Bibliographic record
Abstract
Abstract: Traditionally, shocks to total factor productivity (TFP) are considered exogenous and the response of employment is determined by the impact of the TFP shock on aggregate demand. In this paper, we approach the relationship between TFP and labor differently – raising the possibility that, in response to positive labor supply shocks, firms reduce efforts to increase labor efficiency – essentially picking a lower TFP/higher labor intensity point on the production frontier. In other words, we investigate whether TFP is endogenous. We present evidence using cross-country aggregate information pointing to a strong negative correlation between growth in TFP and labor inputs over the medium- to long-run. This result is robust to changes in datasets, across decades, and after controlling for industry composition. To address the question of causality, we use instruments to capture changes in hours worked that are independent of TFP and find that TFP growth falls following a pickup in hours growth. Our results, though preliminary, could have important policy implications. For instance, Canadian policymakers have been worried about the low productivity growth in their country. However, employment has grown more in Canada than in any other G-7 economy and low productivity growth may partly be a side-effect of its strong labor market performance. By the same token, in countries
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.692 | 0.467 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".