Using Micro Data to Estimate the Intertemporal Substitution Elasticity for Labor Supply in an Implicit Contract Model
Bibliographic record
Abstract
for their assistance. Kris Jacobs of McGill University arranged access to the Current Expenditure Survey (CES) through the Inter-University Consortium for Political and Social Research at the University of Michigan. Wolf Weber of the Bureau of Labor Statistics was extremely helpful in explaining to us the mysteries of using the CES. Eileen Kopchik provided excellent research assistance on the project. Comments by A. Brumwell are gratefully appreciated. We are responsible for any errors. Abstract To: Using Micro Data to Estimate the Intertemporal Substitution Elasticity for Labor Supply in an Implicit Contract Model Economists have devoted substantial resources to estimating the intertemporal substitution elasticity for labor supply because this elasticity plays a crucial role in the real business cycle literature. Generally, the estimates of the elasticity have been too low to explain business cycles. Economists have responded by trying to modify real business cycle models to allow for smaller elasticities, but they have experienced mixed success at best. However, the standard intertemporal substitution model has not done well when tested, and if this model is incorrect, so will be the estimated labor supply elasticities
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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".