Social Indicators Research最新文献

This study aims to investigate social mobility by introducing an innovative data source: surnames in newspapers. The core hypothesis posits that frequent mentions in newspapers are indicative of greater social relevance. Through the collection of local newspaper data in the municipality of Modena, Italy, from 1921 to 2011, we examine variations in the relative representation of surnames in newspapers with respect to their presence in registry data. The results suggest that surnames in newspapers are not a random sample of the population, supporting the assumption that they reflect social significance. Surnames belonging to privileged groups exhibit a higher representation compared to other social groups. This higher relative representation seems to be transmitted to future generations and converges over time toward the mean, with variations depending on the considered high-status group. This kind of analysis could contribute to identifying different mobility patterns at the local level and represents a useful alternative when established data sources, such as income, education, and occupational data, are not available.

The purpose of this article was to analyze the relationship of socioeconomic and educational determinants on life expectancy at birth in the 21 districts of the city of Madrid in 2019. An ecological and cross-sectional study was conducted. The independent variables studied were economic situation, educational level and being a user of public social resources for family care in the city of Madrid. Mean comparison, correlation and multivariate linear regression analysis were performed with the aim of estimating the relationship between these variables and life expectancy at birth in the districts of Madrid. The results show evidence of how the social determinants analyzed by district obtained high levels of association and predictive capacity with respect to life expectancy at birth in Madrid. In this sense, the regression model was able to explain 80.2% of the variance of life expectancy at birth. The study shows that socioeconomic determinants are associated and linearly related to life expectancy in periods prior to the COVID-19 pandemic in the city of Madrid. These results indicate that health policy planning should address social determinants as a starting point to reduce health inequalities and, therefore, to achieve actions aimed at improving the health of all citizens.

This article analyzes the ties of solidarity that exist in the communities that are part of the Purhépecha indigenous region, in Michoacán, Mexico. Through various investigations, we seek to lay the foundations for the construction of an index that would allow the measurement of “social cohesion”, considering that this is generated socially and historically, and is not necessarily determined from a rational-economic vision, but that the bases of social cohesion can be “based on the existence of a collective conscience, c0omposed of a system of values, norms and feelings and ideas common among all the people who make up society” that they are based on the indigenous worldview itself.

In this paper we present the GDL Vulnerability Index (GVI), a new composite index to monitor and analyse the human components of vulnerability to climate change, natural disasters, and other kinds of shocks, for societies and geographic areas across the globe. The GVI is a simple and flexible index designed for use by experts as well as non-experts in the climate field, including researchers, (local) politicians, NGO’s, journalists, advocacy groups and grassroot movements. The GVI is based on an additive formula that summarizes the essence of seven socioeconomic dimensions of vulnerability into a single number. This formula approach sets this index apart from other existing indices. Any person who knows the values of the underlying indicators can compute the vulnerability score of an area by filling in these values in the GVI formula. Validity tests show that the data-driven GVI measures the vulnerability dimensions coping capacity, adaptive capacity and susceptibility as well as major expert-based indices. This offers great prospects for use in situations where no other vulnerability information is available. Here we explain the construction of the GVI, test its validity and present GVI values for (almost) all countries of the world and for major global regions.

The objective of the article is to analyse the degree of persistence of overeducation in the initial stages of the professional career of young workers and the relationship between both the incidence and the persistence of overeducation and the business cycle. We use longitudinal data from administrative records (the ‘Continuous Sample of Working Life’) on the work trajectories of young Spaniards (16–35 years old) who enter the labour market for the first time at various moments in time (2005, 2009 or 2014) and estimate univariate and recursive bivariate probit models. The main findings are that nearly one third of entrants are overeducated in their first job, that their permanence in overeducation five years later is high (above 50%) and that the persistence of overeducation of young entrants is countercyclical.

The World Bank assessed that meeting the goal of eradicating extreme poverty by 2030 appears to be challenging (or even impossible) for the world. This observation requires an urgent need for policymakers to explore potent instruments to combat poverty globally. Numerous studies have examined various determinants of poverty. However, financial literacy—a relatively new concept—remains underexplored, especially on a global scale. As such, this study is conducted to assess whether financial literacy can reduce the likelihood of falling into poverty using a unique dataset of 113 countries. We find that financial literacy has a significant and negative association with the likelihood of falling into poverty. Beyond association, the causal analysis shows that financial literacy exerts a negative effect on poverty. Our findings remain largely unchanged across different sub-samples based on socio-demographic factors, regions and country income levels, and robustness analyses.

