Merchandise trade and growth
Roberta Piermartini
Economic Research and Statistics Division
WTO
CEPR Conference
Modena, 11 May 2015
1
“Fast growth is accompanied by increases in trade
spurred by reduction in trade barriers and costs”
WTR 2014
… BUT
“Do we know by how much does trade fosters
growth?”
Outline
 Merchandise trade and growth … the debate today
 The evolution of merchandise trade and trade policy
… point at an important role of supply chains
 What are the implication for trade and growth
research?
One new research: GVC and technology transfers
3
Merchandise trade correlates to growth
Real per capita GDP growth and merchandise export volume growth, 2000-11
Note: annual growth rate
Source: World Development Indicators and WTO Secretariat calculations.
How does trade affect growth?
 Static gains from trade do not impact on long-run economic growth
 Trade can have a medium-term impact through its effects on factor
accumulation (K)
- a small open economy can sustain growth with capital accumulation only
for an extensive period because return to capital are determined
internationally
 Trade can affect growth in the long-run only if it contributes to
technological progress
- enhances the incentive to innovate (R&D)
- fosters technological spillovers (A)
- improves quality of institution (Q)
Cross-country regression analysis: methodological problems
Correlation versus causality?
If we want to claim that integration affects income, we need to show that arrows (4) and (5) are
the relevant ones, while arrows (6) – omitted variable – and (7) – reverse causality – are relatively
insignificant
6
Terminator II: Rodrik et al. (2004)
Rodrik et al.(2004) claim that “institutions rule”
critique of Frankel and Romer (1999): “geography” –their instrument- affect institutions
Once institutions (property rights, rule of law – instrumented with settlers’ mortality)
are controlled for, trade has no effect on income per capita
Openness contributes to income per capita only indirectly, via its positive effect on
institutions
7
Further evidence
Evidence based on within-country estimates
Some countries grow much faster than others after opening up to trade…
…others actually see their growth decline after opening up to trade
(Waczairg and Welch (2008))
Why this heterogeneity?
 Bad regulation Freund and Bolaky (2008)
… debate still going on
Evidence on trade and technology transfers
Keller (1998) Randomly created trade patterns give rise to positive international R&D spillover
estimates, which are often larger, and explain more of the variation in productivity across
countries than if true bilateral trade patterns are employed
The finding casts doubt on the claim that patterns of international trade are important in driving
R&D spillovers…
8
What do we know
about merchandise trade?
 Merchandise trade account for an increasing share of
GDP
Merchandise trade account for an increasing share of GDP
Source: World Bank World Development Indicators, WTO merchandise trade data and IMF WEO data
What do we know
about merchandise trade?
 Merchandise trade account for an increasing share of
GDP
 New players have risen to prominence in world trade ..
World centre of gravity has moved toward the East
WTR, 2013
Figure B.5
Shares of selected economies in world merchandise exports by level of development, 1980-2011
(per cent)
1980
Mexico, 1%
China, 1%
2011
Other
developing and
emerging, 15%
Other
developing
economies,
16%
European
Union (15),
37%
Singapore, 1%
Chinese
Taipei, 1%
European
Union (15),
30%
Malaysia, 1%
Brazil, 1%
Indonesia, 1%
South Africa,
1%
Nigeria, 1%
Iraq, 1%
Fommer Soviet
Union, 4%
Developing
and
emerging
economies,
34%
Chinese
Taipei, 2%
Developed
economies,
66%
Thailand, 1%
Brazil, 1%
India, 2%
Mexico, 2%
Saudi Arabia,
2% Singapore, 2%
Developing
and
emerging
economies,
47%
Developed
economies,
53%
Russian
Federation, 3%
Saudi Arabia,
Kingdom of,
5%
Other
developed,
11%
Source: WTO Secretariat.
