econometric projek lithem

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SCHOOL OF MATHEMATICAL SCIENCESFIRST SEMESTER, ACADEMIC YEAR 2013/2014

SEP 221: Applied Statistics and Econometric

TITLE: THE ROLE OF GOVERNMENT IN

HUMAN DEVELOPMENT

Prepared by

LIM LI THEM 113786

Checked by

Dr. Che Normee Che Sab


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Table of Contents

Chapter Title Page

Abstract 1

1 IntroductionProblem StatementObjectiveScope of StudySignificance of StudyDistribution of Chapter

2 - 4

2 Literature Review 5 - 6

3 Methodology3.1 Descriptive Statistics

3.2 Unit Root Test3.3 Correlation3.4 Multiple Regression Analysis3.5 Autocorrelation3.6 Heteroscedasticity3.7 Newey-West Test3.8 Specification Error Test3.9 Multicollinearity

7 - 14

4 Result Analysis and Discussion

4.1 Descriptive Statistics4.2 Unit Root Test4.3 Unit Root Test for Residual4.4 Correlation4.5 Autocorrelation

- Durbin-Watson test- Breusch-Godfrey Serial Correlation LM Test

4.6 Heteroscedasticity- Graphical Method- White s Heteroscedasticity Test

4.7 Newey-West Test4.8 Specification Error Test- Ramsey RESET Test- Recursive Estimates (CUSUM-Squared Test)- Chow s Breakpoint Test- Wald Test

3.9 Multicollinearity- Variance Inflation Factor (VIF) Test

15 - 25

5 Conclusion 26 -27

Reference 28

Appendix 29


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ABSTRACT

This paper examines the role of government in human development by using multiple

regression model. The effect of government expenditure in education, health,

infrastructure, security and transport sectors, from the year 1981 until year 2010, onthe value of Human Development Index is investigated and their relationship is

studied. The result of regression model, after taking into account of autocorrelation,

heteroscedasticity and multicollinearity, suggests that only the government

expenditure in transport sector has a significant positive relationship with HDI while

the government expenditure in internal security sector has significant negative

relationship with HDI. The government spending in sectors like education, health and

infrastructure do not have a significant effect on HDI.

Keywords: Government expenditure, HDI, multiple regression model


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CHAPTER 1: INTRODUCTION

In an economic planning, human development always is the main concern for both

developed and developing countries. According to the 1990 Human Development

Report, hu man development is defined as a process of enlarging peoples choices.

This means that people have the freedom to make their own choices and have the

opportunities to realize them. These choices include political freedom, guaranteed

human rights, accessibility to education, a long and a healthy life, a good standard of

living and so on. Human Development Index (HDI) is introduced by Mahbub ul Haq,

a Pakistan economist and Amartya Sen, an Indian economist in 1990. Its used by

United Nations Development Programme in Human Development Report as one of

the tools in measuring the human development level of the countries in the world.HDI is a composite index of life expectancy, education and income indices (refer to

figure 1) . Its served as a frame of reference for both social and economic

development and is expressed as a value between 0 and 1.

Based on this index, countries with HDI greater than 0.8 is considered as high human

development, countries with HDI between 0.5 to 0.8 is medium human development

while countries with HDI less than 0.5 is classified as low human development.

According to the Human Development Report 2012, Malaysias HDI value is 0.769

which is in the high human development category, ranked at the position of 64 out of

187 countries and territories in the world. Between 1980 and 2012, Malaysias HDI

value had increased from 0.563 to 0.769, an increase of 37% or average annual

increase of about 1.0%.

Government expenditure or government spending, on the other hand, is government

acquisition of goods and services for current use to satisfy individual or collective

needs (government final consumption expenditure) or for future benefits (government

investment). These two types of government expenditure, on final consumption and

on gross capital formation, together constitute one of the major components of gross

domestic product. In Malaysia, the federal government expenditure is divided into

operating expenditure and development expenditure. Operating expenditure are

expenses that incurred to maintain the operations and other regular activities of the

Government, for example, the wages of public service officers and operating grants to

statutory boards and aided educational institutions. While the development
http://en.wikipedia.org/wiki/Mahbub_ul_Haqhttp://en.wikipedia.org/wiki/Mahbub_ul_Haq


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expenditure are expenses that represent a longer-term investment and results in the

formation of asset of the Government. Examples of such spending are the investment

of housing and building of roads. There are four main categories under the

government expenditure, which are security, social service, economic service and

general administration. Since year 1970, the federal government expenditure is on the

upward trend, especially the government development expenditure. This shows that

Malaysia is trying to move from developing country status towards a developed and

industrialised nation.

Problem Statement

Its not doubt that when a national budget is planned, the Government will consider

both economic growth and social benefit. The government spending is planned

carefully so that it can achieve both goals of economic growth and human

development. However, is the government expenditure that spent really improving

human development? Or it just brings to the growth of country economic? Which

sectors spending can affect the human development? Well find out the answers

through the study.

Objective and Significant of Study

The main objective of this paper is to study the role of government expenditure on the

human development. By determining the role of government in improving the human

development, the social benefit can be increased and the spending of government can

be more efficient. And knowing that which sectors spending can affect the human

development can help the government in planning their budget with the main goal to

improve the social benefit. The study also helps to determine whether the government

spending is efficiency or not. If the overall government expenditure is efficient,

definitely it can increase the value of HDI.

