Holdups and Overinvestment in Physical Capital Markets with Matching Frictions
Bibliographic record
Abstract
This paper considers an economy with matching frictions in the allocation of physical capital. In the absence of binding ex-ante contracts, the speci city created by this friction gives rise to a holdup problem. In partial equilibrium, rms react strategically to the holdup problem by overinvesting in order to reduce the marginal productivity of capital and thus the rental rate. In general equilibrium, overinvestment in physical capital contaminates other factor markets and, together with the matching friction, distorts the allocation of resources. Quantitative exercises in a full-blown general equilibrium model with capital and labor suggest that these ine¢ ciencies can be large, implying excessive capital accumulation and consumption losses of several percentage points relative to the social optimum. I thank David Arseneau and Sanjay Chugh for comments; and Randy Wright for encouraging me to explore the topic. Financial support from the SSHRC is gratefully acknowledged. yContact address: Andre Kurmann, Universite du Quebec a Montreal, Department of Economics, P.O. Box 8888, Downtown Station, Montreal (QC) H3C 3P8, Canada. Email: kurmann.andre@gmail.com:
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".