Social Indicators Research最新文献

{"title":"Bullying Among Pupils at School and a Country’s Educational System: An Efficiency Evaluation of Educational Performance in Europe by Means of an Extended Data Envelopment Analysis","authors":"Kouhei Kikuchi, Soushi Suzuki, Peter Nijkamp","doi":"10.1007/s11205-024-03376-x","DOIUrl":"https://doi.org/10.1007/s11205-024-03376-x","url":null,"abstract":"<p>A strong and attractive educational system serves normally as a cornerstone for enhancing a nation’s long-term socio-economic development potential. In recent years, bullying among pupils or students has become a pressing issue in many schools, with significant negative repercussions for both pupils (or students) and their educational environment. Bullying not only diminishes the quality of school education but also erodes the students’ motivation and wellbeing. Thus, it plays a critical role in educational performance, prompting an urgent need for an assessment of its negative implications. This paper seeks to design and test a new model-based approach to evaluate the negative role of bullying at school in educational performance. A prominent avenue of evidence-based research on the quantitative evaluation of educational outcomes can be found in the use of Data Envelopment Analysis (DEA), a multidimensional comparative assessment tool for judging the efficiency of a set of relevant decision-making agents by examining the ratio of outputs to inputs. Among the various applications of DEA, the Distance Friction Minimization (DFM) approach has emerged as a promising tool. Nevertheless, the conventional DFM approach has also a serious limitation: it considers normally only one input and one output element in its projection. To address this shortcoming, this paper introduces a new Improved Ratio Minimization (IRM) approach. The IRM method overcomes the above-mentioned constraint, by allowing for the distribution of efficiency improvement projections among all input and output items contributing to efficient outcomes. Subsequently, this paper seeks to demonstrate the practical relevance of the IRM approach in DEA by applying it to an assessment of educational efficiency, with a particular focus on the effects of bullying in secondary education in European countries. Drawing from an extensive international dataset, the IRM-DEA model generates a variety of comparative empirical findings regarding the overall wellbeing and efficiency loss caused by bullying among students in European countries. The paper also explores new policy avenues for enhancing educational performance in the context of bullying at school across Europe.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"25 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foreign Direct Investment, Income Inequality and Pollution Reduction: Policy Trilemma for India","authors":"Soumen Rej, Barnali Nag, Md. Emran Hossain","doi":"10.1007/s11205-024-03390-z","DOIUrl":"https://doi.org/10.1007/s11205-024-03390-z","url":null,"abstract":"<p>India, the third largest carbon-emitting country currently faces a three-dimensional policy challenge on one hand enhancing economic growth through foreign direct investment (FDI) and reducing income inequality and on the other hand complying with its intended nationally determined contributions (INDC) commitment to reducing carbon emissions. This study aims to contribute to the current policy discourse of India by examining the asymmetric and symmetric effects of income inequality and FDI on carbon emissions with the augmentation of non-linear and linear autoregressive distributive lag model technique and using time series data from 1990 to 2021. Findings indicate that positive shock on income inequality reduces emissions, while the same on FDI increases emissions. Further, negative shock on both income inequality and FDI shows an insignificant influence on carbon dioxide emissions. The study not only confirms the presence of the pollution haven hypothesis for India but also provides evidence of conflict between the sustainable development goal (SDG-10) of reducing income inequality and the goal of climate change mitigation (SDG-13). In addition, the human development index has been found to aggravate carbon emissions. The study highlights the policy challenges of harmonizing India's SDGs with its economic growth. It suggests significant policy changes to strategically prioritize foreign direct investment projects that are in line with SDG13.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"25 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Income, Ill-being, and Gender Inequality: Explaining Cross-National Variation in the Gendered Risk of Suffering Among the Poor","authors":"Satoshi Araki, Francisco Olivos","doi":"10.1007/s11205-024-03358-z","DOIUrl":"https://doi.org/10.1007/s11205-024-03358-z","url":null,"abstract":"<p>Scholars have long investigated the positive link between income and well-being, including its gender difference. However, little is known about (1) how low income is linked to ill-being among women and men; and (2) how their association varies depending on societal-level gender (in)equality. Filling this knowledge gap is crucial not only for scholarship but for social policy to tackle income-based disparities of ill-being. In this study, using the European Social Survey and the joint European Values Study-World Values Survey data, we conduct country-specific regressions and cross-national multilevel analyses to examine the relationship between low income, subjective ill-being (SIB), and macro-level gender parity. We first confirm that low-income individuals, regardless of gender, are more likely than their affluent counterparts to suffer from SIB in many countries. This indicates the applicability of implications derived from conventional approaches focused on the positive association between higher income and better well-being to the studies on low income and SIB. Nevertheless, the SIB risk significantly differs depending on the degree of gender inequality in that (1) both women and men face a higher likelihood of SIB in gender-inegalitarian societies; and importantly, (2) the psychological penalty for the poor is intensified under such gendered circumstances, especially among men. These results suggest that gender inequality not merely induces women’s ill-being but punishes low-income men possibly by exacerbating pressure as a breadwinner and imposing stigmas when they cannot meet gendered social expectations.