Free Trade Agreements (FTAs) are often touted as engines for boosting trade between member countries. But how can we measure an FTA’s effectiveness in simple terms? One straightforward way is to look at changes in trade volumes – are member countries trading more with each other than they did before the agreement? However, effective assessment of the impact of a Free Trade Agreement (FTA) involves a combination of quantitative and qualitative methods which helps evaluate its impact on not only trade flows, but also economic growth, sectoral performance, and other relevant outcomes.

In this blog post, we will explore the key concepts and analytical tools used to assess the effectiveness of FTAs.

Trade Creation vs Trade Diversion: The Basics

When evaluating an FTA’s impact on trade, economists distinguish between trade creation and trade diversion. These concepts, introduced by Jacob Viner in 1950, describe two opposing effects of removing tariffs within a bloc:

• Trade Creation: This is the positive outcome – lower tariffs lead member countries to buy more from each other instead of producing everything domestically. In other words, an FTA “creates” new trade by allowing consumers and firms to access cheaper or better goods from partner countries. For example, the EU–South Korea Free Trade Agreement (2011) significantly boosted bilateral trade, particularly in sectors like automobiles, machinery, and electronics, where South Korea had a comparative advantage. Importantly, this increase did not come at the expense of non-EU suppliers, indicating trade creation rather than diversion.
• Trade Diversion: This is a potential downside – an FTA might cause members to import from each other instead of from non-member countries that might actually be more efficient producers. In this case, trade is simply “diverted” away from a low-cost external supplier to a higher-cost partner, only because the partner enjoys a tariff advantage. For instance, in Mercosur, Brazil and Argentina increased intra-bloc trade in machinery and transport equipment despite lacking comparative advantage, diverting imports from more efficient global suppliers (Yeats, 1998). Early EU integration also led to reduced imports from non-member European countries like EFTA members, signaling diversion (Bayoumi & Eichengreen, 1995).

A successful FTA is generally one where trade creation outweighs trade diversion, meaning the agreement stimulates a net increase in trade and efficiency. Empirical studies try to detect these effects by examining trade patterns. In practice, many analyses find that trade creation has been the dominant effect in major FTAs, with limited evidence of harmful diversion. For example, an IMF study of several trade agreements found significant trade creation in Europe and Asia, and observed clear trade diversion in only one case (NAFTA)¹. Even in NAFTA’s case, the overall verdict has been that the pact greatly expanded regional trade (more on that below) and any diversion was relatively small in context.

Quantitative Methods for Analysing the Impact of FTAs

Gravity Model: The Workhorse for FTA Impact Analysis

One of the most widely used methods to assess FTA effectiveness is the gravity model of trade. In economics, the gravity model is a simple but powerful tool that predicts trade flows based on economic size and distance. It’s analogous to Newton’s law of gravity: larger economies attract more trade, and distance (or other trade costs) reduces trade. A basic gravity equation might say that trade between Country X and Y is proportional to the product of their GDPs (bigger economies trade more) and inversely proportional to the distance between them (distant pairs trade less), among other factors.

To evaluate an FTA, researchers include an FTA dummy variable in the gravity model – essentially an on/off indicator of whether a pair of countries has a free trade agreement. The years for which an FTA exists between two countries/ blocs, the FTA dummy will take a value 1, otherwise 0 for the rest of the years. By analyzing a long time series of data, we can estimate how much having an FTA boosts trade between two countries, after accounting for economy size, distance, and other factors. If the FTA dummy is positive and significant, it suggests the agreement is associated with higher trade volumes between members.

The basic form of the gravity equation is:

log(T_ij) = β₀ + β₁ log(GDP_i) + β₂ log(GDP_j) − β₃ log(Distance_ij) + δFTA_ij + ε_ij

Where:

• T_ij is the value of trade between country i and country j
• GDP_i, GDP_j are the GDPs of the exporting and importing countries
• Distance_ij is the geographical distance between them
• FTA_ij is a dummy variable = 1 if an FTA exists between i and j
• δ captures the average effect of the FTA on trade
• ε_ij is the error term

This is the structural gravity model — it allows us to interpret coefficients more causally, especially when enriched with fixed effects.

