What does a relative price of investment wedge reveal about the role of investment-specific technology?
Bibliographic record
Abstract
Abstract In order to identify investment-specific technology (IST), most DSGE models assume a perfect inverse relationship between IST and the relative price of investment (RPI). This paper explores this relationship and provides evidence that the RPI also responds to changes in market power, which I find constitutes a third of volatility in the RPI. To corroborate this conclusion, two competing models are produced; the first is a two-sector model with a wedge separating the identification of IST with the inverse of the RPI. The RPI wedge is then estimated using Bayesian estimation techniques. A second, richer two-sector model is produced, where firms can vary markups depending on the number of competitors. This paper finds that changes in relative markups are highly correlated with the RPI wedge and help explain the sudden increase in the RPI following the Great Recession in the United States. In addition, with endogenous price markups, non-IST shocks can explain over a third of the volatility observed in the RPI, with marginal efficiency of investment contributing approximately 30 percent of the volatility in the RPI.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".