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代写论文价格 The Factors Determine Tax Revenue In Malaysia Economics Essay

The Factors Determine Tax Revenue In Malaysia Economics Essay

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INTRODUCTION

Malaysia is a federation of 13 States and the Federal Territories of Kuala Lumpur and Labuan. The Federal Constitution contains special provisions regarding sources of revenue that are assigned to the Federal and the State governments.Sources that are assigned to the State governments include revenue from land, forest, entertainment, mining, water supply, bank interests, returns from investments, fines including forfeitures (other than imposed by Federal Courts) and fees for licences and permits (but not licences relating to motor vehicles and registration of businesses). All the other revenues that are not specifically assigned to the states, are Federal Government revenues.

Taxation become crucial economic tools to govern economics for any country. The pattern of tax revenues and economic growth accross countries has become a significant concern to Malaysian economists since Malaysia is one of the developing country with the rapid trend toward globalization and internationalization. Recently, Malaysia has performed well and shows the similar growth pattern in economy. Therefore, the fund collected from taxation in Malaysia are used by the government to provide facilities for its population and for the development of the country. Other than that income tax is one of the surest way to make sure that the government fund is available for spending.

Inland Revenue Board (IRB) has play their main role as an agent of Malaysian Government in administering, assessing, collecting, and enforcing payment of income tax and other revenue as may be agreed between Government and the Board. For many years, IRB has presumed that its activities promote better tax collection starting from Official Assessment System (OAS) until Self Assessment System (SAS).

Malaysia Federal Government revenues are broadly classified into three that is tax revenues, non-tax revenues and non-revenue receipts. Tax revenues include both Direct and Indirect Taxes. Direct taxes are collected by the Inland Revenue Board (IRB) and includes taxes such as income tax on individuals, income tax on corporations, petroleum income tax, stamp duty and real property gains tax. While for indirect taxes the responsibility of collection is taken by the Royal Customs and Excise Department. Indirect taxes include import duties, export duties, excise duties, sales tax, service tax and last but not least; goods and services tax (GST) that replace sales tax and service tax.

Non-tax revenues of Malaysian Government consists of fees for issue of licences and permits, fees for specific services, proceeds from sale of government assets, rental of government property, bank interests, returns from Government investments, fines and forfeitures. The non-revenue receipts consist mainly of repayments and reimbursements such as refunds of overpayments in previous years and repayment of loans from the Federal Government’s Consolidated Fund (Revenue Account) received from other Federal Government Agencies and State Governments.

The trend of tax collection in Malaysia is inconsistent, changing upward and downward depending upon economic conditions. However, over a 30 period, most years show an increasing incremental in total tax collection but sometimes has an exception. The exceptions are when there is an abnormal economic condition arise such as financial crisis, war or increase in world oil prices.

During the early stages of its development which is in year 1960, Malaysia similar with most developing countries relied heavily on indirect taxes accounted for 76.7% (Kasipillai, 2006). However as the economy developed and with the tax reform less reliance was placed on indirect tax which starting from year 1999 the major contribution to government revenue is come from direct tax (69%). In 2008 the collection of direct tax represents 52% of the Government total revenue (Economic Planning Unit, Ministry of Finance and Bank Negara Malaysia). It is believed that the encouraging growth in Gross Gomestic Product (GDP) in 2009 stood at 23% contribute positively to the national revenue collection (9MP).

After brief introduction the remainder of this paper is structured as follow. Chapter 2 provide some sort of literature review regarding all the variables included in this research. Chapter 3 consist of research methodology and design, data collection, theoretical framework, hypothesis statement, and data analysis. Chapter 4 provides data description and result analysis and finally Chapter 5 gives conclusions and recommendations of the study.

BACKGROUND OF STUDY

The main sources of income for government is tax. Tax is defined as a fee charged (levied) by a government on a product, income, or activity. If tax is charged directly on personal or corporate income, then it is called as direct tax. But if tax is charged on the price of goods or services, then it is called as an indirect tax.

Malaysia is a very tax friendly country. Income tax are comparaly low and many taxes which are raised in other countries, does not exist in Malaysia. All earnings of companies and individuals acccumulated in, derived from or remitted to Malaysia are liable to tax.