Fixed Effects and Robust Estimation

Modern gravity models go beyond the basic version above. They control for multiple unobserved factors using fixed effects, which account for characteristics that don’t vary over time or across countries. These refinements improve causal identification and reduce bias. Common specifications include:

• Country-Pair Fixed Effects: Control for all time-invariant factors unique to a country pair — geography, shared language, colonial history, etc.
• Time Fixed Effects: Control for global shocks in a given year — financial crises, pandemics, global demand booms.
• Exporter-Year and Importer-Year Fixed Effects: Account for country-specific trends like GDP growth, trade policy changes, or currency movements.
• Product Fixed Effects (if using disaggregated HS-level data): Useful when analyzing FTA impact at HS 6-digit or 4-digit product level, to control for product-specific trends (e.g., rising global demand for electronics or food staples).

So a more robust gravity model specification would look like:

log(T_ijkt) = α_ij + γ_k + λ_t + δFTA_ijt + ε_ijkt

Where:

• T_ijkt is trade in product k between countries i and j in year t
• γ_k is a product fixed effect
• α_ij, λ_t are country-pair and year fixed effects
• δ captures the effect of the FTA

This structure ensures that we compare trade between FTA partners before and after the agreement — controlling for all other influences — and that we compare this against non-FTA pairs over the same time period.

Fixed effects matter because FTAs are not randomly assigned — countries that already trade a lot or are politically aligned may be more likely to sign FTAs. Without fixed effects, a gravity model might overstate the true FTA impact. By using exporter-year, importer-year, and country-pair fixed effects, researchers control for this selection bias and better isolate the causal effect of the agreement.

A landmark study by Baier and Bergstrand (2007)² applied an enhanced gravity model with robust fixed effects to evaluate the impact of FTAs on trade flows across multiple countries over time. Using a panel dataset of 96 countries over 20 years (1960–2000), the authors controlled for country-pair fixed effects, time effects, and endogeneity in FTA formation—factors often overlooked in earlier studies.

They found that, on average, a Free Trade Agreement led to a doubling of bilateral trade between member countries over ten years. This estimate was significantly more precise and causally robust than earlier models because it adjusted for the likelihood that countries with already-high trade volumes are more likely to sign FTAs (selection bias).

This study remains a benchmark in FTA impact assessment because of its methodological rigor. It demonstrated that when you account for hidden factors like political alignment, proximity, or historical trade ties, the trade-creating effect of FTAs is both large and statistically significant.

Gravity-based analyses have been done for many FTAs around the world. They not only tell us the overall boost to trade, but can also be nuanced to detect trade diversion (for instance, by seeing if members import less from the rest of the world post-FTA). The strength of the gravity model is its predictive power and solid theoretical foundation, but it’s essentially a statistical correlation tool. To strengthen causal interpretations (i.e. the FTA caused the increase in trade), researchers often combine gravity models with qualitative techniques as well (discussed later in the post). In sum, gravity models are a key starting point: if an FTA is effective, we should see actual trade between members exceeding the “normal” predicted trade, and gravity analysis makes that clear.

Difference-in-Differences: Isolating the Before-and-After Impact

Another powerful method to gauge an FTA’s impact is the difference-in-differences (DiD) approach. This technique is widely used in social sciences to evaluate policy changes. The core idea is to compare the change in trade for the countries involved in the FTA (before vs. after it took effect) with the change in trade for a similar “control group” that did not experience the FTA. This helps isolate the effect of the agreement itself, filtering out broader trends that affected everyone.

In practice, implementing a difference-in-differences analysis for trade might work like this:

• Identify the treatment group: the country pair or set of countries that signed the FTA (for example, Mexico–USA for NAFTA).
• Identify a control group: countries or pairs that did not sign a new FTA in that period but are otherwise comparable (for example, the USA’s trade with other similar countries that didn’t have an FTA with the USA).
• Examine trade volumes in both groups before and after the FTA implementation date.
• The FTA’s effect is inferred by the difference in the trade growth of the treatment group relative to the control group. The assumption is that any general global forces (economic growth, commodity price swings, etc.) would affect both groups similarly, so the extra jump seen only for the FTA partners can be attributed to the agreement. This approach is useful for pinpointing causality.

