1. No category

IS33- ID547 ISI RSC 2014, Proceeding Internatinional Statistical Institute Regional Statistics Conference
2014, Bank Negara Malaysia, Kuala Lumpur
WHERE ARE PENINSULAR MALAYSIA’S MOST DEPRIVED AREAS?
Soo-Fen Fam1, Noriszura Ismail2, Abdul Aziz Jemain3
1
Universiti Teknikal Malaysia Melaka [email protected]
2,3
Universiti Kebangsaan Malaysia [email protected] ; [email protected]
Abstract
The country’s economy should be allocated optimally and effectively so that all
levels of the communities receive appropriate economic distribution. To ensure
the achievement of this goal, a suitable method that can be used to describe such
efforts is needed. Among the measures that can be undertaken by policymakers is
to make decisions based on Deprivation Indices (DI). The use of DI in the
planning and allocation of resources have long been used by developed countries
such as United States, Canada, Europe, New Zealand and Australia. However, this
approach has not received attention in Malaysia. Therefore, this study develops DI
by using principal component analysis method (PCA) namely the General Index
of Deprivation (GID) to determine the socio-economic deprivation level of 81
administrative districts (ADs) in Peninsular Malaysia. For this purpose, the
Population and Housing Censuses in 1991, 2000 and 2010, Vital Statistics,
juvenile data and health data are utilized. The results of GID indicate that the
ADs can be ranked and classified into four quartiles; the most affluent, the
moderately affluent, the moderately deprived and the most deprived. Analysis by
choropleth maps depict that the majority of affluent areas were located in the
westcoast of Peninsular Malaysia whereas the most deprived areas were mainly
scattered in the northeast of Peninsular Malaysia for the three censuses year
respectively. These phenomena indicate the existence of patchiness and strong
spatial disparities of socioeconomic deprivation in several ADs. Thereby, GID
denotes the importance of considering geographic localization and spatial
condition of each AD for allocating resources and implementing efficient policies
in Peninsular Malaysia.
Keywords:
1
Deprivation index; Principal component analysis; Correlation
INTRODUCTION
Malaysia is a country that is rich in diversity with multiracial, multi religious and
multicultural population. It is indeed a nation of diversity in unity where various
ethnic groups practicing different religious live together and strive for peace,
success and harmony. To preserve the prosperity, wellbeing and security enjoyed
over the years and as a lesson emitting from the racial riot in Kuala Lumpur on 13
May 1969, inequality of socioeconomic has received much attention and has
become one of the goals in the New Economic Policy established in 1972 which
was later on known as National Development Policy (NDP) in 1991. This policy
is designed to eradicate poverty and reduce inequality of socioeconomic across
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the nation. Many socioeconomic indicators, either single indicator or composite
indices, have been devised with the aim of depicting socioeconomic information
about geographic areas across states in Malaysia. Currently there are two
composite socioeconomic indices that have received attention from government
agencies and policy makers. These two indices are the Malaysian Urban
Indicators network (MURNInet) and Human Development Index (HDI).
MURNInet, proposed by Department of Town and Country Planning, is used to
gauge town sustainability. On the other hand, HDI is a composite index employed
by UNDP for measuring key dimensions of human capabilities (Leete, 2006).
Although these indices significantly conceptualize the spatial dimension for
measuring variation of socioeconomic status, no study has been done for small
area geographic units to depict the socioeconomic status across areas in Malaysia.
The objective of this study is to develop a pioneer composite area-based index of
socioeconomic deprivation in small area geographical unit, namely the General
Index of Deprivation (GID) by using principal component analysis method
(PCA). In order for greater understanding and interpretation of the distribution of
socioeconomic patterns across 81 administrative districts (ADs) in Peninsular
Malaysia by relating the ADs to the newly developed GID, choropleth maps are
provided to visualize and identify the preliminary scenario.
