Transcript Deep Agreements and International Production

Deep Agreements and the
Internationalization of Production
Alberto Osnago, Nadia Rocha
WTO
Michele Ruta
IMF
University of Exeter, 30 May 2013
Deep agreements and int. production
• Preferential trade agreements (PTA) are usually
thought as tariff reductions (i.e. shallow agreements)
• Deep agreements cover in addition many other
provisions:
–
–
–
–
–
–
–
Technical barriers to trade (TBT) measures
Sanitary and phytosanitary (SPS) measures
Investment
Intellectual property rights (IPR) protection
Anti-corruption
Competition policy
…
Deep PTAs and vertical FDI
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Deep agreements and int. production
• Depth of PTAs and the international fragmentation of
production have changed over time
Source World Trade Report (2011)
Deep PTAs and vertical FDI
3
Deep agreements and int. production
• This paper:
• Digs further into the relationship between deep trade
agreements and the internationalization of production
•
Specific question:
•
•
How are deep agreements and vertical FDI related?
General idea:
•
•
•
Deep provisions improve the contractual environment
However, different provisions affect the contractibility
of various inputs differently
FDI respond to (and, possibly, determine) these
institutional changes
Deep PTAs and vertical FDI
4
Related literature
• International organization of production
– Antras (2003); Antras and Helpman (2004); Nunn and Trefler
(2012); …
• Domestic institutions and intra-firm trade
– Grossman and Helpman (2005); Antras and Helpman (2008);
Bernard et al. (2010); …
• Trade agreements and internationalization of
production
– Lawrence (1996); Baldwin (2010); Orefice and Rocha (2012);
Blanchard and Matschke (2012); Baltagi et al. (2008); …
Deep PTAs and vertical FDI
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Outline
1.
Theory: Vertical FDI and deep PTAs
– Antras and Helpman (2008)
2. Data description and methodology
– Depth and composition of deep agreements
– Vertical FDI
3. Empirical findings
– Depth of PTAs and vertical FDI
– Composition of PTAs and vertical FDI
4. Summary and work ahead
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Based on Antras and Helpman (2008)
– Property rights approach of the international organization
of production under asymmetric contractual frictions
• Two countries: North and South
– North is high-cost and has complete contracting
– South is low-cost and has weaker contracting institutions
relative to North
• Final good producers located in North
– They have to produce headquarter (HQ) services, but can
source components domestically or from South
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Final good production in an industry combines HQ
service (h) and components (m) with technology
𝑞 θ =θ
ℎ 𝜔
η
η 𝑚 𝜔 1−η
1−η
• Sectors vary by intensity with which they use HQ
services (η)
• Firms within sectors vary by productivity (θ)
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Each input is produced with a continuum of activities
in the interval [0,1]
𝑙 = exp
1
log 𝑥ℎ
0
𝑖 𝑑𝑖
with 𝑙 = ℎ, 𝑚
• Imperfect contractibility in South
– Only activities in range 0, 𝜇𝑙 with 0 ≤ 𝜇𝑙 ≤ 1 are
contractible
• Contractibility depends on domestic institutions in
South (λ) and on PTA provisions (γ)
𝜇ℎ = ℎ(λ, 𝛾1 , … , 𝛾𝑇 )
and 𝜇𝑚 = 𝑚(λ, 𝛾1 , … , 𝛾𝑇 )
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Differences in contractibility across production processes
and countries reflect technological and institutional
variation
• Deep provisions in PTAs are a determinant of institutional
variation and affect contractibility of inputs
• Examples:
– Anti-corruption provisions reduce contractual insecurity (↑𝜇ℎ and ↑𝜇𝑚 )
– IPR provisions improve contractibility of HQ services (namely R&D) by
protecting patents (↑𝜇ℎ )
– TBT/SPS provisions improve contractibility of components by promoting
standardization / mutual recognition (↑𝜇𝑚 )
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Final good producer has three alternatives to obtain
components (m)
– Domestic sourcing (D)
– Foreign outsourcing (O)
– FDI, i.e. foreign integration (V)
• Profits depend on control and location choice and
can be expressed with the standard form
𝜋𝑖 = 𝑍𝑖 𝜗 − 𝑤𝑁 𝑓𝑖 with 𝑖 = 𝐷, 𝑂, 𝑉
where 𝜗 is a function of productivity θ and profitability 𝑍𝑖
depends on HQ intensity η (Antras, 2003)
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
• Domestic production, foreign outsourcing and FDI
coexist
πV
πO
πD
-wNfD
-wNfO
ϑO
ϑD
Domestic prod
ϑV
Outsource
ϑ
FDI
-wNfV
Deep PTAs and vertical FDI
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Vertical FDI and deep PTAs
πV ’
πV
πO ’
πO
πD
-wNfD
ϑD
ϑO ’ ϑO
ϑ V ’ ϑV
ϑ
-wNfO
-wNfV
Outsourcing
Deep PTAs and vertical FDI
FDI
13
Vertical FDI and deep PTAs
• PTA provisions that improve contractibility of HQ
services (↑𝜇ℎ ) decrease FDI
πV ’
πV
πO ’
πO
πD
-wNfD
ϑD
ϑO ’ ϑO
ϑV ϑV ’
ϑ
-wNfO
