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

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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

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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

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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

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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

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Global Financial Crisis and US Imports from Pakistan and the World

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Exports in 2002==1