Scope of Study

The study focuses on the development expenditure in education sector, health,

internal security, public infrastructure and transport sectors. The government

expenditure in internal security means that the spending of government in fighting the

local crime rate and increase social security, for example, the increase in number of

police. The government expenditure in public infrastructure and transport sectors


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means that the government investment in providing and maintenance the public

utilities and public transports. The human development is measured by using the HDI

value from year 1970 until year 2012.

Distribution of Chapter

Chapter 1: Introduction of the Human Development Index. Objective, significant

of study and scope of study is stated in this chapter.

Chapter 2: In this chapter, a selection of previous studies which examine the

influence of government expenditure in different sectors on human

development.

Chapter 3: Several methods are used to analyze the data and will be discussed in

this chapter.

Chapter 4: A few tests are carried out using Eviews and results are shown in this

chapter. The effect of the independent variables (lnEDU, lnHEALTH,

lnINFRA, lnSAFETY and lnTPT) towards the ln HDI is studied. A

regression model is generated at the end to investigate the relationship

between the independent variables and the dependent variables.

Chapter 5: Conclusion is drawn based on the results of analysis.


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CHAPTER 2: LITERATURE REVIEW

A few studies had been done regarding the relationship between government

expenditure and the human development. Social indicators are improving in many

developing countries as public spending, especially the expenditures in health and

education sectors, are increasing.

In A.D Prasetyo and U. Zuhdi paper, they investigated the efficiency level of

government expenditure per capital in health and education sectors towards the

human development in 81 countries by using Data Envelopment Analysis (DEA)

approach from 2006 to 2010. Government expenditure per capita on education and

health sector were used as the inputs while HDI as the output. Results showed that

how efficiency of government expenditure could affect the human development

progress. Countries that were efficient in managing their expenditure tend to have

high HDI value, for example, Singapore. Another paper by Andrew and Vinaya (2007)

also showed that public spending was more effective and had impact on the related

sectors with good governance from the government. Study found that the health

spending lowers child mortality rates more in countries with good governance.

Similarly, public spending on primary education became more effective in increasing

the primary education attainment in countries with good governance. The difference

in the efficacy of public spending, therefore, can be explained by the quality of

governance.

Same research was carried out in Iran to examine the effect of government health

expenditure on HDI by using ordinary least square method (OLS) over the period

1990-2009 in Iran (M.J. Razmi, 2012). The investigation revealed that there was a

positive and significant relationship between government health expenditure and HDI.

However, Granger Causality Test in that research indicated that there was no bilateral

relationship between the government health expenditure and HDI.

A related study had been done also in Malaysia by R.A. Mohd Dzubaidi, Rahmah

Ismail and Tamat Sarmidi in 2013. The study showed that there was a positive

relationship between government expenditure and human development and at the

same time impacts the growth of economics. Variables like HDI, total government

expenditure, government expenditure in health and education sectors, Gross Domestic


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Product per capital, urban population, accessibility to water source and sanitation

facilities.

Other similar studies were Afonso and St. Aubyn (2004) paper that measured the

efficiency in education and health sectors in OECD countries. Several inputs such asthe expenditure per student, average class size, in-patient beds, medical technology

indicators and etc. are used. While for outputs, performance of students on reading,

mathematics and science literacy scales, life expectancy, infant and maternal mortality

rate are used in the paper. Herrera and Pang (2005) did the same study but their scope

of study was in the developing countries. They used government expenditure per

capital on education and health sectors ranged from 1996 to 2002 as the inputs.

Whereas, school enrolment, literacy of youth, average years of school, learning scoresare the outputs for education sectors and life expectancy at birth are the outputs for

health sectors.

.


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CHAPTER 3: METHODOLOGY

In this project, Eviews is used to perform our analysis. A regression model is

developed to test the effect few selected variables on the value of HDI. The estimated

regression model is constructed with time frame from year 1981 until year 2010 as

below:

l n H DI ln EDU l n HEALTH+ l n I NF RA ln SAFETY+ ln TPT

HDI=Human Development Index

EDU= Expenditure that spent in education sector (RM millions)

HEALTH= Expenditure that spent in health sector (RM millions)

INFRA= Expenditure that spent in building infrastructure (RM millions)

SAFETY= Expenditure that spent in internal security (RM millions)

TPT= Expenditure that spent in transport sector (RM millions)

A few tests are carried out on the estimated regression model above in order to

develop the most suitable regression model by taking into account the possibility of

autocorrelation, heteroscedasticity and multicollinearity. The tests and analyses arecarried at the significant level of 5% or = 0.05.

3.1 Descriptive Statistics

The mean, median, maximum, minimum and standard deviation of each data series is

summarised in a table for the easy comparison. The skewness and kurtosis of each

data series show the distribution of the data. The normality of each variable is testedtoo through probability of the Jarque-Bera test. The hypotheses of Jarque-Bera test are

made as below:


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3.2 Unit Root Test

A unit root test tests is a test that use to determine whether the time series variable is

non-stationary using an autoregressive model. There are few unit roots test, for

example, Dickey-Fuller test, Phillips-Perron KPSS test and so on. A well-known

test that is valid in large samples is the augmented Dickey Fuller (ADF) test. A data

series that has unit root is not stationary while data series that do not have unit root is

stationary. The hypotheses below are made for unit root test:

(The variable is not stationary or has unit root)

(The variable is stationary)

The null hypothesis is rejected if p-value < 0.05 or 1, k nt t or 1, k nt t , which

means the variable is stationary at = 0.05. The null hypothesis is not rejected if p -

value > 0.05 or 1, k nt t or 1, k nt t , which means the variable is not stationary

at = 0.05. In this study, the unit root test is conducted at both level and 1 st difference.

