Finance and Green Growth: A Comment on De Haas and Popov (2023)
Bibliographic record
Abstract
De Haas and Popov (2023) used a forty-eight-country, sixteen-industry and twenty-six-year panel to test how the size and structure of a country’s financial sector affect CO|$_2$| emissions. This comment revisits the results by correcting two coding errors: failing to cluster the SEs and improperly implementing the generalised method of moments (GMM) estimator. Robust SEs are only significant at 10%, while the magnitudes of the GMM results are reduced by a factor of 2 to 4.1 There are inconsistencies between how SEs are calculated in the scripts provided by De Haas and Popov (2023), and how they are described in the main text. For their country panel, the authors stated that the SEs are clustered at the country level. However, in their script, the authors either only adjusted the SEs to account for heteroscedasticity or did not make any SE adjustments. The SEs in Table A1 in Appendix A are clustered at the country level for the first four columns, and account for heteroscedasticity for the GMM estimator. Compared to the results in Table 2 of De Haas and Popov (2023), the precision of the coefficients of interest is reduced. The results are now either statistically insignificant or only significant at the 10% level.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".