ex-post evaluation of the implementation of the eu-mexico free … · 2016. 8. 5. · table of...

Ex-post evaluation of the

implementation of the EU-Mexico

Free Trade Agreement

Annexes to the Interim Technical Report

Client: European Commission - DG TRADE

Rotterdam, 11 May 2015

2

NL1027685

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Table of contents

3

Ex-post evaluation of the implementation of the EU-Mexico Free Trade Agreement

Annex A: Literature list 5

A.1 Sources for the economic analysis 5

A.2 Sources for the social analysis 5

A.3 Sources for the environmental analysis 8

Annex B: Economic analysis 17

B.1 Descriptive economic analysis 17

B.2 Gravity analysis 17

Annex C: Social analysis 21

Annex D: Quantitative environmental analysis 23

D.1 Changes in the environmental quality 23

D.2 Data and definition of sectors 24

D.3 Monetizing externalities 26

D.4 Data sources for air pollution 27

Annex E: CGE tables 29

E.1 Macroeconomic effects 29

E.2 Micro-economic effects 35

Annex F: Overview of Tariff Rate Quotas 55

5


Annex A: Literature list

A.1 Sources for the economic analysis

Calderon-Madrid and Voicu (2004), Total Factor Productivity Growth and Job Turnover in Mexican

Manufacturing Plants in the 1990s. Discussion Paper No. 993, The Institute for the Study of Labor

(IZA).

Hernández Laos, E. (2005), Productivity performance in developing countries – Country case study

Mexico. UNIDO Productivity Performance Project. http://www.unido.org/data1/wpd/Index.cfm.

IIDE Discussion Papers 20130101, Institue for International and Development Economics.

Joseph Francois & Olga Pindyuk, 2013. "Consolidated Data on International Trade in Services".

McKinsey&Company (2014), A tale of two Mexicos: Growth and prosperity in a two-speed

economy. McKinsey Global Institute, March 2014.

OECD (2013a), Getting It Right, Strategic Agenda for Reforms in Mexico. OECD Publishing.

OECD (2013b), OECD economic surveys: Mexico. OECD Publishing, May 2013.

Pedro Cavalcanti Ferreira, P., De Abreu Pessôa, S. & Veloso, F. (2011), On the Evolution of TFP in

Latin America. Fundação Getulio Vargas, Ensaios Econômicos, No. 723.

PWC (2013), PwC-IMMEX Maquiladora Guide. Doing Business in Mexico. March 2013.

Rabobank (2014), Country Report Mexico. Rabobank Economic Research Department, 6 June

2014.

The Economist (2013), Mexico’s maquiladoras: Big maq attack. Tijuana, 26th of October 2013.

United Nations & WTO (2012), A practical guide to trade policy analysis.

A.2 Sources for the social analysis

Aleman Castilla, B. (2006): The effect of trade liberalization on informality and wages: Evidence

from Mexico, Centre for Economic Performance,

http://eprints.lse.ac.uk/19779/1/The_Effect_of_Trade_Liberalization_on_Informality_and_Wages_E

vidence_from_Mexico.pdf.

Angeles Villareal, M. (2010): NAFTA and the Mexican economy,

http://fas.org/sgp/crs/row/RL34733.pdf.

Arias, J., Artuc, E., Lederman, D., Rojas, D. (2013): “Trade, Informal Employment and Labour

Adjustment Costs”, World Bank Policy Research Working Paper 6614,

http://econ.worldbank.org/external/default/main?pagePK=64165259&piPK=64165421&theSitePK=4

69372&menuPK=64166093&entityID=000158349_20130920085912&cid=decresearch

http://www.unido.org/data1/wpd/Index.cfm

6


Artecona, R. & Cunningham, W. (2002): Effects of Trade Liberalization on the Gender Wage Gap in

Mexico, http://siteresources.worldbank.org/INTGENDER/Resources/wp21.pdf.

AS (2013): Bringing Youth into Labor Markets: Public-Private Efforts in Mexico, http://www.as-

coa.org/sites/default/files/Mexico%20Country%20Report_Final.pdf.

Bosch, M. & Maloney, W. (2006): Gross Worker Flows in the Presence of Informal Labor Markets.

The Mexican Experience 1987-2002,

http://econ.worldbank.org/external/default/main?pagePK=64165259&piPK=64165421&theSitePK=4

69372&menuPK=64216926&entityID=000016406_20060414122943.

Brambila Macias, J. and Cazzavillan, G. (2009): The dynamics of parallel economies. Measuring

the informal, Research in Economics, Vol.63.

Cardoso, A. (2004): Industrial relations, social dialogue and employment in Argentina, Brazil and

Mexico, Employment Strategy Papers, No. 7, Geneva: ILO,

http://www.ilo.org/wcmsp5/groups/public/---ed_emp/---

emp_elm/documents/publication/wcms_114326.pdf.

Center for Economic Policy and Research (2014): Did NAFTA help Mexico? An assessment after

20 years, http://www.cepr.net/documents/nafta-20-years-2014-02.pdf.

CEPAL (2007): Employment Challenges and Policy Responses in Argentina, Brazil and Mexico,

http://www.cepal.org/publicaciones/xml/3/29503/lcg2333iErnstOtros.pdf.

Coverrubias, A. (2013): Social Norms and Women’s Participation in Salaried Employment: The

Case of the Tehuac´an Region of Mexico, Bulletin of Latin American Research, Vol. 32, No. 1.

Duval-Hernández, R. and Romano, P. (2009): A Cohort Analysis of Labor Participation in Mexico,

1987-2009, Discussion Paper, The Institute for the Study of Labor (IZA),

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1472563.

ECLAC (2012): Social protection systems in Latin America and the Caribbean: Mexico,

http://www.cepal.org/cgi-

bin/getProd.asp?xml=/publicaciones/xml/6/48986/P48986.xml&xsl=/publicaciones/ficha-

i.xsl&base=/publicaciones/top_publicaciones-i.xsl.

FAO (2010): Gender dimensions of agricultural and rural employment: Differentiated pathways out

of poverty - Status, trends and gaps, http://www.fao.org/docrep/013/i1638e/i1638e00.htm.

Human Rights Watch (2014): World Report 2014, Mexico, http://www.hrw.org/world-

report/2014/country-chapters/mexico?page=3.

Jonkin, J. (2009): Labour and Its Discontents: The Consequences of Orthodox Reform in

Venezuela and Mexico, The Journal of Development Studies, Vol.45, No.8.

ILO (2010): World Social Security Report, http://ilo.org/global/research/global-reports/world-social-

security-report/lang--en/index.htm.

ILO (2011): Social Dialogue Indicators: International Statistical Inquiry 2008-09.

7


Technical Brief,

http://laborsta.ilo.org/applv8/data/TUM/TUD%20and%20CBC%20Technical%20Brief.pdf.

ILO (2013): Cash transfer programmes, poverty reduction and empowerment of women: A

comparative analysis, http://www.ilo.org/wcmsp5/groups/public/---dgreports/---

gender/documents/publication/wcms_233599.pdf.

ILO (2013): Green Jobs in Mexico, http://empleosverdes.mex.ilo.org/wp-

content/uploads/byhand/GJ_Boletin_in_English.pdf.

ILO (2013): Labour Overview. Latin America and the Caribbean,

http://www.ilo.org/wcmsp5/groups/public/---americas/---ro-

lima/documents/publication/wcms_242634.pdf.

