Quantifying the Mega-regional Trade Agreements

The document assesses the capabilities of various trade models—primarily Computable General Equilibrium (CGE) models—in quantifying the effects of mega-regional trade agreements like the TPP and TTIP. It identifies significant gaps in modelling foreign direct investment, value chains, and 'behind the border' issues such as intellectual property and government procurement. The authors argue that while CGE models are the most comprehensive tool available, they must be supplemented by gravity models, partial equilibrium models, and meta-analysis to provide a realistic assessment of these complex agreements.

• Computable General Equilibrium (CGE) models are the most comprehensive tool for analysing mega-regional agreements because they are the only models with the structural features needed to cover services, investment, and 'behind the border' issues alongside traditional tariffs.
• Current CGE models have significant limitations: few model foreign direct investment (FDI), and even fewer capture Mode 3 sales or Mode 4 labour movement.
• No major CGE model effectively captures the impact of tariffs and non-tariff barriers (NTBs) on global value chains or the effects of tariff escalation on the composition of trade between raw and processed goods.
• Quantifying the impact of Rules of Origin (ROOs) remains imprecise, often relying on 'guesstimation', and the specific effect of ROOs regionalization on input tradability is not currently tractable in these models.
• Several key areas of mega-regional agreements are currently beyond the reach of modelling due to data shortages, specifically government procurement, e-commerce, labour and environmental chapters, and intellectual property (IPR) chapters focusing on pharmaceuticals and media content.
• The impact of intellectual property rights (IPR) is difficult to model because it acts as a 'two-edged sword', providing incentives for innovation while simultaneously slowing the diffusion of that innovation.
• Government procurement is difficult to quantify in CGE models because databases do not map cross-border transactions by institutional sector, and much of this trade occurs through commercial presence (Mode 3).
• Environmental and labour standards are often modelled in a way that attributes welfare losses to their enforcement, as some models assign negative weights to these chapters by treating them as increases in non-tariff barriers for goods.
• Because no single model is fully comprehensive, the most effective way to capture the impact of mega-regionals is through a meta-analysis of a suite of CGE-based studies, using satellite models for detail and sufficient statistics for reality checks.

APA

Narayanan, B., Ciuriak, G. D., & Singh, H. V. (2015). Quantifying the Mega-regional Trade Agreements. International Institute for Sustainable Development. https://www.iisd.org/system/files/publications/quantifying-mega-regional-trade-agreements.pdf

Chicago

Narayanan, Badri, G. Dan Ciuriak, and Harsha Vardhana Singh. Quantifying the Mega-regional Trade Agreements. International Institute for Sustainable Development, 2015. https://www.iisd.org/system/files/publications/quantifying-mega-regional-trade-agreements.pdf.

Wikipedia

{{cite report |last1=Narayanan |first1=Badri |last2=Ciuriak |first2=G. Dan |last3=Singh |first3=Harsha Vardhana |title=Quantifying the Mega-regional Trade Agreements |publisher=International Institute for Sustainable Development |date=10 April 2015 |url=https://www.iisd.org/system/files/publications/quantifying-mega-regional-trade-agreements.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{narayanan2015quantifying, author = {Narayanan, Badri and Ciuriak, G. Dan and Singh, Harsha Vardhana}, title = {{Quantifying the Mega-regional Trade Agreements}}, institution = {International Institute for Sustainable Development}, year = {2015}, month = apr, url = {https://www.iisd.org/system/files/publications/quantifying-mega-regional-trade-agreements.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }