# Non-Europe: The Magnitude and Causes of Market Fragmentation in the EU

Keith Head, Thierry Mayer

<figure><img src="image_1.png" /></figure>

## To cite this version:

Keith Head, Thierry Mayer. Non-Europe: The Magnitude and Causes of Market Fragmentation in the EU. 1999. halshs-03589644

HAL Id: halshs-03589644

https://shs.hal.science/halshs-03589644

Submitted on 25 Feb 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
Cahiers
de la
MSE

Non-Europe: The Magnitude and Causes of
Market Fragmentation in the EU

Keith HEAD

Thierry MAYER

1999.04
Non-Europe:
The Magnitude and Causes of Market Fragmentation in the EU*

Keith Head†

Thierry Mayer‡

20th September 1999

Abstract

In 1985 the European Commission diagnosed its member states as suffering from excessive market fragmentation, a state of affairs it later referred to as “Non-Europe.” In response, the European Union launched an ambitious program to unify its internal market by removing non-tariff barriers. We examine the empirical basis for the Commission’s diagnosis using a trade model derived from monopolistic competition. We then investigate the links between the initial size and subsequent evolution of border effects within the European Union. Our findings support the view that European consumers act as if imports from other members were subject to high non-tariff barriers. However, there appears to be almost no relation between market fragmentation and the barriers that were identified and removed by Europe’s Single Market Programme.

Résumé

En 1985, la Commission Européenne a publié un livre blanc stigmatisant la fragmentation des marchés européens, une situation qui fût plus tard qualifiée de “Non-Europe”. Afin de réduire cette fragmentation, un ambitieux programme fût lancé par l’Union Européenne visant à unifier le marché européen en supprimant les barrières non tarifaires au sein de l’Union. Nous évaluons la mesure dans laquelle le diagnostic de la Commission était justifié en utilisant les prédictions empiriques en termes d’échange international d’un modèle théorique de concurrence monopolistique. Nous étudions ensuite le lien entre le niveau initial de la fragmentation et son évolution. Nos résultats confirment que les consommateurs européens se comportent comme si les biens des autres pays de l’Union Européenne étaient soumis à de fortes barrières non tarifaires. Nous ne trouvons néanmoins pratiquement aucune relation entre la fragmentation observée et les barrières non tarifaires qui avaient été identifiées et qui ont été supprimées par l’Acte Unique Européen.

JEL classification: F12, F15

Keywords: Borders, European Union, gravity, non-tariff barriers

Mots-clés : Frontières, Union Européenne, gravité, barrières non tarifaires

<footnote>*We are grateful to Syoum Negassi for providing us with European production data.</footnote>

<footnote>†Corresponding author: Faculty of Commerce, University of British Columbia, 2053 Main Mall, Vancouver, BC, V6T1Z2, Canada. Tel: (604)822-8492, Fax: (604)822-8477, Email:keith head@ubc.ca</footnote>

<footnote>‡Université de Paris I Panthéon Sorbonne / TEAM-CESEFI 106-112 Bd de l'hopital 75647 Paris CEDEX 13 Tel: (33) 1 55 43 42 67 Email: tmayer@univ-paris1.fr</footnote>
# I Introduction

Trade between European Union members has been essentially free of tariffs and quotas since 1968. Nevertheless, 27 years later the European Commission argued that Europe's internal market was far from unified. The Commission issued a White Paper in 1985 that identified three primary barriers to intra-EU trade: differences in technical standards, delays and administrative burdens caused by frontier controls, and national biases in government procurement. The Commission then launched a research project to establish the costs of what they referred to as "Non-Europe" or the "present market fragmentation of the European Community" (Commission of the EC, 1988). By the time the 16 volume study was released, the member nations had already legislated the Single European Act (SEA). The Act contained roughly 300 proposals from the White Paper designed to "complete" Europe's internal market by the end of 1992.

In this paper we evaluate the success of Europe's Single Market Programme (SMP). The initial step is to establish the extent of "market fragmentation" at the time of the White Paper. We define a market as fragmented when national borders influence the pattern of commercial transactions. Borders matter when firms have greater access to domestic consumers than to consumers in other nations. We measure the effect of borders as the average deviation between actual trade and the trade that would be expected in an economy without border-related barriers. We calculate industry level border effects for the European Union and examine the extent that they can be explained by the barriers identified in the White Paper. Next, we turn to the effects of the implementation of White Paper measures during the period 1986 to 1993. How much did border effects decline as the SEA was implemented? Were the declines related to the policy changes?

Our results suggest that the European Commission may have mis-assessed the causes of "Non-Europe". Thus, we empirically validate Geroski's (1991) caveat that "...it is by no means
clear that obstacles to trade are the main reason why few European firms operate on a truly
European scale". We propose an alternative explanation for market fragmentation: consumers
exhibit a bias towards domestic goods. This may be the outcome of cultural differences or the
legacy of past protection that caused domestic suppliers to adapt their product offerings to
suit local tastes. This line of argument supports Geroski's conjecture that European market
fragmentation was mostly due to its diversity in national and regional tastes and that the 1992
Programme would do little to reduce this type of fragmentation.

The methodology we employ is the monopolistic competition model of trade introduced by Krugman (1980). That model establishes a relation between the relative amounts consumers spend on foreign and domestic goods and their relative prices net of transport costs. The border effect measures divergence from the predicted consumption ratios. Using data from the European Commission's own studies, we categorize industries according to importance of the three types of barriers identified by the White Paper. We then correlate these barriers to levels and changes in border effects.

The remainder of the paper proceeds as follows: Section II reviews the existing work on border effects. A theoretical model of monopolistic competition with a border effect is provided in section III. We introduce a new method to calculate internal and external distances in a consistent manner in section IV. The results for pooled data and industry-specific regressions are presented in section V and section VI concludes.

## II Literature review

The objective of this paper is to measure and explain the remaining sources of market frag-
mentation in the European Union. Formal trade barriers should not be important in Europe
as tariffs and quotas have been removed since 1968. Non-tariff barriers were still thought to be
significant impediments to trade within the European Community. Neven and Roller (1991) estimate the impact of non-tariff barriers on the share of EU imports in apparent consumption of the four major European countries for the years 1975-85. Their measure for NTBs comes from Buigues et al (1990). They find no evidence that higher NTBs hampered trade within the EC but a significant negative effect on imports from the rest of the world suggesting paradoxically that the SMP might generate more benefits to firms from outside the EU.

The removal of non-tariff barriers (NTBs) was the objective of the Single Market Programme (SMP) initiated in 1986 and completed by the end of 1992. Most research on the SMP consisted of prospective studies. Smith and Venables (1988), for instance, employed a numerical calibration of an imperfect competition model to project the welfare gains achievable through greater market integration. However, there have been few *ex post* empirical studies assessing the impact of the SMP.

Fontagné et al., (1998) study the impact of the SMP on intra-European trade at a much more detailed level using combined nomenclature 8-digit trade data from Eurostat. They estimate in particular whether the removal of remaining barriers to trade changed the proportion of inter-industry, horizontally differentiated, and vertically differentiated trade. They find that the removal of NTBs (here again mainly based on Buigues et al, 1990) had an important effect on the composition of intra-European trade, raising the volume of inter-industry trade while reducing the share of intra-industry trade in horizontally differentiated products. Our work differs from Fontagné et al in that we examine how NTBs affect consumption of foreign goods relative to consumption of domestic goods. The empirical construct we employ is the border effect.

The literature on border effects was established by McCallum (1995) who analyzed trade between Canadian provinces and between US states and Canadian provinces. The border effect measures the extent that domestic subunits trade more with each other than with foreign units
of identical size and distance. Using data on interprovincial and international trade by Canadian provinces, McCallum (1995) and Helliwell (1996) showed that the border effect on US-Canadian trade for the 1988-90 period was extremely large. Interprovincial trade is estimated to be more than 20 times larger than trade between Canadian provinces and American states. This large border effect is remarkable given that Canada and the US were thought to be highly integrated, with low tariffs that were being phased out by the 1988 Free Trade Agreement. Furthermore, Canada's unusual economic geography (85% of Canadians live less than 100 miles from the US border) and interprovincial trade barriers might have even delivered the opposite result.

The use of gravity equations to estimate the magnitude of the effects of various forms of regional economic integration is well established in the empirical trade literature. What made it possible for McCallum and Helliwell to estimate the importance of the Canadian Economic Union was the availability of comparable data on trade flows for subnational units. Such data are rare. McCallum and Helliwell use data from Statistics Canada's Input-Output Division. Since that data fails to identify the state of origin or destination for trade flows, they use customs clearance data to allocate trade across states. Anderson and Smith (1997) investigate whether this procedure inflates estimates of border effects by considering the US as a single source/destination. They find a border effect of 12, smaller than the earlier two studies, but still suggestive of a large trade-impeding effect of crossing the border. Hillberry (1998) also estimates Canada-US border effects using data from the 1993 commodity transportation survey carried out by the US Transportation Department. He finds border effects slightly over 20, remarkably similar to those obtained by McCallum and Helliwell.

