Childcare and Children’s Development: Features of Effective Programs
Bibliographic record
Abstract
Abstract An increasing number of countries have introduced pay transparency policies with the aim of reducing gender inequality in the labor market. Firms subject to transparency requirements must disclose publicly or to employees’ representatives information on their employees’ pay broken down by gender, or indicators of gender gaps in pay and career outcomes. The argument at the base of these policies is that gender inequality may in part persist because it is hidden. On the one hand, employers rarely keep track of employees’ pay and career progression by gender, and, on the other hand, employees rarely engage in conversations with their colleagues about pay. The lack of information on within-firm disparities by gender may therefore hamper progress toward a more egalitarian labor market. Transparency policies have the potential to improve women’s relative pay and career outcomes for two reasons. First, by increasing the salience of gender gaps in the labor market, they can alter the relative bargaining power of male and female employees vis-à-vis the firm and lead lower-paid individuals to demand higher pay from their employer. Second, together with pressures from employees, the public availability of information on firms’ gender-equality performance may also increase public pressure for firms’ action in this domain. A clear message emerges from the literature analyzing the impact of pay transparency policies on gender inequality: these policies are effective at pushing firms to reduce their gender pay gaps, although this is achieved via a slowdown of men’s wage growth. Related results point to a reduction in labor productivity following the introduction of transparency mandates but no detrimental effect on firms’ profits because this effect is compensated by the reduction in labor costs. Overall, the findings in this literature suggest that transparency policies can reduce the gender pay gap with limited costs for firms but may not be suited to achieve the objective of improving outcomes for lower-paid employees.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".