The Geography of Trade in theEuropean Single Market

Shawn W. Tan

The World Bank

IMF/WB/WTO Trade Research WorkshopNovember 30, 2016

0 / 18

Borders within the European Single Market (ESM)

Trade should move freely within the ESM, unimpeded by tari�s andtrade barriers.

Yet trade is still impeded by spatial frictions: distance and borders

Many papers have estimated border e�ects in Europe

I Find that internal trade �ows higher than trade �ows that crossnational/regional borders

Issues with these studies

I International data may be a factor of geographical aggregationI Regional data are limited in coverage: Spanish regions trade with otherEU countries (e.g. Garmendia et al., 2012) and French and Germanregions (Helble, 2007)

1 / 18

What I do in My Paper

Use a unique dataset to estimate the e�ects of spatial frictions ontrade �ows within the ESM

I 28 countries, 270 European regions, 13 sectorsI Best available data of sector-level inter-regional trade in Europe

Find that spatial frictions exert a strong negative e�ect on trade

I Aggregate trade falls rapidly over short distancesI Driven largely by fall in the extensive margins: in particular, total andaverage number of shipments

I Intensive margins are less a�ected by distance

Possibly explained by trade in intermediate inputs and co-location of�rms

I Use probits to estimate probability of two regions tradingI Find that the probability of regional trade is higher when industrialdemand of a sector at destination is higher

2 / 18

Literature

Estimation of border e�ects

I Europe: Head and Mayer (2000), Nitsch (2000), Chen (2004),Minondo (2007), Gil-Pareja et al. (2005), Ghemawat et al. (2010),Requena and Llano (2010), Llano-Verduras et al. (2011), Garmendia etal. (2012)

I U.S. and Canada: McCallum (1995), Anderson and Van Wincoop(2003), Balistreri and Hillberry (2007)

Reasons behind border e�ects

I Omitted variable bias: Anderson and Van Wincoop (2003)I Intermediate inputs and co-location of �rms: Hillberry and Hummels(2008), Chen (2004)

3 / 18

Outline

1 Introduction

2 Empirical Framework

3 Data

4 Decomposing Trade Responses to Spatial Frictions

5 Trade in Intermediate Inputs and Co-location of Firms

6 Conclusion

4 / 18

Empirical Framework

Decompose aggregate trade into six components following Hummelsand Klenow (2005) and Hillberry and Hummels (2008)

Tij = Nij ×PQ ij

Tij = Nkij ×N f

ij︸ ︷︷ ︸=Nij

×P ij ×Q ij︸ ︷︷ ︸=PQ ij

I Aggregate trade (Tij )I Total number of shipments

(Nij

)F Number of unique shipments

(Nkij

)F Average number of shipments per sectors (N f

ij )

I Average value of each shipments(PQ ij

)F Average price (P ij )F Average quantity

(Q ij

)I Where i is origin regions, j is destination regions and k are sectors

5 / 18

Estimation techniques

1 Kernel regressions

I Non-parametric technique to uncover the relationship between distanceand trade components

I Gives a picture of how trade reacts to distances, especially at very shortdistances

2 Linear regressions

I Examine importance of region and country bordersI Logs of aggregate trade and its six components used as dependentvariables in separate regressions

I Regressed on lnDij , (lnDij )2, regionij , countryij with origin and

destination �xed e�ects

6 / 18

Freight Data

Eurostat Road Freight Transport Survey

I Collected by EU member countries and three EFTA countries in 2011I Random strati�ed survey of road vehicles over one week for eachquarters in 2011

I Each country follows same survey methodologyI Data adjusted with weights and then provided to Eurostat

Data is available at the origin-destination-goods level

I 13 sectors: broad categories of goodsI 270 regions in 28 countries

Actual distance covered for each shipment

7 / 18

Converting Quantity to Values

Freight data only has quantities expressed in kg

Solution: use 2011 unit prices from CEPII unit price dataset (Berthouand Emlinger, 2011)

I Unit prices between reporter and partner countries for products (HS 6)I Concordance between HS 6 and the 13 sectorsI Matched regions in freight data to countries