Wei (1996) introduced a method that obviates the need for trade data on sub-national units. His procedure calculates a country's trade with itself by starting with domestic output and subtracting aggregate exports to other nations. The remainder measures the domestically made goods that are "exported" to domestic consumers. The effects of crossing a border can
then be estimated by including a dummy variable indicating when trade occurs with self and when it occurs with other nations. Wei's estimate of the border coefficient for an average OECD country over the 1982-94 period is just under 10 in the traditional gravity equations and falls to only 2.6 when taking into account common language and contiguity. Helliwell (1997) revisits the OECD data, using a different remoteness measure and including language effects in the border effect. He reports border effects of 12.

When considering the European Community, Wei's estimates imply a ratio of imports from self to imports from other European countries of only 1.7. This seems to imply that European integration succeeded in removing significant trade barriers. Moreover, Wei finds that the border effect for EC members declined by 50% between 1982 and 1994. A recent paper by Nitsch (1997) reestimates the EU border effects, including three countries omitted by Wei, using different trade data, and employing new measures of internal distance (we discuss these important measurement issues in greater detail in section IV). Nitsch estimates border effects that are larger than Wei's, but they vary substantially across specifications (from 2.5 to 16).

Wolf's (1997) study of trade between states in the US raises an important caveat in the use of Wei's methodology to estimate border effects. He finds significant state border effects (3.0 to 3.7, depending on specification) for trade within the United States. Given that states have been constitutionally prevented from erecting trade barriers, these border effects are difficult to explain. Unlike the situation in Europe, cultural and institutional differences between American states seem too small to serve as explanations for border effects. Instead, Wolf argues that geographic clusters of vertically-linked industries promote large volumes of intra-state trade. This suggests that border effects would be particularly strong for intermediate goods. When we present our industry-level results, we will reconsider Wolf's hypothesis. It is also possible that positive border effects could be estimated without actual trade barriers because the internal distance of the state is overestimated. Our paper investigates the impact of different internal
distance measures on the magnitudes of estimated border effects.

Haveman and Hummels (1997) point out that the use of aggregate data could generate misleading results on border effects. This will tend to be the case when proximate nations alter their production mixes to further exploit trade opportunities with each other. For instance, they might choose to specialize in different industries. Or, as suggested by Wolf, they might both concentrate in vertically related industries. In either case, the border effect would not simply measure substitution away from costly imports by consumers. Rather it would reflect choices by producers. By working with trade and production data that has been disaggregated to 3-digit industries, we should minimize the potential for such production composition effects to influence the magnitude of the border effect.

Previous work has not been able to establish what portion of the border effect can be attributed to non-tariff barriers. This paper is, to our knowledge, the first to estimate industry-level border effects and to use industry-level data on European NTBs to assess the importance of this determinant in the tendency of countries to trade excessively with themselves. Once NTBs are taken into account in the model, the remaining border effect consists only of differences in consumers' preferences. This determinant might indeed be an important explanation of trade patterns, particularly between European countries. We now proceed to develop a trade model that includes NTBs and consumer biases as determinants of border effects.

# III Border Effects in a Monopolistic Competition Model

We begin with a fairly general specification of preferences. The utility of the representative consumer in country *i* depends on the quantity of each variety *h* consumed from each country *j*.<fnref n="1" />

<footnote n="1">¹The model we develop is one of trade in final goods. Note that an equivalent estimating equation could be derived for an industry comprising a large number of differentiated producers of intermediate goods. In that case the consumer utility function would be replaced with the downstream industry's production function. Solution of the cost minimization equation results in the same functional form as equation 3. This equivalence is valuable since a large amount of trade occurs in intermediate goods.</footnote>
All varieties are differentiated from each other but products from the same country are weighted equally in the utility function. Thus, denoting quantity consumed with c and the preference weight with a, the constant elasticity of substitution utility function is given by:

$$U_i = \left( \sum_{j=1}^{N} \sum_{h=1}^{n_j} (a_{ij}c_{ijh})^{\frac{\sigma-1}{\sigma}} \right)^{\frac{\sigma}{\sigma-1}}$$

Denoting $m_{ij}$ as the C.I.F. value of imports of country i from country j ($m_{ij} = c_{ij}p_{ij}$) and $m_i = \sum_k m_{ik}$ as expenditures on goods from all sources (including the home country), then bilateral imports are given by:

$$m_{ij} = \frac{a_{ij}^{\sigma-1} n_j p_{ij}^{1-\sigma}}{\sum_k a_{ik}^{\sigma-1} n_k p_{ik}^{1-\sigma}} m_i. \quad (1)$$

We may derive a gravity equation from this expression. Note first that the Dixit-Stiglitz model of monopolistic competition yields proportionality between production and the number of varieties. Denoting the value of production in country j as $v_j$, the quantity produced by each firm as q, and the mill price of each variety as $p_j$, we obtain $v_j = qp_jn_j$. We will use this equation to eliminate the $n_j$ from the equation since the number of symmetric varieties produced by each country is not observed.

Assume that transport costs and preferences take the following forms. The price paid by consumers in country i for products of country j is a multiplicative function of distance ($d_{ij}$), non-tariff barriers (NTB$_{ij}$), and the mill price ($p_j$). Consumer preferences consist of a home bias (HB) plus a random component:

$$p_{ij} = (1 + \text{NTB}_{ij}) d_{ij}^{\delta} p_j,$$

$$a_{ij} = \exp(\text{HB}_{ij} + e_{ij}) \quad \text{where } e_{ij} = \text{i.i.d. error term.}$$
We also assume the following definitions of trade barriers and aversion to foreign products:

$$NTB_{ij} = \begin{cases} \nu & \text{if } i \neq j \\ 0 & \text{if } i = j, \end{cases}$$

$$HB_{ij} = \begin{cases} -\beta & \text{if } i \neq j \\ 0 & \text{if } i = j. \end{cases}$$

Thus, $\beta$ measures the typical consumer's aversion to foreign products (or, looked at from the other direction, their home bias). Using $n_j p_j = v_j/q$, substituting for $a_{ij}$, $p_{ij}$, and $n_j$ in (1), and taking logs leads to a formulation of the gravity equation:

$$\ln m_{ij} = \ln m_i + \ln v_j - (\sigma - 1)\delta \ln d_{ij} - \sigma \ln p_j - I_i - (\sigma - 1)[\beta + \ln(1 + \nu)]B_{ij} + (\sigma - 1)e_{ij}, \quad (2)$$

where $B_{ij}$ is a cross-border dummy variable set equal to one for $i \neq j$ and where $I_i$, the importer's "inclusive value," is defined as follows.

$$I_i = \ln \left( \sum_k \exp[\ln v_k - \sigma \ln p_k + (\sigma - 1)(-\delta \ln d_{ik} - [\beta + \ln(1 + \nu)]B_{ik} + e_{ik})] \right).$$

The inclusive value describes the full range of potential suppliers to a given importer, taking into account their size, distance and relevant border effects.<fnref n="2" />

The first three terms of equation 2—exporter output, importer expenditure, and distance between importer and exporter—appear in some form in all estimated gravity equations. However, the gravity equations estimated in the trade literature generally differ from (2) in several

<footnote n="2">²We label this term the "inclusive value" in reference to its resemblance to the utility of the whole choice set in a logit model. For an analysis on the connections between the logit model and the monopolistic competition model, see Anderson et al (1992).</footnote>
important respects. First, most studies omit the “inclusive value” term, $I_i$. In some cases a related measure consisting of the remoteness of each trading partner from other possible partners is used instead.<fnref n="3" /> Traditional gravity equations also use the GDPs of the two trade partners instead of using a goods production measure for $\ln v_j$ and an expenditure measure for $\ln m_i$. Furthermore, the theoretical coefficients of 1 on $\ln m_i$ and $\ln v_j$ are almost never imposed. Although the theory clearly requires that we control for differences in FOB price levels (the $p_j$), Bergstrand (1985) is one of the only papers to do so. In contrast, while the model predicts that trade depends only on total consumption and production, it has become commonplace to augment the basic gravity equation with the product of the two trading partners’ per-capita incomes.

The estimation of the influence of $I_i$ is difficult because it depends on parameters that are already in the equation to be estimated. Another problem related to this term is that even in studies like ours that restrict the sample to a particular subset of countries, the $I_i$ term is supposed to contain attributes of all possible origin countries for the product. The monopolistic competition model predicts that imports from a particular country in a given industry are related to alternative sources in the whole world. To avoid these difficulties, we work with log odds ratios that allow us to sidestep the problem of estimating $I_i$. The derivation proceeds as follows: set $j = i$ in equation 2 to obtain an expression for $\ln(m_{ii})$. Subtract from $\ln m_{ij}$ and one obtains

$$ \ln\left(\frac{m_{ij}}{m_{ii}}\right) = \ln\left(\frac{v_j}{v_i}\right) - (\sigma - 1)\delta\ln\left(\frac{d_{ij}}{d_{ii}}\right) - \sigma\ln\left(\frac{p_j}{p_i}\right) \\ -(\sigma - 1)[\beta + \ln(1 + \nu)] + \epsilon_{ij}, \quad (3) $$

<footnote n="3">³Wei (1996), Wolf (1997), and Helliwell (1997) each adopt different formulations of the remoteness variable involving distance and GDP. They also use separate measures for the exporting and importing country whereas the theory requires only a measure for the importer.</footnote>
where $\epsilon_{ij} = (\sigma - 1)(e_{ij} - e_{ii})$. This expression relates the decisions of consumers in a given country on how to allocate expenditure between goods from a particular foreign country and goods produced at home.<fnref n="4" /> It exploits the Independence from Irrelevant Alternatives (IIA) property of the CES demand function to obtain a formulation in which relative demand for a given foreign country depends only on ratios of explanatory variables for that country and the home country.

