Social Indicators Research最新文献

Intimate partner violence (IPV) is a worldwide issue. One of the latest developments in its theoretical framework deals with the concept of polyvictimisation – the simultaneous occurrence of physical, emotional, and sexual abuse. However, the literature lacks an overall measure of violence for surveys. The aim of this research is to study IPV within the framework of the ecological model. A model-based composite indicator that takes into account the relationship between domestic abuse and individual characteristics of respondents, family dynamics, and community and societal traits is built using survey data. The data are from the Demographic and Health Survey collected in eleven African countries on women aged 15–49. The employed structural equation model shows the importance of individual characteristics while community and societal factors are less relevant. The composite indicator is also used for classification and ranking purposes, allowing areas where socio-educational interventions are more urgent to be identified.

Is the association between debt and feeling of depression so fundamental that it occurs across time and place? Are some countries better at mitigating the depression related to debt than others? This paper addresses these questions by taking advantage of three harmonised longitudinal surveys, consisting of older adults in 21 European countries and the US. A series of logistic regression models show that, net of differences in other socioeconomic variables, people with household non-mortgage debt have higher odds of depression, measured using dichotomised versions of depressive symptom scores, in all countries. These associations are in many countries as strong as the association between low education level and depression. The association is particularly strong in countries with poor debt discharge legislation or low levels of indebtedness, both of which may be regarded as indicators of stigma related to debts. Overtime the association between debt and depression seems to be elevated within countries when the unemployment rate increases. These findings demonstrate how the links between debt and mental health are embedded in its institutional and economic contexts.

Exogenous events have the potential to affect citizens’ risk-perceptions, (in)security sentiments, electoral choices, policy preferences and institutional trust levels. It has been shown that migrant and refugee flows also affect citizens’ trust and policy preferences. The 2015 refugee crisis as it has come to be known, shook Europe’s cohesion and tested the existing immigration and asylum policies as well as acted as a further stimulus to populist/right wing Eurosceptic parties with an agenda of anti-immigrant restrictive policies. Expectedly, it has attracted considerable attention in the relevant literature. Building on existing studies that examine various aspects associated with the 2015 refugee crisis, the present paper examines its effect on institutional trust levels in Europe. Specifically, it focuses on trust towards the national government, the EU and the two state security services the police and the armed forces. To this effect, the study draws data from successive Eurobarometer surveys before and after the crisis. Employing segmented-type regression models as well as Oaxaca decomposition models it probes into the 2015 refugee crisis’ impact on the levels of institutional trust using over 400,000 responses to the relevant Eurobarometer questions. The results reported herein suggest the presence of “a rally around the flag effect”. Nonetheless, macroeconomic factors such as GDP growth and unemployment as well as differences in behavioural responses also explain a large part of the observed gap in trust before and after the 2015 refugee crisis.

This study introduces an innovative tool to analyse how various inequality factors, including geography, race, and gender, contribute to overall inequality. Traditional approaches typically partition populations into groups based on a single factor and assess inequality by additively decomposing an inequality measure into within- and between-group components. After discussing the theoretical impossibility of additively decomposing the Gini index into within- and between-group components, in fact, we propose a Gini decomposition into two highly informative within- and between-components, with substantial improvement upon the usual assessment of horizontal inequality. This method represents a significant advancement over the traditional horizontal inequality assessment, which only compares group means and overlooks the complexities of differences between groups. Our approach accurately captures the nuances of group disparities, offering a robust measure of horizontal inequality. Through rigorous simulations and empirical analysis of the OECD Income Distribution Database, we validate the effectiveness of our method in evaluating and understanding inequality. This work enriches the toolkit available to researchers in the field by offering a framework for selecting the most suitable measure of horizontal inequality, along with the code for implementing the proposed decomposition.

In this paper we estimate the Head Count Ratio (HCR) and two fuzzy poverty measures at provincial level in Morocco using data from the Household Budget Survey (HBS). As the sample size is not always sufficient to provide reliable direct estimates, we use a Fay–Herriot model with additive logistic transformation and meteorological covariates to obtain estimates with lower mean squared errors. Among our main results, we find out that the Fuzzy Monetary measure provides more accurate estimates than the Head Count Ratio when conducting small area estimation exercises. Also, we empirically notice that the set of covariates at our disposal allows us to obtain better estimates for each supplementary poverty measure that we identify.