In the coming sections, we will discuss on the background and uses of deprivation
indices. Next, we will proceed to describe the construction of the pioneer
socioeconomic deprivation index. Data is analyzed using R freeware statistical
package (version 2.9.1), Microsoft Excel 2007 and SPSS statistical package
(version 17.0). The results are presented in tables and spatial analyses are
illustrated by choropleth maps using R freeware. Overall, this paper documents a
modest investigation on the developed index for Peninsular Malaysia, including
identifying change over time when using the three censuses data. Discussion and
conclusion are available in the last section.
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DEPRIVATION INDEX
Deprivation index is an ecological approach used to measure socioeconomic
conditions in specific geographical areas to overcome the problem of absence of
individual level data. In addition, it is very simple, inexpensive and is generally
composed of easily available census variables (Testi and Ivaldi et al. 2008;
Bartley and Blane 1994). Therefore it has been favored by many researchers.
There are many uses of deprivation indices. Deprivation indices have been used to
capture health inequalities (Havard et al. 2008; Mcintyre et al. 2002; Pampalon
and Raymond 2000), to facilitate the allocation of resources (Abas et al. 2003;
Salmond et al. 2007), to predict the breastfeeding duration (Brown et al. 2009)
and to measure individual’s satisfaction with own life (Bellani and D’Ambrosio
2011). Some researchers have used deprivation index and investigate its
relationship with mortality (Sridharan et al. 2007; Rey et al. 2009; Choi et al.
2011) while other researchers have related deprivation with crime (Kawachi et al.
1999).
2
There are several techniques to construct composite indices such as simple
additive method and PCA method. Simple additive method was used in 1980s by
researchers from Europe such as Townsend (1987) and Carstairs (1989) to
develop composite indices of deprivation describing material deprivation. Since
1990s studies have reverted to the use of Principal Component Analysis (PCA) to
construct area-based deprivation indices. A few examples would be the
development of the index of deprivation in New Zealand namely NZdep91,
NZdep96, NZdep2001 and NZdep2006 with three principal purposes in mind:
resource allocation, research, and advocacy (Crampton et al. 1997; Salmond et al.
1998; Salmond and Crampton 2002, Salmond et al. 2007). In Canada, PCA was
used to develop a material and social deprivation index to facilitate the monitoring
of social inequalities for health planning (Pampalon et al. 2009).
The construction of deprivation index by employing simple additive method using
Townsend’s approach has been discussed in our previous paper (Fam et al. 2011).
Thus in this paper, we proposed the use of an alternative method -PCA - to
construct a composite index of deprivation for Peninsular Malaysia.
According to Felipe and Resende (1996) and Kallmann (1997), one of the most
notable of the multivariate techniques employed in composite indexing is the
principal component analysis – which is deployed in this paper. The plus point of
this method is that it assigns each variable a weight that takes the relations
between the variable into account (Havard et al. 2008). It also provides an easy
procedure of letting the data to determine the optimal weights which accounts for
the largest proportion of the variance (Ram 1982). Compared with other statistical
method, PCA is computationally easier and the data used can be easily collected
in household surveys (Jobson 1992). Another advantage of PCA is its flexibility
when it comes to adding the number of variables for deriving a composite index,
as it imposes very little cost on the analysis and the researcher could also include
many related variables for deriving the principal components (Ram 1982). In
order to ensure the accuracy of the deprivation index, Gordon (1995) advises that
component variables should be additive and differential weighting should be
assigned to variables. The approach used in this study, PCA, is the statistical
technique that fulfils these two criterions (Mcintyre et al. 2002).
3
MATERIALS AND METHODS
3.1
Data
The data analyzed in this study was provided by Department of Statistics,
Malaysia. The data analyzed comprises of census data and administrative
registration data for 81 administrative districts in Peninsular Malaysia for the
years 1991, 2000 and 2010.
3.2
Variables
The initial variables of interest for possible inclusion in the PCA in this study was
mainly based on the literature review. In addition, the selected variables had to
fulfill the following criterions:
i) Relevance to the concept of deprivation in the Malaysia context.
ii) Availability of data by administrative districts.
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iii) Compatibility and applicability of data over time.
After taking these criterions into consideration, 23 variables that are eligible to
describe deprivation in Peninsular Malaysia were identified for the three censuses
year respectively.
