Bibliographic record
Abstract
The purpose of this research was to question whether the behavioural tendencies of men and women could help explain the gender wage gap of recent university graduates. It was conducted after discovering that a 2013 study found that even once accounting for observable characteristics such as age, experience, industry, occupation, and field of study, female graduates were still earning 6-14% less than their male counterparts. Using the willingness of a graduate to gamble a current job offer for a potentially better job offer in the future as a proxy for risk, this research investigates the impact risk preferences have on the gender wage gap. More specifically, it attempts to calculate how much of the observed wage gap can be attributed to the greater risk aversion of women. Our data was from the National Longitudinal Survey of Youth 1997. Using a McCall Job Search model and an MLE, we found that women take approximately 4.5 fewer weeks to accept a job, accept significantly lower starting salaries, and are systematically offered lower salaries than their male counterparts. Furthermore, we found that women have an Arrow-Pratt coefficient almost 1.25 times that of men. These results suggest that women are more willing to accept lower wage positions offered to them today because they are less willing to gamble that a higher wage position will come along tomorrow. Moreover, they propose that this female unwillingness to gamble can explain up to a quarter of the difference in the wages accepted by men and women.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".