjay Kumar (2009) shows that growth in the Indian Automobile Sector is declining due to inflation. Increase in fuel price makes it worse. Indian car manufacturer like Tata Motors, Maruti try hard to increase their production and sales by giving discount to the buyers. The inflation is not only affected the car manufacturers but also the car dealer and car financers. Result shows that in the year of 2008, the Indian automobile industry fall about 9%.

Other study made by Tian Ying and Ying Dao (2010) indicates that passenger car deliveries in China at a slowest momentum in 15 months due to due to rising price and it will reduce consumer’s purchasing power. Inflation rate in China is at 2.9% and it’s the highest level during 2010. They said that demand in the world’s largest vehicle market may actually fall in the second half of 2010. It makes some dealers and automakers panic and offer the largest discounts they can. If the prices of necessities good keep increasing, consumer’s expectations of their future financial security will be undetermined, so it will reduce their desire to buy a car.

In a other studies made by analyst in Vietnam Business News (2011), shows that in mid February 2011, after State Bank of Vietnam announced the 9.3% dong devaluations, Toyota Vietnam lead the sale price increase movement by raising the sale prices by 34 to 174 million dong per car. Followed by other automobile manufacturers such as GM Daewoo (Vidamco), Truong Hai (Kia), Ford Vietnam and Honda Vietnam also announced the increasing in sale price of their cars. It will makes people who dream to own car rethink their plan. To avoid demand for cars drop sharply, car dealers decided

to reduce the numbers of imports in February. In February 2011, only 4500 cars were imported with the total revenue of 76 million dollar. This figure is much less than in January 2011. In January, 6100 were imported with the total revenue of 103 million dollar.

2.0.3 Gross Domestic Product (GDP)

Study made by (Phil Goodwin, 2003) indicate that if real income increase by 10 %, number of vehicle and the total amount of fuel they consume will both rise nearly 4% within a year and by over 10% in the long run. It also indicates that as income increase, the car ownership is more attract to buy new car that reduce fuel usage.

Other study made by Ballew and Schnorbus (1994) shows that automotive industry is one of the best examples of how durable goods drive economic activity. In the U.S, automotive industry contributes about 4% of the national GDP.

Other study made by economist in China indicates that car ownerships levels of China are very slow and incomes now increased to a level where relationship between GDP and vehicles ownership is very strong. In other words, trends car sales in china are increase extremely now and even in the future. This proves by trends sales in China are increased from $ 4 million per annum in 2005 to $ 9 million in 2009.

From the research made by Centre of Automotive Research (2009), the analyst says that there are positive correlation between vehicle sales growth and annualized GDP. If the GDP growth rate is below 1 percent, vehicle sales will fall, if the GDP growth is above 3 percent, vehicles sales will increase. According to University of Michigan’s Research Seminar in Quantitative Economics (RSQE), they forecast annualized GDP is about -1.9 percent in 2009 and it will reduce vehicle sales over the year.

CHAPTER 3: RESEARCH METHODOLOGY

This chapter will discuss on the analysis on the data collection, technique of data analysis and the regression model to analyze the relationship between independent variables and dependent variables.

3.1 DATA DESCRIPTION

3.1.1 Data Collection

All the data and information for this research are collected from the secondary data. The data are gathered from year 1991 until 2010.

3.1.2 Data Sources

Most of the data are collected from Bursa Malaysia’s library and Bank Negara Malaysia’s library. The data also gathered from articles, journal and annual report of the company. Some articles regarding the research were also obtained from internet. For example: economywatch.com.

3.1.3 Data Analysis

The data that has been obtained will be calculated by using regression analysis in order to examine the relationship between dependent variables and independent variables. This analysis is used to predict the value of one variable on the basis of other variables.

For this study, multiple regression analysis is applied since there are many variables need to be test with dependent variables. In order to analyze and process the data, Statistical Package for Social Science (SPSS) was to be used.

