A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

This paper proposes a methodology for measuring Quality of Employment (QoE) deprivation from a multidimensional perspective in six Central American countries (Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama) using a dataset specifically designed to measure employment conditions. Building on previous work on multidimensional poverty and employment indicators, the paper uses the Alkire/Foster (AF) method to construct a synthetic indicator of the QoE at an individual level. It selects four dimensions that must be considered as essential to QoE deprivation: income, job stability, job security and employment conditions. These dimensions then subdivide into several indicators, a threshold for each indicator and dimension is established before defining an overall cut-off line that allows for the calculation of composite levels of deprivation. The results generated by this indicator show that Central American countries can be divided into three distinct and robust performance groups in terms of their QoE deprivation. Overall, approximately 60% of the deprivation levels are attributable to non-income variables, such as occupational status and job tenure. The methodology used can allow policymakers to identify and focus on the most vulnerable workers in a labour market and highlights the fact that having a formal written contract is no guarantee of good job quality, particularly in the case of women.

     

Indigenous perspectives for strengthening social responses to global environmental changes: A response to the social work grand challenge on environmental change

ABSTRACT

The “Grand Challenges for Social Work,” is a call to action for innovative responses to society’s most pressing social problems. In this article, we respond to the “Grand Challenge” of Creating Social Responses to a Changing Environment from our perspective as Indigenous scholars. Over the last several decades, diminishing natural resources, pollution, over-consumption, and the exploitation of the natural environment have led to climate change events that disproportionately affect Indigenous peoples. We present how environmental changes impact Indigenous peoples and suggest culturally relevant responses for working with Indigenous communities. We propose a decolonizing cyclical, iterative process grounded in Indigenous Ways of Knowing.     

Minority parents’ coping efforts to improve their children’s academic achievement: the Tanodas in Hungary

ABSTRACT

Our paper is based on a study carried out among students of Hungarian tanodas – second chance educational institutions in Hungary – and their parents. The findings of the interview research did not support deficit models and suggest that schooling is a vital issue for both Roma/Gypsy families and their children. All families have limited resources and opportunities to achieve desired goals, and despite their strong intent and determination, they often seem to lack a clear vision of the steps to be taken. Our results show that children have to cope with negligence, stigmatization, exclusion, discrimination, etc. Turning to tanodas for help has become a sort of coping strategy in and of itself. We could identify, as common parental strategies, the ‘squeezing out’ of the maximum available scarce opportunities, building on social networks, and monitoring their children’s studies. Students’ coping mechanisms regarding identity-threats were more varied and ranged from confrontation via proxy control and social support, to disengaging from school and taking on a negative identity or, the opposite, taking up positive roles.     

The use of macroinvertebrates and algae as indicators of riparian ecosystem services in the Mexican Basin: a morpho-functional approach

Urban Ecosystems - Peri-urban rivers have been subjected to poor environmental management, especially in developing countries, due to urban expansion and channel transformation in the context of a...     

Building relationships on social networking sites from a social work approach

ABSTRACT

Our current age of connectedness has facilitated a boom in interactive dynamics within social networking sites. It is, therefore, possible for the field of Social Work to draw on these advantages in order to connect with the unconnected by strengthening online mutual support networks among users.

The aim of this article is to examine whether ‘connectedness’ in social networking sites improves online social capital and resilience of social service users.

Through our analysis of social networks carried out on an experimental model, we observed the patterns of connectedness on Facebook of 50 social service end-users from Málaga, Spain. The detection of online communities through the modularity algorithm has allowed us to ascertain whether individuals’ offline realities mirror their online realities. At the same time, we examined the influence certain interactions (likes, comments, etc.) have on leadership through online ethnography. Finally, online social capital, understood as the combination of connectedness and online interaction, has been correlated with users’ resilience. The results reveal that both connectedness and interaction feed themselves and have correlations with resilience.     

Bayesian space-time modeling of malaria incidence in Sucre state, Venezuela

Malaria is a parasitic infectious tropical disease that causes high mortality rates in the tropical belt. In Venezuela, Sucre state is considered the third state with most disease prevalence. This paper presents a hierarchical regression log-Poisson space-time model within a Bayesian approach to represent the incidence of malaria in Sucre state, Venezuela, during the period 1990–2002 in 15 municipalities of the state. Several additive models for the logarithm of the relative risk of the disease for each district were considered. These models differ in their structure by including different combinations of social-economic and climatic covariates in a multiple regression term. A random effect that captures the spatial heterogeneity in the study region, and a CAR (Conditionally Autoregressive) component that recognizes the effect of nearby municipalities in the transmission of the disease each year, are also included in the model. A simpler version without including the CAR component was also fitted to the data. Model estimation and predictive inference was carried out through the implementation of a computer code in the WinBUGS software, which makes use of Markov Chain Monte Carlo (MCMC) methods. For model selection the criterion of minimum posterior predictive loss (D) was used. The Moran I statistic was calculated to test the independence of the residuals of the resulting model. Finally, we verify the model fit by using the Bayesian p-value, and in most cases the selected model captures the spatial structure of the relative risks among the neighboring municipalities each year. For years with a poor model fit, the t-Student distribution is used as an alternative model for the spatial local random effect with better fit to the tail behavior of the data probability distribution.     

Correction to: Vertical and Horizontal Inequality in Ecuador: The Lack of Sustainability

Social Indicators Research - The article Vertical and Horizontal Inequality in Ecuador: The Lack of Sustainability, written Ivan Gachet, Diego F. Grijalva, Paúl A. Ponce and Damián...     

Nonparametric Forecasting in Time Series—A Comparative Study

The problem of predicting a future value of a time series is considered in this article. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables, smoothing parameter selection, and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behavior of the methods is also illustrated by a detailed analysis of two data sets.     

Illicit Financial Flows: Another Road Block to Human Development in Low- and Middle-Income Countries

Social Indicators Research - This article analysed the relationship between illicit financial flows (IFFs) and human development, as measured with the United Nations Human Development Index (HDI),...     

Four comments for the paper from Liu et al. (2019)

We give comments for the paper from Liu et al. (2019) about the Item Response Theory (IRT) model under consideration, the justification to compute Marginal likelihood, about what we learn with the data analysis performed and finally, about the computational issues in this paper.