Abstract
The study of segregation of deprivation can provide a tool to determine the economic, social and institutional factors associated with spatial unevenness in the distribution of wealth. Segregation is linked to social exclusion, diminished opportunities for human capital development and lower access to public services. In comparison to descriptive measures of poverty segregation, a multilevel structural equation modelling approach allows us to make statistical inferences about segregation, and to assess the extent to which segregation can be explained by contextual variables. Previous research using multilevel models to analyse segregation is extended to handle a continuous latent variable, measured by multiple binary indicators. The proposed approach is used to quantify the extent to which household deprivation is clustered within communities in Bolivia and to explore contextual factors associated with between-community differences in deprivation. Bolivia had one of the worst performances in poverty headcount ratio and chronic malnutrition in Latin America in the first decade of the twenty-first century, according to World Bank data. Bolivia is found to have a high level of segregation, since the main source of variation in deprivation arises from differences across communities, rather than within communities. Ethnicity, education, administrative region, distance to urban centres, and drought-induced migration significantly predict differences in the mean level of deprivation across Bolivian villages. This analysis helps to identify clusters of deprivation and highlights crucial sectors to be developed in order to reduce unevenness in the distribution of deprivation.
Highlights
Segregation can be defined as a form of physical separation where population groups are isolated into different neighbourhoods or schools, “shaping the living environment at the neighbourhoods [or school] level” (Kawachi and Berkman 2003).Geographical clustering of deprived people is commonly associated with economic, ethnic, or physical segregation, being the consequence of variation in characteristics under study across areas
This paper proposes a general structural equation modelling (SEM) approach to the study of geographical segregation, by extending the multilevel modelling approach proposed by Goldstein and Noden (2003) to handle constructs measured by multiple indicators
The proposed multilevel SEM approach is applied in a study of deprivation segregation in Bolivia, a country that presented among the highest indicators of poverty and deprivation in Latin America (Coa and Ochoa 2009)
Summary
Geographical clustering of deprived people is commonly associated with economic, ethnic, or physical segregation, being the consequence of variation in characteristics under study across areas. Segregation of deprivation may be related to social exclusion, with important consequences for social and health policies. In Bolivia, for instance, social exclusion has been identified as a possible mechanism through which individuals belonging to certain ethnic groups reside in areas that tend to have lower education and income (Gray-Molina et al 2002). There is some evidence that the opportunities and even the conduct of people residing in certain neighbourhoods is shaped, among other factors, by the characteristics of their neighbourhood (Jencks and Mayer 1990). The analysis of deprivation and poverty segregation can help to identify the most deprived areas, which are economically and socially isolated from the more developed areas. Since a higher mortality rate and higher exposure to infectious diseases is likely to be found in contexts of concentrated deprivation (Fiscella and Franks 1997; Szwarcwald et al 2002), reducing the differences in deprivation among communities might be associated with the better health outcomes
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