Bibliographic record
Abstract
Firms engaged in product transactions are financially linked through various financing arrangements, most notably trade credit, yet many aspects of this linkage remain unclear. This dissertation aims to contribute to the understanding of firms' financing choices and their role in the dynamics of firm-to-firm trade relationships, particularly in an international context. In the first chapter, I thoroughly survey the existing theories and empirical evidence related to the use of trade credit. The literature on financing practices between firms offers numerous theories that attempt to explain why suppliers often finance transactions. Additionally, in the international context, several studies have explored how exporters' and importers' choices of finance arrangements are influenced by cross-country differences in contract enforcement and financial market conditions. By linking these theories to existing empirical works, I provide readers with a comprehensive overview of this extensive literature. The second chapter of this study examines the evolution of trade finance usage within trade relationships as they interact more over time. Using transaction-level customs data with buyer and seller identities, I find that within-relationship evolution contributes only minimally to the previously implicated and observed pattern in the existing literature. Specifically, the observation that transactions conducted by older relationships are associated with a higher likelihood of using trade credit than new relationships is primarily driven by compositional effects. Relationships of longer longevity consistently use more trade credit at every stage. Furthermore, the heterogeneity in trade finance use across relationships is primarily explained by variations across exporters and importers, rather than variations across products. The last chapter investigates whether the use of trade finance in trade relationships shaped their reaction to the recent global financial crisis. Using the same dataset as in the second chapter, I find that trade relationships relying more extensively on trade credit experienced significantly smaller reductions in trade value and a smaller increase in exit rates compared to others during the crisis. Moreover, this divergence persisted even after the crisis, suggesting a lasting impact of the shock. The results hold firm when accounting for various fixed effects and applying Inverse Propensity Score Reweighting to make trade relationships using trade credit to different extents more comparable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".