Is the border effect an artefact of geographic aggregation?

IS THE BORDER EFFECT AN ARTEFACT OF GEOGRAPHIC AGGREGATION?

CARLOS LLANO-VERDURAS ASIER MINONDO-URIBE FRANCISCO REQUENA-SILVENTE

FUNDACIÓN DE LAS CAJAS DE AHORROS DOCUMENTO DE TRABAJO

Nº 570/2010

De conformidad con la base quinta de la convocatoria del Programa de Estímulo a la Investigación, este trabajo ha sido sometido a eva- luación externa anónima de especialistas cualificados a fin de con- trastar su nivel técnico.

ISSN: 1988-8767

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Las opiniones son responsabilidad de los autores.

Is the Border Effect an Artefact of Geographic Aggregation?

Carlos Llano-Verduras*

Asier Minondo-Uribe**

Francisco Requena-Silvente***

Abstract:

The existence of a large border effect is considered as one of the main puzzles of international macroeconomics. We show that the border effect is, to a large extent, an artefact of geographic concentration. In order to do so we combine international flows with intra-national flows data characterised by a high geographic grid. At this fine grid, intra-national flows are highly localised and dropping sharply with distance.

The use of a small geographical unit of reference to measure intra-national bilateral trade flows allows to estimating correctly the negative impact of distance on shipments. When we use sector disaggregated export flows of 50 Spanish provinces in years 2000 and 2005 split into inter-provincial and inter-national flows, we find that the border effect is reduced substantially and even becomes statistically not different from zero in some estimations.

Key words: border effect, distance, interregional trade, international trade, Spanish provinces

JEL Classification: F14, F15

Corresponding author: Asier Minondo, Dpt Economics, Universidad de Deusto–ESTE, Camino de Mundaiz, 50. 20014 San Sebastián (Spain). E-mail: [email protected].

* Universidad Autónoma de Madrid

** Universidad de Deusto-ESTE

*** Universidad de Valencia

Acknowledgements: Carlos Llano acknowledges financial support from the Education Department of the Regional Government of Madrid (project TransporTrade S2007/HUM/497 www.uam.es/transportrade). Asier Minondo acknowledges financial support from the Department of Education, Universities and Research of the Basque Government. Francisco Requena acknowledges financial support from the Spanish Ministry of Science and Innovation (project number ECO 2008- 04059/ECON) and Generalitat Valenciana, project Prometeo/2009/098.

1. Introduction

Since McCallum's (1995) seminal paper, a large body of literature has tried to explain why countries trade much more with themselves than with other partners. The notoriety of the border effect is such that it has been argued to be “one of the six major puzzles in international macroeconomics” (Obstfeld and Rogoff, 2000).

Researchers have followed four different routes to solve the border puzzle. First, some authors (Rauch, 2001; Combes et al., 2005; Schulze and Wolf, 2009; Nitsch and Wolf, 2009) argue that large border effects should not constitute a puzzle, because information barriers increase gently the costs of doing business not only for partners located in different countries but even for patterns located in different regions within a country. Second, other authors point out that even when trade costs were low, a large elasticity of substitution between domestic and foreign goods may lead to large home-bias effects (Evans, 2003). Chaney (2008) demonstrates that the presence of fixed costs of exporting and productivity heterogeneity across firms also magnify the aggregated trade elasticity with respect to trade barriers. Rossi- Hansberg (2005) and Yi (2010) show how endogenous firm location and vertical specialisation in the presence of intermediate goods may help to magnify initial small trade frictions along national borders. A third group of researchers argue that large border effects arise due to a misspecification of the gravity model used in the econometric analyses (Anderson and van Wincoop, 2003) or to identification problems (Heinemeyer et al, 2008).

Finally, a fourth group of authors have indicated that the mismeasurement of physical

distance may explain the large border effects. This concern arose in studies that

lacked data on flows across sub-national units and had to input intra-national trade

as the difference between production and exports (Wei, 1996; Nitsch, 2000; Head

and Mayer, 2000; Helliwell and Verdier, 2001). In these studies the distance travelled

by goods in the intra-national flow plays a crucial role. If the internal distance is not

estimated accurately, the estimated border effect will not be reliable. In particular, if

intra-national distance is overestimated, a larger border effect will be needed to

explain the preference for national goods with respect to foreign goods. Head and

Mayer (2002) show that most studies that used imputed intra-national flows

overestimate intra-national distance, leading to illusory border effects.

