multinominal approach to estimating the determinants of occupational segregation
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multinominal approach to estimating the determinants of occupational segregation by Rebecca A. Knudson

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Published .
Written in English


  • Occupations -- Sex differences.,
  • Vocational interests -- Sex differences.,
  • Vocational qualifications -- Sex differences.

Book details:

Edition Notes

Statementby Rebecca A. Knudson.
The Physical Object
Pagination53 leaves, bound. ;
Number of Pages53
ID Numbers
Open LibraryOL17645144M

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A multinominal approach to estimating the determinants of occupational segregation. By. Abstract. Graduation date: The purpose of this study is to analyze the sources\ud of occupational segregation between men and women.\ud Specifically, three hypotheses are tested. First, do\ud stiff specific training requirements for an occupation\ud.   A comparison of the actual and predicted females' occupation distribution revealed a large over‐representation of females in unskilled occupations., – The paper makes an original contribution to our understanding of occupational distribution by demonstrating that occupational segregation by gender is a large and economically significant factor in the Russian labour market, Cited by: 8. Purpose ‐ The purpose of this paper is to focus on the determinants of occupational choice of workers in the handloom industry in Assam and to examine the variables that influence the Author: Tayyeb Shabbir. occupational segregation across the various race-sex groups. In the second part of the analysis, I will first estimate simple multinomial regression models that include only race, then only sex, and then both, with no controls in order to see the main effects of race and gender. Race will be.

To calculate this segregation data from Pakistan labor force survey has been used and Duncan index technique was used to calculate the segregation index. Researcher considered nine major professions to check the segregation index. After finding the segregation indices this paper finds the determinants of this by: 3. Occupational Gender Segregation and Its Determinants, an Analysis of Pakistan Labor Force Market. Muhammad Irfan 1,, Sofia Anwar 2, Waqar Akram 3, Irum Waqar 3. 1 Economics research scholar, G.C University Faisalabad, Punjab, Pakistan. 2 Department of Economics G.C University Faisalabad, Punjab, Pakistan. 3 Department of Business Administration, Institute of Business . Sample size: Multinomial regression uses a maximum likelihood estimation method. Therefore, it requires a large sample size. It also uses multiple equations. Therefore, it requires an even larger sample size than ordinal or binary logistic regression. Complete or quasi-complete separation: Complete separation implies that only one value of a. Cambridge House, /4 Ansari Road, Daryaganj, Delhi , India Cambridge University Press is part of the University of Cambridge. It furthers the University’s File Size: KB.

Design/methodology/approach - Multinomial logit was chosen as the estimation technique for this analysis. Findings - It was found that gender significantly affects occupational distribution after controlling for human capital and other characteristics during all years.   This paper examines gender differentials in earnings in Macedonia, with special emphasis on the role of occupational segregation. The lower earnings of women in Macedonia cannot be explained by gender differences in measured human capital endowments. There is a high degree of segregation of jobs along gender lines, the end product of which is lower earnings for women relative Cited by: 5. The multinomial logit model can allow us to estimate a set of coefficients β j corresponding to each occupational as follows: [1] Where i index the individuals; j represents the six nominal Author: Zafar Nasir.   The role of occupational segregation in the determination of gender wage differentials is assessed. It is found (1) that occupational segregation plays less of a role in explaining wage differentials than do traditional human capital variables; (2) that earnings profiles generated with data that include a percent female (PF) measure of occupational segregation are not ideal for testing human Cited by: