Upload
others
View
15
Download
1
Embed Size (px)
Citation preview
Car wars: Trade integration and tradedisruption in the worldwide car industryConsequences for output and consumers
Thierry MayerLisbon, July 2019
1
Outline of talk
Motivation: trade policy is not boring anymore
Estimating Frictions to Multinational Production
Policy counterfactuals
2
Motivation: trade policy is notboring anymore
Incredible times for trade economists
1. There are many mega-regional free trade agreementsbeing signed or discussed
2. Trump + Brexit are shaking what seemed like a steady pathto ever more trade integration.
3. The automobile industry is of central interest in thoseevents, with very rich data
3
Deep integration and trade disruption w/ MNCs
• Regional and mega-regional (TTIP, TPP, EU-Mercosur)integration agreements have major implications not just forgoods flows but also for multinational production (MP).1. Preferential tariff cuts increase the attractiveness of all member
states as production bases.2. Deliver deeper integration than just trade cost reductions:
• Investment and IPR measures protect investments, trademarks• Mutual recognition or harmonization of regulations• Free(r) movement (at least for professionals)
• Deeper integration shifts the focus towards re-allocationof production within multinationals.• Trade disruption... undo those effects. 4
TrumpWarnings to ... many firms
German newspaper Bild quoted Trump as saying:“I would tell BMW that if you are building a factory inMex-ico and plan to sell cars to the USA, without a 35 percenttax, then you can forget that.”
5
Is it credible? (Fajgelbaum et al. (2019)
6
When academic interest meets policy interest
Head and Mayer, “Brands in Motion, How Frictions ShapeMultinational Production”, forthcoming AER had evolvingmotivation over 5 year production process:
• 2014-2015: Rich data to estimate Frictions on Mult. Prod.• 2015–2016: Mega-regionals deals with Deep Integration• 2016–2018: Trade Disrupted (Brexit / Trumpit)• 2019: Trade wars looming with a focus on cars
7
Estimating Frictions toMultinational Production
The model in one graph
Draws:
Periods:
Decisions:
1 2 3
Productivity ϕb,# of models Mb,assembly sites Lb`,brand-entry fixed costs F d
bn
Opendealerships Dbn
Model-entry fixed costs F emn
Offermodels Imn
Model-locationproductivities zm`
Sourcing Sm`n,prices pmn,quantities qmn
• Four decisions to estimate• Three dimensions of frictions to recover• Two key substitution elasticities
1. Sourcing (cost minimization) ' 82. Consumer price elasticity (utility maximization) ' 4 8
Richness of the IHS data
• For each car model we know brand home, number of unitsby assembly location and destination.• We use passenger cars sales 2000-2016• Dimensions of the data (2016, after cuts)
• 50 different assembly countries (almost all world production)• 74 different markets (countries that record brand/origin)• 120 brands (Fiat) of 55 parents (FCA) from 20 HQ• 1406 sales nameplate (500)• 2128 models (Fiat 500 convertible “FF” program)
9
A (quantified) taxonomy of Multinational FrictionsHeadquarters
&%'$ Market
&%'$
Assembly location&%'$
i - n
@@@@@@@@@R
`
����������
δin(33%), δein(9.7%), δdin(26%)
γi` (31%
) τ `n(24%)
Variable and fixed marketing costs
HQ inputtransfers
Tradecosts
10
Fit of the solved model to data
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
0.6 0.8 1.0 1.2 1.4 1.6
0.5
1.0
1.5
2.0
Estimate of log Pn
Equ
ilibr
ium
log
Pn
ARE
ARG
AUS
AUT
BEL
BGR
BHRBIHBLR
BRA
CAN
CHE
CHL
CHN
COL
CZE
DEU
DNK
DZA
EGY
ESP
ESTFIN
FRA
GBR
GRC HKGHRV
HUN
IDN
IND
IRL
IRN
ISLISR
ITA
JPN
KAZ
KOR
KWT
LTU
LUX
LVA
MAR
MEXMKD MYS
NLD
NOR
NZL
OMN
PERPHL
POL
PRT
QAT
ROM
RUS
SAUSGP
SRB
SVKSVNSWE
THA
TUR
TWNUKR
URY
USA
VNM
ZAF
Correlation: 0.98
●
●
● ●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●●●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●
●
● ●
●●
●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
● ●
●
●
●
●
● ●
●●
●
●
●
● ●
●●
●●
●
●
●●
●
●●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●●
●
● ●
●
●
●
● ●●
●
●
●●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●●
●
● ●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
● ●●
●
●
●
● ●●
●
●
●● ●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●●
●
●
●●
●
●
● ●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
● ●
●
●
●
●●
●
●
●
●●●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
● ●●
●●
●
●
●●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●●
● ●
●
● ●
●●●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●● ●●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●● ●
●
●
●●●
●
●●
●
●
●
●● ●
●
●
●
●
●
●
●
●
● ●●
●
●●
●
●
● ●
●
●●
●
●
●
●●
●●
●●●
●●
●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
● ●● ●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●●
