Abstract
Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding and accounting of spatial liaisons are needed in order to effectively forecast regional economic variables. In this research household deprivation (HD) is viewed from the perspective of living standards index (LSI). Historically, governorates in Iraq suffered inequalities of living standards for many reasons, such as government’s focus on the heart of the city, specifically Baghdad, Basra, and Nineveh. Question is raised whether the spatial pattern of HD is existed in Iraq? If so, can household economic status (HES) and education indexes explain this pattern?The objective is to investigate spatial structure of LSI and its spatial correlation to spatial structure of each of HES and education. This investigation will provide implications for policy makers, finding local clusters and showing visual picture for each of LSI, HES and education.The study utilizes a cross-sectional census data collected in 2004 for 18 governorates. Mapping is used as the first step to conduct visual inspection for LSI using quartiles. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically referenced data. Two statistics of spatial autocorrelation, based on sharing boundary neighbours, known as global and local Moran's I, are carried out. Wartenberg’s measure is used to detect bivariate spatial correlation.The hypothesis of spatial clustering for LSI was confirmed by a positive global Moran’s I of .34 with p= .002 and permutation p=.006, while for education was not confirmed by a negative global Moran’s I of -.16 with p=.451 and for HES was confirmed by a positive global Moran’s I of .28 with p= .010. Bivariate spatial correlation between LSI and education wasn’t found significant .09 with p=.165 and between LSI and HES was found significant .31 with p=.002.In conclusion, based on visual inspection of mapping global clustering was found in LSI on the northern and southern parts. It was confirmed by the significant global Moran’s I statistic. Out of 18 governorates; eight, seven, and one were found as local clusters in LSI, HES, and education respectively based on local Moran's Ii. The bivariate spatial correlation between LSI and education was not found significant while between LSI and HES was found significant.
Highlights
Spatial statistics relates to the analysis of the spatial aspect of data sets
In a spatial analysis conducted in Madrid, it was found that mortality increased with deprivation (Pozo et al, 2010).Exploratory spatial data analysis conducted by Sridharan, Koschinsky, and Walker (2011) revealed concentrations of high standardized mortality ratios and deprivation in the West of Scotland and concentrations of low values for both variables in the rest of the country
The highest estimated factor loadings of factor 1 were appeared for infrastructure, housing conditions, and housing (.99, .82, and .62 respectively).This strong interaction between these variables reflects the effect on living standards index (LSI)
Summary
Spatial statistics relates to the analysis of the spatial aspect of data sets. The reasons why spatial statistics are of import for many areas are threefold: (i) Data that are spatially close, are usually more similar than those that are further apart. It is often understood in terms of material goods and resources but applicable to psychological factors. The concept of poverty refers to the lack of income and other resources, that make those conditions inescapable or at least highly likely (Townsend, 1987). Hooghe et al (2011) investigated the impact of deprivation indicators on crime in Belgian municipalities for the period 2001–06. A spatial regression analysis used by Hooghe et al demonstrated that unemployment figures have a strong and significant impact on crime rates. In a spatial analysis conducted in Madrid, it was found that mortality increased with deprivation (Pozo et al, 2010).Exploratory spatial data analysis conducted by Sridharan, Koschinsky, and Walker (2011) revealed concentrations of high standardized mortality ratios and deprivation (hotspots) in the West of Scotland and concentrations of low values (coldspots) for both variables in the rest of the country
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