Ethnic poverty and social vulnerability data sources from the census of the United States and the American community survey

Includes bibliography

The UK government's approach to monitor its strategy to tackle poverty and social exclusion: opportunity for all

Includes bibliography

A Dinâmica agrária do município de Ortigueira(PR) e a reprodução social dos produtores familiares: uma análise das comunidades rurais de Pinhalzinho e Vila Rica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Hambre y cohesión social: cómo revertir la relación entre inequidad y desnutrición en América Latina y el Caribe

Incluye Bibliografía

Social Panorama of Latin America 1994

This third edition of the Social Panorama of Latin America is an expression of the ECLAC secretariat's continuing effort to incorporate the social dimension into the Commission's annual appraisals of regional development. The analysis presented in this edition emphasizes core issues concerning children and the familiy, as a result of the secretariat's joint activities with the United Nations Children's Fund (UNICEF), in order to provide up-to-date information on opportunities for access to well-being from childhodd onwards. This report is prepared periodically by Statistics Development Division of ECLAC, which collaborated with the Economic Development Division in producing the present edition. The information analysed yields an ilustrative profile of trends in the early 1990s in important facets of social development such as poverty, income distribution, employment, social expenditure, children, the family, education, pay levels and a social agenda of the main issues in this field that have captured public attention in the countries of the region during the past year.

An assessment of big data for official statistics in the Caribbean: Challenges and opportunities

The data revolution for sustainable development has triggered interest in the use of big data for official statistics such that theUnited Nations Economic and Social Council considers it to be almost an obligation for statistical organizations to explore big data. Big data has been promoted as a more timely and cheaper alternative to traditional sources of official data, and one that offers great potential for monitoring the sustainable development goals. However, privacy concerns, technology and capacity remain significant obstacles to the use of big data. This study makes a case for incorporating big data in official statitics in the Caribbean by highlight the opportunities that big data provides for the subregion, while suggesting ways to manage the challenges. It serves as a starting point for further discussions on the many facets of big data and provides an initial platform upon which a Caribbean big data strategy could be built.

De movimento social comunitário à institucionalização: análises sobre o Programa Municipal de Alfabetização e Cidadania - PROMAC

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Grandes projetos urbanos, segregação social e condições da moradia em Belém e Manaus

O objeto central da pesquisa que deu origem a esta tese de doutorado foi analisar os efeitos segregativos que os grandes projetos urbanos provocam sobre as condições de moradia nas metrópoles amazônicas de Belém e Manaus. Adotam-se as projeções teóricas que interpretam a dinâmica urbana enquanto produto da acumulação do capital e que gera segregação social, numa perspectiva que permite comparar intervenções urbanas nessas duas metrópoles. Em cada uma dessas cidades, grandes projetos urbanos estão sendo implantados. Para efeito deste estudo, as experiências do Projeto Portal da Amazônia, em Belém-Pará e o Programa de Saneamento Ambiental dos Igarapés Manaus (PROSAMIM), na cidade Manaus-Amazonas, foram analisados como experiências de grandes projetos urbanos na Amazônia. O referencial teórico-metodológico teve a contribuição das teorias produzidas pela Escola Sociológica francesa, as anglo-saxônicas e as brasileiras, permitindo a construção de um pensamento crítico sobre a lógica que permeia os grandes projetos urbanos nas metrópoles amazônicas. Para isso, elegeu-se um procedimento operacional do tipo quali-quantitivo, tendo em dados primários e secundários as principais fontes de informação, materializadas por documentos históricos, oficiais, dados estatísticos, observação direta e realização de entrevistas com lideranças dos movimentos em defesa da moradia e da reforma urbana, moradores das áreas afetadas direta e indiretamente pelos programas em estudo e agentes de órgãos públicos. Os principais resultados são a constatação de que nas metrópoles amazônicas o processo de urbanização vem se dando desde o final do século XIX, com o advento da economia gomífera, intensificando-se a partir do Golpe Militar de 1964, quando foram fortalecidos os processos de exploração de recursos naturais e de adensamento populacional, com consequentes alterações físico-territoriais em Belém e Manaus. Nos anos recentes, as duas cidades vêm acompanhando o movimento de globalização do capital, ao adotarem os grandes projetos urbanos como a principal estratégia de renovação urbana, com suporte técnico e financeiro do Banco Interamericano de Desenvolvimento (BID). Por fim, estes resultados apontaram efeitos segregativos, determinados pela implantação destes grandes projetos urbanos, uma vez que as ações de deslocamento compulsório impactaram de forma negativa a condição de moradia e trabalho de expressivas frações das classes trabalhadoras, tornando inacessível o Direito à Cidade, tanto em Belém como em Manaus.

A saúde de jovens mães: desafios para os sitemas de proteção social

Pós-graduação em Serviço Social - FCHS

A inserção social do banco dos cocais no município de São João do Arraial - PI

Pós-graduação em Geografia - IGCE

Spatio-temporal patterns of tuberculosis incidence in Ribeirao Preto, State of Sao Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

Introduction: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirao Preto, State of Sao Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. Methods: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. Results: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirao Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. Conclusions: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.