3.1.4 Data Testing

3.1.4.1 Multiple Regression Model

In testing the data, simple regression is to be used. In this model, there are several statistical techniques that can be used. However, in this research only 4 statistical techniques that are to be used in order to testing the data. The techniques are T-Statistic, F-Statistic, Durbin-Watson Statistic and Pearson Coefficient Correlation. In using this regression, the estimated regression model based on the sales of Proton cars should develop.

The equation is as follows:

•

Where:

• Dependent variable (Sales of Proton cars)

c constant

β Beta

X independent variable (GDP, Inflation, and Fuel)

ε Error

3.1.4.2 T-Statistic

A measurement uses to determine whether hypothesis will be rejected or accepted. In order to test the hypothesis, the computed t-value needs to be compared with the value of T-distribution table. T-distribution formula as below:

n =number of observation

k =number of independent variable

If t-value >T-distribution table, it indicate that there are significant relationship between independent variable and dependent variable. Therefore, accept H1 and reject Ho.

If t-value<T-distribution table, it indicate that the insignificant relationship between independent variable and dependent variable. Therefore, reject H1 and accept Ho.

3.1.4.3 F-Statistic

It is used in order to know how reliable the overall model. It provides an overall appraisal of the regression equation to evaluate the significance of each dependent competent of the entire regression model.

The F-Statistic equation as follows:

F critical value

k no. of independent variable

n no. of observation

If F-computed > F critical value, it indicate that there are significant relationship between independent variables and dependent variable.

If F-computed<F critical value, it indicate that there are insignificant relationship between independent variables and dependent variable.

3.1.4.4 Coefficient of Determination (

is a statistic that will give some information about the goodness or fit of a model. In regression, the will explain the extent to which the variation in explanatory variables can explain the variation in the dependent variable. The value of must range from 0 to 1. The measurement of is shown as follows:

It indicates that there is no relationship between independent variable and dependent variables.

0.1 to 0.5

It indicates that there is weak relationship between independent variable and dependent variables.

0.6 to 0.9

It indicates that more than 60 percent of the dependent variable is strongly explained by the independent variables.

1

In indicates that the dependent variable is perfectly explained by independent variables.

3.1.4.5 Pearson Correlation Analysis

A Pearson Correlation matrix will indicate the direction, strength and significance of the bivariate relationship of all variables in the study. If the result is below than 0.5, it will indicate that there is no multi collinearity exists between independent variables. According to the Pearson Correlation analysis, the result can be ranked as follows:

Less than 0.30

It indicates that there are weak relationships between all variables.

0.30-0.49

It indicates that there are moderate relationships between all variables.

0.50-0.69

It indicates that there are strong relationships between all variables.

0.70-0.99

It indicates that there are very strong relationships between all variables.

1.0

It indicates that there are very strong relationships between all variables.

3.2 THEREOTICAL FRAMEWORK

FIGURE 3.2 Theoretical Framework of Demand for Proton Car

INFLATION RATE

DEMAND FOR PROTON CAR (SALES)

GROSS DOMESTIC PRODUCT (GDP)

DEPENDENT VARIABLE

FUEL PRICE

INDEPENDENT VARIABLES

3.3 HYPOTHESIS

Hypothesis 1

: There are no significant relationships between all independent variables

(GDP, inflation, fuel) and dependent variable (Proton total sales).

: There are significant relationships between all independent variables

(GDP, inflation, fuel) and dependent variables (Proton total sales).

Hypothesis 2

: There are no significant relationship between fuel price and demand of Proton cars.

: There are significant relationship between fuel price and demand of

Proton cars.

Hypothesis 3

: There are no significant relationship between inflation rate and demand of Proton cars.

: There are significant relationship between inflation rate and demand of Proton cars.

Hypothesis 4:

: There are no significant relationship between Gross Domestic Product (GDP) and demand of Proton cars.

: There are significant relationship between Gross Domestic Product (GDP) and demand of Proton cars.

