Capital Gains Taxes and Acquisition Activity: Evidence of the Lock‐in Effect*
Bibliographic record
Abstract
The lock-in effect proposes that capital gains taxes represent transaction costs that increase the reservation price for security owners and, ceteris paribus, reduce trading volume. Consistent with the lock-in effect, previous empirical research documents price and reactions to enacted changes in the capital gains tax rate. We investigate whether the volume hypothesis predicted by the lock-in effect extends to corporate acquisition activity. In particular, we analyze whether aggregate corporate acquisition activity is inversely associated with shareholder capital gains tax rates. We measure quarterly corporate acquisition activity from 1973 through 2001 using (1) the percentage of traded firms acquired in a calendar quarter and (2) the percentage of market value of traded firms acquired in a calendar quarter. In supplemental analysis, we measure acquisition activity at the industry level (i.e., as the percentage of firms and percentage of market value acquired by industry annually). In each analysis we model acquisition activity as a function of the maximum long-term capital gains tax rate for individuals and other macroeconomic factors previously hypothesized to be associated with acquisition activity. Consistent with a lock-in effect for corporate acquisitions, we find a significant negative association between corporate acquisition activity and the capital gains tax rate whether we measure acquisition activity in the aggregate or at the industry level. In addition, we find that this negative association is attributable to increased (decreased) taxable acquisition activity during periods of low (high) capital gains tax rates. These results suggest that, ceteris paribus, capital gains taxes represent significant transaction costs that influence the level of corporate acquisition activity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".