The assumption is that any general global forces (economic growth, commodity price swings, etc.) would affect both groups similarly, so the extra jump seen only for the FTA partners can be attributed to the agreement. This approach is useful for pinpointing causality.

To illustrate, consider a study on the United States–Korea (KORUS) FTA that came into force in 2012. Suppose we observe that U.S.–Korea trade rose by 20% after 2012. Is that because of the FTA, or because of other factors (like recovery from a recession or currency changes)? A difference-in-differences analysis would compare the U.S.–Korea trade growth to the growth in U.S. trade with similar countries (e.g. other industrial nations) over the same period. If U.S. trade with those other countries only grew, say, 5% in that time, while U.S.–Korea grew 20%, we might attribute the extra 15 percentage points to the FTA’s effect. In the KORUS case, researchers indeed set up such an analysis using other major trading partners as a control group, and controlled for factors like economic growth and exchange rates³. By doing so, they aimed to causally isolate the FTA’s impact on trade balances. Their findings supported the notion that the KORUS FTA contributed to a rising U.S. trade deficit with Korea beyond general trends (in line with some political criticisms of that deal).

The basic form of DiD model is:

Y_it = α + β₁Treat_i + β₂Post_t + δ(Treat_i × Post_t) + ε_it

Where:

• Y_it is the outcome variable (e.g., trade volume) for unit i (could refer to country/ country-pair/sector) at time t
• Treat_i is a dummy variable = 1 if unit i is in the treatment group (FTA partner), 0 otherwise
• Post_t is a dummy variable = 1 if year t is after the FTA was implemented, 0 otherwise
• Treat_i × Post_t is the interaction term identifying treated observations in the post-FTA period
• δ is the DiD estimator — it captures the causal impact of the FTA
• ε_it is the error term capturing unobserved influences

Difference-in-differences has been applied in case studies of various FTAs. For example, analysts have used it to assess NAFTA by comparing North American trade growth with that of other similar country pairs in the same era. One challenge, however, is finding a good control group – countries that are similar enough but unaffected by the policy. Nonetheless, when done carefully, DiD provides an intuitive and convincing measure of an FTA’s effectiveness by answering, “Did member countries trade more than they would have, compared to others, after the FTA?”

Trade Elasticity: Gauging How Sensitive Trade Is to Tariff Cuts

Another lens to evaluate FTA effectiveness is through trade elasticities. An elasticity measures how much one thing responds to a change in another – in this case, how much trade volumes respond when trade costs (like tariffs) go down. An FTA typically reduces or eliminates tariffs among members, effectively making imports cheaper. Trade elasticity with respect to tariffs tells us how big of a trade boost to expect from that cost reduction.

For example, imagine an FTA removes a 10% tariff on a partner’s goods. If the elasticity of import demand is, say, 4, this suggests that the volume of imports could eventually increase by about 40% (4 times the 10% price drop) due to the tariff elimination. Higher elasticities mean trade is very responsive to tariff changes, which implies an FTA can have a large impact on trade volumes; lower elasticities mean the response is more muted. Different products have different elasticities – trade in commodities might respond strongly to price changes, while trade in niche products might be less sensitive.

Economists often estimate these elasticities from data or use established values from prior research. Gravity models themselves are connected to elasticity: the coefficient on trade costs in a gravity equation reflects the average elasticity of trade flows to those costs. A common finding in trade literature is that the elasticity of trade with respect to tariff or trade cost changes is in the range of 4 to 8 for many goods. This implies FTAs can lead to substantial increases in trade volumes given significant tariff cuts. For instance, one European Central Bank study found that incorporating realistic elasticities implies FTAs increase trade by about 54% after ten years on average⁴ – a result consistent with other gravity-based estimates and reflecting a meaningful responsiveness of trade to reduced barriers.