Government will used this tax revenues to fund all spending made by government in order to achieve an economic growth and also to promote a sound of economy.

Government will present their budget in Parliament around September each year. Determination of budget is based on estimation of government revenue and spending. If government’s revenue increase, there will be an increasing in the allocation for government spending. The tax rate is one of the components in government budget. The government will decide whether to increase or reduce the tax rate or to remain unchage based on the goals of government in each budget every year.

PROBLEM STATEMENT

Malaysia is facing budget deficit every year since government expenditure exceed government revenue. If the government’s budget are not sufficient, some of the macroeconomic factors can’t be achieved. Government cannot reduce unemployment and inflation rate and also cannot increase the economic growth and promote currency stability if they cannot reach a sufficient budget to cover all the expenditure.

Tax is the main component of government revenue that will use to finance all the government expenditure to stabilize the economy. The expenditure here means the used of government’s revenue for the development and operational expenditure that will bring an economic growth.

This study is undertaken to discover factors determinant of tax revenue which are independent variables namely Gross Domestic Product (GDP), inflation rate, unemployment and openness (trade) on dependent variable which is tax revenue. It tries to grasp those variables volatility impact on tax revenue in a given economic environment and horizon.

Besides, this study was brought up to strenghten the prove of previous similar study. However, due to the changing environment of the economy, past researchers cannot be deem a suitable for current application. There is a need to revise the findings from the previous researchers, so it is consistent with current economic situation. The horizon of the research will cover from 1990 to the ending 2009. From this, all the independent variables are important towards dependent variable.

Therefore the problem statement for this study is which variables that have strongly positive significant relationship towards tax revenue?

RESEARCH QUESTION

In order to realize the factors determining tax revenue, this question must be taken into consideration.

The question is:

What is the relationship between GDP and tax revenue?

What is the relationship between Inflation rate and tax revenue?

What is the relationship between Unemployment and tax revenue?

What is the relationship between Openness and tax revenue?

This question must be taken into consideration because the questions will answer the overall study and to make sure whether the problem lies within this factor or the others factor.

OBJECTIVES OF STUDY

General objective

The general objective of this study is to identify the factors determine tax revenue in Malaysia from year 1990 to 2009 which is 20 years.

Specific objectives

To know what are the factors that will increase or reduce the total tax revenue collected by government.

To determine whether GDP in Malaysia significantly affect tax revenue collected by government.

To determine whether inflation in Malaysia significantly affect tax revenue collected by government.

To determine whether unemployment in Malaysia significantly affect tax revenue collected by government.

To determine whether the degree of openness in Malaysia significantly affect total tax revenue collected by the government.

SCOPE OF STUDY

The scope of study is as follow:

This study focus on factors determining tax revenue collected by government. The data will be collected from 1990 to 2009 which is twenty years in yearly.

Four variables are choosen which are GDP, inflation rate, unemployment, and openness.

Software that used as a regression tool is Statistical Package for Social Science (SPSS) 16.0.

SIGNIFICANCE OF STUDY

This research study can help the researcher to determine the most significant independent variables to the dependent variable.

From this study, it can help the relevant parties to know which variables can give influence to the tax revenue collected government.

The findings from this research can provide the information to the other researcher for future research that is similar or related with this study.

LIMITATIONS OF STUDY

Cost

Cost is one of the limitations in doing this research because the researcher needs to bear all the cost and expenses in completing this research without getting any sponsorship. The cost that incurred such as stationeries expenses, photocopying, printing, transportation expenses and others are fully support by the researcher.

Choice of Variables

Choice of variables is the other limitation of the study. There have many variables that are determinants tax revenue and the researcher need to choose the exact variables so that it is suitable with the dependent variable. The variables that are choosen in this study are GDP, inflation rate, unemployment, and openness.

Data Collection

Data collection is one of the limitation of the study. The data covered a period of twenty years which is from 1990 to 2009 in yearly. Besides that, there have difficulties while choosing the exact journal and literature review that are strongly support all the variables.

