Draft: 02.26.09 Direct and Indirect Effects of Voluntary Certification: Evidence from the Mexican Clean Industry Program
Bibliographic record
Abstract
on Economic Growth. We wish to thank Naresh Kumar who processed and provided us with the satellite-based AOD measures for Mexico over the relevant period. We also with to thank Raul Tornel, Jaime Garcia Sepulveda, and Jose Domingo Morales of PROFEPA who provided us with the inspection and certification data and described in some detail the nature of their programs and the Mexican Ministry of Economics, which provided the firm-level data. We also thank seminar participants at Brown, Vanderbilt, University of Illinois, Guanajuato, ITAM, Université de Montréal and Banco de México for their comments and suggestions. All errors are ours. In this paper we develop a model of environmental regulation in a developing country that integrates firm and regulator behavior and incorporates a combination of voluntary and mandatory controls. The implications of this model are then tested using a data set that has been newly assembled to examine the effects of the Mexican Clean Industry Program, in which firms are provided a Clean Industry Certificate if they are willing to establish, via a privately financed audit that, they meet the legal emissions standards. In particular, by imposing some structure on the cost of participation and the cost of compliance and drawing out the resulting implications, we are able to establish using data at the
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.295 | 0.026 |
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".