Regulation, Market Structure and Service Trade Liberalization
Bibliographic record
Abstract
In this paper, we develop a method to quantify the importance of regulation and market structure on the success of trade liberalization. For this purpose, we incorporate a single imperfectly competitive service sector that can take on various market structures into a standard computational general equilibrium model. We apply our framework to analyze the impact of allowing a single foreign telecom provider to enter Tunisia. If the regulation environment guarantees competition, Tunisia's welfare can improve up to 0.65 percent. If a cartel is formed between the domestic incumbent and foreign entrant, however, Tunisia's welfare can drop up to 0.25 percent. Our results thus call for Tunisia among other developing countries to step up its procompetitive regulatory reforms while liberalizing its telecom sector. Dans ce papier, nous développons une méthode permettant de quantifier l'importance de la réglementation et de la structure des marchés sur la libéralisation du commerce et sur son succès. À ces fins, nous incorporons un secteur unique et imparfaitement compétitif pouvant intégrer différentes structures de marché dans un modèle standard de calcul d'équilibre général. Nous appliquons notre cadre d'analyse afin d'étudier l'impact de l'entrée d'un seul fournisseur étranger en Tunisie. Nous trouvons que si la réglementation du marché y garantit la compétition, le bien-être de la Tunisie peut augmenter de 0, 65 %. Cependant, s'il y a formation d'un cartel entre le réseau domestique et l'entrant étranger, le bien-être de la Tunisie peut baisser de 0, 25 %. Nos résultats démontrent que tout en libéralisant son secteur des télécommunications, la Tunisie bénéficierait de réformes visant des régulations pro-compétitives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".