Accuracy of Data

Accuracy also become a limitation of the study. Researcher used secondary sources in conducting this study to collect data. The secondary sources such as annual reports, books, article, journal that the researcher found from internet and library. So, the accuracy of data depend from all the secondary sources that found in various materials. It means that, the researcher trying to maintain the originality and quality of the journal but the data needed depend on the materials.

CHAPTER TWO

2.0 LITERATURE REVIEWS

Gross Domestic Product (GDP)

Clausing (2007), analyze the impact of the size and the profitability of the corporate sector on revenues from corporate tax. The result of her regression analysis confirm that the share of the value added of the corporate sector, profit level GDP per capita and GDP growth have a positive impact on revenues from corporate tax, whereas the unemployment level has a negative impact.

Saeed et al (2010), have studied the impact of corruption index on the tax revenues over 27 developing countries and use annual data for the 2002 – 2006 periods found that GDP per capita is positive but it is significance at 12 percent level. The coefficient of the ratio of exports and imports (openness) to GDP is positive but not significance at even 10 percent level.

Gupta (2007), found that several structural factors like per capita GDP, share of agriculture in GDP and trade openness are statistically significant and strong determinants of tax revenue performance.

Inflation

Tanzi (1992), in his research provide an evidence that tax revenue is negatively affected by inflation, the so-called Olivera-Tanzi effect. This inverse relationship is usually explained by the fact that the real value of tax revenue is erode by inflation, since it exists for some tax categories a time-lag between the date of imposition and the effective collection of these taxes. Therefore, by theoretically maintaining inflation at low levels, and therefore by increasing the real value of tax revenue, Inflation Targeting may attenuate the government’s tax collection effort.

Agbeyegbe et al (2004), in their research examine the deternminants of total tax revenue shares by two different measures of trade liberalization. Using the first measure of trade liberalization, although per capita income is not significant, agricultural share, industrial share, government consumption, and the terms of trade all exert a positive effect on total tax revenue, and inflation exerts a negative effect. However, using the second measure, they find a different pettern of results. Industrial share is positive and marginally significant. The real exchange rate and inflation are both negative and significant, suggesting that real exchange rate appreciation and higher inflation depress revenues, consistent with Tanzi’s hypotheses. Appreciation of the exchange rate and increases in inflation generally speaking lead to lower overall tax revenue, though the results vary by component of taxes.

Unemployment

Kubatova et al (2009), from their regression analysis found that the cyclicality of economic growth has a statistically significant impact on revenues from corporate tax. All of their examined factors (GDP growth, inflation and unemployment) were statistically significant. Along with the growth of GDP comes the growth of revenues from corporate tax. Inflation also has a similar effect. Conversely, higher unemployment leads to a decrease of the revenues from corporate tax. The cyclicality of the economy is therefore an important factor influencing the revenues from the taxation of corporations.

Openness

Christopher et al (2001), in their research examine the relationship between tax revenue, exchange rates, and trade openness in Sub-Saharan Africa, using a difference General Method of Moments (GMM) dynamic panel estimation. Although not a focus of their work, they proxy trade liberalization through an openness variable. They conclude that openness raises overall tax revenue in CFA franc countries while t has little effect in non-CFA franc countries, though the disaggregated revenue outcome suggest that it raises trade tax revenue and lowers goods and services tax revenue.

Baunssgard et al (2005), in their research found that openness is significantly positively related to domestic tax revenue, and aid per capita negatively so. The dummy indicates a significantly negative impact on domestic tax revenues, but interaction terms indicate a positive effect that increases with the level of national income and a negative effect (albeit in this case insignificant) that increases with openness.

Bretschger et al, in their research found that globalization captured by open has a positive impact on the measure of corporate taxation; the estimated parameter is significant in estimations. Openness is also positive in the same estimations but not significant. They conclude that only a change of the dependent variable from corporate tax to corporate share changes the key result.

Becker et al (2007),in their research found that tax rate has a negative but mostly insignificant impact on tax revenue. Whereas total trade (internationalization or openness) has significantly positive influence on tax revenues.

