Gender, income and mental health: The Turkish case

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Köse T.

PLOS ONE, vol.15, no.4, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1371/journal.pone.0232344
  • Journal Name: PLOS ONE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE, Food Science & Technology Abstracts, Index Islamicus, Linguistic Bibliography, MEDLINE, Pollution Abstracts, Psycinfo, zbMATH, Directory of Open Access Journals
  • TED University Affiliated: No


Gender gaps in health outcomes are frequently observed. Mental health disorders also display gender differences in various countries. This paper explores gender differences in mental health outcomes of individuals in Turkey. It aims to deliver additional evidence on associations between gender, income and mental health status by providing an empirical analysis from a developing country, Turkey. This study employs a nationally representative data set from Turkish Health Survey of 2016. It constructs an index for mental health at individual level by using polychoric principal component analysis. Conditional mixed process models are estimated for quantification of associations between gender, income and mental health measures. Empirical findings indicate that there is endogenous and positive relationship between household income level and mental health status of individuals in Turkey. Turkish females report lower mental health statuses than Turkish males. Furthermore, females are more likely to use mental health services in Turkey. Gender gaps in both mental health status and mental health service use are present in the Turkish case. Results of this study imply that mental health policies should avoid applying one-fit-all approaches.