Does economic policy uncertainty impact inventories and firm value? Evidence from the US economy
Bibliographic record
Abstract
This paper develops an empirical model to explore how EPU impacts inventory levels in U.S. firms and the effect on firm value under high EPU. This is the first paper to investigate the impact of macroeconomic risk measured by the EPU on inventory and firm value. We apply panel data regression methodology to financial data from a COMPUSTAT sample of 330,905 quarterly observations between 2002 and 2023 for U.S.-based firms. We measure firm value using the market-to-book ratio of assets, which enables us to link inventory policy to market valuation directly. We find that with increased EPU, the total inventory levels, raw material, and finished goods inventory increase, while work-in-process (WIP) inventory level decreases. This indicates that during high EPU, firms increase raw material and finished goods inventory to hedge against supply and demand shortages, while streamlining their internal production processes and workflow, which lowers the WIP inventory, achieving higher inventory leanness. Our findings also indicate that inventory levels and firm value follow an inverted U-shaped relationship. A higher inventory level due to EPU is value-enhancing until it reaches a threshold point, beyond which a firm's value decreases.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".