CHAPTER 4: ANALYSIS AND FINDINGS

4.1 INTERPRETATION OF TREND ANALYSIS

Table 4.1 Total Sales of Proton Cars from 1991 until 2010

YEAR

Total Sales

1991

1,786,100

1992

2,191,000

1993

2,286,507

1994

3,037,884

1995

3,622,733

1996

4,955,710

1997

5,945,460

1998

6,041,695

1999

3,039,314

2000

5,399,636

2001

6,902,906

2002

8,571,089

2003

7,674,265

2004

5,259,781

2005

1,488,839

2006

111,097

2007

667,983

2008

41,203

2009

362,357

2010

101,203

Sources: Bursa Malaysia

Figure 4.1 Trend Analysis of Proton Total Sales from 1991 until 2010.

The trend in figure above shows that the movement of Proton total sales from 1991 to 2010. The total sales keep increase from 1991 until 1998. However, the total sales drop sharply in 1999 due to economic crisis. When the economy starts to recover in 2000, the Proton total sales started to rise back until 2002. Due to low quality and high price, the total sales decline every year from 2003 and it reach at lowest total sales at RM 111,097 billion in 2006. In 2007, the total sales for Proton car are fluctuated and it reaches 101,203 billion in 2010.

4.2 MULTIPLE REGRESSION ANALYSIS

4.2.1 REGRESSION RESULT

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

15.853

2.176

gdp

32.708

18.512

inflation

-1.003

.342

-.461

-2.933

.010

fuel

-9.367

2.482

-1.339

-3.774

.002

TABLE 4.2.1

Regression Model

•

•15.853 32.708 GDP 1.003 INF 9.367 FUEL ε

Where:

Y Demand of Proton Cars (Sales)

GDP Gross Domestic Product

INF Inflation

FUEL Fuel Price

Based on the regression analysis above, GDP has positive relationship with the Proton sales. If GDP increase by RM 1, the Proton sales will increase by RM 32.708. For inflation, it has negative relationship with Proton sales. It means, every increase of 1 percent in inflation will decrease 1.003 percent in Proton sales. Same with fuel price, it also has negative relationship with Proton sales. If fuel price increase by RM 1, Proton total sales will decrease by RM 9.367. This is consistent with economics theory.

4.3 COEFFICIENT OF DETERMINATION (R)

Model

R Square

Adjusted R Square

1

.678

.618

TABLE 4.3

From the regression above, it indicates that 61.8 percent of the change in dependent variable (Sales) is explained by the change in all independent variables (GDP, INF, FUEL). Another 38.2 percent is explaining by other variables. This means that, there are other additional variable that are important in influencing the Proton total sales. The relationship can be considered as a strong relationship since it is higher than 50 percent. Therefore, the regression equation can be accepted for forecasting purpose.

4.4 T-TEST

4.4.1 REGRESSION RESULT FROM T-TEST

4.4.1.1 GROSS DOMESTIC PRODUCT (GDP)

TABLE 4.4.1.1 T TEST RESULT OF GDP

Model

T-value

SIG (<0.05)

Outcomes

GDP

1.767

0.096

INSIGNIFICANT

: There are no significant relationship between Gross Domestic Product (GDP) and demand of Proton cars.

: There are significant relationship between Gross Domestic Product (GDP) and demand of Proton cars.

The t-value for GDP is more than 0.05 (0.096>0.05). It means that, there is GDP in manufacturing sector doesn’t give huge impact on demand for Proton cars. From the hypothesis, this research accepts and reject. Therefore, there is insignificant relationship between GDP and Proton total sales.

4.4.1.2 INFLATION RATE

TABLE 4.4.1.2 T-TEST RESULT OF INFLATION RATE

Model

T-value

SIG (<0.05)

Outcomes

INFLATION

2.933

0.010

SIGNIFICANT

: There are no significant relationship between inflation rate and demand of Proton cars.

: There are significant relationship between inflation rate and demand of Proton cars.

The t-value for inflation is less than 0.05 (0.010<0.05). In the other words, inflation rate gives direct impact on demand for Proton cars. From the hypothesis, this research rejects and accepts. Hence, there is significant relationship between inflation rate and Proton total sales.