-wNfV
Outsourcing
Deep PTAs and vertical FDI
FDI
14
Vertical FDI and deep PTAs
• Summary of model’s prediction:
–
Depth of agreements is associated to more offshoring,
but relationship with FDI is ambiguous
• Association is stronger for HQ intensive industries
–
Discipline improving contractibility of components (HQ
services) are associated with increasing (decreasing) FDI
–
Property rights versus transaction cost (TC) model
• Better contractual institutions are always associated to more
outsourcing and less FDI in the TC model
• Empirical analysis provides an indirect test of the two theories
Deep PTAs and vertical FDI
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Depth and composition of PTAs
• We use WTO data on the content of PTAs
• Comprehensive mapping and coding of 100 PTAs signed
between 1958-2011
• We restrict the sample to PTAs signed by Germany (EU),
Japan, and USA
• We are left with 59 agreements (40 signed by EU, 12 by
JAP, and 11 by USA)
• PTA dataset has been constructed following the
methodology of Horn et al. (2010)
– Horn et al. (2010) identify a set of 52 policy areas
– Legal enforceability of PTA obligations is established
according to the language of the agreements
Deep PTAs and vertical FDI
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Depth and composition of PTAs
Country
N. Of
N. Of
Agreements Partners
Avg n. Of
Provisions
Min n. of
Provisions
Max n. of
Provisions
Germany
37
78
21.93
5
35
Japan
11
14
18.51
13
23
USA
11
17
18.06
9
21
All
59
109
20.99
5
35
Deep PTAs and vertical FDI
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Depth and composition of PTAs
• To analyze the depth of PTAs, we construct three
variables (as in Orefice and Rocha, 2013):
– Total count of enforceable provisions (# Provisions)
– Top 5 and top 10 provisions with the highest degree of
commonality across the agreements
• To analyze the composition of PTAs, we classify
provisions into two groups:
– HQ-provisions: GATS, TRIPS, IPR, investment, and
movement of capital
– M-provisions: SPS, TBT, consumer protection, customs, and
export taxes
Deep PTAs and vertical FDI
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Depth and composition of PTAs
Germany
Japan
USA
GATS
11
11
10
TRIPS
22
10
11
IPR
22
7
10
Investment
13
10
8
Movement of capital
23
10
8
SPS
6
7
9
TBT
8
8
8
Consumer protection
4
11
11
Customs
35
11
10
Export taxes
27
5
10
HQ-provisions
M-provisions
Deep PTAs and vertical FDI
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Number of agreements with specific provisions by country
Deep PTAs and vertical FDI
20
Measuring vertical FDI
• FDI data has been constructed using the ORBIS
database assembled by Bureau van Dijk
– ORBIS includes location, ownership, detailed sector level,
and operational data (e.g. revenues) for more than 100
million firms in Europe, Americas, and Asia-Pacific region
• We restrict our analysis to subsidiaries in any country
owned by parent firms located in Germany, Japan,
and USA in 2003, 2007, and 2011
– We have the revenues of 125,212 subsidiaries
– We can identify 42,984 ultimate owner parents
Deep PTAs and vertical FDI
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Measuring vertical FDI
• We follow the methodology in Alfaro and Charlton
(2009) to identify
• the ownership structure:
• We consider a firm to be a parent if it owns a minimum of
25.01% of another firm
• and the type of link between a subsidiary and its
parent firm (i.e. vertical versus other forms of FDI):
• Let S be the set of 6-digits NAICS codes of the subsidiary
and P be the set of 6-digits NAICS code of the parent
• An element x of S is an input of an element z of P if the
total requirements coefficient of the US IO table > 0.03
Deep PTAs and vertical FDI
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Measuring vertical FDI
• We can identify 4 types of connections:
• Horizontal FDI: if S and P share any element
• Vertical FDI: if any element of S is an input of any element
of P
• Complex FDI: if S and P share any element and any
element of S is an input of any element of P
• Non-identified: if none of the above is satisfied
 The share of vertical and horizontal links we obtain is in line with the
findings of Alfaro and Charlton (2009) and Lanz and Miroudot (2011)
Horizontal
Complex
Deep PTAs and vertical FDI
Vertical
23
Measuring vertical FDI
Deep PTAs and vertical FDI
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Depth of PTAs and vertical FDI
• Ideally, we would want information on intra-firm
trade, but data are not available
• Quantification of vertical FDI
– FDIijkt is the aggregate value of the revenues of subsidiaries
in country (destination) j owned by parents operating in
sector k, country i (US, Japan, or Germany) at time t
• Example:
– Vertical FDI of the car sector is the sum of revenues of all
the foreign-owned subsidiaries that produce car inputs,
such as plastic, seat-belts, glass.