The constant in equation 3 captures both the impact of NTBs ($\nu$) and home bias ($\beta$). We estimate this negative intercept for each industry in our sample. Then we examine whether high levels of NTBs identified in a survey made by the European Commission are associated with large negative values of the intercept. This would show that in the industries where firms claimed to suffer from high NTBs, trade between European countries was really more impeded. Then a reasonable part of the observed fragmentation of the European market could be attributed to NTBs, suggesting that the 1992 programme did indeed target the correct causes of Non-Europe.

The third step is an estimation of the evolution of the border effect during the implementation of the SMP.

# IV The Measurement of Internal Distances

A crucial issue in the empirical implementation of the model is the measurement of distance in general and particularly the way we measure intra-national versus international distances. As pointed out by Wei (1996), the magnitude of the border effects can be strongly influenced by the method of calculating a country's distance from itself. If this internal distance is overestimated, then holding international distance constant, the negative effect of distance will be underestimated as the cost of shipping a good internally becomes closer to the cost of shipping

<footnote n="4">⁴Using a different theoretical framework, Eaton and Kortum (1997) obtain a similar dependent variable, normalizing bilateral trade by trade with self.</footnote>
it to another country. As a result, the border effect—which accounts for any excessive amount of trade within a country—will be given more weight in the regression, leading *ceteris paribus* to an overestimated border effect.

We adopt an integrated measure of distance incorporating key characteristics of European economic geography. The usual way of calculating distance between two countries is to take the mileage between their respective principal cities. This amounts to assuming that all bilateral trade occur between these two cities, which means that countries are modeled as points. This sharply conflicts with actual European manufacturing geography. A better measure of distance should take into account the facts that countries consist of geographically dispersed sub-national units and that economic activity is by no means equally distributed between them. A given trade flow between France and Germany might take place between Strasbourg and Bonn but might also occur between Marseille and Hamburg, the true distance from producer to consumer depending on the case. In addition, the relevant distance for a representative product depends on the economic size of the regions, because the volume of trade between major cities like London and Barcelona will be much higher than the trade between, say, Belfast and Bilbao.

Incorporating the considerations discussed above, we construct a measure of distance using regional data available in Europe. We calculate bilateral distances between regions and weight those distances by the economic size of the regions. This method permits the calculation of both international and intra-national distances using the same integrated methodology. Considering two countries *O* and *D* (the origin and destination countries of a given flow), respectively consisting of regions indexed *i* ∈ *O* and *j* ∈ *D*, the following formula provides both external and internal distances.

$$d_{OD} = \sum_{i \in O} (\sum_{j \in D} w_j d_{ij}) w_i$$

We define $d_{ij}$ as the distance between the centers of regions *i* and *j* and $w_i$ as the weight of
region *i*, calculated as $w_i = \frac{GDP_i}{GDP_o}$. The external and internal distances as well as more details about the data used are given in Table 7 in the data appendix.

We also provide the results for three alternative distance measures. The first one comes from Wei (1996) and uses great-circle distances between economic centers for international distance and one quarter of the distance to the nearest foreign economic center for internal distance. We also show results following a procedure initiated by Wolf (1997) which uses the same measure of international distance as Wei but replaces internal distance with the distance between the two main cities of the state. Leamer (1997) proposes another procedure for calculating internal distances that relies on approximating the geography of countries as disks. Their areas can be converted into radii representing the expected distance between randomly selected points in the country.

<figure><img src="image_3.png" /><figcaption>Figure 1: The alternative measures of distance</figcaption></figure>

Figure 1 shows how the alternative measures of distance compare with ours. The logarithm
of the distance measures of Wei (1996), Wolf (1997), and Leamer (1997) are plotted against the
logarithm of our weighted distance measure. Thus, for any given distance, a vertical comparison
with the straight (45 degree) line reveals the difference between each methodology and ours for
a given country pair (including internal distance). First note that all international distances
except ours use main-city distances. It is therefore easy to distinguish between internal and
external distances since the latter are represented by overlaid symbols and are normally much
larger than internal distances.

The first striking feature regarding international distances comparisons is that the region-
weighted measure converges to the main cities distances for the largest values. Indeed, when two
countries are very far from each other, the spatial repartition of GDP inside each country counts
much less. Each region in a country has a roughly similar distance to all regions of the other
country. Furthermore, the most remote countries in our sample (Greece, Portugal, Ireland, and
Denmark) are small countries which reinforces this effect. How does our measure compare with
the main city methodology for international distances between large and proximate countries?
It appears that an important feature of European economic geography is that main cities are
relatively close to each other for large countries such as Germany (Dusseldorf), France (Paris),
Italy (Milan), UK (London) and Spain (Barcelona). Because the main-city procedure does not
give any weight to the much larger bilateral distances between smaller economic regions in these
countries, bilateral distances are considerably underestimated.

Note, then, that there is a systematic relationship between the two alternative external dis-
tance measures: For peripheral countries, the distances are approximately the same. However,
for the core countries, main-city distances tend to underestimate “true” (region-weighted) dis-
tances. The calculation of border effects depends critically on the *relative* magnitudes of external
and internal distance. Hence, it is very important to obtain measures of internal distance that
preserve the true ratio between intra- and inter-national distance.
Our internal distances are fairly similar to the ones calculated with disk-area procedure. As a consequence, the whole set of distances calculated using Leamer's methodology are close to ours. On the other hand, the methodologies using one quarter of the distance to the nearest neighbour and (to a lesser extent) the distance between the two main cities of the country differ in a large and non-systematic manner from region-weighted distances.

# V Results

The data required to implement this specification consist of trade data classified according to industry rather than product. These data are matched with corresponding industry-level production data. Imports from self are defined as production minus exports to other countries. Both sets of data are provided by Eurostat. Unfortunately, production data omits the output of enterprises with less than 20 employees. We use a separate Eurostat database on small enterprises to calculate a scale-up ratio appropriate for each industry and country combination. More details on the construction of the database are provided in the data appendix.

In the estimation of the border effects, three questions are to be answered: How big were border effects before the launching of the SEA? How closely were they related to indicators of NTBs identified by the European Commission? To the extent that border effects have fallen over time, can this decline be attributed to the removal of NTBs under the SMP? We start by reporting the estimates obtained while imposing a common set of coefficients on all industries and then consider a regression of industry-level border coefficients on non-tariff barrier indicators.
<table><caption>Table 1: Border Effects in the EU, 1984–86 Averages, Common Coefficient Regressions.</caption><thead><tr><th rowspan="2">Specification :</th><th colspan="6">Dependent Variable: Ln Partner/Own Imports</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Border</td><td>-2.76*<br>(0.05)</td><td>-3.01*<br>(0.05)</td><td>-3.18*<br>(0.08)</td><td>-3.52*<br>(0.04)</td><td>-3.22*<br>(0.04)</td><td>-2.53*<br>(0.05)</td></tr><tr><td>Ln Rel. Production</td><td>0.86*<br>(0.01)</td><td>0.80*<br>(0.01)</td><td>0.59*<br>(0.01)</td><td>0.66*<br>(0.01)</td><td>0.82*<br>(0.01)</td><td></td></tr><tr><td>Ln Rel. Distance</td><td>-1.21*<br>(0.03)</td><td>-0.95*<br>(0.04)</td><td>-0.53*<br>(0.04)</td><td>-0.68*<br>(0.03)</td><td>-1.15*<br>(0.03)</td><td>-1.32*<br>(0.03)</td></tr><tr><td>Ln Rel. Price</td><td>-0.90*<br>(0.07)</td><td>-0.97*<br>(0.07)</td><td>-1.15*<br>(0.08)</td><td>-0.60*<br>(0.07)</td><td>-0.59*<br>(0.07)</td><td>-1.38*<br>(0.07)</td></tr><tr><td>Not an EU Member</td><td>-0.61*<br>(0.06)</td><td>-0.44*<br>(0.06)</td><td>-0.37*<br>(0.06)</td><td>-0.31*<br>(0.06)</td><td>-0.34*<br>(0.06)</td><td>-0.47*<br>(0.06)</td></tr><tr><td>Both Speak English</td><td>1.44*<br>(0.16)</td><td>1.44*<br>(0.16)</td><td>1.54*<br>(0.16)</td><td>1.47*<br>(0.16)</td><td>1.71*<br>(0.15)</td><td>1.35*<br>(0.16)</td></tr><tr><td>Both Speak French</td><td>1.58*<br>(0.14)</td><td>1.54*<br>(0.14)</td><td>1.58*<br>(0.14)</td><td>1.49*<br>(0.14)</td><td>1.08*<br>(0.13)</td><td>1.49*<br>(0.14)</td></tr><tr><td>Mills Ratio</td><td></td><td>-2.87*<br>(0.25)</td><td>-6.25*<br>(0.20)</td><td>-3.94*<br>(0.24)</td><td>-0.72*<br>(0.27)</td><td>-0.97*<br>(0.22)</td></tr><tr><td>N</td><td>12892</td><td>12892</td><td>12892</td><td>12892</td><td>12892</td><td>12892</td></tr><tr><td>R<sup>2</sup></td><td>0.404</td><td>0.410</td><td>0.391</td><td>0.405</td><td>0.435</td><td>0.226</td></tr><tr><td>RMSE</td><td>2.120</td><td>2.109</td><td>2.144</td><td>2.119</td><td>2.065</td><td>2.133</td></tr></tbody></table>

