Risk-Averse Managers, Labour Market Structures, Public Policies and Discrimination
Bibliographic record
Abstract
Abstract This article presents a model to analyze the effects of first and second-moment statistical discrimination on the labour market. Second-moment statistical discrimination occurs when risk-averse managers make decisions regarding wage and hiring based on productivity variances. We provide a framework exploring managers’ discrimination based on differences in average productivity and in variance of productivity. Furthermore, since discrimination is composed of two types (wage and hiring discrimination), our model allows for the interdependence between hiring practices and wages. Using our model, we examine the effects of various anti-discrimination policies along with changes to the labour market structure. We show that managers’ behaviour may be driven by anti-discrimination policies and labour market structures. A firm reduces hiring when required to implement anti-discrimination policies to address wage inequality. A firm applying policies to promote employment equity must stimulate minority participation. A change in labour market structure does not alter the efficiency of policies promoting employment equity, but it does alter the efficiency of policies aimed at reducing wage differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".