trade facilitation and supply chain security
TRANSCRIPT
Trade Facilitation and Supply Chain Security:
1
WCO-PICARD Conference
27-29 September 2016
Presenter: Salamat Ali
Pakistan Customs/University of Nottingham,
UK
Evaluation of Integrated Cargo Containers Control (IC3)
Program between Pakistan and the United States
Structure
• Introduction of IC3 Program
• Research Question and Findings
• Related Literature
• Data and Methodology
• Main Results
• Summary and Policy Implications
Trade Facilitation and Supply Chain Security:Evaluation of Integrated Cargo Containers Control (IC3) Program between Pakistan and the United States
Motivation: Trade Facilitation and Supply Chain Security
3
4
• Pilot project at Port Qasim, Pakistan; Southampton Port, UK; Puerto Cortès, Honduras
• Intrusive scanning and monitoring via live video link even before loading on vessels
• Limited implementation at ports of Singapore, Hong Kong and Busan (Korea)
• Extension of the 100% scheme to all US-bound cargo from all origins
Integrated Cargo Containers Controls (IC3)
Research Question(s)
• What is the impact of IC3 on firm-level exports of Pakistan to the United States?
• Effect of subsequent policy adjustments in 2011 to facilitate the process
• Examine the heterogeneity of the trade effect across firms (incumbents and switchers)
• Mechanisms of adjustment along:
– Extensive margins of firms and products
– Prices and quantities
– Over time
5
Preview of Results
• Pakistan’s exports to the US relative to the EU drop by 15% in the post-
IC3 period. The drop with respect to India and China is around 40%.
• Adjustment comes along extensive margins of firms and products as well
as along margins of price and quantities.
• Effect of IC3 is persistent for four years until subsequent policy
interventions reverse this trend to some extent.
• The security policy caused a cumulative loss of US market access to the
tune of US$ 8 billion during 2007-2014.6
Related Literature
• Trade Costs: Arkolakis (2010); Feyrer (2009); Anderson and Van Wincoop (2003, 2004);
Baier and Bergstrand (2001); Donaldson (2014)
• Technology and Trade: Bernhofen et al. (2015); Pascali (2014); Hummels (2007)
• Economic Sanctions: Afesorgbor &Mahadevan (2016); Yang et al. (2009); Caruso (2003)
• Trade Diversion: Yang et al. (2014); Liu et al. (2013); Carrere (2006)
• Supply Chain Security: Mirza and Verdier (2008); EC(2009); WCO(2008); GAO (2008)
Uniqueness of this Work
• Exogenous nature of the shock
• Differential effect on trade costs across markets
• Unique data sets and first firm-level study to quantify the effect of typical trade restriction
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Data Description• Sources: Firm-level exports from Pakistan Customs and domestic sales from
Inland Revenue Services (administrative datasets)
• Period: Jan 2000 to Dec 2015
• Frequency: Transaction level
• Observations: 8.6 million; EU (Control)- 4.7m and US (Treatment)-3.9m
• Firms: 24,174
• Variables: Products at HS-8 digit level, unit values, quantities, identity of
exporter and importers, location of production facility of firms, location of their
exporting station, mode of shipment, etc.
• Estimation level: Firm-product-market-year (463, 931 observations)8
Influence of IC3 on US-bound Exports from Pakistan
Beyond-the-border
Eliminating
Transhipment
requirements and
allowing direct
shipments to the US
Behind-the-border
Diversion of US-bound exports from all dry ports and other seaport to Qasim Port, Karachi
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Effect of the IC3 on Trade Costs en Route
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At- and Behind-the-Border Effect of IC3
11
Behind-the-Border Effect
12Source: Pakistan Customs
Percentage of US-bound exports handling at various stations
Influence of IC3 on US-bound Exports
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December 2006
20
00
30
00
40
00
50
00
60
00
Ex
po
rts
Vo
lum
e
2000 2002 2004 2006 2008 2010 2012 2014
European Union United States
Index, 2000=1
Empirical Setting: Diff-in-Diff Estimation Approach
First Treatment (2007)
ln(Xijkt) = β0+ β1(Treat)j + β2(After)t + β3(Treat x After)jt + αi + γk + λt + εijkt………………….………………..(1)
• Xijkt denotes the value of exports of a firm ‘i’ to market ‘j’ of product ‘k’ at a time ‘t’ (intensive
margins). The export volume is measured in PKR millions.