ILO (2013): Marking progress against child labour - Global estimates and trends 2000-2012,

http://www.ilo.org/wcmsp5/groups/public/---ed_norm/---

ipec/documents/publication/wcms_221513.pdf.

ILO (2014): Global employment trends 2014, http://www.ilo.org/wcmsp5/groups/public/---dgreports/-

--dcomm/---publ/documents/publication/wcms_233953.pdf.

ILO (2014): World of Work Report 2014. Developing with jobs.

http://www.ilo.org/wcmsp5/groups/public/---dgreports/---

dcomm/documents/publication/wcms_243961.pdf.

ILO (2014): World Social Protection Report 2014/2015, http://www.ilo.org/wcmsp5/groups/public/---

dgreports/---dcomm/documents/publication/wcms_245201.pdf.

ITUC (2008): The Global gender wage gap, http://www.ituc-csi.org/IMG/pdf/gap-1.pdf.

ITUC (2012): Frozen in time: Gender pay gap unchanged for 10 years, http://www.ituc-

csi.org/frozen-in-time-gender-pay-gap.html?lang=en.

ITUC (2014): Survey of violations of Trade Union Rights, Mexico, http://survey.ituc-

csi.org/Mexico.html?lang=en#tabs-3.

Jonakin, J. (2009): Labour and Its Discontents: The Consequences of Orthodox Reform in

Venezuela and Mexico, The Journal of Development Studies., Vol.45, No.8.

Koujianou Goldberg, P., & Pavcnik, N. (2003): The response of the informal sector to trade

liberalization, Journal of Development Economics, Vol. 72, No.2.

Martin, G. (2000): Employment and unemployment in Mexico in the 1990s, Monthly Labour Review.

Mejia Pailles, G. (2012): A Life Course Perspective on Social and Family Formation Transitions to

Adulthood of Young Men and Women in Mexico, Department of Social Policy of the London School

of Economics and Political Science.

OECD (2008): Declaring work or staying underground: informal employment in seven OECD

countries, http://www.oecd.org/employment/emp/40843646.pdf.

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OECD (2013): The determinants of informality in Mexico's states.

OECD (2013): A Literature Review on Trade and Informal Labour Markets in Developing Countries,

http://www.oecd-

ilibrary.org/docserver/download/5kg3nh4xwxr0.pdf?expires=1406204561&id=id&accname=guest&c

hecksum=BDB6D3325189E68A71516522C178B42F.

OECD (2013): Harmonised Unemployment Rates, http://www.oecd.org/std/labour-

stats/HUR_11e13.pdf.

OECD (2013): Economic Outlook, How does Mexico compare?

http://www.oecd.org/els/emp/Country%20Notes-MEXICO.pdf.

OECD (2014): Society at a glance highlights: Mexico, http://www.oecd.org/mexico/OECD-

SocietyAtaGlance2014-Highlights-Mexico.pdf.

Sinha, A. (2011): Trade and the Informal economy in Jansen, M., Peters, R., Salazar-Xirinachs, J.

(2011). “Trade and Employment: From Myths to Facts”, EC and ILO.

UCW (2012): The Mexican Experience in Reducing Child Labour, Empirical evidence and policy

lessons, http://ucw-project.org/Pages/bib_details.aspx?id=12300&Pag=0&Country=137.

UCW (2013): The NEET trap: A dynamic analysis for Mexico, Working Paper Understanding

Children’s Work (UCW) Programme, http://www.ucw-

project.org/attachment/Youth_Empl_NEET_TRAP_MEXICO20130328_160056.pdf.

Unicef (2009): Child rights in Mexico,

http://www.unicef.org/rightsite/sowc/pdfs/panels/Child%20rights%20in%20Mexico.pdf.

Unicef (2011): The rights of children and adolescents in Mexico: A present day agenda,

http://www.unicef.org/sitan/files/SitAn_Mexico_Eng_Jan_2011.pdf.

Visser, J. (2013): Data Base on Institutional Characteristics of Trade Unions, Wage Setting, State

Intervention and Social Pacts, 1960-2011 (ICTWSS), Version 4.0, http://www.uva-aias.net/208.

A.3 Sources for the environmental analysis

Ricardo Améndola, Epigmenio Castillo & Pedro A. Martínez (2006): Country Pasture/Forage

Resource Profiles: Mexico, FAO Publications, available at

http://www.fao.org/ag/agp/AGPC/doc/Counprof/PDF%20files/Mexico-English.pdf.

Nathalie Jean Baptiste (2007): People, Nature & Waste. The Ecological Value of Waste in Urban

Areas. Case of Jiutepec, Morelos, Mexico. Available at

http://www.wastestudies.com/download.html.

Ron Bisset et al. (2003): Sustainability Impact Assessment of Proposed WTO Negotiations:

Environmental Services with particular reference to water and waste management. Final Report,

commissioned by DG TRADE. Available at

http://trade.ec.europa.eu/doclib/docs/2005/january/tradoc_121223.pdf.

http://www.fao.org/ag/agp/AGPC/doc/Counprof/PDF%20files/Mexico-English.pdfhttp://www.wastestudies.com/download.html

9


Ulrich Brand et al. (2008): Conflicts in Environmental Regulation and the Internationalization of the

State: Contested terrains. Routledge / RIPE series in global political economy. Available at

http://www.untag-

smd.ac.id/files/Perpustakaan_Digital_1/ENVIRONMENTAL%20POLICY%20Conflicts%20in%20env

ironmental%20regulation%20and%20the%20internationalization%20of%20the%20st.pdf.

Stephen B. Brush (2005): Farmer’s Rights and Protection of Traditional Agricultural Knowledge,

CAPRi Working Paper No. 36, International Food Policy Research Institute.

Calder, T. Hofer, S. Vermont and P. Warren (2007):Towards a new understanding of forests and

water. In: Unasylva 229, Vol 58, pp. 3-10, available at

ftp://ftp.fao.org/docrep/fao/010/a1598e/a1598e00.pdf.

Victor G. Carreón-Rodriguez et al. (2003): The Mexican Electricity Sector: Economic, Legal and

Political Issues, Stanford Program on Energy and Sustainable Development Working Paper 5,

available at http://pesd.fsi.stanford.edu/sites/default/files/WP5%2C_10_May_2004.pdf.

Beatrice Chaytor (2009), Environmental provisions in economic partnership agreements:

Implications for developing countries, in: BIORES – Analysis and news on trade and environment,

Vol 3:1, available at http://www.ictsd.org/bridges-news/biores/news/environmental-provisions-in-

economic-partnership-agreements-implications#_ftn18.

Commission for Environmental Cooperation (2004): Maize and Biodiversity. The Effects of

Transgenic Maize in Mexico. Available at http://www3.cec.org/islandora/en/item/2152-maize-and-

biodiversity-effects-transgenic-maize-in-mexico-key-findings-and-en.pdf.

CONAGUA (2008): National Water Program 2007-2012. Published by SEMARNAT. Available at

http://www.conagua.gob.mx/english07/publications/National_Water_Program_2007-2012.pdf.

CONAGUA (2010): Statistics on Water in Mexico, 2010 edition. Published by SEMARNAT.

Available at http://www.conagua.gob.mx/english07/publications/EAM2010Ingles_Baja.pdf.

CONAGUA (2013): Estadísticas del Agua en México, Edición 2013. Published by SEMARNAT.

Available at http://www.conagua.gob.mx/CONAGUA07/Noticias/SGP-2-14Web.pdf.