Qazi (2010), in his paper attempts to search the determinants of tax buoyancy of 25 developing countries. He found that growth in import and manufacturing sectors have positive and significant impact on tax buoyancy which shows with the increase in growth of import sector tax revenue collection increases through import duties, tariff, sales tax on import stage and withholding income tax at import stage.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

The method used to analyze the data in sthis study was Multiple Regression Correlation Analysis. A multiple regression analysis involves more than one independent variable. It will focus on a relationship between a dependent variable and one or more independent variable. The regression analysis help the researcher to understand how the typical value of the dependent variable changes when any one of the independent variable is varied, while the other independent variables are held fixed.

3.1 RESEARCH DESIGN

3.1.1 Purpose of Study

The purpose of this study is to determine the factors determinant tax revenue in Malaysia namely Gross Domestic Product (GDP), inflation rate, unemployment and openness.

3.1.2 Research Interference

Most of the data used in this study are obtained from the secondary sources from various resources that have been analyzed. The data are collected from an internet resources.

3.1.2.1 Accuracy and Data Reliability

Multiple regression analysis and a correlation research design are selected as the method of this study in order to investigate the variables that are associated with the problem. Two random variables are positively correlated if high values of one are likely to be associated with high values of the other and negatively correlated if high values of one are likely to be associated with low values of the other known as correlation. A statistical method used with one dependent variable and more than one independent variable known as multiple regression analysis. Thus, the accuracy and the data reliability of the data may partly depend on the published materials.

3.1.3 Study Setting

Secondary data from various resources have been analyzed. Research here is a field study where it is non contrive setting with minimial interference.

3.2 DATA COLLECTION

In completing this study, data is the most important thing needed. From the data collected, the researcher can make analysis and interpret the output to find out the result.

Secondary Data

It refer to the data collected by someone for some other purposes. The sources include census reports, organizational records, surveys and annual reports. This secondary data used by the researcher to gain the idea and information to develop the literature review and complete this study.

3.2.1.1 Internet and website

Google Search

The major sources that the researcher choose to find and gather journal that related with this study. This website are useful to the reasercher because help the researcher to gain the information about this study.

3.2.1.2 Library Research

The researcher find the journal and books through the library reserach. Some of the information from journals and published materials can be used as references to the researcher to get a better picture of the situation.

DATA ANALYSIS

In this study, the data analysis need to be explained clearly. The data also consists of independent variable and dependent variable which is GDP, inflation rate, unemployment and openness . Pearson coefficient of correlation is used to the extent of relationship among different variables. All the data has been analyzed by using Statistical Package for Social Science (SPSS) program. The data will be examine by:

Coefficient of Determination (R-squared)

To know how well the independent variables explain the variation of the dependent variable in the regression.

Beta analysia (Coefficient)

To find out the relationship between independent variables and dependent variable. Does the relationship exist or not.

T-Statistic

Identify significant relationship of each independent variable with the dependent variable

F-Statistic

Testing the significance of the overall independent variables with the dependent variable

Standard Error of Estimation (See)

The objective is to identify whether a particular variableis significant at a certain level of confidence.

Beta Analysis (Coefficient)

Beta analysis is a measurement used in order to find out the relationship between independent variables and dependent variable does exist or not. Therefore, if the result is positive that means the independent variables can explain the changes in the dependent variable.

Coefficient of Determination (R²)

The coefficient of determination is a statistic that will give information the goodness of fit of model. It is a statistical measure of how well the regression line approximates the real data points. Is a descriptive measure between zero and one, indicating how good one term is at predicting another. The value of coefficient of determination is shown below:

Range of R² Strength of relationship

No relationship with dependent variable

0.1 to 0.5 Weak relationship between independent variables

and dependent variable

0.6 to 0.9 Dependent variable is strongly explained by

independent variables

1 Dependent variable ia perfectly explained by

Independent variables

T-Statistic

T-statistic is used to determine whether the significance between the dependent variable and the independent variables exists or not. If the computed T-stat is greater than book T-value, the independent variable is statistically significant or vice-versa. In order to get book T-value, the degree of freedom should be culculated at certain confidence interval.

The degree of freedom can be calculated as follow:

Degree of freedom = n – k – 1

Where: k = Number of Independent Variable

n = Number of Observation

The results for T-statistic:

Accept H1, reject H0

If the computed t-statistic is greater than the book T-value at certain significant level.

