Factors Affecting Urban Poverty

This research is study about the factors affecting urban poverty in Malaysia from year 1981 to 2011. The variables used in this study are unemployment rate, inflation rate and education. Throughout the 30 years of observation from year 1981 to 2011, the results show that all the independent variables have a significant relationship with dependent variable except for inflation. Hence, we can say that unemployment rate and education have mainly factors affecting urban poverty in Malaysia. While one variable name inflation is not consistence with the economic theory, but it does give any impact towards the urban poverty. This is because the error when running the regression model or may be the classification of data is not suitable and not accurate. This is can be prove by the output and the finding show that the inflation is not significant and therefore it should have a direct effect on urban poverty. This is support by UN Report on the World Social Situation 2010, Rethinking Poverty, when the inflation (real wage) elasticity of poverty is found to be significantly less than output which is employment elasticity of poverty. Moreover the majority of the poor are net debtors and inflation can be reduce the real of their debt. So this way inflation may have a negative relationship with poverty and the effect of inflation on poverty is not easy. Based on other research, according to Romer and Romer (1998) studied the impact of the United States’ monetary policy on unemployment, poverty and inequality. Their findings show the change in poverty on the unanticipated change in inflation produced a small and not significant coefficient.

Since the objective of this research is to study the relationship and significance of the variables namely unemployment rate, inflation rate and education in the factor affecting urban poverty in Malaysia from year 1981 to 2011, we can conclude that the research is achieve its objective. Based on the output regression result also shows that the models have good overall fit of regression equation since the variable can be explain the variation of the dependent variable and does not have any regression problem such as multicollinearity and autocorrelation.

Based on the result also, some of recommendation are be made for future researcher and government. Firstly is the sign of inflation. An increase in the inflation rate will increase the rate of poverty. Thus, the relationship between inflation and poverty is positively correlated. Since the sign of this variable is not consistence with the theory, it is recommended for future researcher to be more cautious when choosing the data and use the right proxy to measure it.

To achieve the objective of poverty reduction and eradication, there are some recommendations how to overcome some of the problems discussed.

Social

Housing

A great majority of these categorized as urban poors live in appalling conditions or to be more specific squatter area. The authorities should build more low cost houses with proper infrastructures affordable to this particular group, houses sold by private developer are not within their reach as the cost are rather prohibitive.

Recreation facilities

Proper recreation facilities like football field, net ball courts futsal, court etc should be provided in these areas. The reason is simple to keep them occupied and indulge in proper activities during their free time. Hence, keeping them away from undesirable influences and activities.

Medical facilities

The government should open more clinics to treat and advise these urban poors on health care. The escalating cost at private clinics had certainly affected them tremendously. The government should provide special fund or free medical expenses for those suffering from terminal illness such as cardiothoracic or neuro problems.

Education

More educational institutions should be provided in certain poor areas as some are too far away from their home. This is true for preschool classes, primary school, secondary school and tertiary or college.

Special training also comes in two categories school drop out and those with tertiary education. The government should provide special skills commensurate with their experiences and qualifications.

Economy

Job

The government should encourage more investors construct and operate their factories in certain designated areas. Job priorities should be given to those from these unfortunate groups as jobs in public sectors are limited.

Job training

Many of those from urban poor families who had graduated from higher institution of learning found it difficult to find proper jobs, therefore the authorities or private sector should provide special skill training or education to enable them to fight and enter the job market.

This recommendation can reduce unemployment rate in Malaysia especially in urban poverty.

Security and safety

Most of the urban poor are prone to criminal activities. Therefore, the government should build more police stations or beat bases as part concerted effort for crime prevention.

The police and other government agencies should be people friendly, more effort and should be organized as part of the concerted efforts to overcome social problems.

Based on the recommendation are given, the government can execute effective and appropriate policies in order to eradicate and combat the incidence of poverty towards the economics. Even the people themselves can have a better individuals understanding to combat with the problems of urban poverty in the future and try to help the government from the microeconomic side.

In addition, the researcher also can include their study to add more observations in order to be more accurate and precise. When time frame longer, it can give good result for their research or study.

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APPENDICES

Appendix 1

LIN LOG

Dependent Variable: POV

Method: Least Squares

Date: 12/25/12 Time: 00:36

Sample: 1 31

Included observations: 31

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LUNP

2.525335

1.210297

2.086541

0.0465

LIN

-0.209822

0.684263

-0.306639

0.7615

LEDU

-1.470277

0.721136

-2.038835

0.0514

17.22166

10.84361

1.588185

0.1239

R-squared

0.624848

Mean dependent var

2.548387

Adjusted R-squared

0.583164

S.D. dependent var

2.350017

S.E. of regression

1.517237

Akaike info criterion

3.791573

Sum squared resid

62.15423

Schwarz criterion

3.976604

Log likelihood

-54.76939

Hannan-Quinn criter.