Deloitte Development LLC (2014), Mexican energy reform: Opportunity knocks, available at

http://www.deloitte.com/assets/Dcom-

UnitedStates/Local%20Assets/Documents/Consulting%20MOs/CSMLs/us_consulting_MexicoEner

gyReformPaper2014FINAL_0342014.pdf.

The Economist (2014), Mexico’s reforms: The power and the glory, available at

http://www.economist.com/news/americas/21606269-foreigners-enthuse-over-enrique-pe-nietos-

reforms-mexicans-are-warier-power-and.

Environmental Protection Agency / SEMARNAT (2006): 1999 Mexico National Emissions Inventory.

Executive Summary, available at

http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_exec_summary.pdf.

Environmental Protection Agency / SEMARNAT (2006): 1999 Mexico National Emissions Inventory:

Final, available at http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf.

http://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/ENVIRONMENTAL%20POLICY%20Conflicts%20in%20environmental%20regulation%20and%20the%20internationalization%20of%20the%20st.pdfhttp://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/ENVIRONMENTAL%20POLICY%20Conflicts%20in%20environmental%20regulation%20and%20the%20internationalization%20of%20the%20st.pdfhttp://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/ENVIRONMENTAL%20POLICY%20Conflicts%20in%20environmental%20regulation%20and%20the%20internationalization%20of%20the%20st.pdfftp://ftp.fao.org/docrep/fao/010/a1598e/a1598e00.pdfhttp://pesd.fsi.stanford.edu/sites/default/files/WP5%2C_10_May_2004.pdfhttp://www.ictsd.org/bridges-news/biores/news/environmental-provisions-in-economic-partnership-agreements-implications#_ftn18http://www.ictsd.org/bridges-news/biores/news/environmental-provisions-in-economic-partnership-agreements-implications#_ftn18http://www3.cec.org/islandora/en/item/2152-maize-and-biodiversity-effects-transgenic-maize-in-mexico-key-findings-and-en.pdfhttp://www3.cec.org/islandora/en/item/2152-maize-and-biodiversity-effects-transgenic-maize-in-mexico-key-findings-and-en.pdfhttp://www.conagua.gob.mx/english07/publications/National_Water_Program_2007-2012.pdfhttp://www.conagua.gob.mx/english07/publications/EAM2010Ingles_Baja.pdfhttp://www.conagua.gob.mx/CONAGUA07/Noticias/SGP-2-14Web.pdfhttp://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/Consulting%20MOs/CSMLs/us_consulting_MexicoEnergyReformPaper2014FINAL_0342014.pdfhttp://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/Consulting%20MOs/CSMLs/us_consulting_MexicoEnergyReformPaper2014FINAL_0342014.pdfhttp://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/Consulting%20MOs/CSMLs/us_consulting_MexicoEnergyReformPaper2014FINAL_0342014.pdfhttp://www.economist.com/news/americas/21606269-foreigners-enthuse-over-enrique-pe-nietos-reforms-mexicans-are-warier-power-andhttp://www.economist.com/news/americas/21606269-foreigners-enthuse-over-enrique-pe-nietos-reforms-mexicans-are-warier-power-andhttp://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_exec_summary.pdfhttp://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf

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European Commission (2007), Mexico Country Strategy paper, available at

http://eeas.europa.eu/mexico/csp/07_13_en.pdf.

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Term Review, Annex, available at

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General for Trade: Market Access Database, available at

http://madb.europa.eu/madb/barriers_details.htm?barrier_id=960156&version=11.

Carolyn L. Deere and Daniel C. Esty (eds.) (2002): Greening the Americas: NAFTA's Lessons for

Hemispheric Trade, MIT Press.

Adrián Fernández-Bremauntz (2008): Air Quality Management in Mexico. In: Journal of Toxicology

and Environmental Health, Part A, 71, pp. 56–62.

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Kevin P. Gallagher (2004): Free Trade and the Environment: Mexico, NAFTA, and Beyond,

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Aurélien Genty et al. (2012): Final Database of Environmental Satellite Accounts: Technical Report

on their Compilation. WIOD Deliverable 4.6.

Ana Givaudan (2014): Overhauling Mexico’s CFE and Electric Power Industry, available at

http://www.renewableenergymexico.com/?p=1098.

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IMF / Philippe Karam (2013): Energy Subsidy Reform. Lessons and Implications. Presentation

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(2011): Transboundary Diagnostic Analysis. Available at

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res/sesiones/tda_gomlm11oct2011.pdf.

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Annex B: Economic analysis

B.1 Descriptive economic analysis

Grubel-Lloyd index

The Grubel-Lloyd index indicates the level of similarity between import and export products (intra-

industry trade). It is calculated based on the following formula:

(1) 𝐺𝐿𝑘𝑖𝑗

= 1 −|𝑋𝑘

𝑖𝑗− 𝑀𝑘

𝑖𝑗|

𝑋𝑘𝑖𝑗

+ 𝑀𝑘𝑖𝑗

where 𝑋𝑘𝑖𝑗

represents country 𝑖 exports to country 𝑗 of good or sector 𝑘. 𝑀𝑘𝑖𝑗

represents imports by

country 𝑖 from country 𝑗 of good or sector 𝑘.

If a country is either only an importer or an exporter of a certain product or product group, the

second term of the formula will equal 1, so that the outcome of the formula is zero. However, if a

country both imports and exports a certain product or product group, the GL index will be closer to 1

as similarity in import and export values increases. So by construction, this index is between 0 and

1, whereby higher indices represent higher levels of intra-industry trade.

Herfindahl index

The Herfindahl index indicates the level of diversification of exports, and is calculated according to

the below formula:

(2) ℎ𝑖 = ∑ (𝑠𝑘𝑖 )

2

𝑘

where 𝑠𝑘𝑖 represents the share of sector 𝑘 in the total exports of country 𝑖 to country 𝑗. Because the

value of 𝑠𝑘𝑖 is always between 0 and 1 and squared, ℎ𝑖 becomes smaller when more products or

product groups are exported, or sectors are relatively small. Therefore, the value of ℎ𝑖 ranges from

1/𝑘 to one.

Balassa index

The Balassa index is used to investigate which sectors of the economy have a revealed

comparative advantage. The index is calculated according to the following formula:

(3) 𝑅𝐶𝐴𝑘𝑖 =

𝑋𝑘𝑖 /𝑋𝑖

𝑋𝑘/𝑋

where 𝑋𝑘𝑖 is country i’s exports of good or sector k, 𝑋𝑖 = ∑ 𝑋𝑘

𝑖𝑘 its total exports, 𝑋𝑘 = ∑ 𝑋𝑘

𝑖𝑖 world

exports of good or sector k and 𝑋 = ∑ ∑ 𝑋𝑘𝑖

𝑘𝑖 total world exports. An 𝑅𝐶𝐴𝑘𝑖 above 1 indicates that

country 𝑖 exports relatively more of good or sector 𝑘 than the world average, and therefore has a

comparative advantage in good or sector 𝑘.

B.2 Gravity analysis

In this annex we describe gravity-based estimation of trade volume effects between the EU and

Mexico, beyond volume effects explained by tariff concessions alone. This requires working with

18


data on Non-tariff barriers. Identifying suitable indicators for NTBs can be particularly challenging.

Data are available for a limited set of countries from UNCTAD. However, the data also have severe

limitations in terms of country coverage. Two other sources used here are the NTM database of the

WTO (based on notifications), and the World Bank’s index of burden from customs procedures.

