Determinants and Drivers of Large Negative Book-Tax Differences: Evidence from S&P 500
Bibliographic record
Abstract
Temporary book-tax differences (BTDs) serve as critical proxies for understanding corporate earnings management and tax planning. However, the drivers of large negative BTDs (LNBTDs)—where book income falls below taxable income—remain underexplored. This study investigates the determinants and components of LNBTDs, focusing on their relationship with deferred tax assets (DTAs) and liabilities (DTLs). Utilizing hand-collected data from the tax disclosures of S&P 500 firms’ 10-K filings (2007–2023), I analyze 4685 firm-year observations to identify specific accounting items driving LNBTDs. Findings reveal that deferred revenue, goodwill impairments, R&D, CapEx, environmental obligations, pensions, contingency liabilities, leases, and receivables are significant contributors, often generating substantial DTAs due to timing mismatches between book and tax recognition. Notably, high-tech industries, like the pharmaceutical, medical, and computers and software industries, exhibit pronounced LNBTDs, driven by upfront revenue recognition for tax purposes and deferred recognition for financial reporting, capitalization, amortization and depreciation effects, and other deferred tax components. Regression analyses confirm strong associations between these components and LNBTDs, with asymmetry in reversal patterns suggesting that initial differences do not always offset symmetrically over time. While prior research emphasizes large positive BTDs and tax avoidance, this study highlights economic and industry-specific characteristics as key LNBTD drivers, with limited evidence of earnings manipulation via deferred taxes. These insights enhance the value relevance of deferred tax disclosures and offer implications for reporting standards, tax policy, and research into BTD dynamics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".