3.3 Correlation

Correlation is the statistical relationship involving dependence of two or more random

variables or data sets. When two sets of data are strongly linked together we say they

have a high correlation. When the values increase together, there is positive

correlation; when the values decrease together, there is negative correlation. A closer

value of the Pearsons correlation coefficient to -1 or 1 indicated there was a higher

negative or positive relationship between the variables respectively (refer to the

diagram 1 below).

Diagram 1 Positive and Negative Correlation
http://en.wikipedia.org/wiki/Time_serieshttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Time_series


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3.4 Multiple Regression Analysis

Regression analysis is a statistical process for estimating the relationships among the

variables. It helps us understand how the dependent variable changes when any one of

the independent variables is varied, assuming that the other independent variables areheld fixed. A multiple regression function is a function that contains two or more

independent variables, and one dependent variable, . The general form of

multiple regressions is

where is responding variable of ith experimental unit

is the ith observation on the jth independent variable

is the regression intercept

is a random error term

There are a few assumptions for the regression analysis:

1. There is a linear relationship between independent and dependent variables.

2. are n independent variables and ), i=1,2,.,n 3. The mean error, is denoted as zero. E( =0.

4. ,.., are independent.

5. are independent and is normally distributed.

6. The variance of the error is constant across observations (homoscedasticity) .

3.5 Autocorrelation

Autocorrelation is a mathematical representation of the degree of similarity between a

given time series and a lagged version of itself over successive time intervals. It is the

same as calculating the correlation between two different time series, .

The same time series is used twice - once in its original form and once lagged one or

more time periods. It also known as serial correlation. It is used to detect the non-

randomness in a given data or to identify an appropriate time series model if the data

are not random.
http://en.wikipedia.org/wiki/Homoscedasticityhttp://en.wikipedia.org/wiki/Homoscedasticity


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3.5.1 Durbin-Watson Test

Durbin-Watson (DW) Test is a test statistics that used to detect the presence of the

first order autocorrelation. The DW statistics has range from 0 to 4 and is calculated

using the formula below:

where T is the number of observation and d is approximately equal to 2(1- ). The

hypothesis testing for this statistical test can be written as below:

H0: = 0 (no autocorrelation)

H1: >0 (for positive autocorrelation) or


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test for the presence of serial dependence that has not been included in a proposed

model structure and which, if present, would mean that incorrect conclusions would

be drawn from other tests.

3.6 Heteroscedasticity

In a regression analysis, heteroscedasticity refer to a situation in which the variance of

the dependent variable varies across the data . The presence of heteroscedasticity is a

major concern in the application of regression analysis as it can invalidate statistical

tests of significance that assume that the modelling errors are uncorrelated and

normally distributed . Whites Heteroscedasticity is used in this study to detect the

presence of heteroscedasticity in the regression model with the following hypotheses:

: Hoteroscedasticity does not exist (Homoscedasticity exists)

: Heteroscedasticity exists

If the p-value obtained from F-statistic is larger than 5% level, is not rejected and

homoscedasticity exists. The alternative method to detect the presence of

heteroscedasticity is by graphical method.

3.7 Newey-West (HAC) Test

Newey-West (HAC) Test is used when autocorrelation and heteroscedasticity is

detected in a regression model. It is used to try to overcome autocorrelation, or correlation,

and heteroscedasticity in the error terms of the models. This is often used to correct the

effects of correlation in the error terms in regressions applied to time series data.

3.8 Specification Error Tests

Specification is the process of converting a theory into a regression model. This

process consists of selecting an appropriate functional form for the model and

choosing which variables to include. The specification errors include:

Incorrect functional form

Omitted-variable bias Irrelevant variable that is included in the model
http://en.wikipedia.org/wiki/Serial_dependencehttp://en.wikipedia.org/wiki/Serial_dependence


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The system has dependent variables

Measurement error

3.8.1 Ramsey REST Test

In a multiple regression analysis, a regression equation can be nonlinear in any or all

the explanatory variables. Ramsey Regression Equation Specification Error Test

(RESET) test, therefore, is used to detect the nonlinearity in a model . If the newly

added non-linear explanatory variables have any power in explaining the response

variable, the model is said to be mis-specified or in other word, suffers from

specification error. The following hypotheses are made for the Ramsey RESET test:

H 0: 0654321 (the model does not have specification error)

H 1: Not all 0 j ; j=1, 2, 3, 4, 5, 6 (the model has specification error).

3.8.2 Recursive Estimates (CUSUM-Squared Test)

The cumulative sum squared test (CUSUM-squared test) is used to test the

consistency of the coefficient in a particular model. The trend of the model will be

displayed in a graphical representation and the two bounds will be constructed parallelto each other that represent the 5% significance level. If the trend touches or exceeds

the bound at any point of time, it implies that structural break occurs at that particular

time and correction need to be done to reduce the influence of this structural break

towards the predictability of the model.