The distance mismeasurement problem does not arise, in principle, in those studies, such as McCallum (1995), that combine intra-national flows across sub-national units and across those sub-national units and foreign sub-national units (or countries), as long as the same procedure is followed to calculate intra-national and inter-national distances. However, these studies may also face limitations if they use geographically large sub-national units, such as US states or Canadian provinces. In a recent work, Hillberry and Hummels (2008) shows that the relationship between shipments and distance is highly non-linear: at the beginning, there is a sharp reduction in value with distance; however, once a distance-threshold is achieved the negative effect vanishes. If most of intra-national trade happens at short distances, the choice of the sub-national unit will play a crucial role in the border effect analysis.

If the sub-national unit is geographically large, trade between sub-national units may not pick the sharp reduction in value that happens at short distances. Hence, econometric analyses may underestimate the negative contribution of distance on trade, and a larger border effect will be needed to explain the preference for national goods with respect to international goods.

Hillberry and Hummels (2008) analyse whether aggregation may explain why

previous studies, such as Wolf (2000) and Hillberry and Hummels (2003), find that

US states trade more with themselves than with other US states. They show that the

intra-national home bias vanishes once geographically finely disaggregated data,

which picks the non-linear relationship between distance and shipments value, are

used. Our paper extends Hillberry and Hummels´ findings to the international trade

level and analyse to what extent large border (frontier) effects can also be an artefact

of geographical aggregation. For that purpose we use a unique database that reports

the value of Spanish small geographical units (provinces) trade flows to other small

geographical units and to other countries in years 2000 and 2005. Previous research

by Gil et al. (2005) use trade flows between Spanish large geographical units

(regions) to the rest of Spain and to other countries over the period 1995-1998. They

found that Spanish regions traded 21.8 times more with the rest of Spain than they

did with other foreign countries, a result very similar to that found by McCallum for

Canada. We show that the Spanish border effect is reduced substantially, and even

disappears, once we allow data to show the high geographic concentration of intra-

national trade flows. These results are in line with recent research, such as Gorodnichenko and Tesar (2009), which using price rather than quantities has also put into question the excessively large border effects found by previous studies.

The rest of the paper is organised as follows. Section 2 analyses whether there is a non-linear relationship between distance and the value of trade flows in Spain.

Section 3 presents the empirical model and Section 4 the main results of the econometric analyses. The final section summarises the main conclusions of our paper.

2. The geographic concentration of trade flows

Aggregation will lead to an overestimation of the border effect as long as there is a non-linear relationship between distance and the value of trade flows. To document this feature in the case of Spain, we use a new database, C-Intereg, which combines fine grid intra-national trade flows data with international exports data (Llano et al., 2010).

It offers the value of total trade flows from Spanish provinces (Eurostat NUTS III) to other Spanish provinces and to 17 European countries, disaggregated by 13 manufacturing industries.

Distance data is obtained from the Spanish Permanent Survey on Commodity Transport by Road (SPSCTR), both for internal and external trade flows. Our distance measure is the actual average distance travelled by heavy trucks in their deliveries of commodities for the inter-provincial and international flows. This measure of distance has the virtuosity of capturing the distance travelled

1 The C-intereg database (see www.c-intereg.es) combines the most accurate data on Spanish transport flows of goods by transport modes (road, rail, ship, plane, pipe and electric network) with additional information used to estimate specific export price vectors, province of origin, transport mode and type of product . The methodology also includes a process for correcting problems in the original transport flows database that arise from multi- modal transport flows and international transit flows hidden in the interregional flows. After this first screening process, an initial estimate of interregional trade flows in tons and in current euro is obtained, based on a combination of transport magnitudes and price information. Finally, a process of harmonization is applied to produce flows magnitudes in tons and euros that are consistent with total output magnitudes from the Spanish Industrial Survey, the National Accounts and the international trade figures from Customs. This final process of harmonization allows offering international trade flows with origin (destination) in any of the Spanish provinces and destination (origin) in any country of the world, compatible with the internal trade estimates and the official output figures at the provincial and the sector level. This procedure brings the trade flows to a position close to the paradigm of trade in continuous space.