3.3
Final selection of indicators to be included in PCA
PCA work optimally when variables are correlated (Vyas and Kumaranayake
2006). Before undertaking PCA, the initially selected variables were subjected to
Pearson correlation to examine the correlation between the twenty three variables.
Variables that are highly correlated with each other in which the correlation
coefficients are significant at 1% level were retained to be included in the PCA, as
suggested by Alderman and Morris (1967).
3.4
Calculation of deprivation index
To compute deprivation index, we must first get the component score. The
component score for a particular AD for a particular component is the sum of each
variable’s z-score multiplied by its component score coefficient for that particular
component. The z-score is the standardized score of each variable. The component
score coefficients were derived from the first principal component. Variables with
higher loadings have larger component score coefficient. In this paper, the
component scores were estimated through the dimension reduction by using SPSS
procedure. Each AD was given a score on each component extracted. To compute
the deprivation index, these component scores were later taken in combination
with the proportion of variance explained by each component as weight.
3.5
Division of 81 ADs into four quartiles according to their index values
The ADS were then ranked and classified into four quartiles (Q1-Q4) according to
their respective index values with each quartile has an approximately equal
number of ADs (around 20ADs per quartile). Quartile 1 represents the most
affluent area and quartile 4 represents the most deprived area.
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RESULTS
From the Pearson bivariate correlation matrix, only 13 variables were highly
correlated with each other at the 1% significant level for the over the three period
of time. These variables were retained to be included in the PCA. The coefficients
of the thirteen variables of the first principal component, after standardization are
provided in Table 1. It can be seen that all coefficients are positive and similar,
indicating that the thirteen variables contribute similar weights to the first
principal component. In 1991, the highest weight is 0.315 contributed by
percentage of private household without a refrigerator, followed by percentage of
population without education carrying the weight 0.311 then percentage of private
household without a fixed telephone line with the weight 0.303. Whereas for year
2000, the highest weight is 0.307 contributed by percentage of private household
without a fixed telephone line, followed by percentage of private household
without a refrigerator and total dependency ratio both with the same weight 0.303.
In 2010, the highest weight is 0.331 contributed by total dependency ratio,
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followed by percentage of private household without a toilet with flush system
carrying the weight 0.329 and percentage of population without education with
the weight 0.323. The proportion of the variation explained by the first principal
component for the three censuses are 71.99%, 70.08% and 62.58% respectively.
Table 1: Coefficients for the thirteen variables and proportion of the variation
explained from the first principal component for the three censuses
Coefficient
Variables
1991
2000
2010
0.289
0.303
0.331
0.29
0.295
0.235
Percentage of private household without a radio
0.286
0.277
-
Percentage of private household without a television
0.289
0.25
-
Percentage of private household without a fixed telephone line
0.303
0.307
-
Percentage of private household without a refrigerator
0.315
0.303
-
Percentage of private household without a washing machine
0.296
0.293
-
Percentage of unemployed population aged 15-64 years who are
economically active
0.277
0.256
-
Percentage of private household without treated piped water
0.267
0.262
0.227
Percentage of private household without a toilet with flush
system
0.299
0.278
0.329
Percentage of population without education
0.311
0.295
0.323
Percentage of population aged 10 years and above without
receiving SPM certificate
0.063
0.226
-
Relative risk of infant mortality
0.223
0.246
0.28
Relative risk of neonatral
-
-
0.242
Relative risk of stillbirth
-
-
0.248
Percentage of private household without a handphone
-
-
0.264
Percentage of private household without internet service
-
-
0.313
Percentage of private household without an air-conditioner
-
-
0.321
Percentage of private household
facilities
-
-
0.32
Total dependency ratio (100 population aged 15-64 years)
Percentage of private household without a car
without
Proportion of the variation explained
waste disposal
71.99%
70.08%
62.58%
The three censuses GID are then applied to the dataset of eighty-one ADs in
Peninsular Malaysia. The ADs are ranked and classified into four quartiles,
quartile 1, quartile 2, quartile 3 and quartile 4, corresponding respectively to the
most affluent, the moderately affluent, the moderately deprived and the most
deprived ADs. In 1991, quartile 1 ranging from -6.299to -1.797, quartile 2 ranging
from -1.797 to -0.424, quartile 3 ranging from -0.424 to 1.861, and quartile 4
ranging from 1.861 to 6.789. For 2000, quartile 1 ranging from -4.569 to -2.078,
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quartile 2 ranging from -2.078 to -0.419, quartile 3 ranging from -0.419 to 1.974,
and quartile 4 ranging from 1.974 to 7.845. Whereas for year 2010, quartile 1
ranging from -5.678to -1.748, quartile 2 ranging from -1.748 to 0.101, quartile 3
ranging from 0.101 to 2.352, and quartile 4 ranging from 2.352 to 5.404, Further
illustration is showed in Figure 1 where the lighter shades indicate areas with
relatively lower level of deprivation and the darker shades indicate areas with
relatively higher level of deprivation.
