Exporting and Investment Under Credit Constraints
Bibliographic record
Abstract
We examine the relationship between firms’ performance and credit constraints affecting export market entry. The existing research assumes that variation in firms’ financial conditions identifies credit constraints. A critical assumption is that financial conditions do not affect real outcomes (performance, exporting, or investment). To relax this assumption, we focus on the direct effect of firms’ fundamentals and financial conditions on firms’ performance. This approach distinguishes between firms that choose not to export because it is unprofitable from firms that do not export because of binding credit constraints. Our empirical specification allows firms’ characteristics to enter both the selection into exporting and return from exporting regressions. The leverage response heterogeneity identifies the presence of credit constraints. Using administrative Canadian firm-level data, our findings show that new exporters (a) increase their productivity, (b) raise their leverage ratio and (c) increase investment. We estimate that 48 percent of Canadian manufacturers face binding credit constraints when deciding whether to enter export markets. Alleviating these constraints would increase aggregate productivity by 0.97–1.04 percentage points.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".