Decomposing the gender gap in subjective well-being: evidence from a machine learning-based switching framework
Humanities and Social Sciences Communications · 2026
Abstract e pubblicazione originale
L’abstract è consultabile nella fonte originale. Non viene ripubblicato qui perché non risulta verificata una licenza compatibile.
Leggi nella fonte originale ↗Parole chiave
Psychological Well-being and Life SatisfactionGender, Labor, and Family DynamicsIncome, Poverty, and Inequalitygender gapsubjective well-beinghuman well-beinginstitutional flexibilitycross-national comparisonGallup World Pollgender disparitiessocial normsgender inequalitygender stereotypesgender equity policyGender, Health, and Social Inequality
Come citare questo studio
Jie Mi, Chao Li, Alexander Ryota Keeley, Jiaxu Zhang, Shunsuke Managi. (2026). Decomposing the gender gap in subjective well-being: evidence from a machine learning-based switching framework. Humanities and Social Sciences Communications. DOI: 10.1057/s41599-026-09543-4
Consulta la pubblicazione originale ↗