Long-term Changes in Married Couples' Labor Supply and Taxes: Evidence from the US and Europe Since the 1980s
Bibliographic record
Abstract
We document the time-series of employment rates and hours worked per employed by married couples in the US and seven European countries (Belgium, France, Germany, Italy, the Netherlands, Portugal, and the UK) from the early 1980s through 2016.Relying on a model of joint household labor supply decisions, we quantitatively analyze the role of non-linear labor income taxes for explaining the evolution of hours worked of married couples over time, using as inputs the full country-and year-specific statutory labor income tax codes.We further evaluate the role of consumption taxes, gender and educational wage premia, and the educational composition.The model is quite successful in replicating the time series behavior of hours worked per employed married woman, with labor income taxes being the key driving force.It does however capture only part of the secular increase in married women's employment rates in the 1980s and early 1990s, suggesting an important role for factors not considered in this paper.We will make the non-linear tax codes used as an input into the analysis available as a userfriendly and easily integrable set of Matlab codes.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".