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