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Jaime Ahcar Olmos Under the supervision of : Jean-Marc Siroën P SL Université Paris-Dauphine

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## Jaime Ahcar Olmos Under the supervision of : Jean-Marc Siroën P SL Université Paris-Dauphine

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**Deep Integration:Free trade agreements heterogeneity and its**impact on bilateral tradeSéminaire DIAL JaimeAhcar Olmos Under the supervision of : Jean-Marc Siroën PSL Université Paris-Dauphine LaboratoireLEDa-DIAL 24avril, 2014 Contents 1) Introduction 2) Methodology and Data 3) Results 4) Conclusions 5)References**Introduction**• International trade economistshave apparently successfully dealt with the question : does free trade agreements increase trade flows? Rose (2004), Baier & Bergstrand (2007), Martinez et al. (2009) • This has been done neglecting thorny issues like heterogeneity and the concept of deep integration.**Introduction : Questions**• So we now try to answer new questions : • Are all free trade agreements equal? What happensif not ? • Does a deeper free trade agreement really increase trade more than a shallow agreement ? • What would be a good indicator of deep integration like? • Are other provisions related to trade but out of the traditional WTO framework of negotiation really important to expand trade ?**Introduction : Importance**• Preferential Trade Agreements PTA heterogeneity little explored • Indicators of heterogeneity • Implications for the interpretation of PTA coefficients • Implications for our understanding of the limits of trade liberalization**Literature review**• Bourgeois, Dawar & Evnenett (2007). A Comparative Analysis of SelectedProvisions in Free Trade Agreements. • Magee (2008) controlled for some levels of trade integration. • Vicard (2009) on the depth of the agreements found that their trade creation effect does not statistically differ according to the depth of the RTA. • Vicard, V (2011) By using interactions terms found that large, similar and close countriesn benefit more from RTA. This approach doesn’t care about their dose or their design.**Literature Review**• Horn,Mavroidis& Sapir (2010) and non-traditional WTO provisions in the analysis and explored legal enforcement effects, but ramained on the descriptive approach. • Orefice & Rocha (2013) carried out empirical analysis from Horn et al. observarions and found positive evidence. • Dür, Baccini & Elsig (2014) enlarged the database and introduced latent trade analysis.**Database**• Two different classification of agreements databases: 1) WTO(2011) Research division for the World TradeReport 2) Design of Trade Agreements DESTA-WTI (2014) Then, we introduce this information in our gravity model unbalanced panel data set which encompasses 153 countries and 613.030 bilateral trade flowsfrom 1980 to 2012.**Databases and methodology :**• Following (Horn et al.2010)approach: • 1) WTO+provisions:topics under the competenceof the WTO agreements. • 2) WTO-X provisions:topics outside the current mandate of the WTO.” • This database is exploited by Orefice & Roche (2013) • Their regressions account for 66 PTA from 1980–2007. We include in our calculations 103 PTA including agreements for Peru & Colombia from 1980-2012.**Databasevariables**Source: Orefice & Rocha (2013), Horn et al. (2010). WTO(2011) Research division for the World Trade Report**Database variables**Source: Orefice & Rocha (2013), Horn et al. (2010).WTO(2011) Research division for the World Trade Report**Methodology**• Probit approach • We put forward two main kinds of indicators to try to capture deep integration. • They depend ona set of variables (provisions)that characterize PTA texts. • 1) Additive indicators • 2) Distilled indicators taken from PCA related methodologies.**Methodology Contributions: Additive indicators**• Does an additive indicator tell us something about the depth of the agreement? • Reasonably yes. One can expect that more comprehensive agreements are deeper. • Isn’t the magnitude of the reduction on tariffsthe main indicator of depth? Maybe not, as nontariff barriers can play a major role to block trade.**Methodology Contributions :Distilled indicators**• We calculate a Multiple Correspondence Analysis MCA indicator for deep integration: • MCA is a technique that can be applied to obtain an indicator that summarizes in just one dimension the variability of a set of variables related with the content and design of PTA, and then with the depth of the integration.**Limits of the analysis**• “Europe’s single market is probably the best example globally of successful deep integration” ClaarS. & Nölke A. (2010). • Nevertheless, because of methodology consistency requirements, Dür et al.