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 1 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. 2 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. 3 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. 4 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, 4 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, 5 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. 6 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 7 of deprivation. 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