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Record W3191372487

Adjustment Costs and Incentives to Work: Evidence from a Disability Insurance Program

2019· article· en· W3191372487 on OpenAlexafffund
Arezou Zaresani

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchGovernment of Alberta
KeywordsIncentiveEarningsEconomicsDisability insuranceWork (physics)Labour economicsDemographic economicsMicroeconomicsSocial securityAccounting
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.151
GPT teacher head0.444
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicRetirement, Disability, and EmploymentFrench-language works237,207