Exporter Dynamics Database

A product of the Trade and International Integration Team

(DECTI) Development Research Group

Washington DCMay 24, 2012

A project funded by KCP, MDTF, and RSB

Exporter Dynamics Database Team

Task Manager: Ana Margarida Fernandes

Creators: Caroline Freund, Martha Denisse Pierola

Core Team: Tolga Cebeci, Mario Gutierrez-Rocha

Internal and External Contributors:• Officials from customs agencies

• World Bank colleagues in AFR, LAC, MENA, SAS, EAP, DECPG,PREMTR, PREMGE and especially in DECDG

• Researchers in Europe, the Middle East, and Pacific

Why an Exporter Dynamics Database?• Countries do not export. Firms do.

• Aggregate exports provide a good picture of the flows ofgoods across countries. But so far there was no systematicsource of information on the firms that export.

• Focusing on firm-based information is key to understandthe micro foundations of export growth, the strugglesfaced by exporters and the determinants of export success

• In two years this project collected exporter-level customsdata for 45 countries to develop a novel database withmeasures of exporter characteristics and dynamics that isfreely available to the public

The Exporter Dynamics Database covers 45 countries

37 Developing countries8 Developed countries

Larger concentration of data in Latin America, Sub-Saharan Africa and Middle East and North Africa

How was the Exporter Dynamics Database compiled?

• We collected exporter-level customs data covering the universe of export transactions from customs agencies across countries – For each exporter in a given country we know what are they

exporting in a given year, to where, for what value • Key variables: origin country, firm id, product, destination

country, year, export value– Covering export transactions in agriculture, manufacturing and

mining (excluding HS Chapter 27- oil)

• The cross-country exporter-level customs data is used to compute the Exporter Dynamics Database measures

• Data accuracy is assessed through comparisons to COMTRADE export flows and within-country internal consistency

What measures are included in the Exporter Dynamics Database?

• Basic Characteristics– Number of exporters, exporter size, exporter growth

• Concentration/Diversification– Herfindahl indexes, share of top exporters, number of

products/destinations per exporter, number of exporters per product/destination

• Firm Dynamics– Exporter entry, exit and survival rates

• Product Dynamics– Product entry, exit and survival rates for incumbent exporters and

share of new products in exporter values

• Destination Dynamics– Destination entry, exit and survival rates for incumbent exporters and

share of new destinations in exporter values

• Unit Prices

Measures in the Exporter Dynamics Database are available at different

disaggregation levels

• Country-Year level (1)

• Country-Year-Product level

–HS 2-digit (2)

–HS 4-digit (3)

–HS 6-digit (4)

• Country-Year-Destination level (5)

The Exporter Dynamics Database covers mainly the 2000s

Country 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 TotalAlbania 1 1 1 1 1 1 6

Belgium 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Burkina Faso 1 1 1 1 1 1 6

Bangladesh 1 1 1 1 1 1 1 7

Bulgaria 1 1 1 1 1 1 6

Brazil 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

Botswana 1 1 1 1 1 1 1 1 8

Chile 1 1 1 1 1 1 1 7

Cameroon 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Colombia 1 1 1 3

Costa Rica 1 1 1 1 1 1 1 1 1 1 1 1 12

Dominican Republic 1 1 1 1 1 1 1 1 8

Ecuador 1 1 1 1 4

Egypt 1 1 1 1 1 5

Spain 1 1 1 1 1 5

Estonia 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Guatemala 1 1 1 1 1 1 1 1 8