2 The European countries are Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia and Sweden. The industries are Food, beverages and tobacco; Textiles; Leather and footwear; Wood; Paper, printing and publishing; Chemicals;

Rubber and plastics; Other non-metallic minerals; Basic and fabricated metals; Machinery; Electrical and Optical Equipment; Transport equipment; Other manufacturing.

by trucks between actual origins and destinations.

Spain is divided into 50 provinces, a relatively small administrative unit whose average area is similar to an US 3-digit zip code.

Due to the characteristic of our distance measure, we exclude from the sample those intra-national flows where a Spanish island-province is a partner (Balearic Islands, Las Palmas and Tenerife).

We use a kernel regression estimator to provide a nonparametric estimate of the relationship between distance and the value of Spanish intra-national and international trade flows for year 2005.

As shown in Figure 1, there is a negative relationship between the value of trade flows and distance. However, this negative relationship does not follow a clean pattern. In a first segment, 25 km. to 900 km., the value of shipments drops almost four times, from 65000 thousand euros to 17000 thousand euros. This reduction is especially sharp at short distances, when distance increases from 25 km. to 250 km. In a second segment, 900 km. to 1200 km., paradoxically, we observe an increase in the value of trade with distance. In a third segment, 1200 km. to 2700 km. there is again a negative relationship between the value of trade flows and distance; finally, the relationship becomes flat from 2700 onwards.

3 The estimate of the actual distance travelled by the trucks for the intra provincial and interprovincial flows is obtained straightforward from the survey. The distance for the international flows between each of the Spanish provinces and the European countries implies a more complex procedure: the SPSCTR also captures bilateral flows between the Spanish provinces and a large list of countries (with specification of the distance travelled but not of the international region of destination/origin). Although in theory, every international flow carried out by Spanish trucks is covered by the survey, the number of observations for every year may be not representative enough for some bilateral flows. In order to increase the quality of our distance measure, and taking into account the stable nature of this variable, we increase the number of observations by pooling together all the international shipments for the period 1995-2006. Thus, a database of about 200.000 observations regarding the average actual distance for the international deliveries was obtained for each dyad of Spanish provinces and European countries in that period.

4 The average area of a US 3-digit zip code is 13763 square kilometres and the average area of a Spanish province is 10116 square kilometres (excluding Ceuta and Melilla, two autonomous city-provinces in Africa).

5 We use the Gaussian kernel estimator in STATA, calculated on n=100 points, and allowing the estimator to calculate the optimal bandwidth.

Figure 1. Kernel regression: value of intra-national and inter-national trade flows on distance, year 2005

Kernel regression, bw = __00000F, k = 6

Thousand euros

Kilometers

Grid points

25 250 900 1200 2700 3500

1477 17000 30000 65000

Figure 2. Kernel regression: value of intra-national trade flows on distance, year 2005

Kernel regression, bw = __00000F, k = 6

Thousand euros

Kilometers

Grid points

25 200 800 1200

14400 28000 120000

Next we examine separately the relationship between the value trade flows and distance for the intra-national flows and inter-national flows, separately, in year 2005.

As shown in Figure 2, we now observe a clear non-linear relationship between the value of trade flows and distance for intra-national flows. The value of intra-national trade flows drop from 120000 thousand euros to 28000 thousand euros when distance increases from 25 km. to 200 km. From this distance onwards the relationship is slightly negative and even flat for some distance ranges.

To analyse the relationship between distance and international trade flows, we divide exports by the product of production at origin and demand at destination (adjusted trade), since international partners are very different in their demand capacity. Figure 3 shows that the non-linearity between the value of adjusted trade flows and distance is much sharper for international shipments. The value of trade flows drops more than 80 times when distance increases from 200 km. to 600 km.; from 600 km.

onwards distance does not seem to affect the value of shipments.

Finally, Figure 4 presents the relationship between adjusted trade and distance for intra-national flows when we use highly disaggregated geographical units as origin and destination partners (provinces: PROV-PROV line), and when we use a geographically larger destination partner (Rest of Spain: PROV-ROS line).

Again the relationship between adjusted trade and distance is highly non-linear relationship when province is the destination partner (PROV-PROV line); on the contrary, when Rest of Spain is used as destination partner the relationship is flat for the whole distance-range (PROV-ROS line).

