Credit Constraints, Technology Upgrading, and the Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper develops a tractable general equilibrium model to analyze the effect of credit constraints on production-generated pollution emissions. The model demonstrates that reducing credit constraints increases the scale of production (scale effect) and increases the number of firms taking up production (market-size effect), while it also reduces emissions per unit of output (technique effect) and increases the share of firms investing in the technology upgrade (upgrading-composition effect). Because the former and latter effects are plausibly confounding in nature, the net effect of credit constraints on pollution emissions is an empirical question. This paper demonstrates, using variation in the timing of credit market reforms, that reducing credit constraints significantly improves air pollution. The results are robust using various approaches, including difference in differences (DID) with a rich set of controls, and an alternative DID approach, wherein the time series data are collapsed around credit reforms into a pre- and post-period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it