Iran 1 1 1 1 1 5

Jordan 1 1 1 1 1 1 1 1 8

Kenya 1 1 1 1 4

Cambodia 1 1 1 1 1 1 1 1 1 1 10

Kuwait 1 1 2

Laos 1 1 1 1 4

Lebanon 1 1 1 3

Morocco 1 1 1 1 1 1 1 1 1 9

Mexico 1 1 1 1 1 1 1 1 1 1 10

Macedonia 1 1 1 1 1 1 1 1 1 1 10

Mali 1 1 1 1 4

Mauritius 1 1 1 1 1 1 1 1 8

Malawi 1 1 1 3

Niger 1 1 1 3

Nicaragua 1 1 1 1 1 1 1 1 1 1 10

Norway 1 1 1 1 1 1 1 1 1 1 10

New Zealand 1 1 1 1 1 1 1 1 1 1 1 1 12

Pakistan 1 1 1 1 1 1 1 1 1 9

Peru 1 1 1 1 1 1 1 1 1 1 1 1 1 13

Portugal 1 1 1 1 1 1 1 1 1 9

Senegal 1 1 1 1 1 1 1 1 1 1 1 11

El Salvador 1 1 1 1 1 1 1 1 8

Sweden 1 1 1 1 1 1 1 1 1 1 10

Turkey 1 1 1 1 1 1 1 1 1 9

Tanzania 1 1 1 1 1 1 1 7

Uganda 1 1 1 1 1 1 1 1 1 1 10

Yemen 1 1 1 1 1 5

South Africa 1 1 1 1 1 1 1 1 1 9

Total 8 9 10 14 17 24 29 30 34 39 38 40 39 21 2 354

2003 – 2009 is the most common period

Novel insights based on the Exporter Dynamics Database:

Concentration of exportsChurning in export markets

Survival of new entrants

Remarkable concentration of exports• Exports are concentrated among a very small

number of large firms -the top 1 percent (Freund and Pierola, 2012)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Share of Top 1% Exporters

• Should export promotion target SMEs?

Concentration of exporters is higher in more developed economies

This pattern could be linked to differences in the sectoral composition of exports

ALB

BELBFA

BGD

BGRBRA

BWA

CHL

CMR

COL

CRI

DOM

ECU

EGY

ESP

EST

GTM

IRNJOR

KEN

KHM

KWT

LAO

LBNMAR

MEX

MKDMLIMUS

MWI

NER

NIC

NORNZL

PAK

PER

PRT

SEN

SLV

SWE

TZA

UGA

YEM

ZAF

R2=0.45

.2.4

.6.8

1

Sha

re o

f T

op

1%

Exp

ort

ers

6 7 8 9 10 11Ln GDPpc

Share of Top 1% Exporters - GDPpc

Important differences in concentration across sectors

0%

10%

20%

30%

40%

50%

60%

Share of Top 1% Exporters

Concentration is also lower in developing countries across sectors

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Share of Top 1% Exporters(Developing Countries/Developed Countries)

Almost all sectors are below 1

Churning of firms

In poorer countries, exporter entry rates are high …

ALB

BEL

BFA

BGD

BGR

BRA

BWA

CHL

CMR

COL

CRI

DOM

ECU

EGY

ESP

EST

GTM

IRN

JOR

KEN

KHM

LAO

MARMEX

MKD

MLI

MUS

MWI

NIC

NOR

NZLPAK

PER

PRT

SEN

SLV

SWE

TUR

TZA

UGA

YEM

ZAF

R2=-0.44

.2.3

.4.5

Entr

y R

ate

6 7 8 9 10 11Ln GDPpc

Entry Rate - GDPpc

ALB

BEL

BFA

BGD

BGR

BRA

BWA

CHL

CMR

COL

CRI

DOM

ECU

EGY

ESP

EST

GTM

IRN

JOR

KEN

KHM

LAO

MAR

MEXMKD

MLI

MUS

MWI

NIC

NOR

NZL

PAK

PER

PRT

SEN

SLV

SWETUR

TZA

UGA

YEM

ZAF

R2=-0.36

.2.3

.4.5

.6

Exit R

ate

6 7 8 9 10 11Ln GDPpc

Exit Rate - GDPpc

… but so are exporter exit rates

Exporter churning declines with the level of development

Churning of firms

Surprisingly exporter entry rates are lower in larger countries…

… but so are exporter exit ratesALB

BEL

BFA

BGD

BGR

BRA

BW A

CHL

CMR

COL

CRI

DOM

ECU

EGY

ESP

EST

GTM

IRN

JOR

KEN

KHM

LAO

MARMEX

MKD

MLI

MUS

MWI

NIC

NOR

NZLPAK

PER

PRT

SEN

SLV

SWE

TUR

TZA

UGA

YEM

ZAF

R2=-0.52.2.3

.4.5

Entr

y R

ate

22 24 26 28Ln GDP

Entry Rate - GDP

ALB

BEL

BFA

BGD

BGR

BRA

BW A

CHL

CMR

COL

CRI

DOM

ECU

EGY

ESP

EST

GTM

IRN

JOR

KEN

KHM

LAO

MAR

MEXMKD

MLI

MUS

MWI

NIC

NOR

NZL

PAK

PER

PRT

SEN

SLV

SWETUR

TZA

UGA

YEM

ZAF

R2=-0.40.2.3

.4.5

.6

Exit R

ate

22 24 26 28Ln GDP

Exit Rate - GDP

Exporter churning declines with country size

Survival of New Exporters

• Survival is a challenge everywhere. Particularly for exporters in low-income Africa.