The WTO database provides sector-specific scores on NTMs, while the World Bank index provides

a measure of cross-cutting regulatory costs. Both have explanatory power in our regressions. On

balance, we believe the WTO database is more comprehensive, and comes closer to requirements

for use in the gravity analysis than the UNCTAD database. The WTO data allow us to calculate, for

the product categories in Table 1, the total count of NTMs and the tariff lines affected within a

product category. This count is the sum of the number of barriers x the number of HS lines

affected. We therefore work with an index defined as 𝑁𝑇𝐵𝑊𝑇𝑂 = ln(1 + 𝑐𝑜𝑢𝑛𝑡). Our key

explanatory variables are summarized in the table below.

The World Bank provides and index ranking with the best regime scoring 7.

According to the World Bank, the “Burden of Customs Procedure” index measures business

executives' perceptions of their country's efficiency of customs procedures. The rating ranges from

1 to 7, with a higher score indicating greater efficiency. We have reversed this scoring, with working

with the log of a burden index ranging from 1=no burden to 7=most burdensome, so 𝑁𝑇𝐵𝑊𝐵 =

ln(𝑏𝑢𝑟𝑑𝑒𝑛).

We also include also include both a dummy variable for whether or not a particular country pair is

covered by a free trade agreement, and a simple classification of FTAs into three groups: shallow,

medium, and deep. This is based on the depth of various FTAs from Dür, Elsig and Milewicz

(2014), but not identical. We have taken the Dür et al scores, combined with supplementary

information on the sample of FTAs, to group FTAs as shallow, medium, and deep. The EU is not

technically an FTA, and we represent this with an additional dummy variable. This indicator variable

for intra-European-Union relationships differentiates between the legal and institutional

harmonization associated with EU membership, which clearly goes beyond the liberalization of

policies in other agreements. It reflects the fact that the EU is itself quite different from an FTA,

deep or otherwise.

Table A.B..1 Explanatory variables

name description

ln(1+tariff) log of the tariff (preference margin vis-à-vis MFN)

ln(distance) log of shipping distance in km

polity index index of political similarity (democratic institutions)

common colony dummy for shared colonial history

common

language

dummy for shared common ethnic language

contiguous dummy for common border

colony dummy for former colonial relationship

FTA dummy for all FTAs

EU dummy for intra-EU trade

medium FTAs that are medium depth

deep FTAs that are relatively deep

FTA x 𝑁𝑇𝐵𝑊𝑇𝑂 interaction of all FTAs with WTO NTB index

medium x

𝑁𝑇𝐵𝑊𝑇𝑂

interaction of medium FTAs with WTO NTB index

19


name description

deep x 𝑁𝑇𝐵𝑊𝑇𝑂 interaction of deep FTAs with WTO NTB index

FTA x 𝑁𝑇𝐵𝑊𝐵 interaction of all FTAs with World Bank NTB index

medium x 𝑁𝑇𝐵𝑊𝐵 interaction of medium FTAs with World Bank NTB index

deep x 𝑁𝑇𝐵𝑊𝐵 interaction of deep FTAs with World Bank NTB index

𝑁𝑇𝐵𝑊𝑇𝑂 An NTB index based on the WTO NTB notifications database.

𝑁𝑇𝐵𝑊𝐵 An NTB index based on the World Bank scoring of the burden of different national customs

regimes.

EUNMEX A dummy for trade between the EU and Mexico.

We work with a standard representation of trade in log-linear form, specifying bilateral trade flows in

levels as an exponential function of a log-linear index that is composed of three classes of

determinants: exporter-specific factors (measuring supply potential of exporting countries),

importer-specific factors (measuring demand potential of importing countries), and bilateral factors

(measuring trade impediments in a broad sense) as identified in Annex Table 1 above. We specify

exporter-specific and importer-specific factors as country fixed effects and parameterize bilateral

factors in the log-linear index as a function of observable country-pair-specific variables. Thereby,

we ensure that the parameters on the later exhibit a structural interpretation that permits using them

in a subsequent comparative static analysis of the EU-Mexico FTA in a CGE model that is

calibrated to data on trade and production at the same level of aggregation, where the trade

equations in the model are consistent with those in the gravity model itself. 1

In formal terms, we start with a standard gravity-based equation for trade flows 𝑞𝑖,𝑗,𝑣 in sector v

between source country i and destination country j. Following convention, we base this estimating

equation on what is called a CES-based functional form for demand. Our estimating equation takes

the following form.

[1] 𝑙𝑛(𝑞𝑘,𝑖,𝑗) = ∑ 𝛽𝑔,𝑘𝑋𝑔,𝑘,𝑖,𝑗𝑔 + ∑ 𝛾3𝑘,𝑖𝐷3𝑘,𝑖𝑖 + ∑ 𝛾4𝑘,𝑗𝐷4𝑘,𝑗𝑗 + 𝑒𝑘,𝑖,𝑗

In equation (1), the terms 𝑋𝑔,𝑘,𝑖,𝑗 correspond to the explanatory variables indexed over g in Annex

Table 1, while the terms D represent importer and exporter dummies, and 𝛽𝑔,𝑘 represents the

vector of pairwise coefficients. Based on regression analysis for all 25 goods sectors modelled in

the main text, we find that the combined effect of the current FTA (the EU-Mexico agreement is

classified as medium) and the various interactions with NTB indicator data yield estimates of

positive trade volume effects between the EU and Mexico, above that explained by tariffs, for two

sectors: petrochemicals, and transport equipment. For these sectors, we have reported the

relevant coefficients (FTA indicators and NTB interactions) for these two sectors. Taking the

combined effects of these terms (interacted by NTB indicators as appropriate) yields an estimated

log increase in trade volumes relative to other country pairs. Dividing this by the tariff elasticity, and

taking the exponent, then yields estimated trade costs. For other transport equipment, we estimate

trade cost reductions, expressed as a tariff equivalent, of 17.73 percent for EU exports to Mexico

and 19.96 percent for Mexican exports to the EU. For petrochemicals we estimates trade cost

reductions, expressed as a tariff equivalent, equal to 13.98 percent in the case of EU exports to

Mexico, and 12.01 percent in the case of Mexican exports to the EU. It should be stressed that the

core FTA agreement itself is focused on tariffs, and so these are the basis for our primary ex-post

analysis of the impact on trade volumes, GDP, and other macro and sector indicators. However,

1 We also treat the trade policy variables as jointly endogenous and pursue a control-function approach to reduce the

parameter bias flowing from that endogeneity. In essence we have expanded on the methodology of Egger et al. (2011) to

encompass different types of trade agreements.

20


we also include the contribution of our estimated non-tariff trade cost reductions in the analysis as

well.

Table A.B.2 Coefficient estimates used for trade cost estimates, PQML estimates, 2011 bilateral

trade data

Transport equipment Petrochemicals

ln(1+tariff) -10.643 -5.250

FTA 1.304 -0.888

FTA x 𝑁𝑇𝐵𝑊𝐵 0.741 0.442

medium x 𝑁𝑇𝐵𝑊𝑇𝑂 0.802

FTA x 𝑁𝑇𝐵𝑊𝑇𝑂 -0.653

medium -2.843 1.660

medium x 𝑁𝑇𝐵𝑊𝐵 -1.115 0.120

EUN-MEX 1.770 1.300

Number of observations 1,473 7,964

Pseudo-R2 0.9109 0.8332

Note: all reported coefficients are significant at the .01 level.

Tariff elasticity for petrochemicals is taken from a regression on all merchandise trade. No WTO NTM notifications are included

in the WTO database for petrochemicals. All regressions include country fixed effects and pairwise distance variables (physical

distance, language, etc.) listed in Appendix Table 1.