In the digital age, not only has the scope of indicator monitoring expanded but also the indicators themselves have become increasingly multilevel. Theoretically, the greater the number of levels, the more uncertainty there is in the indicators’ weights. This necessitates the evaluation of the robustness of scheme comparisons to reduce evaluation risks. By using partial order evaluation methods as analytical tools, this study aims to delineate the principles and methods of partial order representation for single-level indicators. Based on this delineation, it then aims to analyze the representation methods for multilevel indicators. The results reveal that for a two-level indicator system, it is only necessary to clarify the order of weights between the first- and second-level indicators. Through a partial order Hasse diagram, the schemes can be ranked and the robustness of the comparison can be analyzed. Furthermore, three- and four-level indicator systems can be decomposed into several two-level indicator systems to complete the partial order evaluation. A case analysis demonstrates that the partial order uncertainty analysis method can integrate decision-makers’ preferences. The Hasse diagram not only ranks the schemes but also achieves stratified clustering, which intuitively reflects the stability of the schemes.

This study assessed the influence of socioeconomic and demographic indicators on different types of crime and explored the spatial and temporal dynamics of crime. Between 2014 and 2020, 174,365 criminal events registered in Quito, Ecuador, were collected and aggregated at an administrative area level. Time-series decompositions, spatial autocorrelations, and regression models were applied, considering different types of crime as dependent variables. A marked seasonal component of crime and crime hotspots in the center of the study area was identified. Crime events are likely to increase significantly by 2025. We also found that unemployment, schooling, unsatisfied basic needs, and especially the density of bars and night clubs are socioeconomic indicators influencing crime. Urban crimes present specific spatial and temporal patterns, and crime events can be explained by urban socioeconomic conditions.

The concept of Health-Related Quality of Life encompasses various factors that contribute to an individual’s overall quality of life in the context of health care. It is the perception of an individual or group regarding their physical and mental health over a period of time. At the individual level, Health-Related Quality of Life considers self-reported perceptions of health, such as energy levels and mood, as well as other factors such as risks and conditions, functional capacities, social support and socio-economic status. Health-related Quality of Life is determined through the administration of questionnaires that may be generic or disease-specific. These questionnaires have become an essential tool for public health surveillance, as they provide valuable indicators of unmet needs and intervention outcomes. The present work focuses on the development of a questionnaire for Epidermolysis Bullosa, which is a rare disease, and proposes an index that defines a patient-designed Quality of Life that correlates with an index of uneasiness based on the patient’s perspective on the disease.

Food security continues to be a global concern and its importance has recently increased for many reasons. Composite food security indices have been widely used to calculate and monitor food security, but farm-level studies are limited. Therefore, the main objective of this study is to construct a Farm-level Food Security Index (FFSI) for dairy farms to assess their contribution to food security, identify potential areas for improvement and guide policy makers. Data were collected from 126 farms in the Thrace Region of Turkey through face-to-face interviews. The FFSI was constructed with four dimensions, briefly called economic, quality, social and natural resources, containing twenty-three variables. Principal component analysis was used for the determination of variable weights, data envelopment analysis for calculating technical efficiency, and the Tobit model for examining the factors influencing FFSI scores. To assess the robustness of the FFSI, Monte Carlo simulations-based uncertainty and sensitivity analysis, dimension extraction approach and Shapley effects sensitivity analysis were performed. With an average score of 56.8, the key result of the FFSI is that dairy farms are using almost half of their potential to fully contribute to food security. Moreover, according to the Tobit model, FFSI scores are significantly affected by the farmer’s age and education level, credit use, livestock unit, fodder crop area and milk marketing channel. The FFSI is robust to weights and sensitive to normalisation, and the social sustainability dimension can cause the largest shift in index scores. Based on these findings, numerous agricultural policy proposals have been developed in this study by identifying the priority areas that need to be addressed to guarantee food security.