Understanding the Dimensions of Education Inequality in China at Different Geographical Scales

Although education equality has been valued and frequently discussed by scholars from different disciplines, theoretical discussions and empirical studies of education equality from a geographical perspective have been somewhat limited. Since the traditional two-dimensional non-spatial framework for measuring and analysing education inequality is inadequate, the research in this thesis is based on a more comprehensive and flexible three-dimensional framework, in which geography is included as an important dimension. China is used as the case study country to examine education inequalities at different geographical scales. At a regional scale, a multidimensional Index of Regional Education Advantage (IREA), underpinned by Amartya Sen’s capability approach, is introduced to evaluate the effectiveness of policies targeted at reducing regional/provincial educational inequalities in China since 2005. At a local scale, the thesis explores the use of geodemographics as a means of assessing potential inequality in access to compulsory education within urban areas. The thesis argues that applying an area classification, one of the first in China, allows consideration of multi-dimensional, socio-spatial influences which affect school choice within urban areas. The ideas are illustrated through a case study of Central Beijing. At the micro scale, multilevel modelling is used to reveal the influence of contextual factors and confounding individual level socio-economic characteristics on pupils’ travel distance to school in Beijing. The results at the regional scale revealed that education in north-eastern China is better than in the south-west of the country, a pattern which lacks conformity with the eastern, middle and western macro-divisions adopted by Central Government as the basis of policy implementation. Furthermore, the social and spatial disparities in terms of access to education facilities within urban areas were also identified. This research has, for the first time, revealed education inequality in China comprehensively from a geographical perspective, and provides some unique insights and crucial policy implications of education inequalities in China at different geographical scales

Extending geodemographics using data primitives: a review and a methodological proposal

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods