Gender Discrimination in the Hiring of Skilled Professionals in Two Male-Dominated Occupational Fields: A Factorial Survey Experiment with Real-World Vacancies and Recruiters in Four European Countries

Bertogg, Ariane; Imdorf, Christian; Hyggen, Christer; Parsanoglou, Dimitris; Stoilova, Rumiana

doi:10.1007/s11577-020-00671-6

Cited by 20 publications

(11 citation statements)

References 52 publications

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“…To understand this, we can only speculate. It could be that the IT sector is more tolerant, pioneering a new work-life gender-egalitarian culture (Faulkner, 2009, but see Bertogg et al, 2020). Alternatively, given the low proportion of women who enter STEM fields, IT employers might believe female applicants are positively selected in unobserved characteristics.…”

Section: Understanding Gender Discriminationmentioning

confidence: 99%

Gender Discrimination in Hiring: Evidence from a Cross-National Harmonized Field Experiment

Birkelund

Lancee

Larsen

et al. 2021

European Sociological Review

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Gender discrimination is often regarded as an important driver of women's disadvantage in the labour market, yet earlier studies show mixed results. However, because different studies employ different research designs, the estimates of discrimination cannot be compared across countries. By utilizing data from the first harmonized comparative field experiment on gender discrimination in hiring in six countries, we can directly compare employers' callbacks to fictitious male and female applicants. The countries included vary in a number of key institutional, economic, and cultural dimensions, yet we found no sign of discrimination against women. This cross-national finding constitutes an important and robust piece of evidence. Second, we found discrimination against men in Germany, the Netherlands, Spain, and the UK, and no discrimination against men in Norway and the United States. However, in the pooled data the gender gradient hardly differs across countries. Our findings suggest that although employers operate in quite different institutional contexts, they regard female applicants as more suitable for jobs in female-dominated occupations, ceteris paribus, while we find no evidence that they regard male applicants as more suitable anywhere.

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Section: Understanding Gender Discriminationmentioning

confidence: 99%

Gender Discrimination in Hiring: Evidence from a Cross-National Harmonized Field Experiment

Birkelund

Lancee

Larsen

et al. 2021

European Sociological Review

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“…Different methods are used to detect discrimination during the recruitment and selection process. Most studies relate to field experiments [7,10,41,46,54], sometimes combined with vignette methods [9]. Relatively often, these studies focus on possible unequal treatment of candidates in job interviews and selection [3,19,54].…”

Section: Labor Market Research On Discriminationmentioning

confidence: 99%

Context-Aware Discrimination Detection in Job Vacancies using Computational Language Models

Vethman¹,

Adhikari²,

Boer³

et al. 2022

Preprint

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Discriminatory job vacancies are a recognized problem with strong impact on inclusiveness and belongingness. It is disapproved worldwide, but remains persistent. Discrimination in job vacancies can be explicit by directly referring to demographic memberships of candidates. On the other hand, more implicit forms of discrimination are also present that may not always be illegal but still influence the diversity of applicants. Although there is a shift towards using implicit forms, explicit written discrimination is still present in numerous job vacancies, as was recently observed for age discrimination in the Netherlands. The studies demonstrated that the lower bound for age discrimination in job vacancies was approximated between 0.16% and 0.24%, while further in-depth analyses showed actual numbers rise above the conservative lower bound. Current efforts for the detection of explicit discrimination concern the identification of job vacancies containing potentially discriminating terms such as "young" or "male". In this way, the automatic detection of potentially discriminatory job vacancies is, however, very inefficient due to the consequent low precision: for instance "we are a young company" or "working with mostly male patients" are phrases that contain explicit terms, while the context shows that these do not reflect discriminatory content.In this paper, we show how state-of-the-art machine learning based computational language models can raise precision in the detection of explicit discrimination by identifying when the potentially discriminating terms are used in a discriminatory context, indicating that the sentence is indeed discriminating. We focus on gender discrimination, which indeed suffers from low precision when filtering explicit terms. First, in collaboration with oversight authorities we created a data set for gender discrimination in job vacancies.Second, we investigated a variety of machine learning based computational language models for discriminatory context detection.Third, we explored and evaluated the capability of these models to detect unforeseen discriminating terms in context. The results show that machine learning based methods can detect explicit gender discrimination with high precision and these state-of-the-art natural * Both authors contributed equally to this research.

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“…Based on this argument, if women are participating increasingly more in the labor market, if they have a greater educational level, and more work experience, this would induce a significant restructuration of their remunerations and their preferences with respect to the type of work that they are employed in or which is offered to them. However, this is in reality not being fulfilled in a large part of the labor market, as is demonstrated by the studies done in this field [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. These indicate that wage discrimination and occupational segregation still exist in the 21st century.…”

Section: Introductionmentioning

confidence: 99%

The Spanish Labor Market: A Gender Approach

Millán

Torre

Rojas

et al. 2021

IJERPH

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The massive incorporation of Spanish women into the labor market is a phenomenon that began in the second half of the 20th century, being many the obstacles that this group has had to overcome to reach the current situation, where getting a job can be an achievement that, in many cases, does not correspond to the capacity and academic training of the worker, creating a labor and economic imbalance (the cost in training is not rewarded with the work done). In this work, the Spanish labor market was analyzed through the labor force survey (EPA) from a gender perspective, demonstrating the existing inequalities at the labor level, both of employment and unemployment rates, and of jobs where the glass ceiling is evident and of economic remuneration where the salary gap continues to be important. In addition, through an ARIMA model, the evolution of the number of Spanish working women was analyzed, and how the economic crisis of 2009 and the sanitaria have affected their employment in the various crises (COVID-19). Measures to solve the problem as well as laws and active policies in favor of the creation of female jobs and a greater awareness of empowerment on the part of the female collective are proposed.

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Gender Discrimination in the Hiring of Skilled Professionals in Two Male-Dominated Occupational Fields: A Factorial Survey Experiment with Real-World Vacancies and Recruiters in Four European Countries

Cited by 20 publications

References 52 publications

Gender Discrimination in Hiring: Evidence from a Cross-National Harmonized Field Experiment

Gender Discrimination in Hiring: Evidence from a Cross-National Harmonized Field Experiment

Context-Aware Discrimination Detection in Job Vacancies using Computational Language Models

The Spanish Labor Market: A Gender Approach

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