Figure 1 General Index of Deprivation for census year (a) 1991 (b) 2000 (c) 2010
The chorepleth maps in Figure 1 depicted the most deprived areas or the areas of
the fourth quartile were mainly scattered in the northeast of Peninsular Malaysia,
where the majority were located in Kelantan and Terengganu, which also
comprised rural areas over the three period of time. Table 2 shows the ranking of
20 most deprived areas in Peninsular Malaysia for year 1991, 2000 and 2010.The
most deprived AD, which was indicated by the highest GID, were Setiu, Gua
Musang and Kuala Krai for the three census years respectively.
Setiu is an AD in the state of Terengganu was ranked the most deprived area in
1991 but had moved to 7th position in year 2000 and 2010. Bachok an AD from
Kelantan was ranked second position for year 1991 and 2000, but has moved to
5th position in year 2010. Gua Musang was ranked in 3rd position in year 1991 but
unfortunately it became the most deprived area in Peninsular Malaysia in year
2000. However in year 2000, Guat Musang has moved to a better position in year
2010 with the ranking number 6.
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5
CONCLUSION
The 2010 GIDs of the eighty-one ADs in Peninsular Malaysia imply that the
majority of affluent areas were located in the westcoast of Peninsular Malaysia,
whereas the most deprived areas were mainly scattered in the northeast of
Peninsular Malaysia.
In Malaysia, several initiatives have been implemented by government and
policymakers to reduce poverty and deprived areas across the nation. In particular,
raising living standards of low-income households has been selected as one of the
government’s six important pillars, or National Key Result Areas (NKRAs),
which is included in a special programme called the Government Transformation
Programme (GTP), newly launched in 2010 (Government Transformation
Programme Roadmap 2010). Examples of initiatives and activities to reduce
poverty and materially deprived areas across the nation which have been
implemented in 2009-2011 by the government through the GTP were the
allocation of federal welfare assistance to those qualified on the first day of each
month, the provision of support to increase home ownership by offering about
44,000 low-cost houses, the delivery of special programmes under 1 Azam which
create and provide jobs, trainings and funds, the creation of employment
opportunities via setting up Jimat 1Malaysia stores, and the increment of number
of urban, rural and mobile medical clinics to cater for low-income households.
Therefore, the GID proposed in this study provides an effective tool for
identifying the most deprived areas in either national, state or district levels, and
can be used as a starting point to consider an in-depth study of a smaller area level
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of deprivation. The GID can also be used as a practical tool in the activities and
initiatives of managing the polarity between the economically well-off and the
deprived to ensure that no Malaysian is left behind. However, geographic
localization and spatial condition of each AD is important to be taken into account
in the process of allocating resources and implementing efficient policies, since
there is a strong evidence of global and spatial autocorrelation in socioeconomic
deprivation across the ADs.
ACKNOWLEDGEMENTS
The authors are grateful to Department of Statistics, Malaysia (DOSM) for
providing the census data for this study.
DISCLAIMER
The responsibility for the use and interpretation of these data is solely that of the
authors. The opinions expressed in this paper are those of the authors and do not
represent the views of DOSM.
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