Japan, 6%
United States,
11%
Korea,
Republic of,
3%
United States,
8%
China, 11%
Japan, 5%
Other
developed,
11%
WTR, 2013
Table B.15A
Share of total trade between geographical regions in world trade, 1990
(per cent)
North
America Europe,
7.8%
EuropeCIS, 3.6%
CIS
To Asia
Europe
North
America
South and
Central
AmericaAsia,
0.8%
CISAsia,
0.7%
South and
Cental
AmericaEurope,
1.6%
North
AmericaSouth and
Central
America,
2.6%
South and
Central
America
North
America Middle East,
0.9%
North
AmericaAfrica,
0.7%
EuropeAfrica,
3.4%
EuropeMiddle East,
2.5%
North
Americ
a-Asia,
10.2%
Europe-Asia,
8.1%
Middle
East
Middle
EastAsia,
3.2%
Asia
To South
and
Central
America
Africa
AfricaAsia,
0.6%
Note: World trade includes intra-EU trade. Arrow weights based on shares in 1990. Trade within regions and with unspecified destinations represented 53% of world trade in
1990.
Source: WTO Secretariat estimates.
WTR, 2013
Table B.15B
Share of total world trade between geographical regions in world trade, 2011
(per cent)
North
America Europe,
4.8%
EuropeCIS, 3.6%
CIS
To Asia
Europe
North
America
South and
Central
AmericaAsia,
2.0%
CISAsia,
1.3%
South and
Cental
AmericaEurope,
1.4%
North
AmericaSouth and
Central
America,
2.1%
South and
Central
America
North
America Middle East,
1.0%
North
AmericaAfrica,
0.8%
EuropeAfrica,
2.3%
EuropeMiddle East,
2.0%
North
Americ
a-Asia,
7.8%
Europe-Asia,
8.8%
Middle
East
Middle
EastAsia,
5.1%
Asia
To South
and
Central
America
Africa
AfricaAsia,
1.7%
Note: World trade includes intra-EU trade. Arrow weights based on shares in 1990. Trade within regions and with unspecified destinations represented 53% of world trade in
1990.
Source: WTO Secretariat estimates.
What do we know
about merchandise trade?
 Merchandise trade account for an increasing share of
GDP
 New players have risen to prominence in world trade ..
World centre of gravity has moved toward the East
 Global supply chains have changed patterns of
international trade ..
- Service matter more than we thought.
BUT …Contribution of services to trade is
bigger than we thoughts
Structure of world exports in
gross terms, 2008
Structure of world exports in
value added terms, 2008
12%
18%
23%
45%
37%
65%
Primary products
Manufacturing
Services
Primary products
Source: WTO Secretariat estimates based on OECD-WTO 2008 data
Manufacturing
Services
BACK TO TEXT
16
What do we know
about merchandise trade?
 Merchandise trade account for an increasing share of
GDP
 New players have risen to prominence in world trade ..
World centre of gravity has moved toward the East
 Global supply chains have changed patterns of
international trade ..
- Service matter more than we thought.
- Trade has tended to become more regionalized (S-S)
WTR, 2013
Figure B.14
Intra-regional and extra-regional merchandise exports of WTO regions, 1990-2011
(US Billion and per cent)
5538
2707
2282
48%
71%
52%
1251
966
1225
706
44%
73%
65%
548
48%
59%
56%
41%
1990
2000
2011
85%
29%
35%
27%
1990
2000
268
2011
91%
Europe (excl.
EU-intra)
North America
94%
1990
594
750
1658
138
52%
15%
2000
2011
51%
739
Middle East
58%
88%
74%
149
106
198
120
86%
74%
26%
26%
1990
2000
2011
South and Central
America
94%
91%
1990
2000
1990
2000
Asia
12%
2011
Africa
Intra
49%
42%
Extra
2011
What do we know
about merchandise trade?
 Merchandise trade account for an increasing share of
GDP
 New players have risen to prominence in world trade ..
World centre of gravity has moved toward the East
 Global supply chains have changed patterns of
international trade ..
- Service matter more than we thought.
- Trade has tended to become more regionalized (S-S)
- Trade grew faster than GDP since 1980
The elasticity of merchandise trade to GDP
World merchandise trade volume and real GDP, 1980-2011
(annual per cent change)
8.0
2.5
7.0
2.0
6.0
5.0
1.5
4.0
1.0
3.0
2.0
0.5
1.0
0.0
0.0
1980-1985
1985-1990
GDP growth (left scale)
1990-1995
1995-2000
2000-2005
Merchandise trade volume growth (left scale)
Source: WTO Secretariat.
Note: Merchandise trade refers to the average of exports and imports.