• ‘Treat’ is a dummy variable equal to ‘1’ if an observation pertains to the US and ‘0’ for the EU.
• ‘After’ is a dummy variable equal to ‘1’ for the period 2007-2014 and ‘0’ otherwise.
• α, γ and λ are firms, products and time fixed effects.
• εijkt is an idiosyncratic error term.
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Second Treatment (2011)
ln(Xijkt) = β0+ β1(Treat)j + β2(After1)t + β3(Treat x After1)jt + β4(After2)t+ β5(Treat x After2)jt + αi + γk + λt + εijkt..(2)
• ‘After1’ is a dummy variable equal to ‘1’ for the period 2007-2014 and ‘0’ otherwise.
• After2’ is a dummy variable equal to ‘1’ for the period 2011-2014 and ‘0’ otherwise.
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The dependent variable is a log of exports per firm by destination
Robust standard errors are in parentheses. These coefficients were obtained using stata 13SE* p < 0.10, ** p < 0.05, *** p < 0.01. The
coefficients on other regressors and fixed effects are not reported.
Main Estimation Results
(1) (2) (3) (4)
Interaction (treat x after) 1st Treatment_2007
-0.200***
(0.022)
-0.134***
(0.019)
-0.157***
(0.018)
-0.151***
(0.018)
2nd Treatment_2011
-0.218***
(0.018)
-0.014 (0.016)
0.036**
(0.015)
0.030**
(0.015)
Firm FE y y y
Product FE y y Time FE y
R2 0.02 0.40 0.51 0.51
Observations 463,931 463,931 463,931 463,931
Heterogeneity of the Effect across Firms
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Incumbents export from Qasim port before and after IC3 while Switchers export
from Karachi port and dry ports before IC3 and switch to Qasim port after IC3
The dependent variable is a log of exports per firm by destination
(1)
1st Treatment_2007 x
Incumbents at PQ -0.089***
(0.029)
Switcher to PQ -0.164***
(0.018)
Continuers at KP -0.149**
(0.075)
2nd Treatment_2011 x
Incumbents at PQ -0.018
(0.026)
Switcher to PQ 0.041**
(0.017)
Continuers at KP 0.038
(0.095)
R2 0.505
Observations 463,931
Mechanisms of Adjustment
17
Speed of Adjustment
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Coeff. SE (1) (2)
Interaction (treat x after) x
int_2007 -0.478***
(0.022)
int_2008 -0.495***
(0.024) int_2009 -0.497
*** (0.024)
int_2010 -0.349*** (0.024)
int_2011 -0.083***
(0.024) int_2012 0.245
*** (0.024)
int_2013 0.367***
(0.024) int_2014 0.398
*** (0.025)
R2 0.50
Observations 463,931
Summary and Conclusion• We investigate the trade effect of IC3 in the wake of 9/11.
• The exogenous nature of this shock and its specificity to one export market allows us
to use a diff-in-diff estimation approach.
• We find that:
– In the post treatment period, Pakistan’s exports to the US relative to the EU drop by
15%, on average.
– The cumulative loss of US market access during 2007-2014 amounts to $8 billion.
– Switchers bear the main loss and the subsequent adjustments reverse this declining
trend to some extent.
• These findings have policy implications to use similar technologies to secure supply
chain and facilitate trade flows in the wake of changing security situation in different
parts of the world.19
Thanks for your attention
Q&A
20
Integrated Cargo Containers Controls (IC3)
21Source: European Commission (2009)
Main Estimation Results
The dependent variable is log of exports per firm by destination
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(1) (2) (3) (4) (5)
Interaction Term
(Treat x After)
-0.371***
(0.025)
-0.133***
(0.022)
-0.131***
(0.021)
-0.106***
(0.021)
-0.106***
(0.021)
Treatment 0.844***
(0.023)
0.262***
(0.021)
0.248***
(0.020)
0.233***
(0.020)
0.233***
(0.020)
After -0.107***
(0.017)
0.178***
(0.014)
0.136***
(0.014)
3.591***
(0.128)
2.451***
(0.202)
Time trend
0.095***
(0.014)
Firm fixed effects y y y y
Prod. fixed effects y y y
Time fixed effects y y
R2 0.011 0.394 0.422 0.437 0.437
Observations 472,258 472,258 472,258 472,258 472,258
Note: Robust standard errors are in parentheses. These coefficients were obtained using Stata 13 SE; * p < 0.10, ** p < 0.05, *** p < 0.01.