Measuring financial capabilities among young people poses a challenge for scholars. In this study, we developed a set of ten scales to assess various behaviors, attitudes, and knowledge, allowing for a comprehensive examination of the construct. We designed a pool of items and invited 746 participants, aged 16 to 29, who were divided into two groups for studying the psychometric properties of the tests. The first group underwent an Exploratory Factor Analysis, while the second group underwent a Confirmatory Factor Analysis. Through these analyses, we identified five dimensions of financial behaviors, four dimensions of financial attitudes, and one dimension of knowledge or financial literacy. The scales were combined to measure two financial capabilities: Financial Saving Capability and Responsible Debt Management Capability. Furthermore, we identified distinct profiles of young people associated with each proposed financial capability. Finally, we offer a conceptual discussion of the financial capabilities construct, present scales with reliable and valid measurements, and propose avenues for future research.

Currently, extensive empirical studies exist on the impact of financial inclusion on socio-economic outcomes such as economic growth, inequality, and poverty. However, research on financial inclusion and population health is limited and still at the infant stage. Therefore, this article adds to knowledge by inquiring into the health implications of financial inclusion using a global panel dataset for 121 countries between 2004 and 2020. After accounting for endogeneity with heteroskedasticity-based instrumental variable regression and cross-sectional dependency with the Driscol–Kraay estimator, we documented that financial inclusion improves population health outcomes. We found that the effect of financial inclusion on population health outcomes also depends on the level of information and communication technology penetration and existing socio-economic conditions in a country. Following this, we showed that financial inclusion improves population health outcomes in countries with lower income inequality and higher GDP per capita, mobile and internet penetration. We further found heterogeneity in the results across geographical regions and income groups. Following these findings, we suggest that financial inclusion policies could enhance population health.

In the context of sustainable development, urban transformation should encourage the use of life cycle thinking tools for infrastructure development. By adopting sustainable economic, social, and environmental criteria, this study presents a review of sustainability assessment tools to build a framework for urban infrastructure. It incorporates a variety of methods that economists and industrial ecologists use for evaluating their projects, in addition to the LCT approach. To recognize the strategic sustainability indicators and criteria, the framework uses LCC, LCA, and SLCA in conjunction with stakeholder evaluation and multi-criteria decision analysis, which recognizes the foremost three pillars of sustainability, i.e., economy, environment, and society. Additionally, system dynamics and agent-based modelling are used to optimise the framework. The prominence of the framework is to understand the route an urban infrastructure development should follow to achieve key sustainability interests involving the interconnected environmental and socio-economic aspects. This sustainability assessment framework can be utilised by consultants or supporting agencies to help organisations carry out and monitor such projects.

Measuring and predicting price dispersion on the real estate market is an important issue for both investors and policymakers. Price dispersion in the housing market can be seen as an additional dimension for measuring social inequality and one of the main goals of public policies that focus on life satisfaction and the accumulation of permanent wealth. The article considers the Polish real estate market and proposes the prediction methods of four measures of dispersion. They are based on quantiles and allow overall measurement of dispersion. In the prediction of dispersion measures the plug-in predictors utylizing longitudinal mixed models are proposed. Furthermore, the ex ante prediction accuracy measure called the quantile of absolute prediction errors (QAPE) is assessed using the residual bootstrap estimators. QAPE allows for a comprehensive description of the distribution of prediction errors, thus fostering discussion of possible various market scenarios.

Amid the rising prevalence of working from home during the COVID-19 pandemic, scholarly interest in the effects of working from home on the fit between work and family life has regained momentum. However, little is known about whether these effects depend on workers’ role salience levels. This study examines the association between the frequency of working from home and two types of work–family conflict: (a) work-to-family conflict (WTFC) and (b) family-to-work conflict (FTWC). We also examine whether these associations are moderated by the salience workers assign to their work and family roles, as well as by workers’ gender and parenting status. To explore these issues, we apply linear regression analyses to data from 4067 employees in Wave 12 (2019–2020) of the German Family Panel Survey. Results show that working from home more frequently is generally associated with both higher WTFC and FTWC for women but not for men. However, among fathers, we found a significant association between working from home and higher WTFC. A moderated association by role salience, where higher work-role salience reduced the positive effect of working from home on FTWC, also only emerged among women. These results suggest that the link between working from home and the fit between one’s work and family is heterogenous: it varies not only by gender and parenthood status, but also partly by the importance workers assign to their various life roles.