By using trade elasticities, we can also do ex-ante simulations to predict an FTA’s effect (before it happens) and ex-post comparisons to see if the outcome matched predictions. Computable general equilibrium (CGE) models, like the Global Trade Analysis Project (GTAP), use a plethora of elasticities to simulate how much trade might grow under an FTA scenario. If actual trade volumes after an FTA closely track these predictions, it’s a sign the FTA had the expected impact. If not, it might mean other frictions (like non-tariff barriers or supply constraints) held back trade growth.

Real-World Evidence: NAFTA, ASEAN, EU, and More

It’s helpful to put these concepts into context with a few real-world examples. FTAs have been studied extensively – let’s look at what happened with some major agreements in terms of trade volumes and what analyses have found:

• NAFTA (North American Free Trade Agreement) – Enacted in 1994 between the U.S., Canada, and Mexico, NAFTA is often cited as a clear case of trade volume expansion. In the two decades after NAFTA’s implementation, regional trade tripled under the agreement⁵. In dollar terms, trade among the three countries soared from about $290 billion in 1993 to over $1.1 trillion by 2016. Studies using gravity models and general equilibrium analysis have quantified NAFTA’s effect: one detailed study by economists Lorenzo Caliendo and Fernando Parro found that NAFTA’s tariff reductions led to a 118% increase in Mexico’s trade with NAFTA partners, a 41% increase for the U.S., and an 11% increase for Canada, relative to what would have happened without the agreement⁶. The larger boost for Mexico reflects how the FTA opened access to the huge U.S. market, whereas the U.S. and Canada were already trading heavily (Canada also had a prior FTA with the U.S.). In terms of trade creation versus diversion, research generally finds NAFTA created a substantial amount of new trade. There was some trade diversion – Mexico and Canada started importing relatively more from each other and the U.S., potentially at the expense of some non-NAFTA countries – but overall trade volumes and efficiency improved. In fact, an analysis shortly after NAFTA’s launch found that other factors (like Mexico’s 1990s unilateral liberalization and peso crisis) also affected trade, but NAFTA still had a noticeable positive though not enormous impact in the early years⁷. Over time, the consensus is that NAFTA significantly increased North American integration, as evidenced by the explosion of supply chains (e.g. auto parts crisscrossing the three countries) and the trade stats above.
• European Union (EU) and its Predecessors – Europe’s experience is often held up as an example of deep trade integration. Starting from the European Economic Community (EEC) in 1957, European countries eliminated tariffs internally and gradually formed a single market. The effect on trade volumes has been dramatic. European intra-regional trade became the dominant share of their trade: by the 2000s, a majority of EU countries’ exports were going to other EU members . (For perspective, intra-EU exports as a share of total exports grew steadily – rising about 8 percentage points from the early 1980s to mid-2010s to reach roughly two-thirds of total trade.⁸) Essentially, most trade for EU countries is now with each other, which signals a high degree of trade creation. Gravity model estimates confirm huge effects – one study found that EU-type agreements (deep integrations) have some of the largest trade impacts, often doubling or tripling trade among members over time⁹. Importantly, Europe’s integration doesn’t appear to have caused major trade diversion in the long run; while EU members trade a lot with each other, they also remain open to global trade. In fact, the EU often reduced external tariffs as well, and collectively the EU trades trillions with external partners. So the EU is a case where internal trade volumes surged (marking FTA success), and due to the scale of liberalization, it likely increased trade with the world as well (sometimes called “open bloc” trade creation ).
• ASEAN (Association of Southeast Asian Nations) – ASEAN formed a free trade area (AFTA) in the early 1990s, aiming to boost trade among its ten member states. The results have been more modest compared to NAFTA or the EU. Intra-ASEAN trade has certainly grown in absolute terms – as the region’s economies developed, they started trading more with neighbors. However, intra-ASEAN trade as a share of the bloc’s total trade has remained relatively low, around 20–25% in recent years¹⁰. That means ASEAN countries still trade more with outside powers (like China, the U.S., Europe) than with each other. Why might this be? Studies suggest that while AFTA lowered tariffs, other issues like non-tariff barriers, lack of economic complementarity in some cases, and competitive similarities limited the upswing in intra-regional trade. Moreover, ASEAN economies had already been quite open to the world, and global demand (especially from China) grew strongly, so trade with non-members also expanded. That said, there is evidence of trade creation within ASEAN in specific sectors. For instance, Malaysia’s and Thailand’s trade with neighbors in electronics and automotive parts grew notably under regional trade agreements. Some research on the ASEAN-India FTA (which is between ASEAN and an external partner, India) found a significant increase in trade in certain commodities, suggesting the FTA removed barriers that were holding back trade^{11 12}. Overall, ASEAN’s FTA effectiveness can be seen as positive but limited – trade volumes rose, yet integration is shallow compared to say the EU. It underlines that measuring effectiveness isn’t just about one number (like intra-bloc trade share) but understanding industry-level changes and whether an FTA met its goals. In ASEAN’s case, ongoing efforts to address non-tariff measures and improve logistics aim to unlock more intra-regional trade in the future.
• Other Examples: Researchers have analysed many other angles while analyzing the impact of FTAs. In India, for example, Veeramani and Dhir¹³ have studied trade patterns and emphasized the role of global production networks in enhancing trade. Their work suggests that simply signing FTAs may not be a magic bullet unless countries also plug into global value chains and boost their competitiveness which would significantly increase India’s exports and job creation. This implies that to maximize an FTA’s benefits (i.e., translate tariff cuts into actual trade growth), complementary policies and business responses matter. It’s a reminder that measuring FTA effectiveness isn’t just a scorecard of trade stats – it ties into how industries adapt and innovate under the new trading conditions.