6 Rest of Spain is the geographical aggregation for the destination sub-national unit used by Gil et al. (2005) in their analysis of the Spanish border effect. As the origin sub-national unit (exporter), they use regions (NUTS - 2). Spanish regions, also called autonomous communities, are aggregations of Spanish provinces. There are 17 regions in Spain, of which seven have only one province. The results are very similar if we use this aggregation for the origin sub-national unit.

Figure 3. Kernel regression: value of adjusted international trade flows on distance, year 2005

Kernel regression, bw = __00000F, k = 6

exports/(gdp_o*gdp_d)

Kilometers

Grid points

200 650 3500

40 3350

Figure 4. Kernel regression: value of adjusted intra-national trade flows on distance, year 2005

5.5110adj_trade

25 150 375 725 1200

km

PR OV-ROS PR OV-PROV

The comparison of the PROV-PROV line and the PROV-ROS line helps to understand how the use of highly aggregated geographical sub-units may lead to an overestimation of the border effect. The use of aggregated units does not allow capturing the highly non-linear relationship between distance and the value of intra- national shipments, leading to an underestimation of the negative impact of distance on trade at short distances.

In this paper we want to examine to what extent such underestimation may lead, in turn, to create an "artificial" border effect. In particular, if distance (and other controls for trade impediments) does not explain accurately interregional trade within a country, then the Spanish frontier will be capturing it, exaggerating the “true” frontier effect. The next sections examine this possibility.

3. Empirical model

We estimate the border effect based on the structural gravity equation developed by Anderson and van Wincoop (2003).

The estimating equation takes the following form:

k j k i ij ij

ij jt

ikt ijkt

P P Partner Nat

Contig Partner

Int Contig

dist Y

Y Border

X

, , 6

5

4 3

2 1

0

_ _ _

_

ln ln

ln ln

































(1)

where X

are exports from province i to a trading partner j of industry k in year t.

Border is a dummy variable that takes the value of 1 if the shipment is intra-national

and zero if the shipment is international; Y

is production of industry k in province i in year t (source: INE); Y

denotes trading partner's GDP j in year t (source: WDI- online). A dummy variable Contig_Int_Partner that takes values of one if the Spanish exporting province is adjacent to an importing international trading partner (a foreign

7 In Figure 3 we observe as well that the value of shipments drops sharply with distance for international flows.

In this case the use of geographically aggregated national units would underestimate the border effect as the distance travelled by goods in inter-national shipments would be overestimated. In any case, this problem seems to be of a lower magnitude, because differences in travelled distance would only be relevant for countries that are very close to Spain (France and Portugal).

8 Helpman, Melitz and Rubinstein (2008) develop a structural gravity model that incorporates heterogeneous firms. They argue that previous studies' estimates are biased because they do not take into account the large number of zeros in bilateral trade. As explained in the text, our estimation technique controls for this problem.

country), zero otherwise, and a dummy variable Contig_Nat_Partner that takes values of one if the Spanish exporting province is adjacent to an importing national partner (a province), zero otherwise.

We expect these variables to be positive if our sample does not capture the whole non-linear relationship between trade and distance that happens at short distances. To control for multilateral resistances, we include industry-province and industry-country fixed effects (P

, Pjk

).

In order to capture the non-linear relationship between distance and the value of shipments we also estimate the following equation:

k j k i ij ij

ij ij jt

ikt ijkt

P P Partner Nat

Contig Partner

Int Contig

dist dist

Y Y

Border X

, , 7

6

2 6

5 3

2 1

0

_ _ _

_

) (ln ln

ln ln

ln

































 (2)

where we have introduced a coefficient for distance and another for the square of distance. As explained before, distance is the actual average distance travelled by heavy trucks in their deliveries of commodities for the inter-provincial and international flows. It is important to stress that our distance measure helps to address the aggregation problem both for international and intra-national shipments.

Finally, due to the existence of zeros in our database, as in Eichengreen and Irwin (1998) and Chen (2004), we express the dependent variable as (1+Ln X

) and estimate the model by a Tobit procedure.

The equations (1) and (2) are estimated using data from years 2000 and 2005.