21


Annex C: Social analysis

Table A.C.1 Social indicators: baseline and DCFTA effects (numbers are in millions of people)

Baseline Total

effect

Price

effect

Wage

effect

Poverty rate (headcount)

Absolute poverty line 58.34 58.13 58.37 58.10

Relative poverty line 47.76 47.76 47.76 47.69

Extreme absolute poverty line 18.87 18.82 19.02 18.83

Poverty gap

Absolute poverty line 14 14 14 14

Inequality indicators

Decile dispersion ratio (at household level) 9.25 9.25 9.24 9.25

Gini coefficient 0.5848 0.5853 0.5851 0.5846

Dispersion of poverty headcount around poverty line

80% of the absolute line 42.49 42.36 42.55 42.25

120% of the absolute line 69.91 69.75 70.04 69.68

80% of the relative line 34.01 34.04 34.04 34.02

120% of the relative line 60.70 60.71 60.71 60.68

Poverty headcount by sex (absolute poverty)

Male 28.17 28.09 28.20 28.06

Female 30.17 30.03 30.16 30.04

Poverty headcount by age (absolute poverty)

0-5 7.68 7.65 7.68 7.64

6-12 9.81 9.80 9.81 9.78

13-18 7.71 7.68 7.73 7.67

19-65 29.69 29.59 29.72 29.57

66+ 3.44 3.41 3.43 3.43

Poverty headcount by education (absolute poverty)

Illiterate 7.31 7.21 7.31 7.22

Preschool 0 0 0 0

Incomplete primary school 13.99 13.96 13.98 13.97

Primary school 12.79 12.76 12.79 12.74

Incomplete secondary school 2.79 2.79 2.81 2.79

Secondary school 14.26 14.19 14.24 14.20

Incomplete prep school 1.36 1.36 1.36 1.36

Prep school 3.77 3.77 3.78 3.76

Incomplete professional school 0.78 0.78 0.79 0.78

Professional school 1.24 1.22 1.24 1.22

Graduate 0.07 0.07 0.07 0.07

Poverty headcount by place of residence (absolute poverty)

Urban 42.45 42.30 42.46 42.32

Rural 15.89 15.82 15.91 15.79

Poverty headcount by geographical region (absolute poverty)

22


Baseline Total

effect

Price

effect

Wage

effect

North 7.71 7.66 7.72 7.63

North center 12.07 12.06 12.09 12.06

Center 22.58 22.51 22.57 22.52

South 15.98 15.90 15.99 15.89

23


Annex D: Quantitative environmental analysis

Our quantitative environmental analysis focused on the effect of DCFTAs on airborne pollutants

and greenhouse gases. We used the methodology that has been applied in the Trade SIA of

DCFTAs between the EU and respectively Morocco, Tunisia, Egypt and Jordan, with small

adjustments. In a nutshell this approach carefully combines:

results of the CGE modelling;

extrapolation of emissions based on emission intensities and decomposing of the estimated

change into a scale and sector composition effect; and

external costs assessment in the case of greenhouse gases, based on the social cost of carbon

concept.

D.1 Changes in the environmental quality

Quantitative modelling of DCFTA-induced environmental impacts is based on the analysis of the

scale and composition effects. These two channels link changes in emissions directly to changes in

economic activity. We abstract from changes in underlying technology that may reduce emission

intensities per unit of output, since aspects of technological change are not explicitly modelled. We

have used output as indicator of economic activity. Theoretically value added is more directly linked

to the economic activity performed within a sector itself, but in practice the results from the CGE

modelling are almost the same for the two indicators. As changes in output are used as an

indication of industry reorganization throughout this report, it appeared consistent to use output

changes in the environmental analysis as well.

Scale effect

The scale effect represents the changes in the emissions resulting from the increased scale of

production. All things being equal, emissions are proportional to GDP and increase or decrease by

the same factor as economic activity as a whole. The FTA-induced level of airborne emissions with

the scale effect for a given scenario2 (denoted by SC) is equal to:

𝐸𝑖𝑆𝐶 = 𝑒𝑖

0 ∙𝑌𝑆𝐶

𝑌0= 𝑒𝑖

0 ∙ (1 + 𝑔𝑌𝑆𝐶) (1)

where 𝑒𝑖0 is emission of pollutant i in the baseline year, Y is total value added, and 𝑔𝑌

𝑆𝐶 is the

change in percentages of total value added as compared with the base year. Since we use the

changes in GDP as our proxy for the growth in total economic activity, our final formula for the scale

effect is:

𝐸𝑖𝑆𝐶 = 𝑒𝑖

0 ∙ (1 + ∆ 𝐺𝐷𝑃 𝑖𝑛 %) (2)

Composition effect

As a result of the FTA, the economic structure of the country is assumed to have changed with

activities shifting to less or more emission intensive sectors to some extent. Environmental impact

analysis should take this composition effect into account. For a given scenario, the composition

effect for a particular pollutant or greenhouse gas is:

2 Note that in the case of the present study, we only looked at the “scenario” of tariff liberalization, due to the extremely

small results coming from the NTB reduction scenario.

24


𝐸𝑖𝑆𝐶 = ∑ (𝑒𝑘

0 ∙𝑦𝑘

𝑆𝐶 𝑌𝑆𝐶⁄

𝑦𝑘0 𝑌0⁄

∙𝑌𝑆𝐶

𝑌0)𝑘 − ∑ (𝑒𝑘

0 ∙𝑌𝑆𝐶

𝑌0)𝑘 =

𝑌𝑆𝐶

𝑌0∙ ∑ 𝑒𝑘

0 ∙ (𝑦𝑘

𝑆𝐶 𝑌𝑆𝐶⁄

𝑦𝑘0 𝑌0⁄

− 1)𝑘 (3)

where k denotes a sector and y is value added per sector.

Finally the total effect combines the scale and composition effects and demonstrates the net

changes in emissions:

𝐸𝑗𝑆𝐶 = ∑ (𝑒𝑘

0 ∙ 𝑤𝑆𝐶

𝑤0 ∙ (1 + 𝑔𝑌

𝑆𝐶)) 𝑘 (4)

where w is a sector share based on total value added. Since we use the change in GDP as a proxy

for changes in total value added, our final equation is:

𝐸𝑗𝑆𝐶 = ∑ (𝑒𝑘

0 ∙ 𝑤𝑆𝐶

𝑤0 ∙ (1 + ∆ 𝐺𝐷𝑃 𝑖𝑛 %)) 𝑘 (5)

D.2 Data and definition of sectors

Emission data comes from the World Input Output Database (WIOD) Environmental Accounts. This

dataset provides emissions of several air pollutants and greenhouse gases in many countries over

the period of 1995 until 2009, at the level of sectors used in all WIOD datasets. These sectors are

quite close to the sectors used in GTAP-based CGE models, but not fully congruent. In order to

arrive at aggregate sectors matching with the CGE model, a general concordance table was

developed between WIOD and GTAP sectors. For goods, WIOD is organized along NACE sectors,

whereas GTAP follows the ISIC classification. Therefore a GTAP-ISIC concordance table was

linked to the ISIC-NACE matching and this was again linked with NACE-WIOD matching. For

services, GTAP matching with other classifications was not available. Therefore the matching was

done manually, using information on the GTAP and NACE/ISIC services sectors.