3.8.3 The Chows Breakpoint Test

A series of data can often contain a structural break, due to a change in policy or

sudden shock to the economy, i.e. Asian Economic Crisis (1997-1998). To carry out

the test, we partition the data into two or more sub-samples. Each sub-sample must

contain more observations than the number of coefficients in the equation so that the

equation can be estimated using each sub-sample. F statistics is used to determine

whether a single regression is more efficient than two separate regressions involving

splitting the data into two sub-samples.


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22,121

21 ~22/

1/k nk

R F k n ESS ESS

k ESS ESS ESS F

3.8.4 Wald Test Coefficient Restriction

The Wald test computes the test statistic by estimating the unrestricted regression

without imposing the coefficient restrictions specified by the null hypothesis. The

Wald statistic measures how close the unrestricted estimates come to satisfying the

restrictions under the null hypothesis. If the restrictions are in fact true, then the

unrestricted estimates should come close to satisfying the restrictions. The hypotheses

for the joint coefficients test can be formulated as follows:

H 0: 1 = 2 = = k = 0

H 1: At least one of j is non-zero, j = 1, 2, , k.

The test statistic used to test the above hypotheses is:

11

1

2

22

k n Rmk R R

k n ESS mk ESS ESS

df ESS df df ESS ESS

F

U

RU

U

U R

U U

U RU R

3.9 Multicollinearity

Multicollinearity occurs when two or more independent variables in a multiple

regression model are highly correlated. There is an approximate linear relationship

between the explanatory variables, which could lead to unreliable regression estimates,

although the OLS estimates are still BLUE. In general it leads to the standard errors of

the parameters being too large, therefore the t-statistics tend to be insignificant. The

problem of multicollinearity can lead to inaccurate estimation from the regression

model and thus reduce the trustworthy of the model. Variance Inflation Factor (VIF)

test can be used to detect and examine the seriousness of the multicollinearity

problem between the independent variables by using the formula below:


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j RVIF

If the VIF value of a variable is higher than the selected value of 10, we can say that

multicollinearity is exist.


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CHAPTER 4: RESULT ANALYSIS AND DISCUSSION

4.1 Descriptive Statistics

Table 4.1.1 Statistics Table

HDI EDUCATION HEALTH INFRA SAFETY TRANSPORTMean 0.673033 3997.933 925.9667 1359.167 591.2000 4007.967Median 0.678500 2067.500 516.5000 932.5000 433.5000 2977.500Maximum 0.763000 12436.00 3780.000 5286.000 1659.000 9450.000Minimum 0.563000 791.0000 53.00000 468.0000 133.0000 1046.000Std. Dev. 0.062555 3805.637 938.3989 1002.103 444.6788 2838.623Skewness -0.130540 1.070335 1.342629 2.264631 1.132522 0.674561Kurtosis 1.706293 2.710527 4.245541 8.929413 3.078749 1.976300

Jarque-Bera 2.177301 5.832830 10.95248 69.59020 6.420787 3.585113Probability 0.336671 0.054127 0.004185 0.000000 0.040341 0.166534

Sum 20.19100 119938.0 27779.00 40775.00 17736.00 120239.0Sum Sq. Dev. 0.113479 4.20E+08 25537183 29122126 5734437. 2.34E+08

Observations 30 30 30 30 30 30

Based on the table 4.1.1, the mean of HDI from 1981 to year 2010 is 0.673033 with

standard deviation 0.062555. This shows that Malaysia has medium human

development for the 30 years. Value of skewness of HDI is -0.130540 and Kurtosis is

1.706293. From the Jarque-Bera Test, the p-value obtained is larger than = 0.05 at

5% significant level. The null hypothesis of normal distribution is not rejected. Thus,

HDI is normally distributed. Same goes to the variable EDUCATION and

TRANSPORT which has p-values larger than 0.05. Both of the variables, therefore,

are normally distributed. EDUCATION and TRANSPORT have means RM3997.933

millions and RM 4007.967 millions respectively with standard deviation RM

3805.637 millions and RM 444.6788 millions each.

For the variable HEALTH, it has mean of RM 925.9667 millions and standard

deviation of RM 938.3989 millions. It has positive skewness of 1.342629 and kurtosis

of 4.245541. Whereas for variable INFRA, it has mean of RM 1359.167 millions and

standard deviation of 1002.103 with value of skewness of 2.264631 and kurtosis of

8.929413. And for variable SAFETY, it has mean of RM 591.200 millions and

standard deviation of RM 2838.623 millions. These variables are not normally

distributed as they have p- value less than = 0.05 at 5% significant level.


