Decision to work by educated married women

The withdrawal of women from the labor market after marriage or after child birth and burdensome housework are cited as the main reasons for lower participation rates among married women in the labor market. Those who leave the labor market are surprisingly women with tertiary level or well educated...

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Published in:Advanced Science Letters
Main Author: Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
Format: Article
Language:English
Published: American Scientific Publishers 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032217961&doi=10.1166%2fasl.2017.9557&partnerID=40&md5=81d3437b7299753e9861d455215e7b27
id 2-s2.0-85032217961
spelling 2-s2.0-85032217961
Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
Decision to work by educated married women
2017
Advanced Science Letters
23
8
10.1166/asl.2017.9557
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032217961&doi=10.1166%2fasl.2017.9557&partnerID=40&md5=81d3437b7299753e9861d455215e7b27
The withdrawal of women from the labor market after marriage or after child birth and burdensome housework are cited as the main reasons for lower participation rates among married women in the labor market. Those who leave the labor market are surprisingly women with tertiary level or well educated. However, as stated by previous research, factors affecting the participation rates for married and educated women, were varied and complex, whereas a single variable usually would not make a significant impact upon women’s decision to work. Based on information collected from 593 women via an online survey, this paper aims to identify the factors affecting the decision of educated and married women to work. The Binary Logistic Regression analysis revealed that participation of educated and married women in the labor market were affected by ethnics, age, level of education, non-labor income, residential location, having children, and husband’s education background. Besides that, this study also describes the two variables that are, access to the internet and non-labor income which have showed different results from the previous studies. © 2017 American Scientific Publishers. All rights reserved.
American Scientific Publishers
19366612
English
Article

author Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
spellingShingle Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
Decision to work by educated married women
author_facet Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
author_sort Amin S.M.; Rameli M.F.P.; Othman A.; Hasan Z.A.; Ibrahim K.
title Decision to work by educated married women
title_short Decision to work by educated married women
title_full Decision to work by educated married women
title_fullStr Decision to work by educated married women
title_full_unstemmed Decision to work by educated married women
title_sort Decision to work by educated married women
publishDate 2017
container_title Advanced Science Letters
container_volume 23
container_issue 8
doi_str_mv 10.1166/asl.2017.9557
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032217961&doi=10.1166%2fasl.2017.9557&partnerID=40&md5=81d3437b7299753e9861d455215e7b27
description The withdrawal of women from the labor market after marriage or after child birth and burdensome housework are cited as the main reasons for lower participation rates among married women in the labor market. Those who leave the labor market are surprisingly women with tertiary level or well educated. However, as stated by previous research, factors affecting the participation rates for married and educated women, were varied and complex, whereas a single variable usually would not make a significant impact upon women’s decision to work. Based on information collected from 593 women via an online survey, this paper aims to identify the factors affecting the decision of educated and married women to work. The Binary Logistic Regression analysis revealed that participation of educated and married women in the labor market were affected by ethnics, age, level of education, non-labor income, residential location, having children, and husband’s education background. Besides that, this study also describes the two variables that are, access to the internet and non-labor income which have showed different results from the previous studies. © 2017 American Scientific Publishers. All rights reserved.
publisher American Scientific Publishers
issn 19366612
language English
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