4.4.1.3 FUEL PRICE

TABLE 4.4.1.3 T-TEST RESULT FUEL PRICE

Model

T-value

SIG (<0.05)

Outcomes

FUEL

3.774

0.002

SIGNIFICANT

: There are no significant relationship between fuel price and demand of Proton cars.

: There are significant relationship between fuel price and demand of

Proton cars.

The t-value for fuel is less than 0.05 (0.002<0.05). It marks that fuel price gives impact on demand for Proton cars. From the hypothesis, this research accepts and rejects. Therefore, fuel price has significant relationship with Proton total sales.

4.5 F-TEST

TABLE 4.5 REGRESSION RESULT FROM F-TEST

MODEL

COMPUTED F-VALUE

SIG

OUTCOMES

1

11.253

0.000

SIGNIFICANT

: There are no significant relationships between all independent variables

(GDP, inflation, fuel) and dependent variable (Proton total sales).

: There are significant relationships between all independent variables

(GDP, inflation, fuel) and dependent variables (Proton total sales).

Based on the result above, at a 95 percent of confidence interval, computed F-value is greater than significant level which is 0.05. Therefore, this research rejects where there is no significant relationship between all independent variable (GDP, inflation, fuel) and dependent variable (Proton total sales) and accepts.

CHAPTER 5: CONCLUSION AND RECOMMENDATION

5.1 CONCLUSION

From this research, as explained in the chapter before is to analyze the relationship between total sales of Proton cars with gross domestic product, inflation rate, and fuel price. In addition, this research is tries to identify which variable (gross domestic product, inflation rate or fuel price) gives most impact on total sales Proton sales. The research carried the data for 20 years starting year 1991 until 2010. All the data and information was gathered from Bank Negara Malaysia (BNM), Bursa Malaysia and internet.

The first objective of this research is to identify whether there is a significant relationship between inflation rate and demand for Proton cars. Result from multiple regression analysis, the model shows that there is negative relationship between inflation rate and demand for Proton cars. The relationship between inflation rate and demand for Proton cars also is tested by hypothesis testing using t-statistics. The result shows that inflation rate is and has negative relationship with total sales of Proton cars.

The second objective of this research is to identify whether there is positive relationship between gross domestic product (GDP) and total sales of Proton cars. Based on multiple regression analysis, it shows that there is positive relationship between GDP and total sales of Proton cars and it represents the highest beta compared the other variables. This means that GDP is the major factor that affects demand for Proton cars. It was supported by hypothesis testing using t-test. It shows that GDP has positive relationship with total sales of Proton cars.

The third objective of this research is to identify whether there is positive relationship between fuel price and total sales of Proton cars. Based on multiple regression analysis, it indicates that there are negative relationship between fuel price and total sales of Proton cars. These two variables also are tested by hypothesis testing using t-test. It shows that there are positive relationships between these two variables.

Based on coefficient of determination () result, the value of is 0.618. This means that 61.8 percent of dependent variable (Sales) has been explained by all independent variable (GDP, INFLATION, and FUEL). Another 38.2 percent unexplained in other variables.

From ANOVA analysis, the F-statistic is lower than F-distribution value. The result indicates that overall model is statistically significant.

As a conclusion, gross domestic product has positive relationship with total sales of Proton cars and major factor that affect demand for Proton cars.

5.2 RECOMMENDATION

Some recommendations need to be taking into consideration in order to improve the research result in the future. First and foremost, Perusahaan Otomobil Nasional Berhad (PROTON) has to provide historical data regarding total sales of Proton cars. Due this problem, researcher faced some difficulties to get the data. With the relevant data and information, it will help researcher to do their research wisely.

Secondly, Malaysian government has takes a look the factors that affect demand for Proton cars since it is a national car. For example, Malaysian government provides specific budget to Proton in order to improve demand for Proton cars.

Thirdly, Bank Negara Malaysia (BNM) should provide longer period for their quarterly statistical bulletin to facilitate researcher to get the information from BNM. It is because BNM played the main role for researcher in order to carry their research.