3.851889

F-statistic

14.99027

Durbin-Watson stat

1.285919

Prob(F-statistic)

0.000006

Appendix 2

DOUBE LOG

Dependent Variable: LPOV

Method: Least Squares

Date: 12/25/12 Time: 00:36

Sample: 1 31

Included observations: 31

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LUNP

0.862764

0.373361

2.310805

0.0287

LIN

-0.040830

0.211086

-0.193429

0.8481

LEDU

-0.502844

0.222461

-2.260370

0.0321

5.594287

3.345111

1.672377

0.1060

R-squared

0.672445

Mean dependent var

0.606235

Adjusted R-squared

0.636050

S.D. dependent var

0.775835

S.E. of regression

0.468048

Akaike info criterion

1.439421

Sum squared resid

5.914854

Schwarz criterion

1.624452

Log likelihood

-18.31103

Hannan-Quinn criter.

1.499737

F-statistic

18.47629

Durbin-Watson stat

1.636499

Prob(F-statistic)

0.000001

Appendix 3

LINEAR

Dependent Variable: POV

Method: Least Squares

Date: 12/25/12 Time: 00:37

Sample: 1 31

Included observations: 31

Variable

Coefficient

Std. Error

t-Statistic

Prob.

UNP

0.652591

0.201685

3.235699

0.0032

0.080399

0.200401

0.401191

0.6914

EDU

-3.31E-06

2.51E-06

-1.319228

0.1982

0.215764

1.945153

0.110924

0.9125

R-squared

0.562361

Mean dependent var

2.548387

Adjusted R-squared

0.513735

S.D. dependent var

2.350017

S.E. of regression

1.638731

Akaike info criterion

3.945635

Sum squared resid

72.50683

Schwarz criterion

4.130666

Log likelihood

-57.15734

Hannan-Quinn criter.

4.005950

F-statistic

11.56492

Durbin-Watson stat

1.235022

Prob(F-statistic)

0.000047

Appendix 4

LOG LIN

Dependent Variable: LPOV

Method: Least Squares

Date: 12/25/12 Time: 00:38

Sample: 1 31

Included observations: 31

Variable

Coefficient

Std. Error

t-Statistic

Prob.

UNP

0.231108

0.062474

3.699285

0.0010

0.052514

0.062076

0.845960

0.4050

EDU

-1.02E-06

7.76E-07

-1.313821

0.2000

-0.339204

0.602528

-0.562969

0.5781

R-squared

0.614729

Mean dependent var

0.606235

Adjusted R-squared

0.571922

S.D. dependent var

0.775835

S.E. of regression

0.507611

Akaike info criterion

1.601711

Sum squared resid

6.957056

Schwarz criterion

1.786741

Log likelihood

-20.82652

Hannan-Quinn criter.

1.662026

F-statistic

14.36020

Durbin-Watson stat

1.623072

Prob(F-statistic)

0.000009

Appendix 5

Variance Inflation Factors

Date: 12/25/12 Time: 00:42

Sample: 1 31

Included observations: 31

Coefficient

Uncentered

Centered

Variable

Variance

VIF

LUNP

0.139398

43.84469

3.015521

LIN

0.044557

8.600688

2.010709

LEDU

0.049489

1063.814

3.436163

11.18977

1583.443

Appendix 6

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

0.614228

Prob. F(2,25)

0.5490

Obs*R-squared

1.451939

Prob. Chi-Square(2)

0.4839

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 12/25/12 Time: 00:57

Sample: 1 31

Included observations: 31

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LUNP

-0.311893

0.497118

-0.627402

0.5361

LIN

-0.083088

0.226933

-0.366133

0.7173

LEDU

-0.134766

0.260802

-0.516738

0.6099

2.193871

4.012157

0.546806

0.5894

RESID(-1)

0.269520

0.243239

1.108044

0.2784

RESID(-2)

0.017704

0.224125

0.078990

0.9377

R-squared

0.046837

Mean dependent var

-1.21E-15

Adjusted R-squared

-0.143796

S.D. dependent var

0.444029

S.E. of regression

0.474882

Akaike info criterion

1.520484

Sum squared resid

5.637822

Schwarz criterion

1.798030

Log likelihood

-17.56751

Hannan-Quinn criter.

1.610957

F-statistic

0.245691

Durbin-Watson stat

1.957760

Prob(F-statistic)

0.938058

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