Financing Constraints, Investment, and Financial Reporting
Bibliographic record
Abstract
The literature on financing constraints has found, as evidence of the existence of financing frictions, that decreases in internal financial resources causes firms to reduce investment activity. However, the literature is inconclusive on the effect of financing constraints on R&D versus capital expenditures. Using a novel setting, this paper finds firms that face a negative shock to internal capital do not uniformly cut back on all types of investment, but allocate capital away from investments with uncertain returns. I use the setting of the 1999 Taiwan earthquake, which disrupted the global semiconductor supply chain and increased production costs for a subset of US high-technology manufacturing firms that sourced semiconductor wafers and other components from Taiwan, causing a drain on internal capital. I find firms negatively impacted by the shock did not cutback on capital expenditures, but reduced R&D investment. In additional analysis, I find affected firms became more aggressive in their revenue recognition practices after the shock, potentially in response to an increase in competition from rivals, or to window-dress financial statements prior to raising equity.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".