Social Indicators Research最新文献

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.

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.

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.

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.

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.

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.

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.

The study investigates the role of digital-government (DG), government utilization, and regional integration on public health services (PHS) by considering E-government and globalization. This study takes public services fragility as a proxy for public health services. In contrast, E-government development as a DG globalization index (GI) has been taken as a proxy of regional integration, and government expenditures (GE) as a fiscal state capacity. The study employed a two-step system generalized method of moments estimation for the sample of 45-panel Asian economies from 2006-to-2022. The results reveal that DG substantially impacted and improved the PHS in Asian economies in the past decade. Moreover, regional integration added fuel to this progression and substantially influenced the PHS. However, GE adversely affects the PHS due to lousy governance and leakage of target spending. Furthermore, the novel DG integration with GE and GI promoted PHS and reduced health fragility through better resource utilization and technology deployment. It also reveals that DG helps in reducing the loopholes of GE and makes the resource implementation transparent and effective, which impacts the PHS. It concludes that these interactions with public policies play a prominent role in comprehensive coverage and healthcare accessibility in Asia through technology deployment with prudent administration strategies. It’s a novel study that integrates digitalization with regional integration and government expenditures from an Asian perspective by considering PHS, which made this study helpful for policy drafting during the COVID-19 pandemic and proposed a better framework to deal with future calamities.

Trust is important for a range of societal outcomes. Despite its significance, there is considerable debate about how best to measure trust. In the context of a newly developed measure of generalized trust—the Stranger Face Trust scale (SFT)—this study evaluates whether different features of survey scales affect the reliability and validity of SFT, which relies on the standard 4-point unipolar scale used by many survey institutes. In a survey experiment conducted with a non-probability sample of U.S. adults (N = 4252), we randomly assigned intensity scale midpoints, polarity, and “don’t know” options to SFT. Results indicate that 7- and 9-point bipolar scales without a “don’t know” option slightly outperform all other scales on some psychometric tests, particularly those related to formal properties of the scales and factorial validity, but not on psychometric tests assessing survey environment or convergent, discriminant, and concurrent validity.

In this article, latent variable analysis is used to construct hybrid measure of political development based on the plausible common variation between objective and subjective indicators of political institutions. For a sample of 167 countries for the period 1810–2018, we chart long-term paths of political development. Our empirical strategy attempts to overcome the existing potential bias in the measures of democracy in the long run by extracting the institutional characteristics of political regimes, voter turnout, expert-based assessments and electoral outcomes into two latent indices of political development that can be compared both across space and time. The evidence reveals the remarkable persistence of multiple peaks in the world distribution of political development and uncovers contrasting long-term trajectories across countries traditionally featured in the political economy literature. Our findings add to the current debate about measurement of democratic backsliding.