Financial Constraint and Productivity: Evidence from Canadian SMEs
Bibliographic record
Abstract
The degree to which financial constraint is binding is often not directly observable in commonly used business data sets (e.g., Compustat). In this paper, we measure and estimate the likelihood of a firm being constrained by external financing using a data set of small- and medium-sized Canadian firms. Our measure separates the need for financing from the degree of constraint, conditional on the need for financing. We find that firm size, the current-debt-to-asset ratio and cash flow are robust indicators that can be used as a proxy for financial constraint. The total debt-to-asset ratio is not, however, a statistically significant indicator of financial constraint. In addition, firms with higher cash flow are less likely to need external financing and to be constrained if they do need it. We then estimate firm-level total factor productivity by taking into account the measured likelihood of binding financial constraint. Estimates of the coefficients for labour and capital in the structural estimation of the production function can be downward-biased if financial constraint is omitted, because production inputs are negatively correlated with the likelihood of being constrained by external financing. This in turn leads to an upward bias of total factor productivity estimates, which is about 4 per cent according to our estimation.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".