Default Risk, Productivity, and the Environment: Theory and Evidence from U.S. Manufacturing
Bibliographic record
Abstract
This paper develops a general equilibrium model with heterogeneous firms to analyze the effect of default risk on production-generated pollution emissions. The model analytically divides the effect of default risk into three distinct effects: the market-size, technology-upgrading, and selection effect. Conceptually, an increase in default risk raises equilibrium borrowing costs, thereby precluding investment in a technology upgrade among a subset of firms (technology-upgrading effect). As a consequence, the economy consists of more numerous (market-size effect) but less productive and more pollution-intensive firms (selection effect). Because the effects are confounding in nature, the effect of default risk on aggregate pollution emissions and emissions intensity is an empirical question. To answer this question, this paper estimates the model’s key parameters using a unique dataset with establishment-level credit scores and a composite measure of pollution emissions for a panel of manufacturing firms in the United States. Using a two-step procedure where default risk is estimated in the first stage, the results indicate that the estimated elasticity of emissions intensity and productivity with respect to default risk is 0.89 and -0.16, respectively. Next, I use the theoretical model to leverage the coefficient estimates to estimate the effect of economy wide default risk on aggregate pollution emissions, demonstrating that default risk increases aggregate emissions and emissions intensity, primarily as a consequence of the technology-upgrading effect. Finally, this paper demonstrates that historical changes in economy-wide default risk can generate economically significant changes in pollution emissions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".