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This research explores how consumers respond to Corporate Social Responsibility (CSR) in China with a multiproduct, comparative survey. Empirical results conclude that (1) Chinese consumers, who show a high level of awareness and trust of CSR, are more likely to transform a good CSR record into positive corporate evaluation, product association, and purchase intention; (2) Consumer responses to CSR vary across different product categories. Those firms selling experience products (vs. search and credence products) are more likely to gain consumers' positive product associations and purchase support through CSR practices; and (3) The relationships between consumer demographics and their CSR responses are not linear, and those consumers with a middle level of age and income would respond to CSR more positively. Managerial implications are provided.

It has long been hypothesized that individuals' migration propensities depend on their risk attitudes, but the empirical evidence has been limited and indirect. We use newly available data from the German Socio-Economic Panel to measure directly the relationship between migration and risk attitudes. We find that individuals who are more willing to take risks are more likely to migrate. Our estimates are substantial compared to unconditional migration probabilities, as well the effects of conventional determinants of migration, and are robust to controlling for a variety of demographic characteristics. We find no evidence that our results are the result of reverse causality.

Electroencephalography (EEG) has proven to be effective for detecting major depressive disorder (MDD), with deep learning models further advancing its potential. However, the performance of these models may be limited by their neglect of demographic factors (e.g., age, sex, and education), which are known to influence EEG characteristics of depression. To address this, we propose DIFNet, a deep learning framework that dynamically fuses EEG features with demographic indicators (age, sex, and years of education) to enhance depression recognition accuracy. DIFNet is composed of four modules: a multiscale convolutional module, a Transformer encoder module, a temporal convolutional network (TCN) module, and a demographic indicator fusion module. The fusion model leverages convolution to process demographic vectors and integrates them with spatiotemporal EEG features, thereby embedding demographic indicators within the deep learning model for classification. Cross-validation between data trials showed that the DIFNet fusing age and years of education achieves a superior accuracy of 99.66%; the dynamic fusion mechanism improves accuracy by 0.72% compared to the baseline without fusing demographic indicators (98.94%), outperforming state-of-the-art methods (SparNet 94.37% and DBGCN 98.30%).

Demographers study population change across time and place, and traditionally they place a strong emphasis on a long-range view of population change. This paper builds on current reflections on how to structure the study of population change and proposes a two-stage perspective. The first stage, discovery, focuses on the production of novel evidence at the population level. The second stage, explanation, develops accounts of demographic change and tests how the action and interaction of individuals generate what is discovered in the first stage. This explanatory stage also provides the foundation for the prediction of demographic change. The transformation of micro-level actions and interactions into macro-level population outcomes is identified as a key challenge for the second stage. Specific instances of research are discussed.

Demographic research frequently reports consistent and significant associations between formal educational attainment and a range of health risks such as smoking, drug abuse, and accidents, as well as the contraction of many diseases, and health outcomes such as mortality—almost all indicating the same conclusion: better-educated individuals are healthier and live longer. Despite the substantial reporting of a robust education effect, there is inadequate appreciation of its independent influence and role as a causal agent. To address the effect of education on health in general, three contributions are provided: 1) a macro-level summary of the dimensions of the worldwide educational revolution and a reassessment of its causal role in the health of individuals and in the demographic health transition are carried out; 2) a meta-analysis of methodologically sophisticated studies of the effect of educational attainment on all-cause mortality is conducted to establish the independence and robustness of the education effect on health; and 3) a schoolingcognition hypothesis about the influence of education as a powerful determinant of health is developed in light of new multidisciplinary cognitive research.

This study investigates the key factors shaping smartphone choice among Azerbaijani consumers, emphasizing demographic characteristics. A questionnaire survey was conducted among smartphone users (n=312) from Azerbaijan, and the results were analyzed using an independent sample T-test to identify statistically significant differences between groups based on gender, and education level. In the analyzed sample, women slightly outnumber men, and students make up the largest group highlighting a tech-savvy generation that's deeply engaged with smartphones. The analyses, done using SPSS version 23, revealed several statistically significant differences, helping uncover the patterns behind smartphone choices in Azerbaijan. The research highlights the importance of features such as phone size, battery life, camera quality, and design. The findings reveal notable gender-based differences: males tend to prioritize technical features like water resistance, while females favor aesthetic design elements. Educational attainment also plays a role, as individuals with higher education levels place greater importance on advanced functionalities like face recognition. These findings underscore the complexity of the smartphone market in Azerbaijan, where demographic factors define consumer behavior. Recognizing these preferences not only allows marketers to tailor strategies for specific consumer segments but also assists policymakers in formulating digital inclusion policies that address diverse demographic needs.

In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals' sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend's friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.

The study of spousal bereavement and mortality has long been a major topic of interest for social scientists, but much remains unknown with respect to important moderating factors, such as age, follow-up duration, and geographic region. The present study examines these factors using meta-analysis. Keyword searches were conducted in multiple electronic databases, supplemented by extensive iterative hand searches. We extracted 1,377 mortality risk estimates from 123 publications, providing data on more than 500 million persons. Compared with married people, widowers had a mean hazard ratio (HR) of 1.23 (95% confidence interval (CI), 1.19—1.28) among HRs adjusted for age and additional covariates and a high subjective quality score. The mean HR was higher for men (HR, 1.27; 95% CI, 1.19—1.35) than for women (HR, 1.15; 95% CI, 1.08—1.22). A significant interaction effect was found between gender and mean age, with HRs decreasing more rapidly for men than for women as age increased. Other significant predictors of HR magnitude included sample size, geographic region, level of statistical adjustment, and study quality.

Using harmonized longitudinal data from the British Household Panel Survey (BHPS), the German Socio-Economic Panel (GSOEP), and the National Longitudinal Survey of Youth (NLSY), we trace career prospects after motherhood for five cohorts of American, British, and West German women around the 1960s. We establish wage penalties for motherhood between 9% and 18% per child, with wage losses among American and British mothers being lower than those experienced by mothers in Germany. Labor market mechanisms generating the observed wage penalty for motherhood differ markedly across countries, however. For British and American women, work interruptions and subsequent mobility into mother-friendly jobs fully account for mothers' wage losses. In contrast, respective penalties are considerably smaller in Germany, yet we observe a substantial residual wage penalty that is unaccounted for by mothers' observable labor market behavior. We interpret this finding as indicating a comparatively more pronounced role for statistical discrimination against mothers in the German labor market.

Although much research has focused on how imprisonment transforms the life course of disadvantaged black men, researchers have paid little attention to how parental imprisonment alters the social experience of childhood. This article estimates the risk of parental imprisonment by age 14 for black and white children born in 1978 and 1990. This article also estimates the risk of parental imprisonment for children whose parents did not finish high school, finished high school only, or attended college. Results show the following: (1) 1 in 40 white children born in 1978 and 1 in 25 white children born in 1990 had a parent imprisoned; (2) 1 in 7 black children born in 1978 and 1 in 4 black children born in 1990 had a parent imprisoned; (3) inequality in the risk of parental imprisonment between white children of college-educated parents and all other children is growing; and (4) by age 14, 50.5% of black children born in 1990 to high school dropouts had a father imprisoned. These estimates, robustness checks, and extensions to longitudinal data indicate that parental imprisonment has emerged as a novel—and distinctively American—childhood risk that is concentrated among black children and children of low-education parents.