Quantifying the allocative efficiency of capital: The role of capital utilization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher dispersion of log average revenue product of capital (ARPK) is commonly associated with lower capital allocative efficiency. We show this is a result of the assumption that capital utilization is fixed. However, when capital utilization is endogenous, higher capital allocative efficiency is associated with lower dispersion of log average revenue product of capital services (log difference between revenue and utilized capital), not ARPK. Furthermore, contrary to the standard relationship, increases to capital allocative efficiency is associated with higher ARPK dispersion when such improvements arise from greater utilization flexibility. We provide evidence supporting the mechanism and demonstrate counterfactuals where allocative efficiency gains are accompanied by higher ARPK dispersion. Lastly, we apply our framework to study the impact of a capital market liberalization reform in India. We estimate the reform improved allocative efficiency by 0.04%, but counterfactual analysis neglecting the response of utilization would have concluded efficiency gains of 5.25%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it