(2014) additive indicator give UE 1992 single market agreement a 5 while Colombia - USA receive a 7 • Orefice & Rocha gives 6-11 to EU27 but 9-27 to EU-Chile. • Enforceability of the agreements. Too subjective, it implies going into the nuances of the language in PTA texts.**MethodologicalLimits**• Due to the large size of our data set, we can not completely control for multilateral resistance in all specifications. Anderson and Van Wincoop (2003);Baldwin & Taglioni (2006) • We try to minimize this setback by introducing time fixed effects and time invariant fixed effects for importers and exporters countries.**Theoretical Model**xij is export fromregioni to region j, σis the elasticity of substitution between all goods, tijis a tradecost factor between i and j; yi and yjare GDP in region i and region j; yWis world nominal income; k is a constant, (1 – σ) and (1 – σ)ρ are coefficients.**Probit model with single EV for the median rta coeffient on**some WTO+ provisions (1)**Probit model with single EV for the median rta coeffieient**on WTO-X provisions**Results**• On a probit model regression with single EV. No evidence that the presence of any particular provisionunder WTO+ would increase trade above the median level, which PTAsare supposed to do. • Only 3 out of 36 WTO-X provisions show significant estimators : environmental laws, labour market regulation and consumer protection. All three show a negative coefficient. • It could open the door to the possibility of PTA that deters trade rather than promote it.**Results**• Robustness. Possible bias in the estimation of the individual PTA coefficients and colinearitybetween provisions. • A better way could be capture the whole variability of the provisions in just one indicator. • This indicator would give us a clue about the depth of the integration.**Specification to estimate with PPML: Control for large zero**value flow observations in GM data sets and gives consistent estimates. (Silva & Tenreyro, 2006; 2011)**Deep integration: Latent trait analysis (Rasch) indicator**Dur and al (2014) and additive indicator on levels and logs. DESTA. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1**Deep Integration: Additive indicators and its log from WTO+**and WTO-X. PPML estimator. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1**Conclusions**• We are now more aware that not all PTA are equal. This would imply that much of previous estimations on the average effect of PTA on trade could be biased. • Through different indicators we have confirmed that deeper PTAs increase trade more than shallow ones. However these indicators also present limits to their interpretation. A better definition of deep integration continues to be a challenge.**Conclusions**• A better deep integration indicator should show EC model as the deepest. Meanwhile, these imperfect indicators present enough power to give us sufficient clues about the direction of the impact of heterogeneity of the agreements on trade. • Other provisions out of the traditional WTO scope present significant positive impact on trade. Yet, some of them could individually bear a negative impact. • More research is needed specially to try to identify which provisions or combination of provisions presents a higher impact.**Data Set : Sources**PTA provisionas database : WTO(2011) Research division for the World Trade Report. http://www.wto.org/english/res_e/publications_e/wtr11_dataset_e.htm World Trade Institute(2014) . The Design of trade agreements database DESTA. Bilateral Exports: International Monetary Fund (IMF)Direction of Trade Statistics Database DOTS (2013). Current GDPand population: World Development Indicators (WDI) database, World Bank, (2013) Area, Island and Landlocked, constructed by the author based on the World Factbook from the Central Intelligence Agency of the United States of America (CIA) Weighted distance, contiguity, col45 and comlang_eth9 : CEPII (2013): Head, K., Mayer, T. & Ries, J. (2010), Gravity dataset, obs. till 2006. Regional Trade Agreements: constructed by the author, based on the Regional Trade Agreements Information System (RTA-IS), World Trade Organization WTO (2013) GATT membership: constructed by the author based on the World Trade Organization WTO information (2013). CEPII: Head, K., Mayer, T. & Ries, J. (2010), Gravity dataset, obs. Till 2006. OCDEmembership: constructed by the author based on the Organisation de coopération et de développement économiques OCDE (2013) information.**References**• Horn, H., Mavroidis, P. C. and Sapir, A. (2010), “Beyond the WTO? an anatomy of EU and US preferential trade agreements”, The World Economy 33(11): 1565-1588 • Bourgeois, J., K. Dawar and S.J. evenett. 2007. A Comparative Analysis of Selected Provisions in Free Trade Agreements, Study commissioned by DG Trade, Brussels: European Commission, mimeo • Claar S. & Nölke A. 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