Deep PTAs and vertical FDI
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Depth of PTAs and vertical FDI
• Other explanatory variables:
– HQ intensity (η) is constructed as the log of capital
expenditures divided by total worker wages using data
from the American Manufacturing Survey (AMS) in 2007
– Rule of law is taken from the Worldwide Governance
Indicators database
– GDP and GDP per capita come from the World Bank
– Distance, contiguity, colony relationship, common
language come from Mayer and Zignago (2011)
Deep PTAs and vertical FDI
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Depth of PTAs and vertical FDI
FDIijkt=α+β1 DEPTHijt + β2 INSTITUTIONSjt + γ1 Xjt +γ2 Xij
+ δt + δk + δi + δit
(1)
where k is sector, t is time, i and j are country indexes (i for the
"origin" country and j for the "destination" country)
• DEPTHijt is # Provisions and Top 5 and top 10 provisions
• Xjt are controls for characteristics of the destination country
that vary over time (GDP and GDP per capita)
• Xij are country-pair variables (distance, contiguity, common
language, colonial relationship)
• δt, δk, δi, δit are time, sector, country (origin), and country-time
fixed effect
Deep PTAs and vertical FDI
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Depth of PTAs and vertical FDI
FDI and Deep Integration
(1)
VARIABLES
PTA
(2)
(3)
FDI (log of revenues in 1000$)
0.527**
(0.208)
N. of Provisions
0.0169**
(0.00736)
log(Top 5)
0.543*
(0.297)
log(Top 10)
Dummy=1 if η>average
Rule of Law
Observations
R-squared
Year FE
Industry-4dig FE
Country FE
Country-Year FE
(4)
0.802***
(0.284)
0.306**
(0.126)
4,951
0.249
Yes
Yes
Yes
Yes
0.806***
(0.285)
0.282**
(0.123)
4,914
0.246
Yes
Yes
Yes
Yes
0.804***
(0.284)
0.282**
(0.124)
4,914
0.245
Yes
Yes
Yes
Yes
0.478**
(0.218)
0.803***
(0.284)
0.290**
(0.125)
4,914
0.246
Yes
Yes
Yes
Yes
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
All regressions control for distance, contiguity, colony relationship, common language, BIT, a dummy for China, GDP, GDP
per capita, and remoteness of the country of the subsidiary.
Deep PTAs and vertical FDI
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Composition of PTAs and vertical FDI
FDIijkt=α+β1 μMijt + β2 μHijt + β3 DEPTHijt + β4 INSTITUTIONSjt
+ γ1 Xjt +γ2 Xij + δt + δk + δi + δit
(2)
where k is sector, t is time, i and j are country indexes (i for the
"origin" country and j for the "destination" country)
• μMijt , μHijt described below and DEPTHijt is as described above
• Xjt are controls for characteristics of the destination country
that vary over time (GDP and GDP per capita)
• Xij are country-pair variables (distance, contiguity, common
language, colonial relationship)
• δt, δk, δi, δit are time, sector, country (origin), and country-time
fixed effect
Deep PTAs and vertical FDI
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Composition of PTAs and vertical FDI
• We construct two variables μℎ and μ𝑚 :
– Dummy μ𝑙 = 1, if there is at least one provision of
the l-type in the PTA
2 if all provisions of l-type in PTA
– Discrete μ𝑙 = 1 if at least one provision of l-type,
0 otherwise
where 𝑙 = ℎ, 𝑚
Deep PTAs and vertical FDI
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Composition of PTAs and vertical FDI
FDI, different provisions, and depth
(1)
(2)
(3)
(4)
(5)
VARIABLES
FDI (log of revenues in 1000$)
Dummy μH
0.678*
-0.665
(0.389)
(0.551)
M
Dummy μ
0.968***
1.448***
(0.288)
(0.393)
H
Discrete μ
0.269
(0.221)
M
Discrete μ
0.569***
(0.172)
N. of Provisions
-0.00476
-0.0114
-0.00283 0.000308 0.000703
(0.0155)
(0.0102)
(0.0155)
(0.0183) (0.00747)
Rule of Law
0.243**
0.267**
0.271**
0.237**
0.286**
(0.108)
(0.110)
(0.109)
(0.108)
(0.111)
Observations
7,108
7,108
7,108
7,108
7,108
R-squared
0.337
0.339
0.339
0.337
0.338
Year FE
Yes
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
Yes
Country FE
Yes
Yes
Yes
Yes
Yes
Country-Year FE
Yes
Yes
Yes
Yes
Yes
(6)
0.133
(0.233)
0.534***
(0.182)
-0.00775
(0.0179)
0.287**
(0.111)
7,108
0.338
Yes
Yes
Yes
Yes
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
All regressions control for distance, contiguity, colony relationship, common language, BIT, a dummy for China, GDP, GDP per capita,
and remoteness of the country of the subsidiary.
