Alena Křížková, Hana Hašková, Kristýna Pospíšilová:
Zdravotní znevýhodnění, gender a vzdělání na trhu práce v intersekcionální perspektivě [102]

« ročník 19, číslo 2/2018: Intersekcionální přístup ve zkoumání sociálních nerovností

Křížková, Alena, Hana Hašková, Kristýna Pospíšilová. 2018. „Zdravotní znevýhodnění, gender a vzdělání na trhu práce v intersekcionální perspektivě.“ Gender a výzkum / Gender and Research 19 (2): 102-134, http://dx.doi.org/10.13060/25706578.2018.19.2.428

The complex nature of the social position of people with disabilities in the Czech labour market is under-researched. Intersectionality is a useful perspective that stresses the socio-contextual, as opposed to the individualist, nature of disability. It explores the dynamics of the interactions between the systems of inequality that create diverse barriers and opportunities in access to employment and to good working conditions for people with disabilities. Using an intersectional perspective, binary logistic regression, and a thematic analysis of interviews with men and women with disabilities and interviews with people working in organisations that support people with disabilities, we set out to answer the following questions: (1) Are people who assess their health as worse more at risk of potentially precarious work than people who assess their health as better, and what other factors contribute to this? (2) How is the risk of precarious work perceived by people with disabilities and how is their experience shaped by various sources of disadvantage? Intergroup intersectional analysis shed light on the systems of inequality and the interactions between them that leave disabled persons more vulnerable to contractual precarity. Intragroup intersectional analysis helped provide a contextual understanding of how gender and education together structure the access of people with disabilities to decent and secure employment, focusing on access to paid work and contractual conditions and remuneration.

Keywords: intersectionality, precarious work, binary logistic regression, thematic analysis of interviews, gender, health status, disability, education

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