2023-09-28 14:46:10 +00:00
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abstract: 'BACKGROUND
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Recent trends show an unprecedented feminisation of migration in China,
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triggered by the increasing demand for cheap labour in big cities and
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the availability of women in the labour market. These trends corroborate
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the evidence that non-agricultural work and remittance from urban labour
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migrants have become the major sources of rural household income.
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OBJECTIVE
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This paper investigates the extent of gender inequalities in job
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participation and wage earning among internal labour migrants in China.
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We hypothesize that female migrants in cities are economically more
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disadvantaged than male migrants in the job market.
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METHODS
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We use data from the 2010 National Migrant Dynamics Monitoring Survey
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conducted in 106 cities representing all 31 provinces and geographic
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regions. The study applies the standard Heckman two-step Probit-OLS
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method to model job participation and wage-earning, separately for
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59,225 males and 41,546 females aged 16-59 years, adjusting for
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demographic and social characteristics and potential selection effects.
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RESULTS
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Female migrants have much lower job-participation and wage-earning
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potential than male migrants. Male migrants earn 26\% higher hourly
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wages than their female counterparts. Decomposition analysis confirms
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potential gender discrimination, suggesting that 88\% of the gender
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difference in wages (or 12\% of female migrant wage) is due to
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discriminatory treatment of female migrants in the Chinese job market.
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Migrants with rural hukou status have a smaller chance of participation
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in the job market and they earn lower wages than those with urban hukou,
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regardless of education advantage.
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CONCLUSIONS
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There is evidence of significant female disadvantage among internal
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labour migrants in the job market in Chinese cities. Household
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registration by urban and rural areas, as controlled by the hukou
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status, partly explains the differing job participation and wage earning
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among female labour migrants in urban China.'
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affiliation: 'Padmadas, SS (Corresponding Author), Univ Southampton, Ctr Global Hlth
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Populat Poverty \& Policy, China Res Ctr, Southampton SO9 5NH, Hants, England.
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Padmadas, SS (Corresponding Author), Univ Southampton, Dept Social Stat \& Demog,
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Southampton SO9 5NH, Hants, England.
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Qin, Min; Li, Bohua; Qi, Jianan, China Populat \& Dev Res Ctr Beijing, Beijing,
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Peoples R China.
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Qin, Min, Univ Southampton, China Res Ctr, Southampton SO9 5NH, Hants, England.
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Brown, James J., Univ Technol Sydney, Sch Math \& Phys Sci, Sydney, NSW 2007, Australia.
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Padmadas, Sabu S., Univ Southampton, Ctr Global Hlth Populat Poverty \& Policy,
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China Res Ctr, Southampton SO9 5NH, Hants, England.
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Padmadas, Sabu S., Univ Southampton, Dept Social Stat \& Demog, Southampton SO9
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5NH, Hants, England.
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Falkingham, Jane, Univ Southampton, ESRC Ctr Populat Change, Southampton SO9 5NH,
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Hants, England.
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Falkingham, Jane, Univ Southampton, China Res Ctr, Southampton SO9 5NH, Hants, England.'
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article-number: '6'
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author: Qin, Min and Brown, James J. and Padmadas, Sabu S. and Li, Bohua and Qi, Jianan
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and Falkingham, Jane
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author-email: S.Padmadas@soton.ac.uk
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author_list:
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- family: Qin
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given: Min
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- family: Brown
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given: James J.
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- family: Padmadas
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given: Sabu S.
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- family: Li
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given: Bohua
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- family: Qi
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given: Jianan
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- family: Falkingham
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given: Jane
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da: '2023-09-28'
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files: []
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issn: 1435-9871
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journal: DEMOGRAPHIC RESEARCH
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keywords-plus: 'DISCRIMINATION; MIGRATION; BIAS; DIFFERENTIALS; TRANSITION; SELECTION;
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WOMEN; GAP'
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language: English
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month: JAN 22
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number-of-cited-references: '53'
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orcid-numbers: 'Li, Bo/0000-0002-7294-6888
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Brown, James J/0000-0002-7535-2874
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Padmadas, Sabu/0000-0002-6538-9374
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Falkingham, Jane/0000-0002-7135-5875'
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pages: 175-202
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papis_id: cebf13a80a83ce846eeeb3eb8feee5c7
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ref: Qin2016genderinequalities
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researcherid-numbers: 'Li, bo/IWL-9318-2023
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Li, Bo/AAA-8968-2020
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Brown, James J/D-7195-2014
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'
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times-cited: '10'
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title: Gender inequalities in employment and wage-earning among internal labour migrants
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in Chinese cities
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2023-10-01 08:15:07 +00:00
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type: article
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2023-09-28 14:46:10 +00:00
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unique-id: WOS:000368521000001
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usage-count-last-180-days: '1'
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usage-count-since-2013: '53'
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volume: '34'
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web-of-science-categories: Demography
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year: '2016'
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