Social Indicators Research最新文献

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.

In promoting China’s comprehensive modernization in pursuit of the grand revitalization of the Chinese nation, the relationship between industry and cities has become increasingly interconnected. National central cities, as the country’s economic, cultural, and financial centers, play a pivotal role in driving the development of their surrounding regions. Therefore, this paper focuses on nine national central cities in China and constructs an assessment index framework for urban–rural integration and modernization. This index system covers two aspects: industrial modernization (with a focus on the modernization of the service sector and industry) and urban modernization (with a focus on the harmonious development of people and nature). Using data from 2021, we determine the weights of individual indicators through the entropy weight method. Moreover, the coupling coordination model is utilized to compute the coupling coordination degree for each city, assessing the level of urban–rural integration and modernization. Using Hubei Province as an illustration, we incorporate the measured coupling coordination degree into the analysis of urban–rural integration effects. Spatial autocorrelation using Global Moran's I is applied to explore the spatial radiation effects of the national central city, Wuhan, on its surrounding cities. Simultaneously, 13 prefecture level cities in Hubei Province were selected as radiation areas, and relevant data from 2017 to 2021 were collected. The Durbin, robust regression, and panel model were employed to analyze the economic radiation impact, learning effect, and transportation accessibility effect of Wuhan as a national central city on surrounding cities. Our findings unequivocally demonstrate the paramount role of the service sector in shaping urban–rural integration, as evidenced by Shanghai emerging with the highest level of such integration and modernization, while Zhengzhou lags with the least progress. Moreover, the innovative prowess inherent in Wuhan, which functions as a national central city, exerts a positive influence on the innovation capacity witnessed in its radiating vicinity. Concurrently, the transportation accessibility quotient between peripheral cities and the central city manifests a positive correlation with the economic development level within the radiation zone. These results furnish invaluable insights into strategies aimed at elevating the echelon of urban–rural integration and modernization within national central cities.

Aim of the paper is to analyse the occurrence of occupational stress across European Union countries, considering gender and job sustainability as determinants, with a specific attention to the effects of home-based work. Although COVID-19 pandemic has brought such issues into a novel spotlight, to detect the response pattern towards occupational stress we chose to employ the latest official data collected by the Sixth European Working Condition Survey developed and carried out in a pre-COVID-19 scenario. This information may provide a reliable picture of working conditions, which are likely to become the “new normal” across Europe, at least for a subset of workers. Descriptive analyses do not seem to help disclosing any different response behaviour with specific respect to reported stress by gender, even when combined with the condition of working from home. Whereas a noteworthy finding of our study is that results from the implemented ordered probit model display that some differences in the response pattern do exist and are even substantial. A question still arises about whether and to what extent hybrid forms of work are here to stay and even to grow in the post-pandemic period. Some of the critical features of teleworking-from-home emerged during the epidemic indicate that the implementation of policies at a national and, ideally, even supra-national level is clearly necessary. However, since both occupations and company organizations are strongly differentiated, it seems also that the enterprises are allowed some flexibility in defining corporate policies for teleworking practices, especially aiming at providing workers with improved and more sustainable working conditions, such as a less distressing environment and more supportive managerial styles.

The spatial clustering of life satisfaction scores noted in recent empirical research suggests that ‘happier’ people may live in specific neighbourhoods or regions. This prompts the questions: Do ‘happier’ people choose to move to specific places? Does living in specific places make people ‘happier’? To answer these questions, this paper explores possible occurrences of selective mobility, and social and ecological influence. Using panel data collected in Australia from 2013 to 2021, we examine the association between life satisfaction scores and selective geographic mobility, and the possible influence that living at specific locations may have on individual life satisfaction trajectory, while controlling for individual personality traits and socio-demographic factors. Our results indicate that urban residents reporting lower life satisfaction scores before the move have a higher probability of moving to a rural area. Similarly, lower life satisfaction scores are associated with a higher probability of moving to a region with a different climate. We also find evidence that moving from the city to the country is associated with an uplift of the life satisfaction trajectory for the individual. A similar conclusion is reached for people who moved to a warmer climate, but not for a move to a cooler climate. To our knowledge, this is the first time the concepts of selective mobility and social and ecological influence have been applied in life satisfaction research. Our work provides an indicator that can be important to demographers predicting population movements. It can also inform policy development around assisting regional and rural areas attract/ retain residents to support regional sustainability.

This research provides an overview of the challenges in analyzing multidimensional social exclusion data using multiple indicators. It highlights the importance of composite indicators in simplifying the understanding of complex realities. Grounded in this literature, the research proposes a new approach to address the issues related to the multispatial and multitemporal analysis of composite indicators, contributing to the existing body of knowledge in this area. To illustrate its potential, social exclusion measures proposed by the Center for Studies and Mapping of Social Exclusion for Public Policies are used for two southern Brazil municipalities. This framework considers demographic, economic, educational, and household dimensions and fourteen variables. The proposed approach offers two significant contributions: firstly, it prevents outliers from heavily influencing the normalized sub-indicators and composite indicators during the scale transformation process. Secondly, it provides a solution compatible with the three-dimensional nature of the problem, thereby enhancing the multitemporal analysis of composite indicators.

Interest in the study of economic insecurity has grown in recent years. However, the ongoing debate about how to measure it remains unresolved. On the assumption that economic insecurity is related both to the forward-looking perception of future outcomes based on past experience and to the perception of one’s own situation compared to others in the present, we propose a class of objective individual composite inter-temporal indices of economic insecurity. The indices are obtained by combining two components, one longitudinal and one cross-sectional. In order to combine the two components, we propose a novel method that takes advantage of the availability of subjective self-assessments of one’s own economic conditions. The composite inter-temporal index is applied to the European Union-Statistics on Income and Living Conditions (EU-SILC) Longitudinal Dataset, encompassing a selection of European countries. Our analysis shows that the proposed class provides new insights into individual perceptions of well-being that are not captured by poverty and inequality measures. It also provides individual measures that can be used to study the relationship between economic insecurity and other phenomena.