Reject H1, accept H0

If the computed t-statistic is lower than the book T-value at certain significant level.

F-Statistic

F-test is an overall test of the null hypothesis that group means on the dependent variable do not differ. It is used when comparing statistical models that have been fit to a data set, in order to identify the model that best fit the population from which the data were sampled. F-test mainly arise when the models have been fit to the data using least squares. In order to get book F-value, it should be culculated at certain significant level.

Formula for book F-value is as follow:

Book F-value = Fα (k – 1, n – k)

Where:

α = Significant level

k = Number of Independent Variable

n = Number of Observation

k – 1 = Numerator

n – k = Denominator

The result for F-Statistics:

Accept H1, reject H0

If the computed F-Statistic is greater than the book F-value at certain significant level.

Reject H1, accept H0

If the computed F-Statistic is lower than the book F-value at certain significant level.

3.3.5 Standard Error of Estimation (See)

It is a measure of the dispersion of tthe data points from the regression line. It’s objective is to identify whether a particular variable is significant at a certain level of confidence. Standard error can be measured in two ways:

Using T-stat

See = b

t-stat

Degree of freedom

Df = n – k – 1

It is also useful in determining the range in which the dependent variable will point to a specified probability.

3.4 MODEL SPECIFICATION AND THEORETICAL FRAMEWORK

3.4.1 MODEL SPECIFICATION

Multiple Regression Analysis

This technique will focus on a relationship between a dependent variable and one or more independent variable. The regression analysis help the researcher to understand how the typical value of the dependent variable changes when any one of the independent variable is varied, while the other independent variables are held fixed.

General Function:

TR = f ( GDP, Inf, Un, Op )

Multiple Regression Equation:

TR = a + b1 GDP + b2 Inf + b3 Un + b4 Op + É›

Where:

TR = Tax Revenue

GDP = Gross Domestic Product

Inf = Inflation Rate

Un = Unemployment

Op = Openness

The dependent variable in the above equation is tax revenue while the independent variables are GDP, inflation rate, unemployment and openness.

3.4.2 THEORETICAL FRAMEWORK

INDEPENDENT VARIABLES

GDP

Tax

Revenue

Inflation Rate DEPENDENT VARIABLE

Unemployment

Openness

Figure 1.0: Theoretical Framework

Based on the figure 1.0 above, it shows the relationship between the dependent variable which is Tax Revenue and the independent variables that includes Gross Domestic Product (GDP), Inflation Rate, Unemployment and Openness (trade). All these independent variables will be test to determine the relationship among these independent variables and dependent variables.

3.4.2.1 Priory Relationship

1. GDP and Tax Revenue : if GDP increase, the total tax revenue collected by government will also increase. This two variable have a positive relationship.

2. Inflation Rate and Tax Revenue : if an inflation rate increase, the total tax revenue collected by government will decrease. This two variable have a negative relationship.

3. Unemployment and Tax Revenue : if unemployment increase, the total tax revenue collected by government will decrease. This two variable have a negative relationship.

4. Openness and Tax Revenue : if the degree of openness increase, the total tax revenue collected by government will also increase. This two variable have a positive relationship.

3.5 HYPOTHESES STATEMENT

The purpose of the hypothesis statement is to illustrates which of the hypothesis is most affect the dependent variable. The hypothesis are:

H0 : GDP is not statistically significant to affect tax revenue in Malaysia

H1 : GDP is statistically significant to affect tax revenue in Malaysia.

H0 : Inflation is not statistically significant to affect tax revenue in

Malaysia

H1 : Inflation is indeed statistically significant to affect tax revenue in

Malaysia.

H0 : Unemployment is not statistically significant to affect tax revenue in

Malaysia.

H1 : Unemployment is indeed statistically significant to affect tax

Revenue in Malaysia.

H0 : Openness is not statistically significant to affect tax revenue in

Malaysia.

H1 : Openness is indeed statistically significant to affect tax revenue in

Malaysia.

