Bibliographic record
Abstract
Purpose To address the empirical aspect of corporate investment patterns by providing evidence in an international setting regarding the critical factors affecting firm investment policy, focusing on the relevance of financial factors. Design/methodology/approach Examines international company‐level data for capital expenditures over the period 1987‐1997 using fixed effects regression analysis. Findings The capital expenditures of firms that are financially constrained are much less sensitive to the availability of internal funds than unconstrained firms. The evidence is particularly strong when firms are classified according to financial health, but is also prevalent for groups formed according to dividend behavior and firm size. The results provide strong support for the generality of the results of Kaplan and Zingales and Cleary. A major reason for the weak investment‐cash flow sensitivity displayed by unhealthy firms is that they appear to be busy building up financial slack, which has long‐term value, as postulated by Myers and Majluf. Research limitations/implications The conclusions in this study relate to the investment behavior of firms operating in well‐developed economies, which may not necessarily hold for firms operating in distinctly different environments. Given the critical importance of stimulating investment in developing economies, an interesting topic for future research would be to extend the analysis to firms operating in developing country environments to see whether the results herein also apply in these environments. Originality/value The results extend empirical evidence to an international setting, providing support for previous US results that had contributed to a debate in the literature. The results also demonstrate that a major reason for the weak investment‐cash flow sensitivity displayed by unhealthy firms is that they are reluctant to invest when debt levels increase, irrespective of the availability of internal funds. This represents an original contribution to the empirical literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".