The result of this general concordance exercise is a table of 25 sectors (14 goods, 11 services

sectors). This overall concordance was then further aggregated in order to take into account the

specific aggregation of the GTAP / CGE model used in this study. The resulting 20 sectors (10

goods, 10 services sectors) can be seen in the third column in the table below.

CGE model sectors WIOD sectors Aggregate sectors for environmental

analysis

Rice: GP 1, 23 sec15t16 Agriculture, forestry, food, beverages,

tobacco, mining, resource extraction Cereals & oilseeds, oils: GP 2, 3,

5, 21

secAtB, sec15t16

Vegetables, fruit, nuts: GP 4 secAtB

Sugar, cane, beet: GP 6, 24 secAtB, sec15t16

Milk and dairy products: GP 11,

22

secAtB, sec15t16

Beef: GP 19 sec15t16

Other meat: GP 20 sec15t16

Other ag.: 7, 8, 9, 10, 12 secAtB

Food products nec: GP 25 sec15t16

Beverages and Tobacco

Products: GP 26

sec15t16

Fisheries: GP 14 secAtB

25


CGE model sectors WIOD sectors Aggregate sectors for environmental

analysis

Energy: GP 15, 16, 17 secC

Other primary, non-ag: 13, 18 sec15t16, secC

Textiles: GP 27 sec17t18 Textiles and wearing apparel

Wearing Apparel: GP 28 sec17t18

Leather Products: GP 29 sec19 Leather products

Petrochemicals: GP 32 sec23 Petrochemicals

Chemicals: GP 33 sec24, sec25 Chemicals, rubber, plastic

Electrical machinery: GP 40 sec29, sec30t33 Machinery and equipment

Motor vehicles: GP 38 sec34t35 Transport equipment (incl. motor

vehicles) Other transport equipment: GP 39 sec34t35

Other machinery: GP 41 sec29, sec30t33 Machinery and equipment

Metals and metal products: GP

35-38

sec27t28 Metals and metal products

Wood and paper products: GP 30-

31

sec20, sec21t22 Wood and paper products

Other manufactures: GP 34,42 sec26, sec36t37 Other manufactures

Electricity: GP 43 secE Electricity, gas, water

Gas manufacture, distribution: GP

44

secE

Water: GP 45 secE

Water transport: GP 49 sec61 Water transport

Air transport: GP 50 sec62 Air transport

Land, other transport: GP 48 sec60, sec63 Other transport (inland and auxiliary)

Finance: GP 52 secJ Finance and insurance

Insurance: GP 53 secJ

Business services: GP 54 sec70, sec71t74 Business services nec

Communications: GP 51 sec64 Post and telecom

Construction: GP 46 secF Construction

Distribution services: GP 47 sec50, sec51, sec52, secH Trade and distribution

Other services: GP 55, 56, 57 secO, secP, secL, secM,

secN, secQ

Public and personal / recreational

services

Due to the specific sector aggregation of the CGE model in this particular case, the first aggregated

sector became quite comprehensive (mainly because forestry is subsumed under primary sectors

in the CGE model, but together with agriculture and fisheries in WIOD; it was also difficult to match

some agricultural and processed food sectors).

For the rest of the sectors, the coverage is quite detailed; it has to be noted that some minor

mismatches remain between GTAP/ISIC and NACE classifications which do not allow for a 100%

sector match:

Clothing products can be classified as textiles, manufactured products, metal products,

chemicals – the mismatch goes both ways, e.g. WIOD classifies as metal what is textiles in

GTAP, and GTAP classifies as chemicals what is textiles in WIOD;

Sometimes also a goods / services mismatch occurs e.g. in the case of records tapes:

publishing of recorded media vs. machinery and equipment; or photographic equipment:

photographic activities vs. machinery and equipment;

26


Nuclear fuel is in chemicals in GTAP, but in coke/petroleum in WIOD;

Furniture is classified as wood products (GTAP) vs. manufacturing (WIOD).

These mismatches were not taken into account in the aggregation. This means that theoretically,

value added data (sourced from the CGE model, with GTAP classification) include economic

activity in some furniture products, while the corresponding emissions (sourced from WIOD) are

reported under manufacturing. Nevertheless, congruence is large enough to have confidence in the

results – the mismatch products so not have a large share in value added / trade of the aggregate

sector. From an environmental perspective, the non-match of nuclear fuel might be relevant when

interpreting the results.

One additional “sector” appears in WIOD and is used in the calculations: consumption by private

households. Their emissions are quite significant and have been assumed to grow in line with GDP.

Thus for households, the total effect is calculated as

𝐸𝐻𝐻𝑆𝐶 = 𝑒𝐻𝐻

0 ∙ (1 + ∆ 𝐺𝐷𝑃 𝑖𝑛 %), (5)

With HH denoting the households sector.

Emission baseline figures were calculated for each aggregated sector (𝑒𝑘0, with k including all 20

sectors plus private households) using the WIOD data from 2000. These can then be used together

with the output changes per sector (and the change in GDP) as reported in the CGE modelling to

calculate the total, scale and composition effects.

D.3 Monetizing externalities

Unfortunately, no external cost of air pollution data are available for Mexico at a level that would

have been usable in this study (there are studies on the costs of air pollution in Mexico, but they are

often specific for a region or specific in other ways; no overview of the costs of a tonne of air

pollutant emissions in Mexico is available).

For valuating CO2 costs, we rely on the concept of the marginal social cost (MSC) of carbon: this is

the cost accruing to society due to the release of an additional unit of carbon emissions (and its

impact on climate change). According to the Stern Review, the size of the impact “depends not only

on the lifetime of the gas, but also on the size of the stock of greenhouse gases while it is in the

atmosphere, and how uncertain climate change impacts in the future are valued and discounted.”3

It also depends on the valuation of simultaneous impacts in different places. Following from this,

estimates for the MSC of carbon depend on several key parameters:

Discount rate. Discounting is an approach to account for uncertainty about the future and how

economic subjects (= people) deal with it. Economic studies usually assume that people are risk

averse, i.e. they are willing to give up part of their income in order to get a guaranteed income.

Risk aversion also means that present income is preferred to future income, because the future

income is uncertain. Social time preference is usually modelled using an annual real discount

rate between 1 and 10%.4 The social cost over a specified time frame is then calculated as the

net present value (NPV) of the costs occurring;

Greenhouse gas emissions. The stock of greenhouse gases changes over time, where the

change depends on economic activity and climate policy. Therefore estimates for the MSC of

3 Stern, N. (2007). The Economics of Climate Change: The Stern Review. Cambridge: University Press.

4 HM Treasury (2011). The Green Book: Appraisal and Evaluation in Central Government. London: TSO. http://www.hm-

treasury.gov.uk/d/green_book_complete.pdf.

http://www.hm-treasury.gov.uk/d/green_book_complete.pdfhttp://www.hm-treasury.gov.uk/d/green_book_complete.pdf

27


carbon in fact need to be dynamically adjusted to the development of GHG emissions, and they

are different for different emission scenarios. Ambitious MSC of carbon estimates additionally

take the inherent uncertainty of scenario results into account by using Monte Carlo simulations

on the underlying climate impact models;5

Equity weighting. Equity weighting takes into account the different effects climate change has

on different world regions – in economic terms. “Economic theory assumes a declining marginal

utility of consumption, i.e. the same absolute consumption change results in a smaller welfare

change for a rich person than a poor person.”6 Equity weighting takes different income levels in

different regions and at different times into account and usually leads to significantly larger

estimates.