CHAPTER FOUR

4.0 DATA ANALYSIS

This chapter focuses on the data result analysis. All the data collected in this study were processed using SPSS program. SPSS program was used to analyze the data from the correlation and regression analysis. The method was used to analyze the data was Multiple Regression Correlation Analysis. A multiple regression analysis involves more than one independent variable.

The process of evaluating is the same with simple regression, but in order to derive the estimated regression, a computer is employed due to the complex nature of data and time required. The presentation of findings is made to examine the relationship among independent variables (GDP, inflation, unemployment and openness) and dependent variable (tax revenue).

This study used Multiple Regression Method Analysis which is the interpretation of Regression Analysis includes Beta Analysis (Coefficient), Coefficient of determination (R-Squared), T-statistic, F-statistic and Standard Error of Estimation (SEE).

4.1 INTERPRETATION OF DATA AND FINDINGS

4.1.1 Research Analysis

From the data obtained, it shows the result of regression output as stated in Table 1 as follows:

Table 1: The results from regression output

Variables

Constant

GDP

Inflation

Unemployment

Openness

Beta Analysis

-144980.369

13.481

1657.557

5860.522

-572.845

T-statistics

8.284

5.562

3.435

2.643

7.017

R-squared : 0.990

F-statistics : 358.696

Standard error of estimation : 6122.50419

4.1.2 Regression Equation

From the result obtained, we can derive the regression linear function as follows:

General function:

TR = f ( GDP, Inf, Un, Op )

Multiple Regression Equation:

TR = a + b1 GDP + b2 Inf + b3 Un + b4 Op + É›

TR = – 144980.369 + 13.481 GDP + 1657.557 Inf + 5860.522 Un

– 572.845 Op + É›

Where:

TR = Tax Revenue

GDP = Gross Domestic Product

Inf = Inflation Rate

Un = Unemployment

Op = Openness

4.2 RESULT OF FINDINGS

4.2.1 Coefficient of Determination (R-squared)

Coefficient of determination or R-squared measures what percentage of a change in the dependent variable can be measured or explained by the change in the independent variables. It is also explains the level of the explanatory power.

If R-squared = 0 (no explanatory power)

This means that none of the change in the dependent variable can be measured by the change in the independent variables. The estimated equation is useless.

If R-squared = 1 (full explanatory power)

This means 100% of the change in the dependent variable can be explained by the change in the independent variables.

From the results obtained, it shows that R-squared is 0.990. This means that 99% change in the dependent variable can be explained by the change in independent variables. However, 1% can be explained by other variables. This means that the dependent variable is strongly explained by independent variables. Besides, it also has an accepted higher explanatory power by 99%.

4.2.2 Beta Analysis (Coefficient)

Beta analysis is a measurement used in order to find out whether a relationship exists between the independent variables and the dependent variable.

Table 2: The results of beta analysis

Variables

Beta

Sign

Interpretations

GDP

13.481

Positive

1 unit increase in GDP will increase tax revenue by 13.481 units. This is a positive relationship and consistent with the economic theory.

Inflation

1657.557

Positive

1 unit increase in an inflation will increase tax revenue by 1657.557 units. This is a positive relationship and not consistent with the economic theory.

Unemployment

5860.522

Positive

1 unit increase in an unemployment will increase tax revenue by 5860.522 units. This is a positive relationship and not consistent with the economic theory.

Openness

-572.845

Negative

1 unit increase in an openness will decrease tax revenue by 572.845 units. This is a negative relationship and not consistent with the economic theory.

Beta analysis for Gross Domestic Product (GDP)

From the results obtained, the increase in GDP will raised up the total tax revenue collected by government. This is because government imposed a higher tax rate for raw material that are imported from outside Malaysia. Therefore, the tax revenue collected from import duties is greater than export duties.

Beta analysis for Inflation

From the results obtained, the increase in inflation will increase the total tax revenue collected by government. This is because the inflation that arise in Malaysia does not seriously hit the Malaysian economic. Therefore people still afford to pay the amount of tax imposed by government to them.

Beta analysis for Unemployment

From the results obtained, the increase in an unemployment will increase the total tax revenue collected by government. It is because the rate of unemployment is not fixed as reported by the Department of Statistics at the end of each year. Jobless people may get a job before the end of the year but not report that to the government and still pay the amount of tax imposed to them. That is why the total tax revenue increase as the rate of unemployment increase.

