The Unintended Effects of the TCJA's Interest Deduction Limitation on the Supply Chain
Bibliographic record
Abstract
ABSTRACT We examine the effects of the 2017 Tax Cuts and Jobs Act's (TCJA) interest deduction limitation on suppliers. Using a difference‐in‐differences design, we find that suppliers with customers subject to the limitation (“affected suppliers”) report increased accounts receivable of between 11.2% and 14.9% relative to their pre‐TCJA average accounts receivable. Using a triple differences design, we provide more granular evidence by documenting that the limitation's effects on affected suppliers' accounts receivable are driven by suppliers with customers that report increased trade credit use (i.e., higher accounts payable). In cross‐sectional analyses, we find that the effects are stronger when suppliers are smaller, have higher peer product similarity, operate in industries with low entry barriers, or are in the early stage of their life cycle, consistent with suppliers with weaker bargaining power providing more trade credit to customers compared to other suppliers. Turning to supplier consequences of increased accounts receivable, we find that affected suppliers' days sales outstanding and operating cycles increase. Next, we use path analyses to find that affected suppliers experience lower cash flows and higher risks due to their increased accounts receivable. Overall, our study provides evidence that the interest deduction limitation yielded externalities on affected firms' supply chains.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".