The definition of a Spanish trading partner plays a key role in our exercise. While the number of international trading partners will be constant and equal to 17 (European) countries in all the estimations, the number and economic size of the Spanish trading partners will change. In the first sample we aggregate all Spanish trading partners in one, the rest of Spain, so each exporting province has 18 trading partners: Rest of Spain plus 17 European countries. We call this sample "SPAIN". Our second sample considers the smallest geographic unit that we have as Spanish trading partner, 47 provinces. In this sample, that we call "PROVINCE", an exporting province has 64

9 When a province sells to the same province the dummy variable takes the value of 1.

10 We use the McDonald and Moffitt (1980) procedure to recover the elasticity at sample means.

trading partners (47 provinces and 17 European countries). Notice that the value of the variable distance will change when we choose different definitions of Spanish trading partner. In particular, the value of distance from one province to Rest of Spain is calculated as the simple average of the distance travelled by trucks from that province to other Spanish provinces. We expect a narrow definition of Spanish trading partners to control adequately for the fact that intra-national shipments are highly localised. Trading with bigger partners (i.e. with the rest of Spain as a whole instead of with each province) will tend to upward bias the border effect since the border effect will be capturing part of the non-linear relationship between distance and shipments.

4. Results of the econometric analyses

Table 1 reports the results of estimating Eq (1) and (2) using the two samples based on the definition of Spanish trading partner. The first two columns use the sample SPAIN and the second two columns the sample PROVINCES. When we aggregate intra-national shipments as the rest of Spain, the coefficient associated to the border effect is around 3.4; that is, a Spanish province trades around 30 times more with the rest of Spain than with a European country. This border estimate is slightly larger than the one obtained by Gil et al. (2005). The discrepancy can be explained by the fact that we use different periods, countries and estimation techniques. In the first estimation, where we are assuming a linear (log-log) relationship between trade and distance, the distance coefficient is negative and statistically significant. In the second estimation, we assume a non-linear relationship between trade and distance;

as expected, the distance coefficient has a strong negative value and the square of

distance coefficient has a positive sign, denoting that trade falls sharply at short

distances and then becomes flat. However, both coefficients are statistically not

significant. It seems that the sample SPAIN is capturing the large non-linearity

between trade and distance for international shipments (Figure 3); however, as the

relationship for intra-national flows is flat (Figure 4; PROV-ROS line), the overall non-

linearity is not very robust. With respect to other independent variables, in both

specifications the coefficients for economic size of trading partners (exporter's

production and importer's GDP) are positive. Finally, the dummy variable on

contiguous countries is statistically not significant.

Table 1. Main results

Sample SPAIN Sample PROVINCES

(1) (2) (3) (4)

Origin

Provinces 47 47 47 47

Destination

Provinces - - 47 47

Rest of Spain 1 1 - -

Countries 17 17 17 17

Sectors 13 13 13 13

Years 2 2 2 2

N 21996 21996 78208 78208

Border 3.514 *** 3.521 *** 0.098 0.497 **

(11.52) (11.58) (0.58) (2.53) Gross output exp. 0.280 *** 0.280 *** 0.147 *** 0.147 ***

(3.94) (3.94) (3.79) (3.78) GDP importer 1.170 *** 1.169 *** 0.783 *** 0.802 ***

(21.02) (19.36) (24.16) (22.66) Distance -1.429 *** -1.291 -1.130 *** -2.404 ***

(-9.10) (-0.67) (-14.34) (-5.37)

Distance^2 0.010 0.109 ***

(0.07) (3.02)

Cont. Int. Part 0.091 0.108 -0.381 *** -0.268 **

(0.30) (0.28) (-2.79) (-1.98)

Cont. Nat. Part 0.664 *** 0.482 ***

(6.51) (5.09)

Pseudo R2 0.301 0.301 0.191 0.192

Note: Tobit estimations, sample mean elasticities. All specifications include industry-specific exporter, industry-specific importer and year dummies. Province-clustered robust t-statistics in parenthesis with

*** denoting significance at the 1-percent level, ** significance at the 5-percent level and * significance at 10-percent level.

Next, we present the results of estimating Eq. (1) and (2) using the sample PROVINCES, that is, other provinces as trading partners. In Table 1 Column (3) we observe that the border effect is positive but not statistically different from zero; and, in Column (4) the border effect is positive and statistically significant but its magnitude is very small (0.497) compared to the border effect of 3.5 in columns (2).