●
●
●
●
●
●
● ●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
● ●●
● ●●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●
● ●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
● ●
●
●●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●●●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●● ●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●●
●●●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●●
●●
● ●
●
●
●
●
●
●
●
●●
●
●●●●●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●●
●
●●
●●
●
●
●
●
●● ●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
● ●●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●●
●●
●●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
● ●●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
● ●
●
●●
●
●
●
●
●
●
●●
● ●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●●
●
● ●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●●
●
●●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●●
●●
●
●
●
● ●●●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●●●
●
●
●●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
● ●
●
●●
●
●
●●●
●●
●
●
●
●
●●
●
●
●
● ●●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●
●●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●● ●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●●
●
●●
●
●
●
●
●
●
●
● ●●●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
● ●●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
● ●
●
●●
●
●
●
●●
●
●●
●
● ●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●●
●
●
●
●
●●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●●●
●●
●●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
● ●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
● ●
●
●●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●● ●
● ●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●●
●
●
● ●
●●
●
●
●
●●
●
●
●●●
●●
●
●
●
●
●
●●
●●
●
●
●
● ●
●●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●
● ●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●
●●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●● ●
●
●
●
●
●
● ●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
● ● ●
●
●
●
●
●
●
●●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●●
●●
●
●●
● ●
●
●
●
● ●
● ●
●
●●
●
●
●
●
●
● ●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●
predicted flow (000s, log scale)
true
flow
(00
0s, l
og s
cale
)
0.01 1 10 100 1000
0.01
110
100
1000 Correlation: 0.63
●●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●
●
● ● ●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●●●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●● ●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●●●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●
●
●
●●●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●●
●
● ●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●●
●
● ●●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
● ●
●
● ●
●
●
●
●
● ●● ●
●
●
●
●●
●●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●
●
●
●
●●●
●●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●●●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
● ●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●● ●
● ●
●●
●
●
●
●
● ●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●●●
●
●
●
●●
●
●
●●●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
predicted flow (000s, log scale)
true
flow
(00
0s, l
og s
cale
)
0.01 1 10 100 1000 10000
0.01
110
100
1000
Correlation: 0.74
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
true production (000s, log scale)
pred
icte
d pr
oduc
tion
(000
s, lo
g sc
ale)
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●●
●
●
●
●
●
●
0.1 1 10 100 1000 10000
1010
010
0010
000
ARG
AUSAUT
BEL
BGR
BLR
BRACAN
CHN
COL
CZE
DEU
DZA
EGY
ESP
FIN
FRA
GBR
HUN
IDN
IND
IRNITA
JPN
KAZ
KOR
MAR
MEX
MYS
NLD
PHL
POLPRT
ROMRUS
SRB
SVK
SVN
SWE
THA
TUR
TWN
UKR
URY
USA
VNM
ZAF
Correlation no IRS: 0.93
Correlation IRS: 0.86
●
●
IRSno IRS
(a) Price indices (Pn) (b) Brand flows (qb`n) (c) Trade flows (q`n) (d) National Output (q`)
11
Cost advantage inferred from sourcing decisions
ARG
AUS
AUT
BEL
BGR
BLR
BRA
CAN
CHN
COL
CZE
DEU
DZA
EGY
ESP
FIN
FRA
GBR
HUN
IDN
IND
IRN
ITA
JPN
KAZ
KOR
MAR
MEX
MYS
NLD
PHL
POL
PRT
ROM
RUS
SRB
SVK
SVN
SWE
THA
TUR
TWN
UKR
URY
USA
VNM
ZAF
−40 −30 −20 −10 0 10
percent cost advantage with respect to the United States12
Policy counterfactuals
The policy experiments
1. US imposes 25% national security tariffs on US leaves NAFTA,imposes 25% tariffs on cars and parts from(a) Canada and Mexico, who retaliate, ending NAFTA (“Trumpit”)(b) the rest of world except NAFTA (“Section 232”)
2. UK exit from the European Union:(a) Soft Brexit: a shallow free trade agreement, UK loses EU FTAs(b) Hard Brexit: EU27 and UK impose 10% MFN duty.
