Adjust Me if I Can’t: The Effect of Firm Incentives on Labor Supply Responses to Taxes
Bibliographic record
Abstract
I provide theoretical and empirical evidence on the importance of statu- tory incidence in labor markets in the presence of asymmetric frictions. Using a theoretical model I show that labor supply responses are stronger when the statutory incidence of taxes or labor rules falls on firms, even when wages can adjust freely. I explore these mechanisms by studying labor responses to incentives generated by the “Mini-Job” program aimed at increasing labor supply of low-income individuals in Germany. Using administrative data, I show evidence of a strong behavioral response – in the form of sharp bunching – to the mini-job threshold that generates large discontinuous changes both in the marginal tax rates and in the total in- come and payroll tax liability of individuals in Germany. Sharp bunching translates into elasticity estimates that are an order of magnitude larger than has been previously estimated using the bunching approach. To ex- plain the magnitude of the observed response, I show that in addition to tax rates, fringe benefit payments also change at the threshold. Mini-job workers receive smaller yearly bonuses and fewer vacation days but are paid higher gross wages than regular workers. These results indicate that lower fringe benefits make mini-jobs attractive to employers, thus facilitating labor supply responses in accordance with the model’s predictions.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".