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Record W2922582848

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

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsClothingCommodityRules of originWelfareEconomicsInternational tradeRegional tradeScale (ratio)International economicsConsumer welfareEconomic welfareBusinessCommercial policyFree tradeGeographyMarket economy
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.074
GPT teacher head0.234
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2004
Admission routes1
Has abstractyes

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