Note: Standard errors in parentheses with * denoting significance at the 1% level. Distances are calculated using Wei's method in column (3), Wolf's method in column (4), and using the disk-area approximation in column (5). The other columns use the weighted sub-national distances described in the text. In column (6) a unit elasticity is imposed on relative production by passing it to the left hand of the regression equation. See text for more details.
V-A The Level of Border Effects before the SEA

We begin with an estimation of the magnitude of the border effect before the implementation
of the Single European Act. We pool the years 1984, 1985, and 1986 and test several different
specifications in Table 1. The regression imposes a common set of coefficients on the 98 industries
in the sample. The two first columns are estimations of equation 3 that include common language
indicators and a dummy to take into account the fact that Spain and Portugal were not members
of the EC before 1986. We find substantially larger language effects than those obtained in the
OECD data studied by Wei and Helliwell. Our coefficients on production, distance, and EU
membership are also somewhat larger than those studies.

The monopolistic competition trade model predicts positive amounts of trade between each
set of partners in each industry. This prediction works remarkably well for most importers.
Only Greece, Ireland, and Portugal have zeros for more than 5% of their industry-partner trade
flows. The endogeneity of which country pairs have positive trade has the potential to generate
selection bias. Column (2) uses Heckman's two-stage procedure to address this concern. First,
we estimate a probit where the dependent variable is an indicator for positive trade. The set of
explanatory variables in this equation now includes the levels of the exporter's production, price,
and distance in addition to the relative values. The probit results (not reported) are similar
in terms of signs and significance level to the OLS results. The exception is relative prices,
which enter with a positive sign. Language effects could not be included in the probit equation
because countries with a common language have positive trade in every industry.<fnref n="5" /> Using the
probit coefficients, we calculate Mills ratios and add them to the original specification. Border
effects are larger (intercepts of -3.01 vs. -2.76) as a result of this selection correction. The
intuition for this result may lie in the fact that countries tend to trade with themselves in every

<footnote n="5">⁵ “Perfect predictors” cannot be included in probit regressions.</footnote>
industry, i.e. the zeros in trade are concentrated in external trade. OLS ignores this while the Heckman procedure, by taking into account zeros through the probit equation, includes this effect in its estimation of the impact of the border. The expansion of the border effect appears to result in slightly lower coefficients on EU membership and distance than those obtained in column (1).

The distance coefficient is more than twice as large as the .6 figure that Leamer (1997) reports as the usual elasticity. While this paper is not alone in obtaining larger distance elasticities, we believe that part of the explanation is our use of an improved measure of distance and the use of a specification that obviates the need to control for “remoteness.” The results reported in column (3) appear to provide some support for these claims. Using main-city distances for international distance and Wei’s one-quarter the distance to the nearest neighbor for internal distance, we obtain markedly lower distance coefficients. We also observe that this specification fits worse in terms of $R^2$ and the standard error of the regression. Wei’s rule leads to some strange internal distances for European countries. For instance, Frances internal distance of 47 miles is three times smaller than Portugal’s internal distance of 157 miles. Our measure gives 239 miles for France and 91 for Portugal. The mismeasurement of distance seems to cause the regression to rely more on border and language effects to explain low external trade.

The use of Wolf’s rule (internal distance equals the distance between the two largest cities) in column (4) raises the distance effect slightly compared to column (3) but also substantially inflates the border effect. Column (5) continues the use of main-city distances but substitutes the disk-area approximations of internal distance (Leamer, 1997). This restores most aspects of the prior results.<fnref n="6" /> Border effects are larger than those obtained with region-weighted distance. The extreme sensitivity of border effects to the different internal distance measures in columns (3),

<footnote n="6">⁶Interestingly, now French seems much less important for trade than English. This may arise because the Brussels-Paris distance substantially underestimates the weighted average distance between Belgium and France (189 vs. 292 miles). In contrast, the UK-Ireland distance is slightly overestimated by the main-city method.</footnote>
(4), and (5) points to the importance of measuring internal and external distances in a consistent manner.

In the final column of table 1, we force the coefficient on the log of relative production to be one, as specified in the Dixit-Stiglitz version of the trade model (Equation 3). In the previous regressions, the coefficient on relative production has been significantly lower than one. Theoretically, this could arise because varieties from countries with larger production are manufactured at a greater scale. Thus, rises in relative production overstate rises in the number of varieties offered. On the other hand, the theory could be correct and econometric problems might lead to an underestimate of the production coefficient. This in turn could lead to bias in other coefficients of interest.

Imposition of a unit elasticity on relative production addresses two different econometric issues. First, as emphasized by Harrigan (1996), output and trade are jointly determined in equilibrium. This could lead to a correlation between relative production and the error term. Harrigan and Wei respond to the simultaneity problem by using factor endowments as instruments for outputs. We adopt a different approach that avoids the need for instrumental variables. By moving $ln v$ to the left hand side of the equation, equation 3 becomes

$$ \ln \left( \frac{m_{ij}}{m_{ii}} \right) - \ln \left( \frac{v_j}{v_i} \right) = -(\sigma - 1)\delta \ln \left( \frac{d_{ij}}{d_{ii}} \right) - \sigma \ln \left( \frac{p_j}{p_i} \right) \\ -(\sigma - 1)[\beta + \ln(1 + \nu)] + \epsilon_{ij}. \qquad (4) $$

Production no longer appears on the right hand side and therefore it cannot cause a simultaneity problem. The imposition of this restriction may also mitigate a second econometric problem—measurement error for production. To the extent that the production data is inaccurate, a bias towards zero may be exhibited in the coefficient on $ln v$. Depending on cross-correlations, the
other right-hand side variables may obtain biased coefficients as well.<fnref n="7" />

The restricted specification results in column (6) differ from previous columns by attributing greater trade reduction to distance and less to border effects. Price effects also become larger. The language effects remain strong and quantitatively similar. Note that the reduction in the $R^2$ occurs because the explanatory power of relative production no longer contributes to the calculation. The root mean squared error (RMSE) of the regression is little changed from column (2).

There are several ways to express the magnitude of border effects. First, we can follow McCallum in using the ratio of imports from self to imports from others, holding other things equal. This is just $exp(3.01) = 20.3$ in column (2) and $exp(2.53) = 12.55$ in column (6). Thus our results lie between the value of 20 obtained by McCallum for Canadian provinces' trade and Wei's value of $exp(2.27) \approx 10$. Expressed in this manner, it appears that border effects in Europe are large but also quite sensitive to specification.

A second way to quantify border effects is to convert them to distance equivalents. This approach is taken by Engel and Rogers (1996) and Helliwell (1996). Despite the differences in the data analyzed (Engle and Rogers examine price variation whereas Helliwell uses an extension of McCallum's data), both papers find that the U.S.-Canada border is equivalent to at least 2000 miles. In our study, crossing a border is equivalent to multiplying distance by $exp(-3.01/-0.95) = 23.75$. Since the average internal distance is 135 miles, this implies an average border "width" of 3206 miles. Thus borders appear to be more than four times the average external distance of 785 miles. Using the estimates in column (6) reduces the implied border width to 918 miles.

Wei considers the tariff equivalent of a border. This requires an estimate of the elasticity

<footnote n="7">⁷We have good reasons to expect measurement error since the original production data was adjusted to take into account unmeasured production by small enterprises. See Data Appendix.</footnote>
of substitution between varieties, or σ in the model we used to obtain the estimating equation.
The *ad valorem* tariff equivalent implied by column (2) is $\exp(3.01/(\sigma - 1)) - 1$. The coefficient
on log of relative price would be a good candidate for $\sigma - 1$. However, our results indicate
unreasonably small values.<fnref n="8" /> Instead, we consider results obtained by Head and Ries (1998) in
a study of the effects of changes in tariffs on Canada-U.S. trade in manufactures. That paper
obtains values of $\sigma - 1$ ranging from 6 to 10.<fnref n="9" /> Using $\sigma - 1 = 8$, we find the tariff equivalent
of a border crossing to be 45%. Using the more conservative column (6) estimates, the tariff
equivalent declines to 37%. Thus, our results suggest that crossing a border in the EU appears
to involve costs that approximate those of the tariffs of the Depression era.