Beta analysis for Openness

From the results obtained, the increase in an openness will reduced the total tax revenue collected by government. However, this result is not consistent with the economic theory maybe due to limitations of number of years studied. Results from past researchers stated that openness have a positive relationship with tax revenue and will raises the overall tax revenue collected by government; see Becker et al (2007) and Adam et al (2001).

4.2.3 T-statistic

T-statistic is used to determine whether the significance between the dependent variable and the independent variables exists or not. If the computed T-stat is greater than book T-value, the independent variable is statistically significant or vice-versa. In order to get book T-value, the degree of freedom should be culculated at a 99% confidence interval.

Degree of freedom = n – k – 1

= 20 – 4 – 1

= 15

From the T-distribution table, the book T-value is 2.947 at 99% confidence interval level.

Table 3: The results of T-statistic

Variables

T-statistics

Findings

GDP

5.562 > 2.947

Statistically significant

Inflation

3.435 > 2.947

Statistically significant

Unemployment

2.643 < 2.947

Not statistically significant

Openness

7.017 > 2.947

Statistically significant

T-statistic for Gross Domestic Product (GDP)

From the results obtained, the culculated T-value is higher than the book T-value (5.562 > 2.947) at a 99% confidence interval.

H0 : GDP is not statistically significant to affect tax revenue in

Malaysia.

H1 : GDP is statistically significant to affect tax revenue in

Malaysia.

Therefore, we accept H1 and reject H0 because gross domestic product (GDP) is statistically significant to affect tax revenue in Malaysia.

This result can be supported by Saeed et al (2010), found that GDP per capita is positive but it is significance at 12 percent level. This result also can be supported by Gupta (2007), found that structural factors like per capita GDP is statistically significant and strong determinants of tax revenue performance.

T-statistic for Inflation

From the results obtained, the culculated T-value is higher than the book T-value (3.435 > 2.947) at a 99% confidence interval.

H0 : Inflation is not statistically significant to affect tax revenue in

Malaysia

H1 : Inflation is indeed statistically significant to affect tax revenue

in Malaysia.

Therefore, we accept H1 and reject H0 because inflation is indeed statistically significant to affect tax revenue in Malaysia.

This result can be supported by Kubatova et al (2009), from their regression found that inflation is statistically significant impact on revenues from corporate tax. They also stated that along with the growth of GDP comes the growth of revenues from corporate tax and inflation also has a similar effect.

T-statistic for Unemployment

From the results obtained, the culculated T-value is lower than the book T-value (2.643 < 2.947) at a 99% confidence interval.

H0 : Unemployment is not statistically significant to affect tax

revenue in Malaysia.

H1 : Unemployment is indeed statistically significant to affect tax

revenue in Malaysia.

Therefore, we accept H0 and reject H1 because unemployment is not statistically significant to affect tax revenue in Malaysia.

However, this result is not the same as the result from past researchers. Again, Kubatova et al (2009), from their regression analysis found that unemployment is statistically significant impact on revenues from corporate tax. They also stated that higher unemployment leads to a decrease of the revenues from corporate tax.

T-statistic for Openness

From the results obtained, the culculated T-value is higher than the book T-value (7.017 > 2.947) at a 99% confidence interval.

H0 : Openness is not statistically significant to affect tax revenue

In Malaysia.

H1 : Openness is indeed statistically significant to affect tax

revenue in Malaysia.

Therefore, we accept H1 and reject H0 because openness is indeed statistically significant to affect tax revenue in Malaysia.

This result can be supported by Becker et al (2007), in their research found that total trade (internationalization or openness) has significantly positive influence on tax revenues. This result also can be supported by Gupta (2007), in his research found that structural factor like trade openness is statistically significant and strong determinants of tax revenue performance.

