The Contribution of Intermediary Factors to Marital Status Differences in Self-Reported Health

1997 ◽  
Vol 59 (2) ◽  
pp. 476 ◽  
Author(s):  
I. M. A. Joung ◽  
K. Stronks ◽  
H. van de Mheen ◽  
F. W. A. van Poppel ◽  
J. B. W. van der Meer ◽  
...  
Keyword(s):  
Marital Status ◽  
Reported Health ◽  
Status Differences

scholarly journals Refining Estimates of Marital Status Differences in Mortality at Older Ages

10.3386/t0182 ◽  
1995 ◽  
Author(s):  
Sanders Korenman ◽  
Noreen Goldman ◽  
Haishan Fu
Keyword(s):  
Marital Status ◽  
Status Differences

Unemployment outflows: the relevance of gender and marital status in Italy and Spain

2014 ◽  
Vol 35 (5) ◽  
pp. 594-612 ◽  
Author(s):  
Chiara Mussida ◽  
Enrico Fabrizi
Keyword(s):  
Marital Status ◽  
Design Methodology ◽  
Geographical Area ◽  
International Comparisons ◽  
Panel Survey ◽  
Labour Mobility ◽  
Content Type ◽  
Status Differences ◽  
Shed Light

Purpose – The purpose of this paper is to shed light on transitions from the state of unemployment to that of employment and of inactivity in Italy and Spain. Design/methodology/approach – First, the paper investigates the determinants of unemployment outflows in these two Mediterranean labour markets. Then, the paper examines discrepancies and similarities between specific outflow determinants, especially the interactions between gender and marital status, by comparing results obtained across countries. Findings – The findings of the paper suggest that gender and marital status influence the probability of unemployment outflows in both countries, although not in the same way, especially with reference to marital status. Discrepancies also emerge in relation to the role of geographical area of residence. Originality/value – International comparisons of unemployment outflows are rather new in the literature, and as far as we know none have been performed using European Union Statistics on Income and Living Conditions data. Further, although studies quite often examine the issue of gender-related labour mobility using the European Community Household Panel survey that took place in the 1990s (Arulampalam et al., 2007; Garcia Pérez and Rebollo Sanz, 2005; Theodossiou and Zangelidis, 2009), one of the main contributions of this paper is that it provides a systematic examination of the issue, considering the influence of gender and marital status differences on patterns of unemployment outflows to employment and inactivity.

Explaining Differences in Mental Health Between Married and Cohabiting Individuals

2005 ◽  
Vol 68 (3) ◽  
pp. 239-257 ◽  
Author(s):  
Kristen Marcussen
Keyword(s):  
Mental Health ◽  
Alcohol Use ◽  
Marital Status ◽  
Relationship Quality ◽  
Psychological Theory ◽  
Coping Resources ◽  
Future Research ◽  
Social Psychological ◽  
Health Differences ◽  
Status Differences

Research on the relationship between cohabitation and mental health tends to ignore social psychological factors that help explain mental health differences between the married and the unmarried, including coping resources and perceived relationship quality. In this paper I draw on social psychological theory and research to clarify differences in depression and alcohol use between married and cohabiting individuals. Using data from the National Survey of Families and Households, I examine the independent and combined influences of socioeconomic status, coping resources, and relationship quality to account for marital status differences in distress. I find that marital status differences in coping resources and relationship quality help explain the gap in depression, but not in alcohol use, between married and cohabiting individuals. I also find that social selection is not responsible for marital status differences in distress. The implications of these findings for future research on cohabitors' mental health are discussed.

scholarly journals Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

2021 ◽  
Vol 18 (8) ◽  
pp. 3886
Author(s):  
Haixia Liu ◽  
Xiaojing Fan ◽  
Huanyuan Luo ◽  
Zhongliang Zhou ◽  
Chi Shen ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Marital Status ◽  
Influencing Factors ◽  
Rural Areas ◽  
The Elderly ◽  
Education Level ◽  
Rural Elderly ◽  
Social Activities ◽  
Reported Health ◽  
Urban And Rural Areas

Depression amongst the elderly population is a worldwide public health problem, especially in China. Affected by the urban–rural dual structure, depressive symptoms of the elderly in urban and rural areas are significantly different. In order to compare depressive symptoms and its influencing factors among the elderly in urban and rural areas, we used the data from the fourth wave of the China Health and Retirement Longitudinal Study (CHARLS). A total of 7690 participants at age 60 or older were included in this study. The results showed that there was a significant difference in the prevalence estimate of depression between urban and rural elderly (χ2 = 10.9.76, p < 0.001). The prevalence of depression among rural elderly was significantly higher than that of urban elderly (OR-unadjusted = 1.88, 95% CI: 1.67 to 2.12). After adjusting for gender, age, marital status, education level, minorities, religious belief, self-reported health, duration of sleep, life satisfaction, chronic disease, social activities and having income or not, the prevalence of depression in rural elderly is 1.52 times (OR = 1.52, 95% CI: 1.32 to 1.76) than that of urban elderly. Gender, education level, self-reported health, duration of sleep, chronic diseases were associated with depression in both urban and rural areas. In addition, social activities were connected with depression in urban areas, while minorities, marital status and having income or not were influencing factors of depression among the rural elderly. The interaction analysis showed that the interaction between marital status, social activities and urban and rural sources was statistically significant (divorced: coefficient was 1.567, p < 0.05; social activities: coefficient was 0.340, p < 0.05), while gender, education level, minorities, self-reported health, duration of sleep, life satisfaction, chronic disease, social activities having income or not and urban and rural sources have no interaction (p > 0.05). Thus, it is necessary to propose targeted and precise intervention strategies to prevent depression after accurately identifying the factors’ effects.

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