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December 11, 2024

How job ads shape gender and racial segregation in UK workforce

Average marginal effects of labor force gender/racial composition on gender/EDI language in job ads. Linear effects in A) and nonlinear effects in B). Credit: PNAS Nexus (2024). DOI: 10.1093/pnasnexus/pgae526
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Average marginal effects of labor force gender/racial composition on gender/EDI language in job ads. Linear effects in A) and nonlinear effects in B). Credit: PNAS Nexus (2024). DOI: 10.1093/pnasnexus/pgae526

In the UK, equality, diversity and inclusion (EDI) language in job advertisements (ads) could unintentionally have the reverse effect on attempts to create a more gender-balanced workplace, says a new study led by Lancaster University.

The study also shows that actively using EDI language designed to appeal to in job ads is not working.

Workforces with a larger share of women tend to include language associated with family-friendly policies and flexible work arrangements in job ads. Such language, the study suggests, tends to appeal more to female rather than male applicants, which, in turn, intensifies gender segregation in these workforces.

While workforces with a larger share of racial minority workers tend to include more EDI policy pledges and language signaling workplace EDI culture in job ads, such pledges and language have little impact on workforce , says the research.

The study, "Language in job advertisements and the reproduction of labor force gender and ," is published in .

The findings provide a labor-market-wide audit of how gender/EDI language in job ads helps shape workforce gender/racial composition, as well as how labor force gender/racial composition influences gender/EDI language in job ads.

This , bringing together researchers from universities across the UK, Canada and the U.S. as part of .

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Working in collaboration across multiple disciplines, the team of sociologists, management scholars and data scientists used cutting-edge natural language processing techniques to analyze 28.6 million job ads in the UK in combination with ONS labor force statistics between 2018 and 2023, making it the most comprehensive and up-to-date study of its kind.

"Understanding and tackling persistent labor force gender and racial segregation are crucial to facilitating equality and diversity in the labor market," says the lead author, Professor Yang Hu, of Lancaster University.

"Job ads are important because they are the first point of contact between job seekers and employers," said Associate Professor Nicole Denier of the University of Alberta.

"By signaling characteristics expected of an "ideal candidate," job ads "gatekeep" the labor force and configure its composition by shaping both candidates' tendency to apply for a job and the criteria used for shortlisting and interviewing."

The study develops a novel inventory of language in job ads, capturing six dimensions of language related to gender and EDI:

Using this newly developed inventory, the study characterized and mapped the gender/EDI language used in job ads across occupations and industries to the gender and racial composition of the corresponding workforce in these occupation and industry groups across the full UK labor market.

The study is the first of its kind to disentangle how language in job ads shapes labor force gender/racial composition and how labor force gender/racial composition shapes language in job ads in both directions.

The findings reveal three distinct ways in which the interplay between language in job ads and labor force composition reinforces or disrupts labor force/gender segregation:

These findings demonstrate both the benefits and limitations of intervening in the language used in job ads to help reduce labor force gender/racial segregation.

"They provide insights that are crucial to mitigating the impact of job ads on labor force gender and racial segregation," said Professor Hu, "but they also show that 'window-dressing' EDI language in job ads is not sufficient in actually creating EDI in the labor market, at least when labor force gender and racial composition is concerned.

"Our study calls for a major rethink on how employers frame their job ads and coming up with meaningful ways of communicating and doing EDI to help reduce gender and racial segregation in the labor market."

Professor Monideepa Tarafdar, of the University of Massachusetts, Amherst, U.S., co-Principal Investigator of the ESRC–SSHRC project and a Visiting Professor at Lancaster University, added, "With the proliferation of large language models, AI-automated text processing tools are increasingly used to help draft and debias job ads. Our research provides a roadmap for building labor market equality into the design of these tools."

Professor Karen Hughes, of the University of Alberta, added, "Our project also demonstrates the value of collaboration across multiple disciplines to tackle the grand challenges of our time."

More information: Yang Hu et al, Language in job advertisements and the reproduction of labor force gender and racial segregation, PNAS Nexus (2024).

Journal information: PNAS Nexus

Provided by Lancaster University

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