Financial Strain and Intimate Partner Violence Against Married Women in Postreform China: Evidence From Chengdu.
Li ZhengXiaohe XuTing XuLiu YangXiaoxia GuLijuan WangPublished in: Journal of interpersonal violence (2019)
The primary goal of this study is to link both subjective and objective indicators of financial strain to two distinct dimensions of intimate partner violence (IPV) against women-the husband's violent behavior and gendered control-in postreform China. The data for this study were drawn from a community survey conducted in Chengdu, the capital of Sichuan province, in 2017 (N = 340). By utilizing the family stress model and quantitative methods, the following results emerged from a series of multivariate statistical analyses: (a) among married women, self-perceived financial strain is significantly and positively associated with the risk of experiencing the husband's perpetration of violent behavior and financial control; (b) low family income significantly elevates the likelihood of the husband's exertion of personal and financial control over the wife, albeit the effect is weaker for financial control; and (c) unemployment of the husband significantly increases the likelihood of the husband's exertion of financial controlling behavior against his wife. These results underscore the importance of gender and income inequalities in research on IPV against women in postreform China. These findings also cross-culturally substantiate the family stress model that has been utilized previously to examine the multifaceted associations between economic hardship and IPV in the U.S. Policy makers, academic researchers, and health practitioners are urged to recognize both subjective and objective financial strains as social and psychological determinants of IPV against women in postreform China.
Keyphrases
- intimate partner violence
- mental health
- polycystic ovary syndrome
- healthcare
- pregnancy outcomes
- affordable care act
- childhood cancer
- physical activity
- cervical cancer screening
- breast cancer risk
- escherichia coli
- south africa
- high resolution
- social media
- risk assessment
- machine learning
- health insurance
- stress induced
- human health
- health information
- artificial intelligence