The Participation of Civil Society Organizations in Public Policies in Latin America

Throughout Latin America, the relationship between government and civil society organizations (CSOs) has been characterized by opposition, substitution, and submission; and, the incipient path to cooperation is barely noticeable. For their part, participatory public policies make sense within a theory of democratic governance. Democratic governance seeks two propositions: (a) participation from other social actors will give rise to more efficient government action; and (b) citizen support will emerge from the said government action. This paper criticizes the current relationships between the governments and CSOs in this region. In addition, it explores the potential strategies that could be adopted were there a cooperation between these two entities. The paper is supported by theoretical literature as well as by a revision of some cases of participatory public policies that are currently active in the region. The paper proposes that the strategies of opposition to government and government substitution have to be abandoned in this region. The paper focuses on civil organizations (CSOs). It is true that they do not constitute the entirety of civil society; however, they are frequently the most organized compared with other civic actors, such as social movements, families, and individual initiatives. CSOs form only a part of the diversity known as civil society; however, they significantly contribute to the discussion about the public good, and very often they participate in providing such goods. The future of participatory democracy in Latin America is related to our ability to achieve a more complete participation of CSOs in the entire process of participatory public policies—from the formation of public agendas to their design, implementation, and evaluation.     

Understanding multi-methodology: Evaluating the perceived impact of mixing methods for group budgetary decisions

This paper reports on the design and implementation of a multi-methodology intervention intended to support a budget prioritisation decision by a multi-organisational group tasked with tackling the problem of teenage pregnancy in an English borough. The intervention approach involved the combined use of cognitive/causal mapping and a multi-criteria decision analysis method to develop and prioritise a number of projects aimed at alleviating issues associated with teenage pregnancy. The paper describes and discusses the process of developing and applying the intervention approach, and provides an evaluation of its perceived impact by the client group. Drawing on the rich seam of data gathered over the course of our work with the multi-organisational group, we explore the varying degrees of impact that the mapping and multi-criteria evaluation methods achieved during and after the intervention. Analysis of the intervention data suggests that both methods were perceived to facilitate a different way of thinking and learning. In addition, we find that the mapping and multi-criteria evaluation methods successfully addressed participants' democratic and rational concerns. However, despite these reported positive effects, the full recommendations of the intervention were not implemented. An attempt is made to explain this outcome in terms of both the multi-organisational context within which the intervention took place and the nature of the group task. Directions for further research are then proposed.     

Improved Approximation for Breakpoint Graph Decomposition and Sorting by Reversals

Sorting by Reversals (SBR) is one of the most widely studied models of genome rearrangements in computational molecular biology. At present, is the best known approximation ratio achievable in polynomial time for SBR. A very closely related problem, called Breakpoint Graph Decomposition (BGD), calls for a largest collection of edge disjoint cycles in a suitably-defined graph. It has been shown that for almost all instances SBR is equivalent to BGD, in the sense that any solution of the latter corresponds to a solution of the former having the same value. In this paper, we show how to improve the approximation ratio achievable in polynomial time for BGD, from the previously known to for any > 0. Combined with the results in (Caprara, Journal of Combinatorial Optimization, vol. 3, pp. 149–182, 1999b), this yields the same approximation guarantee for n! – O((n – 5)!) out of the n! instances of SBR on permutations with n elements. Our result uses the best known approximation algorithms for Stable Set on graphs with maximum degree 4 as well as for Set Packing where the maximum size of a set is 6. Any improvement in the ratio achieved by these approximation algorithms will yield an automatic improvement of our result.     

A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.     

A Bayesian analysis for the Block and Basu bivariate exponential distribution in the presence of covariates and censored data

In this paper, we introduce a Bayesian Analysis for the Block and Basu bivariate exponential distribution using Markov Chain Monte Carlo (MCMC) methods and considering lifetimes in presence of covariates and censored data. Posterior summaries of interest are obtained using the popular WinBUGS software. Numerical illustrations are introduced considering a medical data set related to the recurrence times of infection for kidney patients and a medical data set related to bone marrow transplantation for leukemia.     

The Role of Survey Data in Microsimulation Models for Social Policy Analysis

Questions about the impact of social policies cannot be answered solely on the basis of official statistics made available in tabular format. The access to microdata allows policy analysts to answer these questions with a larger set of analytical tools, in particular microsimulation models. This paper examines the interface between survey data and microsimulation models. We review different types of microsimulation models (static and dynamic) and their data requirements. We then restrict the attention to survey-based microdata, and examine issues in survey design, questionnaire content, data quality, and dissemination policy that are important from the perspective of microsimulation.     

Psychophysical conditions of air traffic controllers evaluated by the standard shiftwork index

Abstract

In the ambit of a large epidemiological survey on the Italian population of air traffic controllers, an evaluation of their health conditions was carried out using the Standard Shiftwork Index (SSI). The study took into consideration 572 male air traffic controllers, aged between 23 and 59 years, of whom 301 were engaged in three large Regional Centres, 189 in thirteen Airport Towers and 82. formerly active controllers, were working in administrative offices. The former controllers showed worse health levels, in particular minor psychological disorders and chronic fatigue. Sixteen of them had been transferred to administrative jobs because of health impairment, owing to severe diseases of the nervous, gastrointestinal and cardiovascular systems. Such disorders, and musculoskeletal ones, were mostly reported by the active controllers. The controllers engaged in Regional Centres showed more sleep disturbances than those engaged in towers. Beside age, some personal characteristics appeared to influence health and well-being. Neuroticism was correlated with chronic fatigue, digestive and cardiovascular complaints, minor psychological disorders, cognitive and somatic anxiety. On the other hand, morningness was correlated negatively with sleep disturbances on morning shift, and positively with sleep duration both on morning shift and rest days.     

Chain graph models: topological sorting of meta-arrows and efficient construction of $${\mathcal{B}}$$ -essential graphs

Essential graphs and largest chain graphs are well-established graphical representations of equivalence classes of directed acyclic graphs and chain graphs respectively, especially useful in the context of model selection. Recently, the notion of a labelled block ordering of vertices was introduced as a flexible tool for specifying subfamilies of chain graphs. In particular, both the family of directed acyclic graphs and the family of “unconstrained” chain graphs can be specified in this way, for the appropriate choice of . The family of chain graphs identified by a labelled block ordering of vertices is partitioned into equivalence classes each represented by means of a -essential graph. In this paper, we introduce a topological ordering of meta-arrows and use this concept to devise an efficient procedure for the construction of -essential graphs. In this way we also provide an efficient procedure for the construction of both largest chain graphs and essential graphs. The key feature of the proposed procedure is that every meta-arrow needs to be processed only once.     

Population and Deforestation in Costa Rica

This paper addresses a central debate in research and policy on population and environment, namely the extent to which rapid population growth is associated with the massive deforestation currently underway in the tropics. We utilize the experience of Costa Rica during the last forty years to illustrate what the main issues are, discuss the history of deforestation in that country, and present results from conventional regression methods and from the application of spatial analyses. These analyses enable us to estimate the magnitude of the relation between population and deforestation and to identify the factors that are responsible for the linkage between them.