How can we explain such large border effects? First, it seems worth noting the importance
of common language effects. Since internal trade presumably occurs between agents that share
a language, we may infer that border effects would by 76% lower (1 - exp(1.44)) without the
trade-impeding effects of linguistic differences. It is possible, of course, that communication
problems are only part of the observed language effect. It may serve as a proxy for other shared
attributes that promote trade. Our primary focus is on a second potential explanation, namely
that non-tariff barriers fragmented the European market prior to their elimination by the Single
European Act.

To evaluate the extent that NTBs can account for the border effect and therefore see if
the barriers to trade targeted by the White Paper were actually hampering trade before their
removal, the border effect must be estimated at the industry level. The ranking and magnitude
of these coefficients will be crucial in evaluating the impact of NTBs; we thus need to check the
robustness of the results depending on the specification.

We estimate border effects for each industry, allowing all the other coefficients in the spec-

<footnote n="8"><sup>8</sup>We speculate that this is because unobserved variation in relative product quality is correlated with relative product price.</footnote>

<footnote n="9">⁹Using a different methodology, Hanson (1998) obtained estimates of σ that ranged between 6 and 11.</footnote>
ification to vary across industries. The most important reason for this approach is as follows. Suppose one industry has higher transport costs. By forcing it to have the average distance coefficient, we would underestimate the effect of distance on that industry's trade. This might cause the intercept to capture the misspecification, leading to an overestimate of border effects. The six preceding estimations are thus conducted by industry. We refer to the negative of the intercept from each regression as that industry's border coefficient.

<table><caption>Table 2: Correlation matrix of industry-level border effects</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>Mean</th></tr></thead><tbody><tr><th>1</th><td><strong>1.332</strong></td><td></td><td></td><td></td><td></td><td></td><td>2.615</td></tr><tr><th>2</th><td>0.998</td><td><strong>1.374</strong></td><td></td><td></td><td></td><td></td><td>2.645</td></tr><tr><th>3</th><td>0.728</td><td>0.737</td><td><strong>1.524</strong></td><td></td><td></td><td></td><td>2.910</td></tr><tr><th>4</th><td>0.930</td><td>0.934</td><td>0.749</td><td><strong>1.345</strong></td><td></td><td></td><td>3.234</td></tr><tr><th>5</th><td>0.944</td><td>0.945</td><td>0.712</td><td>0.971</td><td><strong>1.302</strong></td><td></td><td>3.079</td></tr><tr><th>6</th><td>0.948</td><td>0.947</td><td>0.676</td><td>0.907</td><td>0.931</td><td><strong>1.299</strong></td><td>2.496</td></tr></tbody></table>

Note: Correlation coefficients between estimates of border effects at the industry level, organized by specification from Table 1. The bold diagonal contains standard deviations for each set of coefficients. The last column contains the mean value of the corresponding border effect.

Table 2 summarizes the results for the six specifications. We note first that average border coefficients are similar in magnitude to the constants in the common-coefficient regressions. They also follow the same size ranking, with specification 6 (the restriction of unit production elasticity) yielding the smallest border effect. Most of the specifications lead to border coefficients that are highly correlated with one another. The exception is specification 3 which uses Wei's 1/4 rule for obtaining internal distances. The disk-area-rule provides coefficients that are generally larger but closely correlated with the weighted-average distance measure. This seems to be because internal distances are somewhat smaller than the disk radius approximation would suggest.

Table 3 gives the border coefficients for specification 2 for each industry. The industries are
<table><caption>Table 3: Border Coefficients by Industry</caption><thead><tr><th>Industry</th><th>Coefficient</th><th>Industry</th><th>Coefficient</th></tr></thead><tbody><tr><td>Motor vehicles-ass and eng</td><td>-0.06</td><td>Meat</td><td>2.27*</td></tr><tr><td>Asbestos</td><td>0.14</td><td>Machine-tools</td><td>2.33*</td></tr><tr><td>Motor vehicles-parts</td><td>0.55</td><td>Knitting</td><td>2.40*</td></tr><tr><td>Electrical apps.-indl.</td><td>0.62</td><td>Industrial chem. n.e.s</td><td>2.47*</td></tr><tr><td>Textile n.e.s</td><td>0.62</td><td>Soap</td><td>2.47*</td></tr><tr><td>Steel tubes</td><td>0.89</td><td>Electrical plant</td><td>2.51*</td></tr><tr><td>Machinery-misc</td><td>0.98*</td><td>Footwear-mass</td><td>2.57*</td></tr><tr><td>Machinery- agricultural</td><td>1.01*</td><td>Clocks</td><td>2.59*</td></tr><tr><td><strong>Office machinery</strong></td><td><strong>1.06*</strong></td><td><strong>Wires</strong></td><td><strong>2.71*</strong></td></tr><tr><td>Household chem. n.e.s</td><td>1.17*</td><td>Oils and fats</td><td>2.72*</td></tr><tr><td>Man-made fibres</td><td>1.18*</td><td>Fish</td><td>2.74*</td></tr><tr><td>Non-ferrous metals-prod.</td><td>1.24*</td><td>Wooden furniture</td><td>2.81*</td></tr><tr><td>Transmission eq.</td><td>1.27*</td><td>Wood-processed</td><td>2.82*</td></tr><tr><td>Receivers-TV and Radio</td><td>1.28*</td><td><strong>Confectionery</strong></td><td><strong>2.89*</strong></td></tr><tr><td>Industrial chem.</td><td>1.35*</td><td>Cork and brushes</td><td>2.97*</td></tr><tr><td>Electrical apps.-domestic</td><td>1.36*</td><td>Clothing</td><td>3.00*</td></tr><tr><td>Machinery n.e.s</td><td>1.37*</td><td><strong>Railway</strong></td><td><strong>3.03*</strong></td></tr><tr><td>Abrasives</td><td>1.44*</td><td>Aerospace</td><td>3.04*</td></tr><tr><td>Steel-preprocess</td><td>1.45*</td><td>Printing</td><td>3.31*</td></tr><tr><td>Optical ins.</td><td>1.47*</td><td>Metals transformation</td><td>3.34*</td></tr><tr><td>Furs</td><td>1.53*</td><td>Paint and Ink</td><td>3.47*</td></tr><tr><td>Glass</td><td>1.60*</td><td><strong>Shipbuilding</strong></td><td><strong>3.52*</strong></td></tr><tr><td>Lighting eq.</td><td>1.61*</td><td>Motor vehicles-bodies</td><td>3.52*</td></tr><tr><td>Musical ins.</td><td>1.64*</td><td>Graphic labs</td><td>3.59*</td></tr><tr><td>Toys and sports</td><td>1.67*</td><td>Foundries</td><td>3.61*</td></tr><tr><td>Ceramics</td><td>1.72*</td><td>Structural metal</td><td>3.63*</td></tr><tr><td>Leather-tanning</td><td>1.74*</td><td><strong>Pharmaceuticals</strong></td><td><strong>3.66*</strong></td></tr><tr><td>Floor coverings</td><td>1.78*</td><td>Dairy</td><td>3.97*</td></tr><tr><td>Cycles</td><td>1.79*</td><td>Distilling</td><td>4.04*</td></tr><tr><td>Jewelery</td><td>1.79*</td><td>Grain milling</td><td>4.08*</td></tr><tr><td>Machinery-engineering</td><td>1.82*</td><td>Used tyres</td><td>4.08*</td></tr><tr><td>Transport eq. n.e.s</td><td>1.82*</td><td><strong>Metal containers</strong></td><td><strong>4.09*</strong></td></tr><tr><td>Pulp and paper</td><td>1.83*</td><td>Bread</td><td>4.10*</td></tr><tr><td>Starch</td><td>1.86*</td><td>Food n.e.s</td><td>4.22*</td></tr><tr><td>Wood n.e.s</td><td>1.87*</td><td><strong>Pasta</strong></td><td><strong>4.25*</strong></td></tr><tr><td>Stone</td><td>1.91*</td><td>Tobacco</td><td>4.32*</td></tr><tr><td>Machinery-textile</td><td>1.94*</td><td>Clay</td><td>4.34*</td></tr><tr><td>Precision ins.</td><td>2.01*</td><td><strong>Beer</strong></td><td><strong>4.51*</strong></td></tr><tr><td>Tools etc.</td><td>2.04*</td><td><strong>Wine</strong></td><td><strong>4.57*</strong></td></tr><tr><td><strong>Telecoms</strong></td><td><strong>2.06*</strong></td><td>Poultry</td><td>4.66*</td></tr><tr><td>Vegetables</td><td>2.07*</td><td><strong>Soft drinks</strong></td><td><strong>4.73*</strong></td></tr><tr><td>Machinery-food and chem.</td><td>2.09*</td><td>Concrete</td><td>4.73*</td></tr><tr><td>Textiles-households</td><td>2.14*</td><td>Cement</td><td>4.74*</td></tr><tr><td>Iron and steel</td><td>2.15*</td><td>Forging</td><td>4.86*</td></tr><tr><td>Plastics</td><td>2.19*</td><td>Wooden containers</td><td>5.38*</td></tr><tr><td>Rubber</td><td>2.23*</td><td>Wood-sawing</td><td>5.47*</td></tr><tr><td>Leather-products</td><td>2.24*</td><td>Oil refining</td><td>5.69*</td></tr><tr><td><strong>Medical eq.</strong></td><td><strong>2.26*</strong></td><td>Carpentry</td><td>5.97*</td></tr><tr><td>Paper processing</td><td>2.27*</td><td>Sugar</td><td>6.40*</td></tr></tbody></table>

Note: Border coefficient (Specification 2) for each industry. The industries identified by Buigues et al (1990) as high and moderate NTBs industries are respectively in bold face and italics.
ordered in terms of increasing magnitude of border effects. It seems noteworthy that ingestible
products—food, beverages, tobacco and drugs—figure heavily among those with large border
effects. Wolf’s hypothesis that border effects reflect vertical industry cluster effects does not
seem appropriate for these industries. Rather, we expect resistance to foreign goods in these
cases might derive from the consumer’s greater experience with and confidence in domestic
varieties.

