Added Worker Effect Revisited: The “Aubry’s Law” in France as a Natural Experiment
Bibliographic record
Abstract
The Added Worker Effect (AWE) refers to an increase in the labor supply of secondary earners in a household in response to a decrease in the income of the primary earner. Most empirical research on the AWE has focused on increases in the labor force participation of married women when their husbands experience unemployment spells, but recent government-mandated decreases in standard hours in several European countries provide an alternative source of exogenous decreases in the work hours of married men. Empirical research in evaluating the effectiveness of such policy, mostly investigated the impact on the workers who were directly affected by the policy. A model of household decision making suggests that work hour restrictions without full wage compensation should have spillover effects on the labor supply of other household members, but little is known about this possible spillover effect. This is the first attempt which empirically investigates the existence of AWE using mandatory reduction in standard working hours in France (Aubry’s Law 1998) as a natural experiment. The results show that the exogenous reduction in standard work hours for husbands does not lead to any unemployment to employment transition of wives but increases the number of hours worked by wives who are already in the market and are not affected by the law themselves. It is also found that in terms of hours worked, AWE is more prominent in low income families and for families with more members as family size is positively correlated with the degree of credit constraint.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".