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4.2 Unit Root Tests Before constructing a regression model and proceeding to estimate the co-integration

model, its necessary to determine if the variables used for the regression model are

stationary. Thus, the variables are tested for unit root to determine the integrationorder of the variables. A stationary test known as Augmented Dickey Fuller (ADF)

with =0.05 is carried out with the hypothesis as below:

(The variable is not stationary or has unit root)

(The variable is stationary)

Table 4.2.1 Result of Unit Root Test

Variables

Level 1 st Difference

None No Trend With Trend None No Trend With Trend

ln HDI -1.952910*** -2.967767 -3.574244 -1.953381*** -2.976263*** -3.587527***

ln EDU -1.952910 -2.971853 -3.580623* -1.953381*** -2.971853*** -3.580623**

ln HEALTH -1.955020 -2.986255 -3.595026*** -1.953381*** -2.971853** -3.580623**

ln INFRA -1.952910 -2.967767 -3.574244 -1.953381*** -2.971853*** -3.580623***

ln SAFETY -1.952910 -2.967677 -3.574244** -1.953381*** -2.986225*** -3.603202**

ln TPT -1.952910 -2.967677 -3.574244 -1.953381*** -2.971853*** -3.580623***

Table 4.2.2 Summary on Unit Root Test

Variables Level 1 st Difference I orderln HDI non-stationary stationary*** 1ln EDU non-stationary stationary*** 1

ln HEALTH non-stationary stationary** 1ln INFRA non-stationary stationary*** 1

ln SAFETY non-stationary stationary*** 1ln TPT non-stationary stationary*** 1

From tables 4.2.1 and 4.2.2, its found that the null hypothesis for the data series

cannot be rejected at the level of the series. Mostly of the data in the regression form

without intercept and trend, with intercept and regression form with intercept and

trend do not reject null hypothesis. The computed ADF t-statistics value (absolute

value) is smaller than the critical values at = 0.01, 0.05 and 0.10 significant level.

There is enough evidence to show that the variables ln HDI, ln EDUCATION, ln


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HEALTH, ln INFRA, ln SAFETY and ln TPT have unit roots. The same test on the

first differences of the data series is carried out and result shows that the null

hypothesis can be rejected at = 0.01, 0.05 and 0.10 significant level . There is no

enough evidence to show that the variables ln HDI, ln EDUCATION, ln HEALTH, ln

INFRA, ln SAFETY and ln TPT have unit roots. Thus, this implies that the series are

integrated of order one [ I (1)] and are stationary in their first differences.

4.3 Unit Root Test for Residual

Table 4.3.1 Result of Unit Root Test of Residual

Null Hypothesis: D(RESID) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=2)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.023344 0.0029Test critical values: 1% level -4.200056

5% level -3.17535210% level -2.728985

*MacKinnon (1996) one-sided p-values.

Now that it has been established that the variables are integrated of the same order.We need to test whether the regression model is a spurious regression. A spurious

regression refers to a regression that shows significant result due to the presence of a

unit root in the variables. The series is co-integrated if the residual in the series is

stationary. The residual is tested for the presence of a unit root. The ADF statistics

(absolute value) is found to be greater than the critical values at = 0.01, 0 .05 and

0.10 significant level. The null hypothesis, thus, can be rejected and the residual is

proved that it do not have unit root or is stationary. The regressions model is not

sprious and co-integration exists.

4.4 Correlation

Table 4.4.1 General Correlation between HDI and Other Variables

LNHDI LNEDU LNHEALTH LNINFRA LNSAFETY LNTPT

LNHDI 1.000000 0.915413 0.909630 0.719905 0.752611 0.926196

LNEDU 0.915413 1.000000 0.915935 0.754489 0.797160 0.908854LNHEALTH 0.909630 0.915935 1.000000 0.684609 0.820601 0.926238


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LNINFRA 0.719905 0.754489 0.684609 1.000000 0.699408 0.723428

LNSAFETY 0.752611 0.797160 0.820601 0.699408 1.000000 0.885159

LNTPT 0.926196 0.908854 0.926238 0.723428 0.885159 1.000000

From the table 4.3.1, its found that ln HDI has strong positive correlation relationshipwith ln EDUCATION, ln HEALTH, ln INFRA, ln SAFETY and ln TPT variables,

with values 0.915413, 0.909630, 0.719905, 0.752611 and 0.926196 respectively. That

means the value of ln HDI increases with the values of ln EDUCATION, ln HEALTH,

ln INFRA, ln SAFETY and ln TPT.

4.5 Autocorrelation

4.5.1 Durbin Watson Test

Table 4.5.1 Multiple Regression Model

Dependent Variable: LNHDIMethod: Least SquaresDate: 12/16/13 Time: 18:21Sample: 1981 2010Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C -1.272230 0.102793 -12.37666 0.0000

LNEDU 0.026928 0.017288 1.557625 0.1324LNHEALTH 0.017042 0.014265 1.194677 0.2439LNINFRA 0.013528 0.015386 0.879282 0.3880

LNSAFETY -0.041885 0.016910 -2.476933 0.0207LNTPT 0.089002 0.026153 3.403086 0.0023

R-squared 0.914958 Mean dependent var -0.400200 Adjusted R-squared 0.897241 S.D. dependent var 0.094028S.E. of regression 0.030142 Akaike info criterion -3.988966Sum squared resid 0.021804 Schwarz criterion -3.708727Log likelihood 65.83450 Hannan-Quinn criter. -3.899315F-statistic 51.64261 Durbin-Watson stat 0.852741Prob(F-statistic) 0.000000

The regression model is shown in the table above. The expected positive relationship

between ln HDI and other variables are consistent with the theory except the

independent variable ln SAFETY which has a negative sign. This implies that In HDI

has a negative relationship with ln SAFETY which is inconsistent with the theory.