The industry-level border coefficients are then regressed on two measures of non-tariff barriers.<fnref n="10" /> The first one comes from survey of 11,000 firms conducted by the European Commission under the “Costs of Non-Europe” project. From this survey we construct three variables representing respectively the magnitude of the NTBs in terms of standard differences, public procurement, and customs formalities. The second set of indicators comes from Buigues et al (1990) which classified European industries into three levels of barriers : low, moderate, and high.

The results in table 4 cast doubt on the proposition that high non-tariff barriers could explain
the market fragmentation found in manufacturing industries in Europe. The explanatory power
is low in each case; NTBs explain at most 10% of the variation in border effects. Moreover,
the effects are often insignificant. Worse, moderate NTB industries appear to have significantly
*lower* border effects than low NTB industries. The results depend somewhat on which specifica-
tion is used to estimate the border effects. However, none of the specifications provides support
for a positive relationship between the NTBs identified by the European Commission and the
border effects we estimate.

<footnote n="10">¹⁰Some border effects are measured more precisely than others. This introduces heteroscedasticity. We respond to this problem by using weighted least squares estimation. The weights are the inverse of the standard error of the estimate of each border effect.</footnote>
<table><caption>Table 4: Explaining Cross-Industry Variation in Pre-SEA EU Border Effects</caption><thead><tr><th rowspan="2">Specification :</th><th colspan="6">Dependent Variable: Constant from Industry Regressions</th></tr><tr><th>(2)</th><th>(3)</th><th>(6)</th><th>(2)</th><th>(3)</th><th>(6)</th></tr></thead><tbody><tr><td>Intercept</td><td>4.275<sup>a</sup><br>(1.631)</td><td>4.643<sup>b</sup><br>(1.913)</td><td>4.564<sup>a</sup><br>(1.504)</td><td>2.678<sup>a</sup><br>(0.158)</td><td>3.017<sup>a</sup><br>(0.184)</td><td>2.550<sup>a</sup><br>(0.149)</td></tr><tr><td>Standards Conflict</td><td>-0.013<br>(0.017)</td><td>-0.016<br>(0.020)</td><td>-0.012<br>(0.016)</td><td></td><td></td><td></td></tr><tr><td>Govt. Proc. Bias</td><td>-0.030<sup>c</sup><br>(0.017)</td><td>-0.024<br>(0.020)</td><td>-0.034<sup>b</sup><br>(0.016)</td><td></td><td></td><td></td></tr><tr><td>Customs Burden</td><td>-0.001<br>(0.020)</td><td>-0.003<br>(0.023)</td><td>-0.008<br>(0.019)</td><td></td><td></td><td></td></tr><tr><td>High NTBs</td><td></td><td></td><td></td><td>0.399<br>(0.379)</td><td>-0.078<br>(0.445)</td><td>0.313<br>(0.370)</td></tr><tr><td>Moderate NTBs</td><td></td><td></td><td></td><td>-0.794<sup>a</sup><br>(0.288)</td><td>-0.579<sup>c</sup><br>(0.338)</td><td>-0.756<sup>a</sup><br>(0.274)</td></tr><tr><td>N</td><td>93</td><td>93</td><td>93</td><td>98</td><td>98</td><td>98</td></tr><tr><td>R<sup>2</sup></td><td>0.058</td><td>0.034</td><td>0.070</td><td>0.101</td><td>0.030</td><td>0.094</td></tr><tr><td>RMSE</td><td>1.269</td><td>1.467</td><td>1.177</td><td>1.221</td><td>1.428</td><td>1.162</td></tr></tbody></table>

Note: Standard errors in parentheses with <sup>a</sup>, <sup>b</sup>, and <sup>c</sup> denoting significance at the 1%, 5%, and 10% level. Weighted least squares regression (see footnote 10)). Specifications 2 and 6 use region-weighted distances. Specification 6 imposes the restriction that the coefficient on relative production be one. Specification 3 is the same as 2 except for using Wei's method of calculating distance.
V-B The Evolution of EU Border Effects

We now turn to the analysis of changes in the border effect over time. We also investigate
whether reductions in border effects since 1986 were higher in industries identified by the Euro-
pean Commission as having high NTBs. During the 1980s what was then the European Com-
munity expanded membership to include Greece (1981), Spain (1986), and Portugal (1986).
Their change in status from outsiders to insiders could influence the temporal evolution of the
border effect. To maintain a constant composition, we now restrict the sample to the nine first
member countries. We first divide the sample into six sub-periods and use Heckman's two-stage
method for each of these sub-periods. As in table 1, we impose a common set of coefficients
on all 98 industries. The results by sub-period are presented in table 5, where we see that the
border effect decreases over time in Europe. The implied ratio of imports from self to imports
from other European countries starts at 21.1 in the late 1970s and falls to 11.35 after the SEA
completion in 1993-5.

This year-by-year evolution of the border effect is better seen in figure 2. The line with square
symbols shows border effects (exp(-intercept)) for a single regression where all coefficients are
held constant from 1976 to 1995 except the constant, i.e. the line shows the coefficients on year
dummies. This specification shows rapid initial decline in the border effect with relatively little
movement since 1986. In fact, the year dummy for 1995 is not significantly different from 1986.

Holding all other coefficients fixed over time may not be reasonable. First, we might expect
declines in transport costs to reduce distance coefficients. The results in table 5 do not provide
much support for this hypothesis. They seem to indicate a U-shaped profile for distance costs.
Second, knowledge of English as a second language is increasing and might therefore reduce the
trade-generating effects of both countries having English as a native language. Some evidence
of this trend seems visible in table 5. The line with circle symbols is based on the intercepts
<table><caption>Table 5: Border Effects for the EU9, 3-year Samples, Common Coefficient Regressions.</caption><thead><tr><th rowspan="2">Period :</th><th colspan="6">Dependent Variable: Ln Partner/Own Imports</th></tr><tr><th>[78-80]</th><th>[81-83]</th><th>[84-86]</th><th>[87-89]</th><th>[90-92]</th><th>[93-95]</th></tr></thead><tbody><tr><td>Border</td><td>-3.05*<br>(0.06)</td><td>-3.05*<br>(0.06)</td><td>-2.83*<br>(0.06)</td><td>-2.68*<br>(0.05)</td><td>-2.43*<br>(0.05)</td><td>-2.55*<br>(0.06)</td></tr><tr><td>Ln Rel. Production</td><td>0.80*<br>(0.02)</td><td>0.75*<br>(0.02)</td><td>0.76*<br>(0.02)</td><td>0.73*<br>(0.01)</td><td>0.75*<br>(0.01)</td><td>0.72*<br>(0.01)</td></tr><tr><td>Ln Rel. Distance</td><td>-0.94*<br>(0.05)</td><td>-0.84*<br>(0.05)</td><td>-0.86*<br>(0.04)</td><td>-0.91*<br>(0.04)</td><td>-1.06*<br>(0.04)</td><td>-0.97*<br>(0.04)</td></tr><tr><td>Ln Rel. Price</td><td>-1.00*<br>(0.08)</td><td>-0.77*<br>(0.13)</td><td>-0.36*<br>(0.11)</td><td>-0.27*<br>(0.09)</td><td>-0.37*<br>(0.09)</td><td>-0.48*<br>(0.07)</td></tr><tr><td>Both Speak English</td><td>1.26*<br>(0.14)</td><td>1.33*<br>(0.14)</td><td>1.15*<br>(0.14)</td><td>0.99*<br>(0.13)</td><td>1.12*<br>(0.13)</td><td>1.00*<br>(0.14)</td></tr><tr><td>Both Speak French</td><td>1.24*<br>(0.12)</td><td>1.21*<br>(0.12)</td><td>1.26*<br>(0.12)</td><td>1.29*<br>(0.11)</td><td>1.27*<br>(0.12)</td><td>1.25*<br>(0.13)</td></tr><tr><td>Mills Ratio</td><td>-5.53*<br>(0.72)</td><td>-5.92*<br>(0.78)</td><td>-6.65*<br>(0.81)</td><td>-7.87*<br>(0.89)</td><td>-6.62*<br>(1.01)</td><td>-9.48*<br>(1.15)</td></tr><tr><td>N</td><td>8052</td><td>7388</td><td>7729</td><td>8111</td><td>7736</td><td>7678</td></tr><tr><td>R<sup>2</sup></td><td>0.378</td><td>0.363</td><td>0.357</td><td>0.352</td><td>0.348</td><td>0.324</td></tr><tr><td>RMSE</td><td>1.918</td><td>1.844</td><td>1.839</td><td>1.76</td><td>1.769</td><td>1.825</td></tr></tbody></table>

Note: Standard errors in parentheses with * denoting significance at the 1% level. Regressions estimated using Heckman two-stage procedure. Greece, Portugal and Spain are excluded.
<figure><img src="image_4.png" /><figcaption>Figure 2: Changes in the border effect 1976–1995</figcaption></figure>
(converted to McCallum-style border effects) for 20 annual regressions where every coefficient
is allowed to vary across years. This method of showing annual border effects points towards
greater decline in market fragmentation during the implementation of the SEA.

