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Record W2011988864 · doi:10.1108/cdi-12-2013-0161

The gender gap in pre-career salary expectations: a test of five explanations

2014· article· en· W2011988864 on OpenAlexaffabout
Linda Schweitzer, Seán Lyons, Lisa Kuron, Eddy S. Ng

Bibliographic record

VenueCareer Development International · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsDalhousie UniversityWilfrid Laurier UniversityUniversity of GuelphCarleton University
Fundersnot available
KeywordsSalaryWageValue (mathematics)Demographic economicsPsychologyGraduation (instrument)Test (biology)Work (physics)EconomicsLabour economicsSocial psychologyStatistics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to investigate the gender gap in pre-career salary expectations. Five major explanations are tested to explain the gap, as well as understand the relative contribution of each explanation. Design/methodology/approach – Data were collected from 452 post-secondary students from Canada. Findings – Young women had lower initial and peak salary expectations than their male counterparts. The gap in peak salary could be explained by initial salary expectations, beta values, the interaction between beta values and gender, and estimations of the value of the labor market. Men and women in this study expected to earn a considerably larger peak salary than they expected for others. Research limitations/implications – Cross-sectional data cannot infer causality, and the Canadian sample may not be generalizable to other countries given that an economic downturn occurred at time of data collection. Research should continue to investigate how individuals establish initial salary expectations, while also testing more dynamic models given the interaction effect found in terms of gender and work values in explaining salary expectations. Practical implications – The majority of the gender gap in peak salary expectations can be explained by what men and women expect to earn immediately after graduation. Further, women and men have different perceptions of the value they attribute to the labor market and what might be a fair wage, especially when considering beta work values. Social implications – The data suggests that the gender-wage gap is likely to continue and that both young men and women would benefit from greater education and information with respect to the labor market and what they can reasonably expect to earn, not just initially, but from a long-term perspective. Originality/value – This study is the first to simultaneously investigate five theoretical explanations for the gender gap in pre-career expectations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.050
GPT teacher head0.244
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations26
Published2014
Admission routes2
Has abstractyes

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