Religiosity, gender, and wage: the differentiated impact of private prayer in Canada
Bibliographic record
Abstract
Purpose – Does religiosity impact wages differently for males and females? Does the impact on wage of different dimensions of religiosity, namely the importance of religion, the frequency of religious practice with others and individually, differ for men and women? The paper aims to discuss these issues. Design/methodology/approach – Using the Canadian Ethnic Diversity Survey, made public in 2004, this paper investigates whether there are evidences for a gender difference in the impact of religiosity on wage. A Mincerean wage regression is estimated using both multiple linear regression and Heckit. Findings – Religious females are found to receive a premium over their labour earnings, through the frequency of private-prayer while the same dimension of religiosity penalizes males’ mean wage. The by-gender impact slightly widens for the subsample of employees, while it diminishes for the self-employed. Research limitations/implications – Making use of the most comprehensive data set available and standard methodology, the paper creates stylized facts that are of interest to the scholars of a multiplicity of disciplines. Practical implications – It advances the body of knowledge about the impact of religiosity on productivity and whether it has a by-gender component. Social implications – The research also informs policy-makers in their decision about the appropriate level of accommodation of religiosity in the workplace. Originality/value – The present work is the first research paper examining the by-gender impact of different dimensions of religiosity on productivity thereby wage.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".