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"2015 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional Multidimensional Attitudes Towards Immigration: Evidence from the European Social Survey Using Small Area Estimation","authors":"Angelo Moretti, Anisa Ahmed","doi":"10.1007/s11205-024-03381-0","DOIUrl":"https://doi.org/10.1007/s11205-024-03381-0","url":null,"abstract":"<p>The present article is the first empirical investigation of attitudes towards immigration in Europe via small area estimation providing reliable regional estimates across European regions. Four dimensions of regional attitudes are considered, i.e., restrictive attitudes towards specific groups, restrictive attitudes towards acquired criteria, threat, and restrictive attitudes towards ascribed criteria. We produce maps of these, as well as validation outputs, demonstrating that our estimates are reliable, hence, have a strong potential in informing policy makers. We show that, although there is a large between-country variation of these attitudes, there are also large spatial differences between-region in some countries. Overall, Swedish public attitudes tend to be quite homogeneous across regions, and located towards the positive side, whereas Eastern European countries tend to show negative attitudes across all the dimensions apart from the acquired criteria. However, in these countries, we can see larger spatial differences across regions, especially in the ascribed criteria and attitudes towards specific groups indicator. In general, the threat dimension does not show a large between-region variability, compared to the other three dimensions.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring Wellbeing Across Culture and Context – are we Getting it Right? Evaluating the Variation in Wellbeing Conceptualisations Throughout the World","authors":"Kate Sollis, Nicholas Biddle, Herdiyan Maulana, Mandy Yap, Paul Campbell","doi":"10.1007/s11205-024-03382-z","DOIUrl":"https://doi.org/10.1007/s11205-024-03382-z","url":null,"abstract":"<p>Researchers, practitioners, and policy makers have been increasingly interested in measuring wellbeing over the last two decades. However, with many measurement tools and frameworks being replicated in contexts distinct from where they were developed, it raises the question as to whether we are measuring the right things. This study draws on data collected through a systematic review of participatory wellbeing frameworks to better understand how wellbeing conceptualisations differ based on country context throughout the world. This analysis is one of the first of its kind, enabling a deeper and more comprehensive insight into cross-cultural understandings of wellbeing. The findings indicate that while there is some degree of universality in how wellbeing is conceptualised in different country contexts, cross-cultural variation is also evident. These findings have important implications for wellbeing measurement throughout the world, indicating that researchers, practitioners, and policymakers should exercise some caution when utilising wellbeing measurement tools and frameworks that were developed in contexts distinct from the population of interest. Furthermore, this study highlights the value of participatory approaches in better understanding these nuanced conceptualisations of wellbeing within different population groups throughout the world. Having greater awareness of cross-cultural differences in wellbeing conceptualisations will help ensure that we are more closely measuring what matters to people.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"14 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organizational Benefits of Commuting Support: The Impact of Flexible Working Hours on Employees’ OCB through Commuting Control","authors":"Zhiyi Gan, Jinhua Gan, Zhiqing E. Zhou, Hanying Tang","doi":"10.1007/s11205-024-03378-9","DOIUrl":"https://doi.org/10.1007/s11205-024-03378-9","url":null,"abstract":"<p>Flexible working hours has many benefits in improving employee’s in-role performance, and thus become more popular in modern cities. However, we knew little about the relationship between flexible working hours and organizational citizenship behavior (OCB). To clarify the question, we constructed a moderated-mediation model to examine the mechanism of commuting control. A total of 284 full-time employees took part in this study. We found that: (1) Employees with flexible working hours had higher levels of commuting control; (2) Then, higher levels of commuting control predicted higher levels of OCB; (3) Commuting control fully meditated the association between flexible working hours and employees’ OCB; (4) Commuting control and road unimpeded interaction to influence OCB. That is, the relationship between commuting control and OCB was stronger when roads were clear. These results not only provide a new explanation of the impact of flexible working hours on commuting control and OCB, but also help to broaden commuting research. These findings additionally have implications for the government and organizations.