Hence, according to our estimates, Spanish provinces trade as much with other

Spanish provinces as with other European countries, once we control for other trade

impediments. As argued in this paper, the disappearance of the border effect is

explained by the use of a more geographically disaggregated sample (PROVINCES),

which allows capturing the high concentration of shipments at low distances. As

shown in Column (4), now both the distance and square of distance coefficients are

statistically significant, confirming the high non-linearity between trade and distance.

In addition the coefficient on contiguity between provinces is positive and statistically significant. As exports to contiguous province encompass the shortest distance shipments, the positive coefficient may point out that our sample still does not capture the whole non-linearity between trade and distance. Surprisingly, the coefficient on contiguous countries is negative and statistically significant. Finally, as in the previous estimations, the origin province output and the destination partner demand coefficients are positive.

To sum up, we can conclude that the border effect disappears when the definition of Spanish trading partner changes from a highly aggregated geographical unit (rest of Spain as a whole ) to a much smaller unit (provinces). Hence, our results indicate that the border effect is, to a large extent, an artifact of the geographic aggregation of intra-national trade flows.

Table 2 reports the industry-specific border coefficients for 13 manufacturing sectors.

In both samples, SPAIN (column 1) or PROVINCES (column 2), there are large

differences in the border coefficients across industries. If we focus on the first sample

(SPAIN - Column 1), we can observe certain heterogeneity across industries: five

sectors exhibit very large border effects (metal products, transport equipment,

mechanical machinery, plastic & rubber and non-metallic mineral products) and two

sectors do not have border effects (textile & apparel and leather & shoes). When we

use the PROVINCE sample, the border coefficients tend to become smaller, and in

many cases even negative, as we use a smaller geographic unit to define Spanish

trading partners.

Table 2. Industry-specific border effect.

Sample SPAIN Sample PROVINCES

(1) (2)

Origin

Provinces 47 47

Destination

Provinces - 47

Rest of Spain 1 -

Countries 17 17

Sectors 13 13

Years 2 2

Observations 21996 78208

Border Food & drinks 3.335 *** 0.600 ***

(15.11) (2.67)

Border Textile & apparel -0.037 -1.478 ***

(-0.09) (-4.90)

Border Leather & shoes -0.199 -1.123 **

(-0.30) (-2.38)

Border Wood products 2.354 *** -1.730 ***

(6.11) (-6.80)

Border Paper & print 3.507 *** -0.474

(9.71) (-1.31)

Border Chemical products 2.203 *** -0.103

(6.04) (-0.34)

Border Plastic & rubber 5.011 *** -0.458

(12.09) (-1.26)

Border Nonmetal mineral 4.437 *** 0.212

(11.38) (0.67)

Border Metal products 6.101 *** 1.306 ***

(14.61) (3.53)

Border Mechanical machinery 5.211 *** 0.784 ***

(13.21) (2.66)

Border Electric & electronic goods 3.470 *** -0.303

(8.01) (-1.02)

Border Transport equipment 6.223 *** 0.232

(13.90) (0.66)

Border Other manufactures 3.271 *** -1.325 ***

(7.46) (-4.51)

Gross Industry Output Exporter 0.209 *** 0.119 ***

(4.41) (3.40)

GDP Importer 1.174 *** 0.780 ***

(19.25) (23.58)

Distance -1.214 *** -2.454 ***

(-0.63) (-5.60)

Distance ^ 2 -0.015 0.112 ***

(-0.11) (3.14)

Contig. Int. Partner 0.117 -0.291 **

(0.31) (-2.17)

Contig. Nat. Partner 0.443 ***

(5.10)

Pseudo R2 0.304 0.193

Note: Tobit estimations, sample mean elasticities. All specifications include industry-specific exporter, industry- specific importer and year dummies. t-statistics in parenthesis with *** denoting significance at the 1-percent level, ** significance at the 5-percent level and * significance at 10-percent level.

