Search results

This paper aims to whether current public expenditure on adult social care services might be associated with the number of delayed days of care attributable to the social care system in England.

Panel econometric models on data from local authorities with adult social care responsibilities in England between 2013–2014 and 2018–2019.

After controlling for other organisational sources of inefficiency, the level of demand in the area and the income poverty amongst the resident older population, this paper finds that a 4.5% reduction in current spending per head on adult social care per older person in one year is associated with an increase by 0.01 delayed days per head the following year.

Given the costs of adverse outcomes of delayed transfers of care reported in the literature, this paper suggests that budgetary constraints to adult social care services would represent a false economy of public funds.

This is the first paper that models the association between public spending on adult social care and delayed transfers of care due to issues originating in the social care system in England.

This chapter applies the five drivers of productivity framework to regional microdata for Egypt and extends it by introducing an index of industrial clusters as an explanatory factor of the productivity performance of local private sector firms. Applying structural equation models, the geographic concentration of sectoral economic activity is found to have a positive and statistically significant effect on labor productivity. The transmission mechanism is conjectured to be the positive spillovers that are created, which local firms can tap into. In contrast, a higher concentration of skilled workers in an industrial sector in a region is associated with lower levels of labor productivity – a finding that suggests there may be structural deficiencies in the allocation of skilled workers. Regional policy should focus on net investments in gross capital formation throughout the country, for which the national and regional governments should improve how public investments are managed and the institutional framework – including the rule of law, bureaucracy and red tape, conflict of interest, transparency, and governance – so that private investment (both local and foreign) may substantially increase.

The purpose of this paper is to present an econometric analysis of hate crime against older people based on data for England and Wales for 2010-2011 disaggregated by Crown Prosecution Service area – a geographical unit which is co-terminus with local authorities.

The authors ran different specifications of structural regression models including one latent variable and accounting for a number of interactions between the covariates.

The paper suggests that the higher the level of other types of hate crime is in an area, the higher the level of hate crime against older people. Demographics are also significant: a higher concentration of older and young people partially explains hate crime levels against the former. Employment, income and educational deprivation are also associated with biased-crime against older people. Conviction rates seem to reduce hate crime against older people, and one indicator of intergenerational contact is not significant.

Due to data availability and quality, the paper only studies one years worth of data. Consequently, the research results may lack generalisability. Furthermore, the proxy variable for intergenerational contact may not be the most suitable indicator; however, there will not be any other indicators available until Census data come out.

The paper suggests that factors underlying hate crime would also influence hate crime against older people. Besides, the results would not support the “generational clash” view. Tackling income, educational and employment deprivation would help significantly reduce the number of episodes of biased criminal activity against older people. Improving conviction rates of all types of hate crime would also contribute to the reduction of hate crime against older people.

This paper presents the first econometric analysis of hate crime against older people.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.