This dependence of MSC estimates on key unknown and changing parameters helps explain the

wide range shown in the literature. Tol (2005) collected 103 estimates from 28 published studies,

arriving at a median of $14/t and a mean of $93/t (in 1995 US$).7 Anthoff (2007), in a study for the

NEEDS report, uses scenario results from the FUND model to calculate MSC of carbon estimates

for several emission years with different parameters.8 Depending on the rate of time preference

equity weighting and the way of taking into account scenario uncertainties he arrives at values for

emissions in the year 2005 ranging from -$34.0 (with a 3% rate of time preference and an equity

weighting approach that normalizes to EU income levels) to $993.0 (with a 0% rate of time

preference and the same equity weighting approach), each without uncertainty calculations.

Given this large range of MSC of carbon estimates and their sensitivity to parameter assumptions, it

seemed most reasonable to draw on median values and estimates used in previous reports in order

to stay in line and ensure comparability with other results. Taking into account Tol’s median,

adjusted to 2011 Euros (14$/t in 1995, equal to 15 € in 2011); the estimate used in the TSIA report

for Egypt and Jordan (20 €/t in 2011); and the estimate used in the TSIA report for Russia (20$/t in

2004, equal to 19€/t in 2011), we assume a value of 20€/t (in 2011 Euros) for the marginal social

cost of carbon in this report.

D.4 Data sources for air pollution

The main data source for air emissions used here is WIOD’s Environmental Accounts,9 which

includes Mexican emissions of NOx, SOx, CO, and NMVOC assigned to sectors based on energy

use data, and calibrated to totals from the EDGAR database.10

For CH4 (which is less linked to

energy use), WIOD assigns emissions to sectors directly based on the EDGAR air emission

inventory. It has to be mentioned that the WIOD / EDGAR totals per pollutant sometimes differ

substantially from the National Emissions Inventory published by SEMARNAT and EPA in the North

American Emissions Inventories,11

especially in the case of SOx and NOx. This is probably due to

the high amount of emissions released from natural emission sources, particularly volcanoes in the

case of SOx, which cannot readily be assigned to economic sectors and thus do not appear in

WIOD (in the case of biogenic sources due to land use, they can be attributed to the agricultural

5 See for example Anthoff, D. (2007). Report on marginal external damage costs inventory of greenhouse gas emissions.

NEEDS Delivery n 5.4 – RS 1b. 6 Anthoff, D., Hepburn, C., Tol, R. (2009). Equity weighting and the marginal damage costs of climate change. In: Ecological

Economics 68:3, pp. 836–849. 7 Tol, R. (2005). The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. In: Energy

Policy 33, pp. 2064-2074. 8 Anthoff, D. (2007). Report on marginal external damage costs inventory of greenhouse gas emissions. NEEDS Delivery n

5.4 – RS 1b. 9 See for the methodology and data sources: Aurélien Genty et al. (2012): Final Database of Environmental Satellite

Accounts: Technical Report on their Compilation. WIOD Deliverable 4.6. 10

http://edgar.jrc.ec.europa.eu/index.php. 11

See http://www.epa.gov/ttn/chief/net/mexico.html or http://sinea.semarnat.gob.mx/sinae.php.

http://edgar.jrc.ec.europa.eu/index.phphttp://www.epa.gov/ttn/chief/net/mexico.htmlhttp://sinea.semarnat.gob.mx/sinae.php

28


sector, but this can only be done based on assumptions, which may differ in the different dataset

methodologies). In our analysis, WIOD data are used whenever possible, because this allows for

complete timelines (with more recent data) and ensures consistency with the impact assessment

section, where sector-specific data are necessary (and where the emissions from natural,

especially geogenic, sources do not matter as they are not influenced by the trade agreement).

However SEMARNAT / EPA data provide information at state level, which can complement the

federal data; also, its detailed information on natural emission sources provides valuable

background information to understand the air quality problems in Mexico.12

Particulate matter emissions are not covered by WIOD; numbers in EDGAR are inconsistent with

values from the National Emissions Inventory. Ozone is not covered in any of the databases (but its

formation is dependent on the existence of its precursors, CH4, NOx, NMVOCs, and CO). Given the

specific nature of particulate matter and ozone with their direct effect on human health and the

relevance of local / ground-level exposure, we show the development of exposure to these two

pollutants rather than their emissions.

12

For a comparison, see the methodologies of both databases: Aurélien Genty et al. (2012): Final Database of

Environmental Satellite Accounts: Technical Report on their Compilation. WIOD Deliverable 4.6; and.

29


Annex E: CGE tables

In this Annex, we present the outcomes of the CGE modelling. For each indicator, we present two

liberalisation scenarios:

Scenario 1 concerns a liberalisation scenario with only tariff liberalisation. This scenario is used

in the main report;

Scenario 2 concerns a scenario with both tariff and NTM liberalisation. In this scenario, NTMs

are reduced for the Petrochemicals and Other transport equipment sectors. For Petrochemicals,

this consists of a trade cost reduction (AVE) of 13.98 percent in the case of EU exports to

Mexico, and 12.01 percent in the case of Mexican exports to the EU. For Other transport

equipment, this is a trade cost reduction (AVE) of 17.73 percent for EU exports to Mexico and

19.96 percent for Mexican exports to the EU (see also results of gravity analysis in Annex B).

E.1 Macroeconomic effects

Table A.E..3 GDP, percent impact

Country/Region Value (in million euros) Scenario 1 (in %) Scenario 2 (in %)