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"20 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of Artificial Intelligence on Financial Inclusion: Cross-Country Analysis","authors":"Yogeeswari Subramaniam, Nanthakumar Loganathan, Fatin Nur Hidayah Taib Khan, Thirunaukarasu Subramaniam","doi":"10.1007/s11205-024-03380-1","DOIUrl":"https://doi.org/10.1007/s11205-024-03380-1","url":null,"abstract":"<p>This study uses panel data from 29 countries that were categorised from low to high in terms of AI adoption from 2017 to 2021 to investigate the impact of artificial intelligence on financial inclusion. The study employed both static and dynamic Generalized Method of Moments (GMM) panel data estimations to achieve the research objective. The findings show that artificial intelligence is a statistically significant determinant of financial inclusion and helps promote financial inclusion in countries that adopt artificial intelligence. Besides that, robustness analysis conducted for alternative measures of AI, and the results continue to demonstrate that AI contributes to financial inclusion by addressing some of the issues that have historically made it difficult for some groups to receive financial services. As a result, significant expansion, and the deployment of artificial intelligence in the finance sector are required to overcome existing financial exclusion and promote financial inclusion. and solve the existing financial exclusion issues.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"11 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intergenerational Educational Mobility in Brazil: Trends and Patterns","authors":"Thiago Henrique Leite, Marina Silva da Cunha","doi":"10.1007/s11205-024-03387-8","DOIUrl":"https://doi.org/10.1007/s11205-024-03387-8","url":null,"abstract":"<p>Socioeconomic mobility across generations is an important measure of how developed and open to equal opportunities a nation is. Understanding the mechanisms behind intergenerational mobility is essential for the implementation of effective public policies toward economic development. This study aimed to measure intergenerational mobility specifically in education, using data from the National Household Sample Survey for the year 2014, where 9 birth cohorts were utilized to obtain regression coefficients, correlation coefficients, and the decomposition of the correlation coefficient. Among the main findings, an increase in intergenerational mobility over time was observed in both the regression coefficient and the correlation coefficient. Additionally, there was limited mobility among groups such as women and non-white individuals. Regarding regions, the Northeast region experienced significant growth in mobility, no longer being the region with the highest persistence over time. As for the type of persistence, it became composed of parents and children who have the same level of education, such as high school and higher education.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subjective–Objective Method of Maximizing the Average Variance Extracted From Sub-indicators in Composite Indicators","authors":"Matheus Pereira Libório, Alexandre Magno Alvez Diniz, Douglas Alexandre Gomes Vieira, Petr Iakovlevitch Ekel","doi":"10.1007/s11205-024-03385-w","DOIUrl":"https://doi.org/10.1007/s11205-024-03385-w","url":null,"abstract":"<p>This research presents an innovative method for constructing composite indicators: the Subjective–objective method of maximizing extracted variance (Sommev). Sommev’s hybrid weighting approach fills an important gap within a highly controversial area of the composite indicators’ literature, which criticizes the statistical assignment of weights disconnected from theory and the errors and judgmental biases inherent in the expert opinion-based weighting approach. These innovations contribute to a more coherent and consistent operationalization of the theoretical framework of multidimensional phenomena, reconciling the non-compensability between sub-indicators and the maximum retention of original information through statistically defined weights, in which the expert’s opinion is considered, but does not determine the sub-indicator’s weights. Twenty simulations were carried out to analyze the application of the method in representing social exclusion in a Brazilian city. Composite indicators constructed by Sommev retain twice as much information as those constructed with equal weights or weights defined by experts. This increased informational capacity favors a more comprehensive representation of the multidimensional phenomenon, having a high potential for application in solving problems of a multidimensional nature in the social, economic, and environmental areas.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"52 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts","authors":"Yuan Zhao, Ronak Paul, Sean Reid, Carolina Coimbra Vieira, Chris Wolfe, Yan Zhang, Rumi Chunara","doi":"10.1007/s11205-024-03386-9","DOIUrl":"https://doi.org/10.1007/s11205-024-03386-9","url":null,"abstract":"<p>We consider the availability of new harmonized data sources and novel machine learning methodologies in the construction of a social vulnerability index (SoVI), a multidimensional measure that defines how individuals’ and communities may respond to hazards including natural disasters, economic changes, and global health crises. The factors underpinning social vulnerability—namely, economic status, age, disability, language, ethnicity, and location—are well understood from a theoretical perspective, and existing indices are generally constructed based on specific data chosen to represent these factors. Further, the indices’ construction methods generally assume structured, linear relationships among input variables and may not capture subtle nonlinear patterns more reflective of the multidimensionality of social vulnerability. We compare a procedure which considers an increased number of variables to describe the SoVI factors with existing approaches that choose specific variables based on consensus within the social science community. Reproducing the analysis across eight countries, as well as leveraging deep learning methods which in recent years have been found to be powerful for finding structure in data, demonstrate that wealth-related factors consistently explain the largest variance and are the most common element in social vulnerability.</p>","PeriodicalId":21943,"journal":{"name":"Social Indicators Research","volume":"67 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}