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Career Mobility and Hiring Bias: Frontiers of Labor Market Research

2021· article· en· W3185218819 on OpenAlexaboutno aff
Claire Daviss, Sara Mahabadi, Weiyi Ng, Ben A. Rissing

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsHarmContext (archaeology)Work (physics)Public relationsMatching (statistics)BusinessMarketingLabour economicsPolitical scienceEconomicsPsychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

The papers in this symposium collectively work at extending and developing the intersections of two current, and burgeoning, streams of contemporary labor market research. First is the move towards a better understanding of the mechanisms of hiring bias. While past research has focused on demonstrating the existence of hiring bias, recent research has endeavored to develop a much richer understanding of how and why it may develop, whether it changes over time, and how it could be mitigated. A second stream of work has tackled the question as to how technological advances will impact the labor market, such as how companies are exploiting the continued externalization of gig-economy labor force hiring and whether and how machine learning matching technologies will benefit or harm employees of employers. In our first presentation, Mahabadi and Cohen ask: How and where do people learn to hire and how does that shape labor markets? The authors explore this question in the context of startups and investigate how founders and hiring mangers there turn to outsiders for help with the critical tasks of hiring. They analyzed over 200 interviews with hiring managers, startup employees, potential job applicants and subject matter experts across over 50 startups; non-participant observation; and document review. They show that founders and hiring managers from many startups described learning from various parties in their ecosystem about several aspects of hiring, including: learning from investors, job candidates, mentors, consultants, recruiters, other entrepreneurs, and people in established organizations; learning through deliberate networking and advice seeking; and learning through less formal and less hiring-focused interactions at events such as startup fairs, conferences and social events. Based on these analyses, they categorized entrepreneurial learning into three broad categories: the very mechanical and transactional aspects of hiring; the aspects of structures closely related to hiring; the more elaborated learning of what they might do to convince people to work for them. The paper then explores the implications of these learnings for labor markets and organizations. In our second presentation, Rissing asks is hiring bias shaped through personal experience or the result of exposure to negative events? The author examines this in the context of H1B visa examiners. He engages two burgeoning, yet largely separate, literatures examining dynamic personal and contextual factors that may shape decision maker bias over time. First, experience-based theories argue that through repeated assessments of individuals belonging to different groups, decision makers may update their attitudes regarding said groups. Second, event-based theories have argued that decision maker bias may be shaped by local shocks, such as crimes or attacks associated with members of a select group that may then be perceived as a threat. There have been few opportunities for scholars to examine how these two key forms of learning might contribute to the updating of decision makers’ attitudes regarding particular groups of workers in an organizational setting. In our third presentation, Daviss and Leung ask whether or not employers change their hiring preferences, and by implication, their biases, over time. Specifically, they explore how employers’ preferences for women or men job candidates vary across multiple hiring decisions. They propose three potential patterns: stability, in which an employers’ bias and by extension their preferences remain consistent across multiple hiring decisions; reduction, in which the strength of an employers’ gender bias is reduced through direct experiences with workers; and reversion, in which an employers’ preference for women or men reverses directions due to the accumulation of moral credentials. To test these patterns, they draw on a unique longitudinal dataset from an online labor marketplace, comprising 4 million bids submitted for more than 450,000 jobs, posted by more than 128,000 employers in 2012. Using logit regression with applicant- and job-level controls, they test whether a bid’s likelihood of being selected is correlated with the gender of the worker, as well as the gender of the employer’s most recent hire and the quality of the employer’s experience with their most recent hire. They find strong evidence suggesting that employers’ preferences for women versus men are largely stable, and weak evidence that employer’s gender preferences are shaped in part by their direct experiences with workers. They do not find, however, that employers’ preferences reverse with the accumulation of moral credentials, even when employers’ moral credentials would presumably be strong. In our fourth presentation, Yang, Bao, and Leung ask whether and how racial hiring bias can be mitigated. Specifically, they examine whether and how employer perceptions of how responsible a job applicant looks may mitigate the hiring bias African Americans face. While employers are known to be biased against hiring black job applicants, when as compared to White applicants, what is less well-understood is whether and how this bias may be mitigated. The authors examine a mobile technology mediated labor market that matches employers looking to hire temporary labor with the gig-economy labor force job seekers. They ask whether or not employer perceptions of responsibility will mitigate the hiring bias black job seekers face. The particularly novel aspect of their paper is the use of a machine learned algorithm to code job applicant photos for how “responsible” they look – thereby introducing us to a computational method to measure human perception in hiring. Finally, Ng and Sherman ask whether the trend by firms who increasingly externalized labor markets to hire is justifiable. Much of the accumulated evidence suggests that it is not. For example, firms tend to pay a significant wage premium for external hires versus comparable candidates promoted from within. Furthermore, research documents declines in the performance of securities analysts, insurance agents, and bankers who switch organizations. Given these results, as well as the financial costs incurred when hiring via third-party recruiters, the rationale for sustaining external hiring at its current levels is not immediately apparent. How do firms capture value from external labor market hiring? Social capital theory suggests that external hires should produce work that is more creative than their otherwise equivalent internal counterparts. Ng and Sherman test this perspective via machine learning methods using a repository of resumes from the website LinkedIn. By relying on a matching estimator the authors find, in a sample of product managers working in large technology firms, that external hires are indeed more creative than observably equivalent internal hires. However, the authors also find that external hires have a higher turnover rate, an effect that is amplified for particularly creative external hires. This suggests that relying on external hires to catalyze creativity may be a difficult strategy to sustain in the long term. Taken together, this research offers some evidence as to why external hiring continues unabated in spite of its demonstrable detriments. Learning by Hiring: How Hiring Processes Facilitate Learning Across Startup-Ecosystem Boundaries Presenter: Sara Mahabadi; McGill U. - Desautels Faculty of Management Presenter: Lisa Ellen Cohen; McGill U. To H-1B or Not to H-1B? The Role of Experience and Events in Shaping Dynamic Bias Presenter: Ben Rissing; Cornell U. Patterned Preferences: Employers’ Preferences for Women Versus Men Across Multiple Hiring Decisions Presenter: Claire Daviss; Stanford U. Presenter: Ming De Leung; U. of California, Irvine How race moderates effect of perceived responsibility on being hired on a low-skilled labor market Presenter: Tiantian Yang; Duke U. Presenter: Jiayi Bao; UNC-Chapel Hill Presenter: Ming De Leung; U. of California, Irvine In Search of Inspiration: External Hiring, Internal Mobility, and Creative Production Presenter: Weiyi Ng; National U. of Singapore Presenter: Eliot Sherman; London Business School

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.329
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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