Deep PTAs and vertical FDI
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Conclusion –future work
• So far we have said little about direction of causality
– Control decisions of firms are expected to respond to depth and
composition of PTAs, but firms lobby on content of trade agreements
– These aspects are not captured by the model and empirical strategy
• On the theory side:
– Need to embed FDI decisions in a model of endogenous PTA formation
• On the empirical side:
– Need to instrument PTA depth and composition
– For PTA depth between country i and country j , we can use
instruments from poli-science literature (e.g. affinity of political
systems) or average depth signed with other countries
– Not obvious how to instrument for composition of PTA
Deep PTAs and vertical FDI
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Conclusion -summary
• We use the AH model to guide our analysis of the
relationship between deep PTAs and the
internationalization of production
• We exploit two new datasets on depth and
composition of PTAs and on vertical FDI
• Consistently with the theory, we find that:
1. Depth of PTA is associated to an increase in FDI (this
finding is not robust)
2. PTA provisions that improve the contractibility of
components relative to HQ activities are associated to
more FDI (this supports PR over TC approach)
Deep PTAs and vertical FDI
33
Transaction costs model
πV
πO ’
πO
πD
ϑD
ϑO ’ ϑO
ϑV
ϑV ’
ϑ
-wNfD
-wNfO
-wNfV
Outsourcing
Deep PTAs and vertical FDI
FDI
34
Intuition PR model
• Higher component contractibility (left) increases the optimal
revenue share of final good producer
• Higher HQ contractibility (right) lowers the optimal revenue
share of final good producer
Β(η)
Β(η)
η
Deep PTAs and vertical FDI
η
35
Sectors with low HQ intensity
• Domestic production and foreign outsourcing
coexist, but no FDI
πO
πD
πV
-wNfD
-wNfO
ϑD
ϑO
Domestic prod
ϑ
Outsourcing
-wNfV
Deep PTAs and vertical FDI
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Sectors with low HQ intensity
πO ’
πO
πD
πV ’
πV
-wNfD
ϑD
ϑO ’
ϑO
ϑ
-wNfO
-wNfV
Outsourcing
Deep PTAs and vertical FDI
37
Final-Good
Producer
inputs
Offshoring
Location
choice
Deep
PTA
Control
choice
Domestic
sourcing
Intra-Firm Trade
Vertical
Integration (FDI)
Foreign
sourcing
Arm’s Length Trade
Foreign
Outsourcing
Location choice
• Location choice in a simple model of global sourcing
with heterogeneous firms
πO
πD
ϑD
ϑO
ϑ
-wNfD
-wNfO
Domestic production
Deep PTAs and vertical FDI
Offshoring
39
Location choice
πO ’
πO
πD
ϑD
ϑO ’
ϑO
ϑ
-wNfD
-wNfO
Offshoring
Deep PTAs and vertical FDI
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Ownership: ORBIS definition
• The definition of ownership “concern the minimum
percentage that must characterize the path from a subject
company up to its Ultimate Owner”
Firm 1
54%
Firm 2
35%
Firm 3
100%
Firm 4
• Considering a path of minimum 25.01%: the ultimate
owner of firm 4 is firm 1
• Considering a path of minimum 50.01% : the
ultimate owner of firm 4 is firm 3
Deep PTAs and vertical FDI
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Distribution of Vertical, Horizontal, and Complex FDI
Type
Number of Subsidiaries
Share
Vertical
25,230
48.39
Horizontal
26,904
51.61
Total
52,134
100
• The share of vertical and horizontal links is in line with the
findings of Alfaro and Charlton (2009) and Lanz and
Miroudot (2011)
Deep PTAs and vertical FDI
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