Shipment values obtained for each origin-destination-sector triad

8 / 18

Geographical distribution of trade �ows

AT11AT12AT13

AT21AT22

AT31AT32AT33AT34

BE10BE21BE22BE23BE24BE25BE31BE32 BE33

BE34BE35

BG31 BG32BG33

BG34BG41BG42

CH01CH02

CH03CH04CH05CH06

CH07

CY00

CZ01CZ02CZ03

CZ04 CZ05

CZ06CZ07CZ08

DE11DE12

DE13DE14 DE21DE22

DE23DE24

DE25DE26

DE27

DE30DE40DE50

DE60

DE71DE72

DE73

DE80

DE91DE92

DE93DE94

DEA1DEA2

DEA3DEA4DEA5

DEB1DEB2DEB3DEC0

DED4DED5

DEE0

DEF0

DEG0

DK01DK02DK03

DK04

DK05

EE00

EL11EL12

EL13

EL14EL21

EL22EL23

EL24

EL25EL30

EL41

EL42

EL43

ES11ES12 ES13 ES21

ES22ES23

ES24

ES30

ES41

ES42ES43

ES51

ES52 ES53

ES61ES62

ES63ES64

ES70

FR10 FR21

FR22FR23

FR24

FR25

FR26

FR30

FR41FR42

FR43FR51FR52

FR53

FR61FR62

FR63FR71FR72

FR81FR82

FR83

HU10HU21HU22

HU23

HU31HU32

HU33

IE01

IE02

ITC1ITC2

ITC3

ITC4

ITF1ITF2

ITF3 ITF4ITF5

ITF6

ITG1

ITG2

ITH1ITH2

ITH3ITH4

ITH5

ITI1ITI2

ITI3

ITI4

LT00

LU00

LV00

NL11NL12NL13NL21

NL22NL23

NL31NL32

NL33NL34NL41

NL42

NO01

NO02

NO03

NO04

NO05

NO06

NO07

PL11PL12

PL21PL22

PL31

PL32

PL33

PL34

PL41

PL42

PL43

PL51PL52

PL61

PL62PL63

PT11

PT15

PT16

PT17PT18

RO11

RO12

RO21

RO22RO31RO32RO41

RO42

SE11SE12

SE21

SE22

SE23

SE31

SE32

SE33

SI01SI02

SK01SK02SK03 SK04

UKC1UKC2

UKD1

UKD6UKD7UKE1

UKE2UKE3UKE4UKF1

UKF2UKF3

UKG1UKG2UKG3 UKH1

UKH2UKH3UKI1UKI2UKJ1UKJ2UKJ3 UKJ4UKK1

UKK2UKK3

UKK4

UKL1UKL2

UKM2

UKM3

UKM5UKM6

UKN0

Percentage = 100 * Within−region / total trade86.5% to 100%73.2% to 86.5%61.8% to 73.2%37.7% to 61.8%0% to 37.7%

9 / 18

Other data used for probits regressions

Additional regional data on population, gross output of a sector, and sectorindustrial demand of a sector

Regional population from Eurostat

Regional gross output of sector is not readily available

I Calculate as proportion of regional GDP determined by sector's laborshares

I Data on regional GDP and sectoral employment data from Eurostat

Industrial demand is the sector's expenditure at destination

I Sum of all shipments by sector at destination (from freight data)I Alternative measures for robustness: number of �rms and number ofemployees in region's sector (from Eurostat)

10 / 18

Kernel regression results (Tij)

010

000

2000

030

000

4000

0M

illio

n U

SD

250 5000 1000 2000 3000 4000 5000Kilometers

Total Value on distance

11 / 18

Kernel regression results Tij = Nij ×PQ ij0

1000

2000

3000

4000

Num

ber

250 5000 1000 2000 3000 4000 5000Kilometers

Total shipments on distance

05

1015

Mill

ion

US

D

250 5000 1000 2000 3000 4000 5000Kilometers

Average value on distance

12 / 18

Kernel regression results Tij = Nkij ×N f

ij ×P ij ×Q ij0

24

68

10N

umbe

r

250 5000 1000 2000 3000 4000 5000Kilometers

Number of Unique Sectors on distance

010

020

030

0N

umbe

r

250 5000 1000 2000 3000 4000 5000Kilometers

Average number of shipments on distance

0.2

.4.6

.81

Mill

ion

US

D

250 5000 1000 2000 3000 4000 5000Kilometers

Average price on distance

510

1520

251,

000

Kilo

gram

s

250 5000 1000 2000 3000 4000 5000Kilometers

Average quantity on distance

13 / 18

Linear regression results

14 / 18

Importance of extensive margins

Spatial frictions have strong negative e�ect on trade �ows, whichreduces sharply at short distances

I Mainly through the extensive margins: Total and average number ofshipments

Two explanations for strong e�ect of spatial frictions on trade �owsI Home Bias: consumers prefer their home goods due to tastes

F Limit to how localized are these preferencesF Hillberry and Hummels (2008) show that border e�ects exist even at

5-digit zip code level in USF More likely to explain consumption goods (food, textile products) but

not homogeneous industrial inputs

I Intermediate inputs and �rm co-location: intermediate goodstransported to regions with matching production structures

F Geographical proximity in matching supplier and customers in Japanand majority of connections between �rms are local � median distanceof 30 km (Bernard et al., 2015)

F Spatial clustering of �rms can increase internal border e�ects (Hillberryand Hummels, 2003; Chen, 2004; Wolf, 2000)

15 / 18

Empirical setup for probit regressions

Test whether industrial demand a�ects likelihood of shipmentsbetween two regions

Control for other in�uences: distance, regional gross outputs, andconsumer demand

Pr(I kij = 1

)= Φ(β

k0

+ βk1lnDij + β

k2

(lnDij

)2

+ βk3Regionij + β

k4Countryij

+ βk5lnGOk

i + βk6lnGOk

j + βk7lnPopj + β

k8lnE k

j ) ∀k = 2, ...,13

I GOki and GOk

j is gross output of sector k of i and jI Popj is population of jI E k

j is industrial expenditure of sector in destinationI Note: distance is between centroids of regions.

16 / 18

Probit regression results

Results robust to alternative measures of industrial demand(E kj

)

17 / 18

Conclusion

Using a unique dataset of freight shipments in the ESM, we �nd thataggregate trade responds sharply to spatial frictions

I Trade falls sharply over short distances (after 250 km)I Largely attributed to fall in total and average number of shipments

Role of extensive margins explained by trade in intermediate goods

I Firms locate near each other to avoid trade costsI Regions are more likely to ship products to other regions with ademand for that input

I Higher industrial demand increases probability of regional shipments

Policy makers concerned about lack of trade integration should worryless about trade barriers and examine roles of value chains.

18 / 18

Thank You

18 / 18

Sectors / Goods Classi�cation

18 / 18

Countries included in estimation

Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark,Estonia, France, Germany, Greece, Hungary, Ireland, Italy, Latvia,Lithuania, Netherlands, Norway, Poland, Slovakia, Slovenia, Spain,Sweden, Switzerland, and the United Kingdom.

Data includes Croatia but the country is dropped from the estimationbecause Croatia was not part of the ESM in 2011 and would havetari� barriers on shipments in and out of the country.

18 / 18

Summary statistics for probit regressions

18 / 18