The line with triangles subtracts the English effect from the border effect to obtain a hy-
pothetical border effect for two nations that speak a common language. Thus it is comparable
to border effects for Canada and the US. This line gives an idea of the level and evolution of
border effects that might have prevailed if all members of the European Union had spoken the
same language. After removing the effect of linguistic differences, the European Union appears
to be substantially more integrated than North America.<fnref n="11" />

Note that in 1993 the border effect appears to jump up under both specifications. After
1992, the European Union could no longer rely on customs declarations for gathering trade
data. The EU member countries adopted a new system called Intrastat in which companies
report exports directly to their respective national statistical institutes. It is likely that Intrastat
underestimates trade flows because not all companies report their exports. As the production
data methodology did not change, there will be some upward bias in border effects since 1993
compared to previous years.

Finally we turn to the evolution of the border effect at the industry level. We saw in table 4
that the industries identified by the European Commission as characterized by higher than
average NTBs were *not* the ones where the estimated border effects were higher prior to the
SMP. A potential explanation would be that governments erected NTBs in those industries
where “natural” border effects were small. Nevertheless, these industries could be the ones
where the SMP most increased trade relative to domestic-products consumption. Thus, there
may be a correlation between the initial importance of NTBs and subsequent *changes* in border

<footnote n="11">¹¹An potentially important caveat is that we use industry level data whereas existing work on Canada-US trade is at the aggregate level.</footnote>
<table><caption>Table 6: Explaining Cross-Industry Evolution in EU Border Effects</caption><thead><tr><th colspan="5">Dep. Var.: Changes in Border Coefficients Since 1984–86</th></tr><tr><th>Model :</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Final Period:</th><th>[93–95]</th><th>[90–92]</th><th>[93–95]</th><th>[90–92]</th></tr></thead><tbody><tr><td>Intercept</td><td>0.412<br>(0.978)</td><td>-0.653<br>(0.906)</td><td>-0.445<sup>a</sup><br>(0.10)</td><td>-0.425<sup>a</sup><br>(0.093)</td></tr><tr><td>Standards Conflict</td><td>-0.006<br>(0.010)</td><td>0.001<br>(0.010)</td><td></td><td></td></tr><tr><td>Govt. Proc. Bias</td><td>0.001<br>(0.010)</td><td>0.007<br>(0.010)</td><td></td><td></td></tr><tr><td>Customs Burden</td><td>-0.011<br>(0.012)</td><td>0.000<br>(0.011)</td><td></td><td></td></tr><tr><td>High NTBs</td><td></td><td></td><td>0.089<br>(0.241)</td><td>0.260<br>(0.222)</td></tr><tr><td>Moderate NTBs</td><td></td><td></td><td>0.021<br>(0.182)</td><td>0.037<br>(0.168)</td></tr><tr><td>N</td><td>93</td><td>93</td><td>98</td><td>98</td></tr><tr><td>R<sup>2</sup></td><td>0.010</td><td>0.008</td><td>0.001</td><td>0.014</td></tr><tr><td>RMSE</td><td>.769</td><td>.714</td><td>.773</td><td>.715</td></tr></tbody></table>

Note: Standard errors in parentheses with <sup>a</sup>, <sup>b</sup>, and <sup>c</sup> denoting significance at the 1%, 5%, and 10% level. Weighted least squares regressions (see footnote 12).

effects.

The border effect is calculated for each industry the same way as for table 3 (Heckman procedure, unrestricted coefficient on relative production) for the periods 1984–86 and 1993–95. In order to keep enough degrees of freedom for individual industries, all EU12 countries are included, albeit with dummy variables for Greece, Portugal, and Spain. The change in the intercept is then regressed on the same measures of NTBs as in table 4.<fnref n="12" /> The change in the border effect is also measured using 1990–92 as the final period in order to avoid any problems that could arise due to the switch to Intrastat.

None of the different measures of NTBs appear to explain changes in the border effect. Thus,

<footnote n="12">¹²As with the previous regressions of levels of border effects on NTBs, we use weighted least squares to take into account differences across industries in the precision of border effects estimates. The weights are the inverse of the square root of the sum of the variances of the initial and final border effects.</footnote>
our results suggest that the removal of different NTBs during the SEA implementation did not provide the greatest benefits to the targeted industries. In particular, the measures taken by the member countries did not lead to the expected rise in the ratio of trade over consumption of domestic products for those industries where high barriers to trade where supposed to cause an excessive aversion to foreign goods.

# VI Conclusion

We employ the monopolistic competition model of trade to estimate border effects for 3-digit industries in the European Union. Our results indicate wide variation in border effects across industries. The average effects are smaller than those estimated for the Canada-US border but still very large. For the average industry in 1985, Europeans purchased 14 times more from domestic producers than from equally distant foreign ones. The tariff equivalent of the border for 1984-86 indicated by the most conservative estimation method is 37%. Since actual tariffs and quotas within the EU were phased out by 1968, the border effects must stem from another cause. Our model decomposes border effects into a part due to government actions that impede trade, i.e. non-tariff barriers, and consumer preferences for domestically made products, i.e. home bias. We find that indicators of the non-tariff barriers identified and eliminated by the Single Market Programme cannot explain cross-industry variation in the pre-SMP border effects. While the impact of borders has declined over time, the reductions began at least a decade before the SMP. Furthermore, during the SEA implementation there is no relation between industry-level declines in border effects and indicators of NTBs. Consequently, our results provide indirect evidence for the consumer bias explanation of border effects.
# VII Data appendix

This paper requires bilateral trade and production data in a compatible classification for 12 European countries over the period 1976-1995. The data were obtained from two Eurostat databases which disaggregate production and trade data to the 3-digit level. The Eurostat industry classification permits the use of previous work on trade impediments in Europe. The European Commission evaluated for instance the NTBs in Europe for the year 1985 at the NACE 3 digit level.

The production data for European countries is part of the VISA database, mainly consisting of the annual survey structure and activity of industry which collects many information on European countries' industries such as the number of firms, employment, and wages. The data covers all manufacturing and consists of 120 NACE 3digit industries.

The bilateral trade data is taken from the COMEXT database. The European trade data made available by Eurostat in this database is separated in two periods : 1975-1987 and 1988-1995. For the second period, a conversion of trade flows from the HS 8 digits to the NACE 3 digits classification is available. However, for the first period, only the 6 digits NIMEXE classification is available. Therefore a concordance table from NIMEXE to NACE constructed by Eurostat (available at http://www.haveman.org) was used to aggregate the more than 10,000 NIMEXE codes into 113 NACE industries. Consistency of the flows between the two periods was checked by verifying that the industries' total flows do not exhibit large jumps between 1987 and 1988 and that the trend does not systematically change between the two periods. The two concordances appear to be consistent with regard to both criteria.

## Production data

The VISA database is mainly based on an annual survey conducted by member countries following Eurostat directives. The annual results only report the data for firms over 20 employees
(except for Spain which reports data for all firms). Comparability between trade and production
data would be threatened since the trade data includes exports of small firms. The underesti-
mation of the production variable entailed by this restriction would result in an underestimation
of trade with self in each country and industry.

Every 5 years member countries conduct a survey including enterprises with less than 20 em-
ployees (*Enterprises in Europe, Fourth Report, Small and Medium-sized Enterprises* database).
We used this data to calculate for each country and each industry the production share of firms
with less than 20 employees. The quality of the data by size classes varies depending on the
country. Number of firms, total employment, turnover, value added and labor costs are the
variables covered by the survey at the 3-digit level for 9 size classes. Turnover was the retained
variable for the ratios calculation due to its proximity to production value. Turnover was how-
ever not available for Italy, so salaried employment was used in this case to calculate the share
of firms with less than 20 employees in each industry. The industrial detail level used was the
finest available: 3 digits when possible, 2 digits otherwise.

