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Enregistrement W2922582848

The Impact of ROO on Africa’s Textiles and Clothing Trade Under AG

2004· article· en· W2922582848 sur OpenAlexaboutno aff
Terrie Walmsley, Sandra A. Rivera

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueGlobal trade and economics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClothingCommodityRules of originWelfareEconomicsInternational tradeRegional tradeScale (ratio)International economicsConsumer welfareEconomic welfareBusinessCommercial policyFree tradeGeographyMarket economy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Objectives: The rules of origin (ROO) agreements have influenced how economic agents invest in liberated markets. In this paper, we will model how the ROO portion of the African Growth Opportunity Act (AGOA) influenced welfare on regional areas of Africa compared to other world regions. We then simulate the lifting of the ROO in the Agreement on Textiles and Clothing and analyze the welfare implications on Africa relative to the rest of the world. Methodology and Data: The data and model used for the analyses are derived from the Global Trade Analysis Project (GTAP), which is widely used for international trade policy analysis. We apply a modified version of the static model to an aggregated version of GTAP v.5 (Hertel and Tsigas, 1997; Dimaranan and McDougall, 2002), which combines detailed bilateral trade, transportation and protection data, and accounts for inter-regional linkages among economies and input/output data bases for inter-sectoral linkages within countries. The model used herein assumes perfectly competitive markets and constant returns to scale technology. The database includes a fully specified record of trade transactions and duties among different regions for the commodities (Gehlhar et al. 1997). The model and database are modified in two important ways to incorporate ROO. First the textiles and clothing commodity in the AGOA countries is split into two distinct commodities: one which does not comply with the ROO and a second commodity which does comply with ROO. There is therefore an additional level of choice for consumers of textiles and clothing. First they choose how much clothing they wish to purchase and then they choose whether they want the ROO compliant variety or the non-ROO compliant variety. Tariffs and quotas (tariff equivalents) are then only reduced on the ROO compliant commodity. The second modification is that imports by firms must now be traced to their source. The data and model are modified so that firms not only chose how much they want to import but also from where. With this modification to the data we can then use an alternative closure to restrict the share of imports from non-AGOA countries and therefore ensure that the ROO restrictions are satisfied for the ROO compliant commodity. This method therefore provides a way of incorporating very simple ROO restrictions. Extending the work of Matoo et al. (2002), we will research how the ROO has been implemented under AGOA, as much of the impact of that trade agreement is linked to the ROO requirements that are in place until November 2004. The Trade Act of 2002 amended AGOA by doubling the limit for clothing made in Sub Saharan Africa beneficiary countries from regional fabric made with regional yards from the previous 1.5 to 3.5 percent of U.S. consumption to a new level of 3 to 7 percent over 8 years (AGOA II). We will assess the impact of the ROO in AGOA, then use that result as the baseline for understanding the impact of the Agreement on Textiles and Clothing (ACT). Success in modeling ROO in CGE models has been limited. This paper will extend the literature in the area, building on work by Rivera, Agama and Dean (2003) to include ROO and hence will give a better understanding of welfare impacts of these agreements. We employ a 14 region (SACU, Other Southern Africa, Rest of SSA, Latin America, Mexico, Central America/Caribbean, China, Old NICs, ASEA, South Asia, USA, Canada, EU-15 and Rest of World) 7 sector aggregation (textiles, clothing, cotton, mining, services, agriculture, and manufacturing) to conduct 2 scenarios: First we execute AGOA and CBTPA, including ROO. Next we simulate the implementation of the ACT in order to better contrast the welfare findings. Anticipated findings: We expect to find that incorporating ROO reduces the beneficial impact of the AGOA agreement on the African economies when compared to studies where tariffs and quotas have been removed without taking ROO into account. Finally when the Agreement on Textiles and Clothing is removed the negative impact on the African economies is expected to be reduced and perhaps even reversed as the African economies are no longer subject to the ROO restrictions. Moreover the African economies are still subject to quotas under the AGOA agreement, following the ATC these quotas will be removed along with the ROO restrictions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,074
Tête enseignante GPT0,234
Écart entre enseignants0,159 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2004
Routes d'admission1
Résumé présentoui

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