Family and Couple Variables Regarding Adherence in Type 2 Diabetes Patients in the Initial Stages of the Disease

This study analyzed which family and couple variables predicted adherence to standard care treatment, in patients recently diagnosed with type 2 diabetes. The sample comprised 224 dyads assessed during the first year of diagnosis (T1) and 4 months later (T2). The results showed that family stress, dyadic adjustment, family coping, and positive support assessed by patients at T1 predicted medication adherence and glucose monitoring at T2. Positive support and dyadic adjustment, assessed by partners at T1, predicted patients' adherence to glucose monitoring and diet at T2. This study highlights the important role of the partner in patient`s adherence. Therefore, standard care in type 2 diabetes should be offered in the context of the dyad.     

Life Stories of International Romanian Adoptees: A Narrative Study

This study sought to explore the life stories of Romanian adoptees who were internationally adopted following the fall of communism in 1989. Ten participants were recruited via social media and took part in a life stories interview. Data were analyzed using narrative analysis, which led to the development of four life chapters centered around identity construction—Chapter 1: Setting the scene–the adoption story; Chapter 2: Constructing the self; Chapter 3: Who am I? Quest for self-discovery; and Chapter 4: Negotiating the selves. Clinical implications, research limitations, and suggestions for future research are discussed.     

Estimation in a change-point non linear quantile model

This paper considers a non linear quantile model with change-points. The quantile estimation method, which as a particular case includes median model, is more robust with respect to other traditional methods when model errors contain outliers. Under relatively weak assumptions, the convergence rate and asymptotic distribution of change-point and of regression parameter estimators are obtained. Numerical study by Monte Carlo simulations shows the performance of the proposed method for non linear model with change-points.     

The Role of the United Nations Special Rapporteur on the Human Rights of Migrants

The position of Special Rapporteur on the Human Rights of Migrants was established in 1999 by the Commission on Human Rights. During the short period since her appointment, the Special Rapporteur has directed her attention to examining ways and means to overcome obstacles impeding the full and effective protection of the human rights of migrants, and also examining difficulties for the return of migrants who are undocumented or in irregular situations. This schedule has involved visits to governments and the dissemination of information on the legal framework and aspects of the mandate designed to explain the complexity of migration. The Special Rapporteur calls for increased efforts to ensure success of the World Conference against Racism, Racial Discrimination, Xenophobia and Related Intolerance, to be held in Durban, South Africa, in 2001. She also encourages those governments which have not yet ratified the International Convention to take the necessary steps.     

Assessing companies' decent work practices: An analysis of ESG rating methodologies

Environmental, social and governance (ESG) rating agencies, as non-financial data providers, have become central actors in the field of responsible investment. Although research has explored the construction of ESG metrics, little is known about how agencies evaluate decent work. Building on the analysis of six rating agencies, this article investigates how these actors measure and assess companies' performance in terms of decent work and related areas and identifies the challenges they face in this endeavour. The authors seek to better understand the capacity of responsible investment and ESG ratings to promote and improve decent work within companies.     

More Inclusive States,Less Poverty Among Immigrants? An Examination of Poverty,Citizenship Stratification,and State Immigrant Policies

Over the last 20 years, policymaking related to immigrant populations has increasingly been conducted at the state-level. State immigrant polices may influence immigrant poverty by determining immigrants’ level of access to social, economic, political, and health resources and by shaping the social environment. Further, these immigrant policies may shape the stratification between citizens and noncitizens, potentially contributing to distinct patterns of disparities in poverty by both citizenship and race/ethnicity. To assess the relationship between immigrant policy and socioeconomic stratification of immigrants across citizenship status and race/ethnicity in the U.S., we combined data from the 2014 American Community Survey and a measure of level of inclusion of state immigrant policies. We estimated fixed-effects logistic regressions to test the associations between poverty and the interaction of level of inclusiveness, citizenship, and race/ethnicity, controlling for state- and individual-level characteristics. Results showed that there are significant disparities in poverty by citizenship status and race/ethnicity. Asian/Pacific Islander (API) noncitizens experienced lower levels of poverty in states with higher levels of inclusion. Both Latino and API citizens experienced lower levels of poverty in states with higher versus lower levels of inclusion. Among Latinos, the gap in poverty rates between noncitizens and citizens is larger in more inclusive than less inclusive ones, suggesting that the potential positive impact of more inclusive environments does not necessarily translate to the most vulnerable Latino group. The level of inclusion was not associated with differences among Whites and Blacks. Findings suggest that states with more inclusive immigrant policies may foster environments that advance the economic well-being of API noncitizens, as well as API and Latino citizens.     

Change-point detection in a linear model by adaptive fused quantile method

A novel approach to quantile estimation in multivariate linear regression models with change-points is proposed: the change-point detection and the model estimation are both performed automatically, by adopting either the quantile-fused penalty or the adaptive version of the quantile-fused penalty. These two methods combine the idea of the check function used for the quantile estimation and the L₁ penalization principle known from the signal processing and, unlike some standard approaches, the presented methods go beyond typical assumptions usually required for the model errors, such as sub-Gaussian or normal distribution. They can effectively handle heavy-tailed random error distributions, and, in general, they offer a more complex view on the data as one can obtain any conditional quantile of the target distribution, not just the conditional mean. The consistency of detection is proved and proper convergence rates for the parameter estimates are derived. The empirical performance is investigated via an extensive comparative simulation study and practical utilization is demonstrated using a real data example.     

The Elasticity of Demand for Microcredit: Evidence from Latin America

Microcredit demand is frequently assumed to be inelastic, yet understanding the price elasticity of demand for microcredit is highly relevant in designing appropriate microfinance institution (MFI) financial products and policy. This article extracts loan demand schedules and elasticities of MFI borrowers in the Dominican Republic using a unique survey instrument. We analyse the intensive margin of microcredit demand and find that client demand elasticities are not homogeneous and are correlated with certain borrower characteristics. Overall results suggest that these micro‐entrepreneurs, who have already entered the MFI market, have close to unit elastic demand for microcredit. The mean demand elasticity for our sample is ‐1.0.