Prosocial Occupations, Work Autonomy, and the Origins of the Social Class Pay Gap
Bibliographic record
Abstract
Despite decades of research on social mobility and wage disparities, it remains a puzzle why people from lower-class families earn less than people from upper-class families even when similar in education and occupational prestige. Taking a sociocultural perspective on social class, we argue that a key contributor to the class pay gap is that people from upper-class origins tend to work in occupations with greater autonomy, whereas their lower-class counterparts tend to work in occupations that are more prosocial. We further propose that autonomous occupations pay better than prosocial occupations. Across two distinct nationally representative samples in the United States, we find that people with upper-class (vs. lower-class) parents are more likely to work in autonomous occupations, but less likely to work in prosocial occupations, even when controlling for education, occupational prestige, and other potential confounds. This pattern of occupational sorting explains a substantial portion of the class pay gap. Our study extends the literatures on social class, occupational segregation, and social mobility, and joins an important scholarly conversation that has, until recently, taken place outside the field of management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".