The p-values of both ln EDU, ln HEALTH and ln INFRA are larger than = 0.05

whereas the p-values of both ln TPT and ln SAFETY are less than = 0 .05. This

means that ln EDU, ln HEALTH and ln INFRA are less significant while ln TPT and


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ln SAFETY are significant under 5% significant level. The high value in the table

shows that the dependent variables and independent variables are closely correlated.

The 91.50% of the changes in ln HDI can be predicted by the current model and

explained by the other independent variables.

With the hypothesis below:

(There is no autocorrelation)

(There is positive or negative autocorrelation)

By referring to the statistics table on Durbin-Watson, there is no autocorrelation if

Durbin-Watson statistic is 2, has positive autocorrelation if less than 2 or negative

autocorrelation if more than 2. From the data series, the Durbin-Watson statistics

obtained is 0.852741. The lower and upper critical values, and are 1.071 and

1.833. Null hypothesis is rejected at 5% significant level and the series data exhibits

positive autocorrelation.

4.5.2 Breusch-Godfrey Serial Correlation LM Test

Table 4.5.2.1 Result of Serial Correlation LM Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 5.436148 Prob. F(2,22) 0.0121Obs*R-squared 9.922303 Prob. Chi-Square(2) 0.0070

Breusch-Godfrey Serial Correlation LM Test is used to test the existence of

autocorrelation. Result shows that the p-value = 0.0121 which is less than = 0.05.

Therefore, null hypothesis is rejected, there is autocorrelation exists in the model at

= 0.05 significant level. The probability value strongly indicates the presence of serial

correlation in the residuals.

4.6 Heteroscedasticity Test

The presence of heteroscedasticity in the regression model is tested by observing the

graph of residual square over the time frame of year 1981 until year 2010 and using

Whites Heteroscedasticity Test.


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Graph 4.6.1 Plot of Residual Square of Estimated Regression

The graph above is the graphical representation of the residual square of the estimated

regression model from year 1981 until year 2010. From the graph, we can see there is

fluctuation over the time period and the residual square is not parallel to the x-axis.

This shows that there is no heteroscedasticity exist within the regression model. In

order to get a more concrete proof regarding the existence of heteroscedasticity withinthe regression model, Whites heteroscedasticity test is used with the fo llowing

hypothesis:

Heteroscedasticity does not exist

Heteroscedasticity exist

Table 4.6.1 Result of White Test

Heteroskedasticity Test: White

F-statistic 1.576167 Prob. F(20,9) 0.2451Obs*R-squared 23.33717 Prob. Chi-Square(20) 0.2725Scaled explained SS 17.98610 Prob. Chi-Square(20) 0.5883

The result of the white test is shown in the table above. The p-value= 0.2451 which is

larger than = 0.05. The null hypothesis is not rejected at 5% significant level. There

is enough evidence to show that is no heteroscedasticity exist in our regression model

at = 0.05.


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4.7 Newey-West (HAC) Test

Table 4.7.1 Result of Newey-West Test

Dependent Variable: LNHDIMethod: Least SquaresDate: 12/16/13 Time: 18:30Sample: 1981 2010Included observations: 30HAC standard errors & covariance (Bartlett kernel, Newey-West fixed

bandwidth = 4.0000)

Variable Coefficient Std. Error t-Statistic Prob.

C -1.201980 0.096558 -12.44831 0.0000LNEDU 0.028289 0.013579 2.083214 0.0481

LNHEALTH 0.016091 0.019274 0.834819 0.4121INFRA 7.00E-06 6.91E-06 1.013081 0.3211

LNSAFETY -0.042261 0.019407 -2.177578 0.0395LNTPT 0.090609 0.017333 5.227565 0.0000

R-squared 0.914997 Mean dependent var -0.400200 Adjusted R-squared 0.897288 S.D. dependent var 0.094028S.E. of regression 0.030135 Akaike info criterion -3.989429Sum squared resid 0.021794 Schwarz criterion -3.709190Log likelihood 65.84144 Hannan-Quinn criter. -3.899778F-statistic 51.66875 Durbin-Watson stat 0.889316Prob(F-statistic) 0.000000 Wald F-statistic 37.23538Prob(Wald F-statistic) 0.000000

Newey-West (HAC) Test is used to improve the regression model and to reduce the

effect of autocorrelation in the model. After conducting the Newey-West Test on the

variables, we can see that there are changes in the significant level of the variables. The

significant level of the variable ln EDU has decreased and become significant at = 0.05.

The R-squared value, Durbin-Watson statistic and Standard Error remain almost the

same as the one before the regression model is corrected.

4.8 Specification Error Test

After checking for the problem of autocorrelation and heteroscedasticity, the

regression model is still not the best yet. More specification errors tests need to be

carried out in order to make the regression model becomes more accurate and precise.

4.8.1 Ramsey RESET Test

Table 4.8.1.1 Result of Ramsey RESET TestRamsey RESET Test


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Equation: UNTITLEDSpecification: LNHDI C LNEDU LNHEALTH LNINFRA LNSAFETY LNTPTOmitted Variables: Powers of fitted values from 2 to 3

Value df ProbabilityF-statistic 1.362658 (2, 22) 0.2768

Likelihood ratio 3.503557 2 0.1735

From the result of Ramsey RESET Test, its found that the value of F -statistic is

3.500796 with probability of 0.2768 which is greater than = 0.05. Therefore the null

of hypothesis that the newly added variables, = 0 is not rejected at 5%

significant level. The newly added variables do not the explanatory power toward the

dependent variable ln HDI. This implies that the estimated regression model is not

suffered from specification error.