Complementing the Numbers: The Role of Qualitative Assessment in FTA Evaluation

While quantitative models like gravity equations and difference-in-differences help isolate the numerical impact of FTAs on trade flows, they don’t capture the full story. Many of the most important effects of an FTA — such as how it reshapes business sentiment, investment climate, supply chains, or regulatory cooperation — often unfold through channels that are better understood qualitatively.

Why Qualitative Assessment Matters?

1. Capturing Institutional and Policy Changes: FTAs often include chapters on standards harmonization, digital trade, intellectual property, dispute resolution, and investment facilitation. These provisions may not immediately show up in trade statistics but can have long-term structural effects. Qualitative assessment helps identify whether these commitments are implemented effectively and whether institutions are adapting accordingly.
2. Understanding Business-Level Impacts: Firm-level interviews, case studies, and exporter surveys help unpack how businesses perceive and utilize FTAs. For example:
   • Are exporters aware of the FTA?
   • Do they face challenges complying with rules of origin?
   • Are certain sectors unable to take advantage due to regulatory or infrastructural bottlenecks? In India, for example, studies have found that a significant share of eligible exporters do not utilize preferential tariffs under FTAs due to documentation hurdles or low awareness (CUTS International, 2018)¹⁴.
3. Assessing Sectoral Readiness and Competitiveness: Even if tariffs are reduced to zero, some sectors may not benefit if they lack scale, technology, or access to inputs. Qualitative frameworks help identify gaps in competitiveness — such as those related to skill levels, logistics, or product quality — which quantitative models might ignore.
4. Policy Feedback and Course Correction: Qualitative evaluations also allow for mid-course corrections. By collecting feedback from industry bodies, export promotion councils, or chambers of commerce, governments can refine FTA outreach strategies, simplify documentation, or adjust support schemes for underperforming sectors.

Methods Commonly Used

• Stakeholder Consultations and Focus Group Discussions (FGDs): With exporters, trade associations, freight forwarders, and customs officials. These reveal ground-level frictions not captured by models — such as port delays, hidden NTBs, or interpretational ambiguities in the FTA text.
• Exporter Surveys and Utilization Audits: Used by agencies like ADB, UNESCAP, and national ministries. These tools measure the rate of utilization of FTAs (i.e., how many eligible firms actually use the preferential tariffs), and the reasons for non-use.

These methods help to reveal how FTAs impact sectors differently — for example, how pharmaceuticals or electronics respond to tariff preferences versus low-value or informal sectors. They also help in formulating Policy Readiness Frameworks, that is, assessing whether a country’s domestic laws, institutions, and infrastructure are aligned with commitments in the FTA (e.g., labor standards in RCEP, data rules in EU FTAs).

