Corporate Investment: Empirical Evidence for Alternative Propensities 1
Bibliographic record
Abstract
We investigate whether previous evidence of the weakness of Tobin's q ratio to explain variation in capital expenditure investment stems from ignoring R&D as an alternative investment. Suppose q theory is true in the general sense that a firm¡¯s incentive to invest increases with its relative market value. However, due to variations in industry technology, individual firms vary in their propensities to make physical vs. intellectual property (IP) investment. The conventionally calculated q failed to make distinction of the propensities for the two investments types, q did a poor job in explaining the firm level investment. We develop a modified q model that account for individual firms' ex ante propensities to make these alternative types of investment. Using data on U.S. firms for 1974-2008, we test the model¡¯s power to explain firm¡¯s investment. The evidence shows strong support for modified model. Q ratio accounting for investment propensities explains more than 27.2% (61%) of capital expenditure (R&D) investment variations compared to about 0.9% (10.6%) obtained via conventional regression. Our approach yields strong and robust support for q theory. To our knowledge, our study is the first to propose a modified q measure to account investment propensity in the empirical corporate investment literature. We also document evidence of the influence of financial constraints on investment.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".