4.8.2 Recursive Estimates (CUSUM-Squared Test)

Graph 4.8.2.1 Result of CUSUM-Squared Test

A CUSUM test is used to test the consistency of the coefficients in the regression

model. From the graph, we can see that there is consistency of the coefficients in the

model for the time frame of year 1981 until 2010. The estimated regression model is

stable.


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4.8.3 Chow s Breakpoint Test

In order to verify the existence of structural change in the regression model for the

certain period, Chow Breakpoint Test is used. Important date like year 1998 (Asian

Economic Crisis) is identified and the following hypothesis is made:

Table 4.8.3.1 Result of Chow Breakpoint Test

Chow Breakpoint Test: 1998Null Hypothesis: No breaks at specified breakpointsVarying regressors: All equation variablesEquation Sample: 1981 2010

F-statistic 2.489226 Prob. F(6,18) 0.0624Log likelihood ratio 18.12525 Prob. Chi-Square(6) 0.0059Wald Statistic 14.93536 Prob. Chi-Square(6) 0.0208

Result found that the there is a structural change in the regression model in year 1998

as the F-statistic has a small probability of 0.0624 which is less than = 0.05. The

null hypothesis is not rejected at 5% significant level. A dummy variable is not

required to add into the regression model.

4.8.4 Wald Test

The estimated equation is shown as below:Estimation Equation:=========================LNHDI = C(1) + C(2)*LNEDU + C(3)*LNHEALTH + C(4)*LNINFRA + C(5)*LNSAFETY + C(6)*LNTPT

Table 4.8.4.1 Result of Wald Test

Wald Test:Equation: Untitled

Test Statistic Value df Probability

F-statistic 3.409866 (3, 24) 0.0337Chi-square 10.22960 3 0.0167

Null Hypothesis: C(2)=0, C(3)=0, C(4)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(2) 0.026928 0.017288C(3) 0.017042 0.014265

C(4) 0.013528 0.015386


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From the table 4.5.1, w notice that the p-value for the independent variables, ln EDU

and ln HEALTH and ln INFRA are 0.1324, 0.2439 and 0.3880 which are larger than

= 0.05. Both of the variables are insignificant at 5% significant level. Thus, these

three variables are subjected to Wald Test to determine if these variables can be

omitted from the current regression model. The hypothesis below is made:

The result implies that the null hypothesis cannot be rejected at 5% significant level as

the p-value = 0.0337 which is less than = 0.05. Thus, it can be concluded that the

three independent variables, ln EDU, ln HEALTH and ln INFRA cannot be omitted

from the current regression model.

4.9 Multicollinearity

The regression model at last, will test for multicollinearity. Multicollinearity refers to

a situation in which two or more explanatory variables in a multiple regression model

are highly linearly related. It means that the independent variables are highly

correlated to an extent that in which the variables are dependent on each other. This

can lead to a serious problem of inaccurate estimation from the regression model and

thus reduce the trustworthy of the model even though it has high value.

As shown in the table 4.4.1, the general correlation between the variable ln TPT and

ln HEALTH has the highest value of 0.926238. There is very high collinearity level

between these variables, hence further analyses was made to test the seriousness of

the multicollinearity problem for each and every independent variable by using theVIF (Variance Inflation Factor) Test. The range of VIF is started from 1. The

multicollinearity problem is very serious if VIF exceed 10.
http://en.wikipedia.org/wiki/Multiple_regressionhttp://en.wikipedia.org/wiki/Multiple_regression


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Table 4.9.2 Result of VIF Test II

Variance Inflation Factors

Date: 12/16/13 Time: 19:50

Sample: 1981 2010

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 0.008617 199.9143 NA

LNEDU 0.000382 555.5256 7.694700

LNHEALTH 0.000231 216.5798 7.337885

LNINFRA 0.000336 388.4226 2.515110

LNSAFETY 0.000290 255.6127 3.451071

From the table 4.9.1 above, it shows that the VIF of ln TPT is higher than 10.

Therefore, the variable ln TPT is omitted and the VIF test is carried out again in order

to get a better regression model. In the VIF Test II, all the VIF values of the variables

obtained are less than 10 which show that there is no serious multicorrelinearity

problem.

After went through a series of tests, the suitable regression model is developed as

below:

ln HDI= -1.272230 + 0.026928 lnEDU + 0.017042 lnHEALTH + 0.013528

lnINFRA - 0.041885 lnSAFETY + 0.089002 lnTPT

Table 4.9.1 Result of VIF Test I

Variance Inflation Factors

Date: 12/16/13 Time: 19:39

Sample: 1981 2010

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 0.010566 348.9104 NA

LNEDU 0.000299 618.0921 8.561322

LNHEALTH 0.000203 272.0020 9.215631

LNINFRA 0.000237 388.9255 2.518367

LNSAFETY 0.000286 359.2304 4.850030

LNTPT 0.000684 1471.113 12.18056


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CHAPTER 5: CONCLUSION

After conducting a various tests and diagnostics, we manage to develop the most

suitable regression model for our study. The regression model obtained is:

ln HDI= -1.272230 + 0.026928 lnEDU + 0.017042 lnHEALTH + 0.013528

lnINFRA - 0.041885 lnSAFETY + 0.089002 lnTPT

From the study, we can know that how is the government expenditure affect the value

of Human Development Index. Result shows that the government spending in internal

security and transport sectors has significant influences on HDI while the spending in

education, health and infrastructure sectors does not has significant impact on HDI.

