Tests of a Deferred Tax Explanation of the Negative Association between the LIFO Reserve and Firm Value*
Bibliographic record
Abstract
Abstract Guenther and Trombley (1994) and Jennings, Simko, and Thompson (1996) document a negative association between a firm's last‐in, first‐out (LIFO) reserve and the market value of its equity. In this paper, we test a deferred tax explanation of this negative association. Specifically, we argue that investors, conditional on adjusting inventory to as‐if first‐in, first‐out (FIFO), estimate a firm's future LIFO liquidation tax burden as its LIFO reserve multiplied by the appropriate corporate tax rate and include this tax‐adjusted LIFO reserve in the valuation of a LIFO firm's net assets. On the basis of this argument, the tax‐adjusted LIFO reserve is in effect an estimate of an off‐balance‐sheet deferred tax liability and, as a result, we predict a negative association between the tax‐adjusted LIFO reserve and market value of equity. We test our deferred tax explanation by estimating a valuation model in which a firm's market value of equity is expressed as a function of the firm's assets, liabilities, deferred tax liability, and tax‐adjusted LIFO reserve; the model is estimated separately in years preceding and following the reduction of tax rates mandated by the US Tax Reform Act of 1986. Test results provide strong support for the deferred tax explanation of the negative association between a firm's LIFO reserve and the market value of its equity.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".