Figure 2 Direct and indirect production gains from TTIP (%)

Source: Vandenbussche et al. (2018)
The EU country that stands to gain most (in relative terms) from TTIP is Ireland, followed by Germany, Belgium, and the Netherlands. This heterogeneity derives from the sectoral composition of these economies and the centrality of their key sectors in the EU production network. This confirms the results of Acemoglu et al. (2012) and others, who claim that it is a sector’s network centrality that determines the impact of an aggregate shock through a cascade effect in the input-output network. A sector that faces large tariff reductions may not have strong aggregate effects if it is not well connected to other sectors, whereas a sector facing small tariff changes can have a large aggregate impact if it is central in the economy and supplies to many other sectors. Hence, from a macro point of view, this granular approach to free trade agreements is important.
ReferencesAcemoglu, D, V M Carvalho, A Ozdaglar and A Tahbaz‐Salehi (2012), “The network origins of aggregate fluctuations”, Econometrica 80(5): 1977-2016.
Berden, K et al. (2009), “Non-tariff Measures in EU-US Trade and Investment. An Economic Analysis”, Ecorys report for EU Commission.
Caliendo, L and F Parro (2015), “Estimates of the Trade and Welfare Effects of NAFTA”, The Review of Economic Studies 82(1): 1-44.
De Gortari (2017), “Disentangling Global Value Chains”, job market paper, Harvard University.
Eaton, J and S Kortum (2002), “Technology, geography, and trade”, Econometrica 70(5): 1741-1779.
European Commission (2018), “Joint U.S.-EU Statement following President Juncker's visit to the White House”, 25 July.
Felbermayr, G (2016), “Economic Analysis of TTIP”, IFO institute working paper.
Imbs, J and I Méjean (2017), “Trade elasticities”, Review of International Economics 25 (2): 383–402.
Johnson, R C (2014), “Five facts about value-added exports and implications for macroeconomics and trade research”, The Journal of Economic Perspectives 28(2): 119-142.
Konings, J and A P Murphy (2006), “Do multinational enterprises relocate employment to low-wage regions? Evidence from European multinationals”, Review of World Economics 142(2): 267–286.
Vandenbussche, H, W Connell and W Simons (2018), “The cost of non-TTIP. A Global Value Chain Analysis”, CEPR Discussion Paper 12705.
Endnotes[1] This joint declarationoccurred in July 2018 when President Juncker visited President Trump.
[2] TTIP was about liberalising trade in goods and services as well as the liberalising investment and public procurement.
[3] NTMs refer to standards and regulatory differences caused by geography, language, preferences, culture or history amongst others.
[4] Felbermayr (2016) provides an excellent overview of existing long-run computable general equilibrium studies of TTIP.
[5] The market structure that we assume is perfect competition, similar to Eaton and Kortum (2002), Caliendo and Parro (2015) and others.
[6] Input-output tables do not reflect the underlying firm heterogeneity in a sector. This discrepancy between firm-level inputs and sector-level inputs was recently documented by de Gortari (2017).But firm-level data is typically only available for one country and does not hold information beyond first round inputs which does not make it suitable for a full assessment of trade policy shocks.
[7] Konings and Murphy (2006) provide estimates of employment elasticities with respect to value added in manufacturing and services.
[8] We use existing sector-level trade elasticities from Imbs and Méjean (2017), to arrive at 16 different sectoral elasticities in manufacturing.
[9] Berden et al. (2009) provides estimates of the reduceable part of NTMs in the US-EU trade relations at sector-level and finds that on average only 50% of NTMs are reduceable.