The study also shows that the government expenditure in education, health,

infrastructure and transport sector has positive relationship with HDI as what we

expected in the initial prediction. This is because HDI takes into account of life

expectancy, expected and means year of schooling and gross national income per

capital. The more government spending in educational and health sectors, for example,

the building of hospital and school, the increase in the number of teachers and health

care workers and the increase in the usage of technology can improve the life

expectancy and literacy rate among the Malaysian. The government investments in

infrastructure and transport sector also increase the value of HDI as it improves the

living standard of the residents. On the other hand, its not consistent with the theory

that the government spending in internal security sector affect the HDI negatively. It

might be due to the inefficiency of the polices in fighting against the upward trend of

crime rate and the residents have the feeling of unsecure.

Throughout the study, the estimated regression model doesnt have heteroscedasticity

and serious multicollinearity problem but it contains positive autocorrelation.

Autocorreltaion that happens in the model might due to the natural of the data series

and is har to avoid. Besides that, there is no structural change across the sub-sample in

the regression model and the model, thus, doesnt suffer from specification error.

Following is the summary of results of a few main tests that we had carried out.


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Tests ResultAutocorrelation:

Positive autocorrelation exists.Durbin-Watson TestBreusch- Godfrey Serial Correlation LM Test

Heteroscedasticity:There is no heteroscedasticity.Graphical Test

Whites Heteroscedasticity test

Model Specification Error:

Structural change across the sub-samples does not exist.

Ramsey RESET TestRecursive Estimates (CUSUM-Squared Test)Chows Breakpoint Test Wald Test

Multicollinearity:Serious multicollinearity does notexist between independent variable.Variance Inflation Factor (VIF) Test

In conclusion, government expenditure in internal security and transport sectors can

affect the value HDI significantly as shown in the tests above whereby ln SAFETY

and ln TPT can explain and predict the changes in the value of HDI up to 90%.

Government should focus more in these two sectors and plan well for the coming

budget so that the human development can be improved. However, governmentshould not ignore the other sectors as they affect HDI positively although the impact

induced is not significant.

Even though a satisfactory result is obtained throughout the study, some improvement

can be done for further research and to obtain a better regression model. More

independent variables can be added to the model to improve its analysis. More detail

tests can be carried out toward the model to reconfirm the results obtained, for

example ARCH test can be used to reconfirm the result of Whites Heteroscedasticity

Test.


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REFERENCE

Afonso, A., & St Aubyn, M. (2005). Non-parametric approaches to education andhealth efficiency in OECD countries. Journal of Applied Economics , 8(2), 227-246.

Gupta, S., Clements, B., & Tiongson, E. (1998). Public spending on humandevelopment. Finance and Development , 35, 10-13.

Ministry of Finance Malaysia, Federal Government Development Expenditure 1970-2012, viewed on 11 December 2013,

Pang, G., & Herrera, S. (2005). Efficiency of public spending in developing countries:

an efficiency frontier approach. World Bank Policy Research Working Paper , (3645).

Prasetyo, A. D., & Zuhdi, U. (2013). The Government Expenditure Efficiencytowards the Human Development. Procedia Economics and Finance . pp 615-622.

Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Doesgovernance matter?. Journal of development economics , 86 (1), 96-111.

R.A.M. Dzubaidi, Rahmah Ismail, Tamat Sarmidi. (2013). Peranan Perbelanjaan

Kerajaan Terhadap Pembangunan dan Pertumbuhan Ekonomi. Universiti KebangsaanMalaysia. ISSN: 2231-962X, PROSIDING PERKEM VIII, JILID 2, page 872 879.

Razmi, M. J. (2012). Investigating the Effect of Government Health Expenditure onHDI in Iran. Journal of Knowledge Management, Economics and InformationTechnology , 2.

United Nation Development Programme, Human Development Report, viewed on 11December 2013, < http://hdr.undp.org/en/statistics/hdi/ >
http://www.treasury.gov.my/pdf/ekonomi/dataekonomi/2013/timeseries/FGDE1970_2012.pdfhttp://www.treasury.gov.my/pdf/ekonomi/dataekonomi/2013/timeseries/FGDE1970_2012.pdfhttp://www.treasury.gov.my/pdf/ekonomi/dataekonomi/2013/timeseries/FGDE1970_2012.pdfhttp://hdr.undp.org/en/statistics/hdi/http://hdr.undp.org/en/statistics/hdi/http://hdr.undp.org/en/statistics/hdi/http://hdr.undp.org/en/statistics/hdi/http://www.treasury.gov.my/pdf/ekonomi/dataekonomi/2013/timeseries/FGDE1970_2012.pdfhttp://www.treasury.gov.my/pdf/ekonomi/dataekonomi/2013/timeseries/FGDE1970_2012.pdf


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APPENDIX

(Source: Human Development Report)

Figure 1 Component of HDI

econometric projek lithem

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