2005-2011
Elasticity (right scale)
What do we know about trade policy barriers in
merchandise trade
Trade costs have gone down over time
 MFN rates have fallen .. especially in intermediate inputs
 Preferential trade agreements (PTAs) continue to
proliferate …
- S-S PTAs are diffused
- and are getting deeper and deeper
This correlates with patterns of trade and supply chains,
that are increasingly S-S
MFN rates have fallen
MFN rate
(per cent)
Bound rate
(per cent)
Bound lines
(per cent)
Average 200911
Change since
1996
Average 200911
Change since
1996
Average 200911
Change since
1996
World
8.5
-2.0
27.0
-3.8
80.1
12.9
Developed
2.7
-1.9
6.3
-1.3
98.9
-0.1
Emerging
10.1
-5.5
29.2
-9.8
80.0
7.9
Other developing
13.0
-1.7
29.6
-7.1
87.6
22.4
LDCs
7.1
-2.1
42.2
-2.4
45.5
8.4
Source: WTO Secretariat
Note: Changes are from average 1996-98 to average 2009-11. The sample only includes those country-product pairs for which data are available on the status of bound
lines, bound rates and imports for at least one year both at the beginning and at the end of the period.
WTR, 2014
Figure C.26
Most-favoured nation (MFC) tariffs on parts and components by country group
(per cent)
Note: Underlying data are simple averages of ad valorem rates.
Source: Calculations based on TRAINS database, WITS.
What do we know about trade policy barriers in
merchandise trade
Trade costs have gone down over time
 MFN rates have fallen .. especially in intermediate inputs
 Preferential trade agreements (PTAs) continue to
proliferate …
- S-S PTAs are diffused
- and are getting deeper and deeper
This correlates with patterns of trade and supply chains,
that are increasingly S-S
Number of PTAs in force (1950-2010)
300
250
developing-developing
developed-developing
Number of PTAs
200
developed-developed
150
100
50
0
Year
25
WTR, 2014
Figure C.28
Average number of provisions by country group and period, 1992-2011
(number of PTAs)
Source: WTO Secretariat.
Note: Developed-Developing includes 42 the agreements between developed countries and all developing countries excluding LDCs. Emerging-Other Developing
includes 25 agreements between emerging economies and other developing countries excluding LDCs. Each bar represents the average number of provisions included in
the agreements signed in each time-period.
WTR, 2014
Figure C.29
Share of emerging countries-other developing countries agreements including selected provisions, 1991-2011
(per cent)
Source: WTO Secretariat.
Note: The numbers represent the shares of agreement signed in a particular time-period between emerging countries and other developing countries that include a
particular provision. There are 25 agreements involving both emerging and other developing countries.
What are the implication for trade and
growth research?
The question of how trade affects growth needs to
 account for country’s heterogeneus particiaption in GVC
 account for internalikages between countries through GVC
 Use measures of trade that go
 Beyond merchandise trade... GVCs stress the importance of
services trade and the interlinkages between trade in goods and in
services
 Beyond tariff liberalization GVCs stress the importance of going
beyond tariffs, to include non-tariff barriers and a range of other
trade costs that are determinant of openess
GVC affect R&D spillovers
Source: Piermartini and Rubinova (2014)
Note: "Vertically integrated countries" are defined as those country-pairs with a share of trade in
intermediates above the median
KNOWLEDGE SPILLOVERS
THROUGH SUPPLY CHAINS
Roberta Piermartini (WTO)
and
Stela Rubínova (IHEID)
21 April 2015
Brown Bag Seminar
WTI - NCCR Trade Regulation (University Bern).
Our contributions
1. We measure innovation with number of patents rather than TFP
-we can link industry-level patents and industry level R&D due to the new concordance
tables between IPC and ISIC
2. We can link spillovers to supply chains intensity using the new
WIOD
3. Extend the analysis of knowledge spillovers to a sample of 29
countries (not just developed)
4. Work with a recent time period 2000-2008
5. Address endogeneity
What do we do?
We test whether accessibility of foreign knowledge depend on
supply chains linkages between countries by estimating a steadystate function for the production of innovations where:
Innovationsik =f(R&Djk, wijk)
Where wijk is a measure of supply chain linkages when j is a foreign
country.
We find ….
