IntroductionMigrants without residency permit, known as undocumented, tend to live in precarious conditions and be exposed to an accumulation of adverse determinants of health. Only scarce evidence exists on the social, economic and living conditions-related factors influencing their health status and well-being. No study has assessed the impact of legal status regularisation. The Parchemins study is the first prospective, mixed-methods study aiming at measuring the impact on health and well-being of a regularisation policy on undocumented migrants in Europe.Methods and analysisThe Parchemins study will compare self-rated health and satisfaction with life in a group of adult undocumented migrants who qualify for applying for a residency permit (intervention group) with a group of undocumented migrants who lack one or more eligibility criteria for regularisation (control group) in Geneva Canton, Switzerland. Asylum seekers are not included in this study. The total sample will include 400 participants. Data collection will consist of standardised questionnaires complemented by semidirected interviews in a subsample (n=38) of migrants qualifying for regularisation. The baseline data will be collected just before or during the regularisation, and participants will subsequently be followed up yearly for 3 years. The quantitative part will explore variables about health (ie, health status, occupational health, health-seeking behaviours, access to care, healthcare utilisation), well-being (measured by satisfaction with different dimensions of life), living conditions (ie, employment, accommodation, social support) and economic situation (income, expenditures). Several confounders including sociodemographic characteristics and migration history will be collected. The qualitative part will explore longitudinally the experience of change in legal status at individual and family levels.Ethics and disseminationThis study was approved by the Ethics Committee of Geneva, Switzerland. All participants provided informed consent. Results will be shared with undocumented migrants and disseminated in scientific journals and conferences. Fully anonymised data will be available to researchers.
Mesurer la performance des institutions de microfinance (IMF) n'est pas une tâche aisée. Étudier la seule capacité financière d'une IMF est en effet insuffisant, puisque cela ne constitue qu'une facette de sa performance. De nombreuses IMF étant initialement créées dans l'objectif d'aider les plus pauvres, il est en fait nécessaire de tenir compte d'aspects sociaux. La performance des IMF est par conséquent multidimensionnelle. Ce papier illustre une approche moderne pour évaluer la performance des IMF. L'analyse factorielle est utilisée dans un premier temps afin de construire des indices de performance basés sur de multiples combinaisons de variables potentielles. Les variables de base sont ainsi combinées pour produire plusieurs facteurs contenant chacun une dimension différente de performance. Les scores factoriels assignés à chaque IMF peuvent ensuite être utilisés comme variables dépendantes d'un modèle à équations simultanées. Cette méthodologie nous permet de présenter de nouveaux résultats concernant les facteurs déterminant la performance des IMF.
The measurement of poverty has often been criticized for relying solely on measures of financial deprivation. Poverty being a multidimensional state, related to health, schooling, living environment, psychological state as well as social tides, care should be taken to integrate these various components to have a proper picture of poverty. This is especially true for rich countries where poor financial conditions are often alleviated by social policies like minimum income, unemployment or housing benefits. Social exclusion and poor health can therefore dominate the poverty feeling. We illustrate how some descriptive statistical tools can offer new insights in the context of multidimensional poverty. Factor analysis is used in a first step to construct poverty indicators based on many possible dimensions without posing too many a priori restrictions. The base variables are thus combined to produce common factors which convey some aspect of multidimensional poverty. By ascribing individual scores on each factor, we then use cluster analysis to determine population's subgroups that are unevenly affected by the various dimensions of poverty, what allows us to identify the poor. Finally, a logit regression is run to find the determinants of poverty.
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