Robustness analyses

As mentioned above, the border coefficient we obtain in Table 1 (Columns 1 and 2) is

larger than the one obtained by Gil et al. (2005) using the same sub-national

aggregated destination partner: the Rest of Spain. It is important to analyse where

those differences come from. In order to do so, we reproduce as close as possible

the exercise performed by Gil et al. (2005) using our database. These authors

estimate the Spanish border effect using a random effects model and data on

aggregate shipments from Spanish regions to the rest of Spain and to 27 OECD

countries in the period 1995-1998. Their distance measure is based on geodesic

information. We can match all the characteristics of their sample, except for the

number of countries, which is reduced to 25 OECD countries in our sample and for

the period of analysis (two years, 2000 and 2005). Table 3 - Column (1) reproduces

the results obtained by Gil et al. (2005). Their border effect for Spain is 3.08. Column

(2) reproduces Gil et al.'s exercise using our database. We can see that our border

effect, 2.75, is now smaller than the one obtained by Gil et al. (2005). This result

indicates that the Spanish border effect reported in Table 1 - Columns (1) and (2), is

not driven by the use of a different data source, time period of analysis, estimation

technique, the number of destination partners (OECD partners vs. European

continental partners) or the aggregation level of trade flows.

Table 3. Robustness analysis I. Comparison with Gil et al. (2005)

Gil et al. (2005) estimation Estimation with our database

Border 3.08 (10.27)*** 2.75 (8.09)***

GDP exporter 1.08 (36.71)*** 1.23 (17.67)***

GDP importer 1.08 (36.80)*** 1.07 (24.62)***

Distance -1.28 (-21.69)*** -1.53 (-20.42)***

Time period 1995-1998 2000 y 2005

Estimation technique Random effects model Random effects model

Exporter Spanish region Spanish region

Importer Rest of Spain + 27 OECD

countries

Rest of Spain + 25 OECD countries

Data Aggregated trade Aggregated trade

Note: Gil el al. (2005) coefficients correspond to their Table 1-Column 1 estimation. All estimations include year-specific dummies. t-statistics in parenthesis with *** denoting significance at the 1-percent level.

Second, we analyse the robustness of our results when using alternative estimation

methods. As an alternative method for the treatment of zeros in our database,

following Silva and Tenreyro (2007), we re-estimate the equation using a Poisson

model. In addition to that, we also estimate the equation using OLS. As shown in

Table 4, in both models we observe that the border effect experiences a very large

reduction when we shift from an aggregated intra-national partner to a more

geographically disaggregated intra-national partner. When we use a non-linear

functional form for distance, the border effect drops from 3.645 to 1.745 in the

Poisson model and from 3.753 to 1.091 in the OLS model. This result is consistent

with our previous conclusions. However, the estimated border effects are now

statistically significant. According to the Poisson model, even when we control for

aggregation, Spanish provinces still trade almost six times more with another

Spanish province than with a European country (exp 1.745); according to the OLS

model a Spanish province trades almost three times more with another Spanish

province than with another European country (exp 1.091). Notice that, when we use

the PROVINCES sample and using the Poisson estimation or the OLS estimation,

distance and the square of distance have the opposite signs found in Table 1. In the

Poisson model (column 4) the square of distance is negative and in the OLS model (column 8) distance is positive and the square of distance negative.

Table 4. Robustness analysis II. Estimation with Poisson and OLS models

POISSON OLS (1) (2) (3) (4) (5) (6) (7) (8)

Sample Spain Spain Provinces Provinces Spain Spain Provinces Provinces Origin

Provinces 47 47 47 47 47 47 47 47

Destination

Provinces - - 47 47 - - 47 47

Rest of Spain 1 1 - - 1 1 - -

Countries 17 17 17 17 17 17 17 17

Sectors 13 13 13 13 13 13 13 13

Years 2 2 2 2 2 2 2 2

N 21996 21996 78208 78208 16276 16276 33071 33071

Border 3.837*** 3.645*** 1.929*** 1.745*** 3.800*** 3.753*** 2.211*** 1.091***

(31.58) (32.34) (13.17) (11.43) (15.68) (15.88) (8.19) (3.46) Gross output exp. 0.961*** 0.951*** 0.878*** 0.888*** 0.341*** 0.341*** 0.657*** 0.658***

(44.25) (62.65) (46.68) (45.24) (3.50) (3.50) (8.58) (8.74) GDP importer 0.680*** 0.589*** 0.673*** 0.677*** 0.976*** 0.988*** 0.978*** 0.920***