Real-World Examples

• India’s FTAs with ASEAN and Japan have seen low utilization, especially in sectors like auto parts and processed foods, due to difficulties in meeting rules of origin and complex documentation (FICCI, 2020¹⁵; RIS, 2019¹⁶).
• A qualitative review by UNCTAD of the African Continental Free Trade Area (AfCFTA) emphasized the need for logistics infrastructure and customs modernization before gains could materialize, even though models predicted high trade expansion¹⁷.
• Australia’s Department of Foreign Affairs and Trade (DFAT) regularly conducts post-FTA business consultations, feeding insights into negotiation updates and business support tools¹⁸.

FTA Effectiveness Given India’s Evolving FTA Landscape

India’s renewed push toward bilateral and regional trade agreements makes the question of FTA effectiveness more relevant than ever. In recent years, India has actively pursued high-stakes negotiations with the European Union (EU) and Chile, while also having concluded the much-anticipated India–UK Free Trade Agreement. These agreements are positioned as strategic tools not only to expand market access but also to drive economic diplomacy, integrate into global value chains, and attract foreign investment.

The India–UK FTA, for instance, marks one of India’s most ambitious trade deals with a developed economy outside Asia. Early analyses suggest strong potential for trade creation, especially in sectors like textiles, leather, pharmaceuticals, and professional services. However, actual benefits will depend on how effectively Indian exporters can navigate rules of origin, certification processes, and mutual recognition agreements.

Similarly, the India–EU FTA — currently under negotiation — is expected to be a comprehensive deal encompassing not just tariff cuts, but also chapters on sustainability, digital trade, and investment protection. Evaluating such agreements will require a robust mix of quantitative tools (to assess trade volume changes) and qualitative methods (to understand regulatory and institutional alignment).

As India seeks to position itself as a trusted global trading partner, understanding and measuring the effectiveness of these FTAs is not just a theoretical exercise — it’s a crucial policy tool for ensuring these agreements deliver tangible economic gains.

Conclusion

Free Trade Agreements can be transformative – but we rely on careful measurement to understand their true impact. By focusing on trade volumes, we use indicators like intra-bloc trade growth and methodologies like gravity models, difference-in-differences, and trade elasticity estimates to gauge effectiveness. These tools help answer: Did the FTA boost trade as expected? How much of that trade is newly created versus diverted? The examples of NAFTA, the EU, and ASEAN illustrate that while outcomes differ, FTAs generally tend to increase trade among members, often dramatically. Academic studies and case analyses back this up with hard numbers, finding, for example, that FTAs can double trade over a decade or significantly shift import-export patterns in specific sectors.

For policymakers and the public, these measurements are invaluable. They cut through political rhetoric to show whether an agreement is delivering economically. And for the general reader, the take-home message is clear: one of the best ways to judge an FTA’s success is to look at the trade data – if member countries are trading a lot more freely and extensively with each other than before, that’s a strong sign the FTA is doing its job. Of course, not all effects are equal for all partners (some countries or industries benefit more than others), so nuanced analysis is key.

However, to truly understand whether an FTA is “working,” we must look beyond tariff lines and trade flows. Quantitative models tell us how much trade has changed, but qualitative methods explain why it changed, who benefited, who didn’t, and what structural shifts were triggered. Together, they offer a complete picture — essential for making trade policy inclusive, effective, and future-ready.

