The Insensitivity of Investment to Interest Rates: Evidence from a Survey of CFOs
Bibliographic record
Abstract
A fundamental tenet of investment theory and the traditional theory of monetary policy transmission is that investment expenditures by businesses are negatively affected by interest rates. Yet, a large body of empirical research offer mixed evidence, at best, for a substantial interest-rate effect on investment. In this paper, we examine the sensitivity of investment plans to interest rates using a set of special questions asked of CFOs in the Global Business Outlook Survey conducted in the third quarter of 2012. Among the more than 500 responses to the special questions, we find that most firms claim to be quite insensitive to decreases in interest rates, and only mildly more responsive to interest rate increases. Most CFOs cited ample cash or the low level of interest rates, as explanations for their own insensitivity. We also find that sensitivity to interest rate changes tends to be lower among firms that do not report being concerned about working capital management as well as those that do not expect to borrow over the coming year. Perhaps more surprisingly, we find that investment is also less interest sensitive among firms expecting greater revenue growth. These findings seem to be corroborated by a cursory meta-analysis of average hurdle rates drawn from firm-level surveys at different times over the past 30 years, which exhibit no apparent relation to market interest rates.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.002 | 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".