1. Knowledge spillovers exist only for some forms of participation
in supply chains relationships
2. Supply chains explain knowledge spillovers better than trade or
distance when we extend the sample to 29 countries
3. Supply chains make knowledge spillovers less geographically
localised
Our empirical model
I2P = Imports of inputs to produce
I2E = Imports of inputs to export
I2RE = Imports of inputs to re-export
I2REP = Imports of inputs to re-export into production
Estimated as a Negative binomial with industry and country fixed effects
Variables: patent data
We use number of patent applications (OECD Patent Statistics)
- by inventor’s origin
(applicants can reside in a different country)
- filed under Patent Cooperation Treaty
(to better control for different patent valuse than using national
- by priority date
(closer to the actual invention date)
patent offices)
Weights in R&D Pool
In order of supply chain intensity links, the 4 weights in the knowledge
Production function are constructed as:
I2Pijk = Imported intermediatesjik
I2Eijk = Imported intermediatesjik /Outputik∙ Exportsik
I2REijk = Imported intermediatesjik /Outputik∙ Exportsijk
I2REPijk = Imported intermediatesjik /Outputik∙ Intermediatesijk
Assuming the share of imported intermediate inputs in output
is the same as in exports we get the import content of exports.
Dataset
Averages over period 2000-2008
29 countries – three main supply chains clusters
Factory Asia: China, Japan, South Korea, Chinese Taipei
Factory NAFTA: Canada, Mexico, USA
Factory CEE: Austria, Germany, Czech Republic, Hungary, Poland, Romania,
Slovakia, Slovenia
+ other OECD countries and Russia and Turkey.
13
manufacturing
industries aggregated in 8 groups (sorted by supply chain
25
Rubber and plastics products
intensity)
26
Other non-metallic mineral products
23
29
24
27t28
30t33
Coke, refined petroleum products and nuclear fuel
Machinery and equipment n.e.c.
Chemicals and chemical products
Basic metals, and fabricated metal products, except machinery and equipment
Office, accounting and computing machinery; electrical machinery and apparatus
n.e.c.; radio, television and communication equipment and apparatus; medical,
precision and optical instruments, watches and clocks
34t35 Motor vehicles, trailers and semi-trailers; other transport equipment.
Positive correlation between innovation and
foreign R&D
Result 1: Positive spillovers increase with the
intensity of supply chain linkages
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dependent: Patents
R&D Pool I2P
0.0030*
(0.0017)
R&D Pool I2E
0.0007
(0.0010)
0.0028**
(0.0013)
R&D Pool I2RE
0.0007
(0.0009)
0.0071***
(0.0024)
R&D Pool I2REP
0.0029*
(0.0017)
0.259***
(0.0755)
0.696***
(0.118)
0.523*
(0.276)
0.588***
(0.109)
3.558***
(1.231)
0.253***
(0.0758)
0.738***
(0.116)
0.491*
(0.275)
0.606***
(0.112)
3.422***
(1.234)
0.246***
(0.0728)
0.735***
(0.113)
0.469*
(0.280)
0.609***
(0.112)
3.451***
(1.227)
0.0087***
(0.0025)
0.252***
(0.0708)
0.729***
(0.113)
0.486*
(0.281)
0.595***
(0.107)
3.572***
(1.197)
Industry dummies (8)
Country dummies (29)
yes
no
yes
no
yes
no
Observations
201
201
201
ln(R&D)
ln(GDP)
ln(GDP per capita)
ln(Researchers)
ln(Patent protection)
0.141**
(0.0551)
0.139**
(0.0561)
0.132**
(0.0546)
0.0042**
(0.0018)
0.129**
(0.0524)
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
201
225
225
225
225
Result 2: distance is a less robust determinant of
knowledge spillovers than supply chains
Dependent: Patents
(1)
R&D Pool I2REP
0.0042**
(0.0018)
R&D Pool D
Full Sample
(2)
(3)
(4)
0.0084*
(0.0045)
-0.0056
(0.0035)
0.0044**
(0.0018)
-0.0061*
(0.0036)
(5)
EU Sample
(6)
0.0200*
(0.0110)
0.0078
(0.0053
0.0081