References

1. Eicher, T., Henn, C., & Papageorgiou, C. (2008). Trade Creation and Diversion Revisited: Accounting for Model Uncertainty and Natural Trading Partner Effects (IMF Working Paper No. WP/08/66). International Monetary Fund. https://www.imf.org/external/pubs/ft/wp/2008/wp0866.pdf ↩︎
2. Baier, S. L., & Bergstrand, J. H. (2007). Do free trade agreements actually increase members’ international trade? Journal of International Economics, 71(1), 72–95. https://doi.org/10.1016/j.jinteco.2006.02.005 ↩︎
3. Kim, H., Kim, M. H., Sadana, D., & Zhang, J. (2024). Was the KORUS FTA a horrible deal? (AUWP 2024-07). Department of Economics, Auburn University. https://cla.auburn.edu/econwp/Archives/2024/2024-07.pdf ↩︎
4. Franco-Bedoya, S., & Frohm, E. (2020). Global trade in final goods and intermediate inputs: Impact of FTAs and reduced “border effects” (ECB Working Paper No. 2410). European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2410~09d4199b9f.en.pdf ↩︎
5. Council on Foreign Relations. (2020, January 24). NAFTA’s economic impact. https://www.cfr.org/backgrounder/naftas-economic-impact ↩︎
6. Caliendo, L., & Parro, F. (2015). Estimates of the trade and welfare effects of NAFTA. The Review of Economic Studies, 82(1), 1–44. https://sites.google.com/site/lorenzocaliendo/research/CP ↩︎
7. Krueger, A. O. (1999). Trade creation and trade diversion under NAFTA (NBER Working Paper No. 7429). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w7429/w7429.pdf ↩︎
8. Darvas, Z. (2024, May 8). Chart: Sharp decline in intra-EU trade over the past 4 years. Bruegel. https://www.bruegel.org/blog-post/chart-sharp-decline-intra-eu-trade-over-past-4-years ↩︎
9. Baier, S. L., & Bergstrand, J. H. (2007). Do free trade agreements actually increase members’ international trade? Journal of International Economics, 71(1), 72–95. https://doi.org/10.1016/j.jinteco.2006.02.005 ↩︎
10. Maizland, L., Hong, L., Galina, C., Albert, E., & Fong, C. (2024, May 21). What is ASEAN? Council on Foreign Relations. https://www.cfr.org/backgrounder/what-asean ↩︎
11. Veeramani, C., & Saini, G. K. (2011). Impact of ASEAN-India Preferential Trade Agreement on Plantation Commodities: A Simulation Analysis. Economic and Political Weekly, 46(10), 83–89. https://www.researchgate.net/publication/228348342_Impact_of_ASEAN-India_Preferential_Trade_Agreement_on_Plantation_Commodities_A_Simulation_Analysis ↩︎
12. Rahman, M. M., & Shahbaz, M. (2018). Effects of ASEAN Free Trade Agreement on ASEAN trade: A panel data analysis. Asian Economic Modelling, 2(2), 109–117. https://doi.org/10.1016/j.asemod.2018.11.002 ↩︎
13. Veeramani, C., & Dhir, G. (2019). Dynamics and determinants of fragmentation trade: Asian countries in comparative and long-term perspective (Working Paper No. 2019-040). Indira Gandhi Institute of Development Research. http://www.igidr.ac.in/working-paper-dynamics-determinants-fragmentation-trade-asian-countries-comparative-long-term-perspective/ ↩︎
14. CUTS International. (2018). Maximising the gains from FTAs: A case study of India’s FTAs with ASEAN and Japan. Jaipur: CUTS Centre for International Trade, Economics & Environment (CITEE).
    Retrieved from https://cuts-citee.org/pdf/study-maximising-the-gains-from-ftas.pdf ↩︎
15. Federation of Indian Chambers of Commerce & Industry (FICCI). (2023). Leveraging FTAs for exports: Special focus on agriculture. https://ficci.in/public/storage/sector/Report/22500/U9JYnWhjFQCzxjuNf7cMcC1IEiXbTScm1ctZgypj.pdf ↩︎
16. Seshadri, V. S. (2016). India-Japan CEPA: An appraisal (Research and Information System for Developing Countries Report). RIS. https://ris.org.in/sites/default/files/Publication/India-Japan%20CEPA%20Report_2016.pdf ↩︎
17. United Nations Conference on Trade and Development (UNCTAD). (2020). Economic Development in Africa Report 2020: Tackling Illicit Financial Flows for Sustainable Development in Africa (AfCFTA chapter).
    Retrieved from https://unctad.org/webflyer/economic-development-africa-report-2020 ↩︎
18. DFAT – Have your say: public consultations on FTAs ↩︎