3. Comprehensive Ec. Trade Agreement (CETA): CAN + EU28 ( + USA?)
4. Comp. and Progressive Transpacific Partnership (CPTPP), + USA?13
Section 232 applied to CA+MX (blue) or RoW (red)
Producers Consumers
−1500 −500 0 500 1000 1500
Output change (1000s of cars)
Canada
China
Germany
India
Japan
Korea
Mexico
Spain
UK
USA
Counterfactual:
232 on CAN & MEX 232 on Rest of World
−8 −6 −4 −2 0
Change in consumer surplus (in %)
Canada
China
Germany
India
Japan
Korea
Mexico
Spain
UK
USA
Counterfactual:
232 on CAN & MEX 232 on Rest of World
14
Insufficient Exemptions if 232 applied to ROW
●
●
●
●
●●
●●
●
●
● ●
●● ● ●
●
●
2000 2005 2010 2015
500
1000
1500
2000
2500
3000
Exp
orts
to U
SA
(10
00s
of c
ars)
Canada
Mexico
Section 232 Exemption
(2018 side−letter)
●
●
Section 232 Simulation
15
Brexit soft (blue) and hard (red)
Producers Consumers
−150 −100 −50 0 50 100 150
Output change (1000s of cars)
Austria
France
Germany
Japan
Korea
Mexico
Spain
Turkey
UK
USA
●
Counterfactual: Segments:
Soft Brexit Hard Brexit
noyes
−8 −6 −4 −2 0
Change in consumer surplus (in %)
Austria
France
Germany
Japan
Korea
Mexico
Spain
Turkey
UK
USA
●
Counterfactual: Segments:
Soft Brexit Hard Brexit
noyes
16
It is not that hard to relocate output
17
Brexit is happening
18
CETA (blue), CETA plus TTIP (red)
Producers Consumers
−200 0 200 400
Output change (1000s of cars)
Canada
France
Germany
Japan
Korea
Mexico
Poland
Spain
UK
USA
●
Counterfactual: Segments:
CETA CETA + TTIP
noyes
0.0 0.5 1.0 1.5 2.0
Change in consumer surplus (in %)
Canada
France
Germany
Japan
Korea
Mexico
Poland
Spain
UK
USA
●
Counterfactual: Segments:
CETA CETA + TTIP
noyes
19
Transpacific Partnership (blue), CPTPP (red)
Producers Consumers
−400 −200 0 200 400 600 800
Output change (1000s of cars)
Canada
China
Germany
Japan
Korea
Malaysia
Mexico
Thailand
USA
Vietnam
●
Counterfactual: Segments:
TPP CP−TPP
noyes
0 5 10 15 20 25 30
Change in consumer surplus (in %)
Canada
China
Germany
Japan
Korea
Malaysia
Mexico
Thailand
USA
Vietnam
●
Counterfactual: Segments:
TPP CP−TPP
noyes
20
In sum
1. Trumpit: Disastrous for Canada and Mexico (losses can goup to 1.5 m cars).
2. Section 232: Cuts production in Japan, Korea and Germanyby large amounts (combined losses around 2m cars.)
3. Brexit: Bad for both producer and consumer. Losses ofbetween 2/3 and 3/4 of 2016’s Swindon factory
4. CETA: Gains in production for Canada (56 ths. cars, 8%),followed by Germany (43 ths. cars) and Britain (25ths. cars).
5. TPP/CPTPP: Canada increases output by 33% under TPPand by 42% under CPTPP. 21
What about Portugal?
VW (“good”) plant sells about 100,000 cars a year.
1. Section 232: Increases the market share of VW in Europe, since BMWand Mercedes-Benz US factories are hurt by retaliation. Sourcingfavors Portugal. No sales in the US = no losses. Gain: nearly 5%.
2. Soft Brexit: On UK market, large exports of Mexican andSouth-African plants are cut. PRT benefits. Small gains in output.
3. Hard Brexit: Now 10% tariff also hurts EU plants, VW looses 20%market share in UK market. Losses dominate even for PRT plant:-1.2%.
22