European Union 28 13.217.926 0,01 0,02

Mexico 875.224 0,34 0,38

Turkey 579.516 -0,01 -0,01

Canada 1.330.417 0,00 0,00

United States 11.619.282 0,00 0,00

MERCOSUR 2.143.452 0,00 -0,01

Andean Pact 459.180 0,01 0,01

Central America 127.578 0,00 -0,01

Chile 187.744 0,01 0,01

ACP countries 1.134.822 0,00 0,00

China 5.471.059 0,01 0,01

Japan 4.410.803 0,00 0,00

Rest of World 11.547.863 0,00 0,00

Table A.E..4 - Consumer prices, percent impact

Country/Region Scenario 1 Scenario 2

European Union 28 0,00 0,02

Mexico 0,11 0,12

Turkey -0,02 0,00

Canada -0,02 -0,01

United States 0,00 0,01

MERCOSUR -0,01 0,00

Andean Pact -0,01 0,00

Central America 0,01 0,02

Chile 0,00 0,01

ACP countries 0,00 0,01

China -0,01 0,00

30



Japan -0,02 -0,01

Rest of World -0,01 0,00

Table A.E..5 - Low skill real wages, percent impact



Mexico 0,24 0,28

Turkey -0,01 0,00

Canada 0,00 0,00

United States -0,02 -0,02

MERCOSUR 0,00 0,00

Andean Pact 0,00 0,00

Central America -0,02 -0,02

Chile 0,01 0,01


China 0,00 0,00

Japan 0,00 0,00

Rest of World -0,01 -0,01

Table A.E.. 6 - Medium skill real wages, percent impact



Mexico 0,45 0,50

Turkey -0,01 -0,01

Canada -0,01 -0,01


MERCOSUR -0,01 -0,01



Chile 0,01 0,01


China 0,00 0,00

Japan 0,00 0,00

Rest of World 0,00 0,00

31


Table A.E. 7 - High skill real wages, percent impact



Mexico 0,36 0,41

Turkey -0,01 -0,01

Canada -0,01 -0,01





Chile 0,01 0,01


China 0,00 0,00

Japan 0,00 0,00


Table A.E..8 - Value of exports (total), percent impact

Country/Region value (in million euros c.i.f.) Scenario 1 Scenario 2

European Union 28 4.826.440 0,05 0,07

Mexico 275.325 1,55 1,73

Turkey 125.349 -0,01 0,00

Canada 349.070 -0,12 -0,13

United States 1.443.984 -0,02 -0,01

MERCOSUR 268.189 0,01 0,02

Andean Pact 89.528 0,00 0,02

Central America 56.466 0,02 0,03

Chile 79.224 -0,02 0,00

ACP countries 352.471 -0,01 -0,01

China 1.433.270 -0,02 -0,01

Japan 663.564 -0,04 -0,03

Rest of World 3.991.058 -0,02 -0,01

Table A.E..9 - Value of imports (total), percent impact


European Union 28 4.956.292 0,05 0,07

Mexico 236.943 1,66 1,86

Turkey 172.128 -0,02 0,00

Canada 342.966 -0,12 -0,12

United States 1.845.500 -0,01 -0,01

MERCOSUR 243.407 0,01 0,03

Andean Pact 92.281 0,00 0,02


Chile 56.691 -0,02 -0,01

ACP countries 390.997 -0,01 0,00

China 1.133.544 -0,01 0,00

Japan 646.359 -0,04 -0,03

32



Rest of World 3.767.574 -0,02 -0,01

Table A.E..10 - CO2 emissions, change in MT

Country/Region Value (in MT) Scenario 1 Scenario 2

European Union 28 4.031,99 0,56 0,70

Mexico 405,13 -0,41 -1,21

Turkey 264,26 -0,01 0,00

Canada 555,62 0,18 0,22

United States 5.629,62 -0,10 0,01

MERCOSUR 473,36 0,00 0,00

Andean Pact 122,90 0,03 0,03

Central America 55,76 0,01 0,01

Chile 73,43 0,01 0,01

ACP countries 623,53 -0,06 -0,06

China 5.343,41 0,33 0,39

Japan 1.073,59 0,06 0,08

Rest of World 7.969,68 0,00 0,00

Table A.E..11 - CO2 emissions, percent impact

Country/Region Value (in MT) Scenario 1 Scenario 2

European Union 28 4.031,99 0,01 0,02

Mexico 405,13 -0,10 -0,30

Turkey 264,26 0,00 0,00

Canada 555,62 0,03 0,04

United States 5.629,62 0,00 0,00

MERCOSUR 473,36 0,00 0,00

Andean Pact 122,90 0,03 0,03

Central America 55,76 0,02 0,02

Chile 73,43 0,01 0,01

ACP countries 623,53 -0,01 -0,01

China 5.343,41 0,01 0,01

Japan 1.073,59 0,01 0,01

Rest of World 7.969,68 0,00 0,00

Table A.E..12 - Real income (annual), million of euros

Country/Region value (in million euros) Scenario 1 Scenario 2

European Union 28 13.217.926 1.559 2.319

Mexico 875.224 2.876 3.245

Turkey 579.516 -65 -53

Canada 1.330.417 -80 -87

United States 11.619.282 141 173

MERCOSUR 2.143.452 -84 -92

Andean Pact 459.180 27 28

Central America 127.578 -4 -4

Chile 187.744 12 16

33



ACP countries 1.134.822 -25 -56

China 5.471.059 286 341

Japan 4.410.803 -23 -12

Rest of World 11.547.863 -232 -281

Table A.E.13 - Real income (annual), percent impact


European Union 28 13.217.926 0,01 0,02

Mexico 875.224 0,35 0,40

Turkey 579.516 -0,01 -0,01

Canada 1.330.417 -0,01 -0,01

United States 11.619.282 0,00 0,00

MERCOSUR 2.143.452 0,00 0,00

Andean Pact 459.180 0,01 0,01


Chile 187.744 0,01 0,01

ACP countries 1.134.822 0,00 -0,01

China 5.471.059 0,01 0,01

Japan 4.410.803 0,00 0,00

Rest of World 11.547.863 0,00 0,00

Table A.E.14 - Bilateral trade (change in quantities)


EU exports to Mexico, % 26.937 18,83 20,96

Mexico exports to EU, % 19.523 15,54 16,24

Table A.E..15 - Terms of trade, percent impact



Mexico 0,09 0,08

Turkey -0,01 -0,01

Canada -0,01 -0,01


MERCOSUR 0,00 0,00



Chile 0,00 0,01


China 0,00 0,00

Japan -0,02 -0,02


34


Table A.E. 16 - Low skill nominal wages, percent impact



Mexico 0,36 0,40

Turkey -0,02 -0,01

Canada -0,03 -0,02





Chile 0,00 0,02


China 0,00 0,01

Japan -0,02 -0,01


Table A.E.17 - Medium skill nominal wages, percent impact



Mexico 0,57 0,62

Turkey -0,03 -0,01

Canada -0,03 -0,02





Chile 0,00 0,02


China -0,01 0,01

Japan -0,02 -0,01


Table A.E..18 - High skill nominal wages, percent impact



Mexico 0,48 0,53

Turkey -0,03 -0,01

Canada -0,03 -0,02





Chile 0,00 0,02


China -0,01 0,01

Japan -0,02 -0,01

35




Table A.E..19 - Consumer prices, percent impact



Mexico 0,11 0,12

Turkey -0,02 0,00

Canada -0,02 -0,01


MERCOSUR -0,01 0,00



Chile 0,00 0,01


China -0,01 0,00

Japan -0,02 -0,01


E.2 Micro-economic effects

Table A.E. 20 - Percent impact on output by sector, Mexico

Sector Value added

(share of GDP)

Scenario 1 Scenario 2

1 Rice: GP 1, 23 0,02 -0,45 -0,45

2 Cereals & oilseeds, oils: GP 2, 3, 5, 21 0,50 -0,17 -0,18

3 Vegetables, fruit, nuts: GP 4 1,41 -0,14 -0,15

4 Sugar, cane, beet: GP 6, 24 0,41 -0,60 -0,61

5 Milk and dairy products: GP 11, 22 0,71 0,10 0,12

6 Beef: GP 19 0,17 -0,05 -0,03

7 Other meat: GP 20 0,24 -0,13 -0,12

8 Other ag.: 7, 8, 9, 10, 12 0,91 -0,02 0,00

9 Food products nec: GP 25 2,85 0,10 0,11

10 Beverages and Tobacco Products: GP 26 0,84 0,01 0,04

11 Fisheries: GP 14 0,10 0,04 0,05

12 Energy: GP 15, 16, 17 1,39 -0,08 -0,11

13 Other primary, non-ag: 13, 18 1,41 -0,43 -0,42

14 Textiles: GP 27 0,25 -0,30 -0,21

15 Wearing Apparel: GP 28 0,41 -0,64 -0,61

16 Leather Products: GP 29 0,25 -0,70 -0,67

17 Petrochemicals: GP 32 0,33 -0,13 -1,35

18 Chemicals: GP 33 2,37 -1,24 -1,23

19 Electrical machinery: GP 40 2,60 -11,45 -11,90

20 Motor vehicles: GP 38 3,19 16,54 17,31

21 Other transport equipment: GP 39 0,46 0,04 -0,91

36


Sector Value added

(share of GDP)

Scenario 1 Scenario 2

22 Other machinery: GP 41 2,76 -1,61 -1,70

23 Metals and metal products: GP 35-38 2,51 -2,34 -2,28