The Effect of Tightening Standards on Automakers’ Non‐compliance
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 study investigates how tightening standards can result in greater non‐compliance, especially when market and regulatory interests are misaligned. We confirm a causal relationship that explains the highly publicized auto industry non‐compliance phenomenon where on‐road NOx emissions exceeded standards. Based on a 15‐year on‐road vehicle emissions dataset covering 148,837 vehicles from 42 automakers in the EU, we use regression discontinuity to identify the causal impact of standards tightening on non‐compliance by controlling other confounding factors. Our results suggest that in the absence of effective monitoring, tightening standards directly drives up automakers’ non‐compliance. Furthermore, we find that automakers facing more intense substitution pressure from competitors or with less advanced emissions control technology have a higher non‐compliance rate. Our findings speak to both policymakers as well as managers in the private sector. When setting limit‐based performance goals in situations with conflicting interests and imperfect monitoring, they should anticipate non‐compliance from the regulated parties. Our results suggest that tightening standards in such situations should be accompanied by stricter monitoring or other actions that discourage non‐compliance.
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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 | 0.000 | 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 | 0.001 | 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 it