Adjustment Costs and Incentives to Work: Evidence from a Disability Insurance Program
Bibliographic record
Abstract
How important are adjustment costs for individuals when they face a change in work incentives induced by a policy change? I provide the first estimate of heterogeneous adjustment costs by exploiting a policy change that substantially increased work incentives. The policy change increased the exemption threshold in a disability insurance program. I document strong responses to work incentives as I observe excess mass –"bunching"– right below the exemption threshold where the marginal tax on earnings is low. A puzzling observation is that individuals continue bunching at the former threshold after the policy change. This finding suggests that they face adjustment costs when changing their labor supply. I use the amount of bunching at the new and former threshold to estimate adjustment costs that vary by individuals' ability to work. The estimated adjustment costs are higher for individuals with lower ability; varying from zero to twenty percent of their potential earnings, with an average at eight percent. The estimated elasticity of earnings respect to net-of-tax rate – accounting for heterogeneous adjustment costs – is 0.2, which is double the size of the elasticity estimated with no adjustment costs. To investigate the relative size of the adjustment costs to the work incentives induced by the policy change, I evaluate the overall effect of the policy change on the labor supply using a Difference-in-Differences design. I find that individuals who already work, work more, and those who did not work, start working. Policies designed to increase labor supply will work if the induced work incentives are large enough to offset the adjustment costs. Accounting for adjustment costs then might explain disparate findings on the effects of an increase in work incentives on labor supply in disability insurance programs. These findings have important implications for designing policies and targeting heterogeneous groups to increase labor supply in disability insurance programs.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".