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Measuring Regional Trade Integration 

2019· dataset· en· W2971988552 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAuthorea · 2019
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Economic integrationRegional integrationInternational tradeRegional tradeHomogeneousInternational free trade agreementTrade barrierInternational economicsGravity model of tradeBilateral tradeDeveloping countryBusinessEconomicsFree tradeGeographyEconomic growthComputer science

Abstract

fetched live from OpenAlex

Preferential trade agreements are meant to promote trade within the targeted region. Once such agreements are put into effect, it is interesting to investigate the impact and effectiveness in the targeted region. This paper analyzes world trade network as a graph and introduces the measure to evaluate a change in strength or degree of regional integration. The measure or index introduced also captures the contribution of member countries in the regional integration. INTRODUCTION The Number of preferential trade agreements has been increasing since the 1990s and has increased more than four-fold . Do preferential trade agreements foster trade between the member countries? This question has been as important today as it was when such agreements were formed. This paper analyzes world trade network data to answer this question. The main aim of such agreements is to foster mutual trade in the region and they are considered helpful for promoting the regional economic competitiveness as well. Whereas the impact of such agreements is not homogeneous across countries, the impact is large for industrialized nations and small for developing nations. Several measures of regional integration are devised and found in the literature . Intra-regional trade share ( Si ) measures the ratio of regions i intra-regional trade to total trade . Intraregional Trade Share, Intraregional Trade Intensity Index, and Regional Trade Introversion Index measure the degree of trade interdependence in a certain region . This paper analyze Regional Trade Integration Index and introduce an index, which measures the individual contribution of member countries in the given region. METHODOLOGY This paper introduces an index to study regional trade integration and examines trade data before and after the formation of such agreements. Collection of countries around the world and their trade relationship is represented by graph G(W, E) . W represents a set of all countries and E represents a set of all directed edges or all possible exports. Let eij represents the amount of export from country i to j country. REGIONAL TRADE INTEGRATION INDEX (RTII): This index is the ratio of the sum of exports of all member nations within the region to the sum of export of member nations outside the region. The index range from 0 to 1. Index 0 indicates the member countries do not export within region index 1 indicates the members in a group export everything to other group members. Let g(W′, E′) be a subgraph of G(W, E) and W′ ⊆ W and E′ ⊆ E. In real world, graph G(W, E) all countries in the world and their export relationship. And, subgraph g(W′, E′) represent some preferential trade agreements e.g. NAFTA. Regional Trade Integration Index (RTII) for subgraph g is calculated as Ig. $$I_g=}}e_{ij}}} {}e_{ij}}$$ Individual contribution to the regional integration is computed as Individual Contribution Index (ICI) $$ICI_g^{i}=}e_{ij}}} {}e_{ij}}$$ Where, ICIgi is the ICI for a country i in subgraph g. The weighted sum of Individual Contribution Index is equal to Regional Trade Integration Index . $$I_g = \sum}ICI_g^{i}$$ INDIVIDUAL TRADE INTEGRATION INDEX (ITII): The index indicates how integrated a country is in a certain or group. The index compares the country’s export within the region to export outside the region. Or, the index calculates the ratio of the sum of the export of a country to all another member country in a region to the sum of export of the country to all nations around the globe. This index range from 0 to 1. Integration index 0 indicates the country export within the region is 0 or the country doesn’t export at all to the member countries in the region. Integration index 1 indicates the country’s whole export is within the region and exports nothing outside the region. This paper introduces an integration index Igi, which represents the integration of a country i in some region g or trade agreement or subgraph is given by $$I^{i}_{g} = }{e_{ij}}}{\sum{e_{ij}}}$$ DISCUSSION This paper analyzes and investigates the trend of regional integration for regional trade agreements NAFTA. The ITII of a country with respect to a particular region indicates the country’s contribution to regional integration and RTII represents a ratio of the region’s export within the same region to export to all other countries. Figure [109874] shows RTII for the NAFTA region and the ITII for all countries in the that region. The figure shows RTII for the NAFTA region is almost at the same level in 1990 and 2016 with some fluctuations in between. RTII shown by the red solid line in the figure indicates, the percentage of trade export that NAFTA does within the region compared to all around the world. The RTII trend indicates gradual increment from the inception of NAFTA to downward trend particularly during the global financial slowdowns around the year 2008. The RTII was 0.430114 when the group was formed and reached up to 0.576939 in 2002, then decreased to 0.487429 in 2009, eventually follows an increasing trend after the financial crisis of 2008. Downward trend before 2008 and the upward trend after 1999 is noticeable. If we look at the individual countries ITII, Mexico’s the ITII index is highest among the three countries followed by Canada and the USA. Notably, the ITII is moving parallel for three nations.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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 head0.231
GPT teacher head0.245
Teacher spread0.013 · 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