ln(Xijkt)= β0+ β1(Treat)j + β2(After)t + β3(Treat x After)jt + αi + γk + λt + εijkt
Robustness: Including time-varying Fixed Effects
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(1) (2) (3) (4)
Interaction (Treat x After) -0.144***
(0.030)
-0.148***
(0.029)
-0.253***
(0.018)
-0.150***
(0.030)
Firm-year FE Y Y Y
Product-year FE Y Y
Prod.-market Y Y
R2 0.504 0.534 0.159 0.535
N 472,258 472,258 472,258 472,258
Robust standard errors are in parentheses. These coefficients were obtained using stata 13SE * p < 0.10, ** p <
0.05, *** p < 0.01
The dependent variable is the log of exports per firm by destination
Empirical SettingComposition of Exports to Control (EU) & Treatment (US) Groups
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Source: Pakistan Customs
- 10 20 30 40 50 60 70 80 90 100
01-05_Animal
06-15_Vegetable
16-24_FoodProd
25-26_Minerals
27-27_Fuels
28-38_Chemicals
39-40_PlastiRub
41-43_HidesSkin
44-49_Wood
50-63_TextCloth
64-67_Footwear
68-71_StoneGlas
72-83_Metals
84-85_Mach.Elect
86-89_Transport
90-99_Misc.s
US EU
Share of various products groups in the exports baskets (%), 2013
A: Effect of IC3 on Single and Multiple Market Firms
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Multi-Market Firms (EU &US) Single Market Firms (EU/US)
(1) (2)
Treat x After -0.176***
(0.021)
-0.099***
(0.031)
R2 0.486 0.551
Observations 235, 830 225, 416
All Firms Multi-Market
Firms (EU &US) Single Market
Firms (EU/US) (1) (2) (3)
Treat x After -0.172***
(0.026)
-0.155***
(0.031)
-0.206***
(0.041)
R2 0.471 0.444 0.506
Observations 280,881 148, 494 130, 936
B: Effect of IC3 on the Continuing Cohort of Firms
Collapsing Data to Single Pre- and Post-IC3 Period
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(1) (2)
Treat x after
1st Treatment_2007 -0.173***
(0.021)
2nd Treatment_2011
-0.030
(0.018)
FE (firms, products) y y
R2 0.49 0.50
Observations 295, 668 295,668
Beyond-the-Border effect on US-bound Exports
Source: http://www.searates.com/reference/portdistance/
Note: Firms do not appear to ship to New York via Hong Kong 27
Maritime
Distances A: Maritime Distances in KilometresDestination Direct
New York 14,812 18,424 -19.60% 28,591 NA 14,852 -0.27%
Los Angles 19,564 19,756 -0.97% 19,828 -1.33% 21,754 -10.07%
B: Vessel Sailing Time in Number of DaysDestination Direct
New York 24 30 -20.00% 45 NA 25 -4.00%
Los Angles 31 32 -3.13% 32 -3.13% 35 -11.43%
Via Sri Lanka Via Hong Kong Via Salalah (Oman)
Via Sri Lanka Via Hong Kong Via Salalah (Oman)
Loss of Market Access
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0
1
2
3
4
5
6
7
1998 2000 2002 2004 2006 2008 2010 2012 2014
Bil
lio
n
US
$
Actual
Projected
29
Trading Partners Trade (US$ M) Share %
United States 3,746 14.91
China 2,652 10.56
Afghanistan 1,998 7.95
United Arab Emirates 1,775 7.07
European Union 5,932 23.01
Pakistan's major Export Markets, 2013
Drop in Freight Rates (US$/MT)
Source: Computed using Pakistan Custom’s Dataset
Note: The freight values are in US dollars per metric ton and percentage changes are year-on-year basis
Year Freight % Change Freight % Change Freight % Change
(1) (2) (1) (2) (1) (2)
2003 194 251 285
2004 196 1 226 -10 258 -9
2005 187 -5 241 7 267 3
2006 172 -8 220 -9 274 3
2007 162 -6 209 -5 263 -4
2008 149 -8 194 -7 219 -17
2009 101 -32 159 -18 191 -13
2010 110 9 125 -22 211 10
2011 113 2 134 7 227 7
European Union United Kingdom United States
30
Global Financial Crisis and US Imports from Pakistan and the World
31
Exports in 2002==1