4.2.4 F-statistic

F-statistic is used to test the hypothesis that the variation in the independent variables explained a significant portion of the variation in the dependent variable. The formula of book F-value is as follow:

Book F-value = Fα (k – 1, n – k)

= F0.05 (5 – 1, 20 – 5)

= F0.05 (3, 16)

Numerator Denominator

From the F-distribution table, the book F-value is 3.06 at 5% significant level. The culculated F-statistic is 358.696 > 3.06 that means all the independent variables (GDP, Inf, Un and Op) are said to be statistically significant.

H0 : All the independent variables are not significant enough to

affect total tax revenue collected by government in Malaysia.

H1 : All the independent variables are significant enough to

affect total tax revenue collected by government in Malaysia.

From the results obtained, we accept H1 and reject H0 since there is significance for the overall model.

4.2.5 Standard error of estimation (See)

It is a measure of the dispersion of the data points from the regression line. It’s objective is to identify whether a particular variable is significant at a certain level of confidence. Standard error of estimation can be measured in two ways by using T-statistic and degree of freedom.

It also useful in determining the range which dependent variable will point to within a specified probability. From the results obtained, the standard error of estimation is 6122.50419, which means the smaller the standard error, the closer the data points are to the regression line.

CHAPTER FIVE

5.0 CONCLUSIONS

In this paper, the researcher analyze the determinants of tax in Malaysia for 20 years. This data cover the period from 1990 to 2009. The main focus is to identify the factors determine tax revenue in Malaysia. Tax is the main components of government revenue to finance all the expenditure made by government in providing facilities for public and for the development of the country. Therefore, in conducting this study, Statistical Package for Social Science (SPSS) software were used.

The independent variables such as Gross Domestic Product (GDP), inflation, unemployment and openness have been selected by the researcher to further study on the factors determine tax revenue collected by government in Malaysia.

On conducting this research, the Multiple Regression Correlation Analysis were used by researcher as a method to analyze the data that involves more than one independent variables. The SPSS program were used to measure the relationship between tax revenue and all the independent variables (GDP, inflation, unemployment and openness). The researcher measure the relationship between dependent variable and independent variables in terms of Coefficient of Determination (R-squared), Beta Analysis (Coefficient), T-statistic, F-statistic and Standard Error of Estimation (See).

From the regression model, the result for Coefficient of Determination (R-squared) is 0.990. This means that 99% change in the dependent variable can be explained by the change in independent variables. However, 1% can be explained by other variables. This means that the dependent variable is strongly explained by independent variables.

Based on the Multiple Regression Analysis Results, there is significantly positive between Gross Domestic Product (GDP) and tax revenue collected by government. This positive relationship is consistent with the economic theory. That means, an increase in GDP will raised up the total tax revenue collected by government. This result were supported by many researcher as stated in the literature review on chapter two.

There is also significantly positive between inflation and tax revenue collected by government. However, this positive relationship does not consistent with the economic theory. The result shows that an increase in inflation will increase the total tax collected by government. Even though the positive relationship does not consistent with the economic theory, but it have been support by the past researcher as stated in the literature review.

The result for unemployment is statistically insignificant and have a positive relationship with tax revenue collected by government. It indicates that the increase in an unemployment will increase the total tax collected by government. This positive relationship does not consistent with the economic theory and contra with the result from past researchers.

However, the result for openness is significantly negative with the tax revenue collected by government. It indicates that the increase in openness will decrease the total tax revenue. This negative relationship does not consistent with the economic theory and contra with the result from past researchers that have been included in the literature review.

The F-statistic result shows that the culculated F-statistic is greater than book F-value. Means that all the independent variables are said to be statistically significant at 5% significant level.

Lastly, the result for Standard Error of Estimation (See) is 6122.50419, which means the smaller the standard error, the closer the data points are to the regression line.

5.1 RECOMMENDATIONS

Some recommendation can be made from this study:

It is suggested that a longer time frame should be based for this study because the this topic is not frequently issued. If there a longer time frame, it may give the best result for the findings. Future research could also use the quarterly data to get better result.

The future research may select other economic variables as the measurement in the research because there are many macroeconomic variables that can be used to study on the factors determinats of tax. This topic may have different variable that can be taken into consideration.

It is suggested to do more research on this topic in the near future because there only few research that have been done from the previous period. So that this topic can be an interesting topic to study for future references.

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