(0.0152)
ln(R&D)
0.124**
(0.055)
0.154***
(0.055)
0.133**
(0.057)
0.125
(0.098)
0.165*
(0.089)
0.132
(0.099)
Industry dummies (8)
Country dummies (29/12)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Observations
225
225
225
91
91
91
Country-clustered standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Result 3: trade is a less robust determinant of
knowledge spillovers
(1)
(2)
6.35e-05
(0.0002)
0.0054***
(0.0020)
-0.0002
(0.0002)
(3)
(4)
(5)
Dependent: Patents
R&D Pool I2REP
R&D Pool Imports
R&D Pool Imports share
0.0041**
(0.0020)
0.0073*
(0.0043)
0.0007
(0.0039)
R&D Pool Random
ln(R&D)
0.145***
(0.0533)
0.126**
(0.0507)
0.138***
(0.0515)
0.129**
(0.0526)
-0.0008
(0.00086)
0.145***
(0.0023)
Industry dummies (8)
Country dummies (29)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Observations
225
225
225
225
225
Country-clustered standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Result 4: Supply chains can explain the
decreasing importance of distance over time
Results are robust to endogeneity ...1
First approach: look at the impact of R&D from G5 countries
on innovation in other countries to mitigate the “reflection
issue”
R&D Pool I2REP from G5
R&D Pool I2REP from non-G5
G5
Sample
non-G5
0.0010
(0.0035)
0.0029**
(0.0013)
0.0073*
(0.0041)
0.0115**
(0.0052)
Notes: The coefficient estimates are from regressions with the same
specifi- cation as in Table 3, column (5).
Results are robust to endogeneity .. 2
Second approach: use a panel specification with lagger R&D
and R&D Pool variables to alleviate reverse causality
Patentsikt = exp[α + β · ln(Stock R&D)ikt −1 + γ · Stock R&DPoolikt −1
+δ · ln( Patents)ikt −1 + ιτ + κτ ] + zikt .
Panel regressions
(1)
(2)
(3)
(4)
Dependent: Patents
StockR&D Pool I2Pt −1
0.0084**
(0.0037)
0.0047**
(0.0019)
StockR&D Pool I2Et −1
0.0078***
(0.0023)
StockR&D Pool I2REt −1
0.0075***
(0.0020)
StockR&D Pool I2REPt −1
0.0223**
(0.0106)
0.788***
(0.048)
0.141***
(0.044)
0.0226**
(0.0114)
0.794***
(0.049)
0.136***
(0.045)
0.0195*
(0.0100)
0.785***
(0.048)
0.136***
(0.048)
0.0189*
(0.0108)
0.778***
(0.047)
0.137***
(0.049)
Country-year dummies
Industry-year dummies
yes
yes
yes
yes
yes
yes
yes
yes
Observations
664
664
664
664
ln(StockR&D)t −1
ln(Patents)t −1
Pre-sample fixed effects
Country-clustered standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Instrument=
IV regressions
Industry’s dependence on external finance*
Country’s financial development
Cross-section averages
NegBin
Linear
(1)
(2)
Dynamic panel
NegBin
Linear
(3)
(4)
R&D Pool I2REP
0.0043*
(0.0023)
0.0034*
(0.0018)
0.0080**
(0.0032)
0.0105**
(0.0049)
ln(R&D)
0.124***
(0.048)
0.091**
(0.042)
0.008
(0.010)
0.008
(0.008)
Dependent: Patents
ln(Stock R&D)t −1
Country-year dummies
Industry-year dummies
yes
yes
yes
yes
yes
yes
yes
yes
First Stage F-statistic
327.66
378.47
1092.90
421.86
Observations
225
225
744
744
Bootstrapped standard errors in parentheses in (1) and (3).
Country-clustered standard errors in parentheses in (2) and (4).
*** p<0.01, ** p<0.05, * p<0.1
Conclusions
•
Findings: International production chains favour knowledge spillovers
The effect is larger when the interdependence is stronger
•
Research: The emergence of new countries (China, Singapore, Korea) as new
big contributors to the pool of innovation, may have something to do with
factory Asia … not just these countries invested more in R&D, but factory
Asia also increased the productivity of their R&D.
Look for convergence clubs along the supply chain
•
Policy: There is an economic rationale for government intervention to foster
participation in production networks. But, potential gains depend on the
type of relationships