(13.28) (11.03) (20.22) (22.36) (18.09) (16.90) (19.29) (18.04) Distance -0.486*** 3.569** -0.810*** -0.203 -1.254*** -2.455 -1.488*** 1.730***

(-6.12) (2.23) (-12.23) (-0.84) (-11.62) (-1.67) (-12.23) (3.54)

Distance^2 -0.307** -0.058*** 0.083 -0.279***

(-2.55) (-2.70) (0.80) (-6.49) Cont. Int. Part 0.149 0.275* -0.234* -0.350*** 0.289 0.146 -0.208 -0.569**

(1.14) (1.77) (-1.84) (-2.60) (1.37) (0.57) (-0.88) (-2.37)

Cont. Nat. Part 0.156** 0.184** -0.206 0.181

(2.35) (2.51) (-1.36) (1.31)

Pseudo R2 0.970 0.970 0.797 0.797 0.734 0.734 0.518 0.522 Note: All specifications include industry-specific exporter, industry-specific importer and year dummies. t-statistics in parenthesis with *** denoting significance at the 1-percent level, ** significance at the 5-percent level and * significance at 10-percent level.

Third, we test whether our results are robust to changes in the exporting geographic

unit. In the new sample, instead of provinces, we select regions as the exporting

geographic unit; in this sample, the number of geographic units is reduced from 47 to

15. As shown in Table 5, when we use the sample SPAIN (columns 1-2), the border

coefficient is 3.184 in linear distance specification and 2.843 in the non-linear

distance specification, suggesting that a typical Spanish region trades between 17

and 24 times more with the rest of Spain than with another country. These border

effects are lower than the ones reported in Table 1. When we use the sample

REGIONS (columns 3-4), as expected, we find a large reduction in the border effect:

coefficients drops from 3.184 to 0.431 in the linear specification, and from 2.843 to 0.960 in the non-linear specification. Therefore, the border effect also falls when we use a more aggregated geographical unit. However, such a reduction in the border effect is not as large compared to the sample that uses provinces as geographic unit of reference.

Table 5. Robustness analysis III. Regions as origin

Sample SPAIN Sample REGIONS

(1) (2) (3) (4)

Origin

Regions 15 15 15 15

Destination

Regions - - 15 15

Rest of Spain 1 1 - -

Countries 17 17 17 17

Sectors 13 13 13 13

Years 2 2 2 2

N 7020 7020 12480 12480

Border 3.184 *** 2.843 *** 0.431 0.960 **

(9.53) (7.88) (1.09) (2.05) Gross output exp. 0.654 *** 0.658 *** 0.325 * 0.328 ***

(4.77) (4.74) (1.86) (1.86) GDP importer 1.092 *** 1.149 *** 0.838 *** 0.869 ***

(12.97) (12.75) (8.40) (7.63) Distance -1.414 *** -8.572 ** -2.077 *** -3.564 ***

(-6.68) (-2.20) (-14.88) (-2.61)

Distance^2 0.494 * 0.132

(1.88) (1.18)

Cont. Int. Part -0.302 -0.836 *** -0.960 *** -0.776 **

(-1.32) (-3.34) (-3.61) (-3.27)

Cont. Nat. Part 1.038 *** 1.029 ***

(5.36) (5.19)

Pseudo R2 0.355 0.356 0.203 0.203

Note: Tobit estimations, sample mean elasticities. All specifications include industry-specific exporter, industry-specific importer and year dummies. Regions-clustered robust t-statistics in parenthesis with

*** denoting significance at the 1-percent level, ** significance at the 5-percent level and * significance at 10-percent level.

4. Conclusions

During the last decades a large number of studies have tried to explain why countries

trade more with themselves than with other countries. We show that the border effect

in Spain is, to a large extent, an artefact of geographical aggregation. Based on a

geographic fine grid data, we find that intra-national trade flows are highly localised, with value dropping sharply with distance. If intra-national trade flows are aggregated this localised pattern is lost, which leads to an overestimation of the distance travelled by goods sold domestically and to the creation of an artificial border effect.

Our econometric estimations confirm this fact. While a sizable border effect exists for

Spain when intra-national partners are aggregated, the border effect decreases

substantially, and even disappears in some estimations, once when we allow data to

show the localised nature of intra-national trade flows.

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