Regional data

The regional data used to calculate distances in this paper were extracted from the REGIO
database also published by Eurostat. The indicator used to calculate weights was the GDP of
regions in 1985. This variable is available at several levels of geographic disaggregation called
NUTS. It goes from NUTS 0 which defines countries to the NUTS 5 city level (a little less than
100 000 NUTS units are defined under this last classification). For each country we chose the
lowest degree of disaggregation that provides a reasonable number of sub-national regions. The
geographic level of disaggregation is NUTS 1 for all large countries, NUTS 2 for Portugal, and
NUTS 3 for Ireland. This leaves us with 77 regions. GDP is available for all the European
regions we consider except those in Ireland for whom we used population as the weight. The
distance between two regions was calculated as the distance between the main city of each
region, taking the longitude and latitude of each city and applying the greater circle formula.
Internal distances of regions are assumed to be zero. For reasons of both data availability and
economic relevance, the French départements d'outre mer, Portuguese Azores and Madeira, and
the Spanish Canarias (which are all islands of very little economic weight but very large distance
to the European continent) were excluded from weighted distance calculations. Table 7 gives
our bilateral distances compared to the ones obtained by simply taking the distance between
the economic center of each country and applying Wei's (1996) procedure for internal distance.

Non-Tariff Barriers

The data on non tariff barriers is taken from two major sources. One is the paper by Buigues
et al (1990) identifying the level of NTBs in Europe for 1985 for each 3-digit NACE industry.
These authors isolated 14 industries characterized by high NTBs and 26 with moderate ones
out of the 120 industries. We also used the results of the questionnaire completed by 11,000
European firms and conducted by the European Commission as part of the "Cost of Non-
Europe" project (volume 3). The question we use is how desirable the firms find the removal of
trade barriers. Eight types of barriers are stipulated and firms are asked to rank the removal of
each barrier from "very important" to "satisfaction with the present situation". Coefficients of
importance are then calculated ranging from 0 if all companies considered the removal to be of
little or no importance to 100 if all firms considered the removal very important. This enabled
us to construct three variables (out of the eight proposed in the questionnaire) accounting for
the benefit of removal perceived by firms and thus, implicitly, the trade impact of the NTBs.
The three variables correspond to the three main NTBs identified by the Commission in the
1985 white paper: Differences in standards, national preference in public purchases and customs
formalities.
<table><caption>Table 7: Distances between European countries</caption><thead><tr><th></th><th>FR</th><th>BE</th><th>NE</th><th>GE</th><th>IT</th><th>UK</th><th>IR</th><th>DK</th><th>GR</th><th>PO</th><th>SP</th></tr></thead><tbody><tr><td>FR</td><td><strong>239</strong><br>(47)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>BE</td><td>292<br>(189)</td><td><strong>34</strong><br>(21)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NE</td><td>376<br>(270)</td><td>104<br>(83)</td><td><strong>50</strong><br>(21)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GE</td><td>411<br>(258)</td><td>221<br>(103)</td><td>227<br>(113)</td><td><strong>176</strong><br>(26)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IT</td><td>550<br>(398)</td><td>608<br>(454)</td><td>674<br>(516)</td><td>515<br>(414)</td><td><strong>235</strong><br>(99)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UK</td><td>415<br>(212)</td><td>308<br>(194)</td><td>315<br>(220)</td><td>503<br>(296)</td><td>880<br>(594)</td><td><strong>137</strong><br>(48)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IR</td><td>593<br>(486)</td><td>544<br>(474)</td><td>547<br>(470)</td><td>738<br>(570)</td><td>1093<br>(880)</td><td>270<br>(291)</td><td><strong>81</strong><br>(73)</td><td></td><td></td><td></td><td></td></tr><tr><td>DK</td><td>696<br>(603)</td><td>435<br>(414)</td><td>346<br>(336)</td><td>392<br>(366)</td><td>861<br>(739)</td><td>577<br>(526)</td><td>778<br>(682)</td><td><strong>56</strong><br>(84)</td><td></td><td></td><td></td></tr><tr><td>GR</td><td>1218<br>(1306)</td><td>1236<br>(1314)</td><td>1271<br>(1348)</td><td>1076<br>(1236)</td><td>711<br>(911)</td><td>1533<br>(1487)</td><td>1762<br>(1778)</td><td>1310<br>(1404)</td><td><strong>134</strong><br>(228)</td><td></td><td></td></tr><tr><td>PO</td><td>811<br>(904)</td><td>1022<br>(1088)</td><td>1108<br>(1162)</td><td>1156<br>(1161)</td><td>1096<br>(1049)</td><td>970<br>(989)</td><td>931<br>(1023)</td><td>1449<br>(1497)</td><td>1713<br>(1778)</td><td><strong>91</strong><br>(157)</td><td></td></tr><tr><td>SP</td><td>566<br>(518)</td><td>786<br>(691)</td><td>879<br>(774)</td><td>876<br>(718)</td><td>751<br>(453)</td><td>836<br>(710)</td><td>893<br>(917)</td><td>1209<br>(1084)</td><td>1362<br>(1170)</td><td>396<br>(627)</td><td><strong>249</strong><br>(113)</td></tr></tbody></table>

Note: All distances in miles. Abbreviations for countries are the following: FR: France, BE: Belgium, NE: Netherlands, GE: Germany, IT: Italy, UK: United Kingdom, IR: Ireland, DK: Denmark, GR: Greece, PO: Portugal, SP: Spain. The figures in parentheses are the distances measured between the major city of each country. Internal figures in parentheses are equal to one quarter the distance to the nearest neighbor.
References

ANDERSON, M. AND S. SMITH, 1997, “Do National Borders Really Matter? Canada-US Regional Trade Reconsidered,” forthcoming in *Review of International Economics*.

ANDERSON, S., A. DE PALMA, AND J. THISSE, 1992, *Discrete Choice Theory of Product Differentiation*, Cambridge: MIT Press.

BERGSTRAND, J., 1985, “The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence” *Review of Economics and Statistics* 67(3):474–81.

BUIGUES, P., F. ILKOVITZ AND J-F. LEBRUN, 1990, “The Impact of the Internal Market by Industrial Sector : The Challenge for Member States” *European Economy Social Europe*, special edition.

COMMISSION OF THE EC, 1988, *Research on the Cost of Non-Europe*.

EATON, J. AND S. KORTUM, 1997, “Technology and Bilateral Trade” *National Bureau of Economic Research Working Paper* No. 6253.

ENGEL, C. AND J. ROGERS, 1996, “How Wide is the Border?” *American Economic Review* 86:1112–25.

FONTAGNÉ, L., M. FREUDENBERG, AND N. PÉRIDY, 1998, “Intra-Industry Trade and the Single Market: Quality Matters” *Centre for Economoic Policy Research Discussion Paper* No. 1952.

GEROSKI, P., 1991, “1992 and European Industrial Structure” in G. McKenzie and A.J. Venables, eds. *The Economics of the Single European Act* London : Macmillan Academic and Professional.

HANSON, G., 1998, “Market Potential, Increasing Returns, and Geographic Concentration” NBER Working Paper #6429.

HARRIGAN, J., 1996, “Openness to Trade in Manufactures in the OECD” *Journal of International Economics* 40 : 23-29.

HAVEMAN, J. AND D. HUMMELS, 1997, “What Can We Learn from Bilateral Trade? Gravity and Beyond,” Manuscript.

HEAD, K. AND J. RIES, 1998, “New Trade Versus Neoclassical Trade” Manuscript.

HELLIWELL, J., 1996, “Do National Borders Matter for Quebec’s Trade” *Canadian Journal of Economics*, 29(3):507–22.

HELLIWELL, J., 1997, “National Borders, Trade and Migration” NBER Working Paper #6027.

HILLBERRY, R., 1998, “Regional Trade and the ‘Medicine Line’: The Border Effect in US Commodity Flow Data,” *Journal of Borderlands Studies*, Fall.

KRUGMAN, P., 1980, “Scale Economies, Product Differentiation, and the Pattern of Trade,” *American Economic Review* 70:950–959.

LEAMER, E., 1997, “Access to Western Markets, and Eastern Effort Levels” in S. Zecchini, ed. *Lessons from the Economic Transition : Central and Eastern Europe in the 1990s* Dordrecht ; Boston : Kluwer Academic Publishers.
MCCALLUM, J., 1995, "National Borders Matter: Canada-US Regional Trade Patterns," *American Economic Review* 85:615-23.

NEVEN, D.J. AND L-H. ROLLERS, 1991, "European Integration and Trade Flows" *European Economic Review* 35(6): 1295-1309.

NITSCH, V., 1997, "National Borders and International Trade: Evidence from the European Union." Manuscript.

SMITH, A. AND A. VENABLES, 1988, "Completing the Internal Market in the European Community" *European Economic Review* 32: 1501-1525.

WEI, S-J., 1996, "Intra-National Versus International Trade: How Stubborn Are Nations in Global Integration" *National Bureau of Economic Research Working Paper* No. 5531.

WOLF, H., 1997, "Patterns of Intra- and Inter-State Trade" *National Bureau of Economic Research Working Paper* No. 5939.