0%

10%

20%

30%

40%

50%

60%

70%

One-Year Survival Rate of Entrants

Survival is more challenging when costs of exporting are higher

Source: Brenton, Cadot, and Pierola (2012)

0.2

.4.6

.8

firs

t-yea

r su

rviv

al ra

te o

f e

ntr

an

ts

0 1000 2000 3000 4000cost of export, USD per container

• We need to better understand how to distinguish theeffect of different types of trade costs on entry vs.survival.

• Should policy target more entry or longer survival?

Using exporter-level data: concentration and export superstars

• Superstars –the top 1% of exporters by size-represent over three quarters of total export growth –average across countries over their latest three-year period.

Non-Superstars,

22%

Superstars, 78%

Source: Freund and Pierola (2012)

Using exporter-level data: survival

Cadot, Iacovone, Pierola, and Rauch (2011)

• What determines success and survival of exporters (beyond the first year) in Malawi, Mali, Senegal and, Tanzania?

• Survival probability increases with:

– The number of firms exporting the same product to the same destination (cross-firm synergies driven by information spillovers)

– Firm diversification in terms of products and especially in terms of markets

Looking Ahead: Using the Exporter Dynamics Database

What can the Exporter Dynamics Database be used for?

• For in-depth analysis of the performance of the export sector in a country, benchmarking against relevant regional or income-group comparators.

Example – Peru Trade Report

• For cross-country and cross-sectoral comparisons to help policy-makers identify areas where their exporters are facing more challenges in terms of entry or survival.

• For analyzing the effects of: – Trade costs (logistics costs, customs delays)– Trade barriers (tariffs, non-tariff measures, contingent protection measures)

Example – Gravity equation– Business environment factors and policy or economic shocks (exchange rates)

• As a tool for the impact evaluation of certain types of trade facilitation reforms and export promotion programs.

Study of non-traditional exports• Peruvian exporters are very diversified in

terms of destination markets in vegetables

0

1

2

3

4

5

6C

HL

CO

L

CR

I

DO

M

ECU

MEX

PER

CH

L

CO

L

CR

I

DO

M

ECU

MEX

PER

CH

L

CO

L

CR

I

DO

M

ECU

MEX

PER

CH

L

CO

L

CR

I

DO

M

ECU

MEX

PER

7 8 9 20

Vegetables (fresh) Fruits (fresh) Vegetables andFruits (prepared)

Coffee, tea and spices (includes paprika and

piquillo pepper)

Average number of destinations per exporter

Source: Fernandes, Gutierrez-Rocha, and Pierola (2012)

High barriers to bilateral trade shrink the numbers of exporters

Coeff in total exports reg. =

Coeff in number of exporters reg. + Coeff in average exports per firm reg.

Ln Total Exports

Bilateral

Ln Number of

Exporters

Bilateral

Ln Mean Exports

per Firm

Bilateral

Ln GDP of exporter 1.171*** 0.860*** 0.312***

(0.028) (0.015) (0.021)

Ln GDP of destination 0.851*** 0.500*** 0.351***

(0.020) (0.011) (0.014)

Ln bilateral distance -1.649*** -1.253*** -0.396***

(0.052) (0.031) (0.032)

Ln bilateral tariffs -1.098*** -1.040*** -0.058

(0.079) (0.048) (0.053)

Observations 2445 2445 2445

R-squared 0.544 0.658 0.224

Dependent Variable is:Traditional

gravity equation

New gravity

equationwith EDD measures

Public and Free Access to the Exporter Dynamics Database: Website

Demonstrationhttp://econ.worldbank.org/exporter-dynamics-database

Thank You!