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# Evaluating Trade's Competitive Effect from Overall Seller Concentration Estimates: The Case of the OECD Countries

Daniel Mirza

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Daniel Mirza. Evaluating Trade's Competitive Effect from Overall Seller Concentration Estimates: The Case of the OECD Countries. 2000. halshs-03774123

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Cahiers de la MSE

Evaluating Trade's Competitive Effect from Overall Seller Concentration Estimates: The Case of the OECD Countries

Daniel MIRZA

2000.108
# Evaluating Trade's Competitive Effect from Overall Seller Concentration Estimates: The Case of the OECD Countries

Daniel Mirza

TEAM university of Paris1-Panthéon- Sorbonne-CNRS,

106-112, Bd. de l'hôpital,

75647 Paris cedex 13.

Email: danmirza@univ-paris1.fr

First draft: June 2000; This draft: October 2000

This paper benefited from financial support of the Commissariat Général au Plan, Paris, France (N.4-98). I am indebted to Marie-Laure Cheval, Andrew Clark, Matthieu Crozet, Lionel Fontagné, Keith Head, Thierry Mayer, Claude Montmarquette and Leo Sleuwaegen for their useful remarks. I thank as well David Hummels for sending some of his bilateral freight costs data and Séverine Chédor for providing relative costs indicators between exporter and non-exporter firms from the EAE french database.
Abstract

This paper presents a new method that estimates the relative seller-concentration of an industry over both domestic and foreign sellers in 14 OECD countries, on the basis of a joint Structure-Performance and Brander-Krugman type model. Results suggest that *overall* seller-concentration is significantly lower than domestic concentration in only 7 out of 25 ISIC industries. In most other industries the two indexes appear to be very similar. However, foreign exporters contribute up to 40-50% in determining market structures, especially in Europe. Finally, in most of the highly-penetrated industries, relative seller-concentration did not decrease over the 1971-1994 period suggesting that openness did not enhance competition in OECD markets.

Resumé

A partir d'un modèle théorique combinant les théories de Structure-Conduite-Performance et de Brander-Krugman, on estime dans ce papier le degré de concentration d'une industrie qui prend en compte, à la fois, producteurs domestiques et étrangers dans 14 pays de l'OCDE. Les résultats suggèrent que l'indice de concentration *globale* est significativement inférieur à celui de la concentration domestique seulement dans 7 industries sur les 25 étudiés. Dans la plupart des autres industries, les deux indices semblent être similaires. Pourtant, le taux de contribution des exportateurs à la détermination de la structure de marché s'élèverait à près de 40-50% en Europe. Enfin, dans la plupart des industries les plus pénétrés sur le marché de l'OCDE, la concentration *globale* ne semble pas avoir baissé sur la période 1971-1994, suggérant que l'ouverture n'a pas accru la concurrence dans les pays de l'OCDE.

JEL codes: F12, F14, L13, L60

Keywords: Trade, Competition, Market Concentration

Mots-clés: Commerce, Concurrence, Concentration du Marché
# 1 Introduction

It is commonly believed by most policy makers that trade openness enhances competition since it increases the number of competitors in national markets<fnref n="1" />. This is true under similar cost characteristics of domestic and foreign competitors that serve the newly opened market. However, following the pioneering work of Clarke and Davis (1982) it should be kept in mind that if the new firms entering the market have lower costs than incumbents, and are thus more efficient, this market could become more concentrated<fnref n="2" />.

Several attempts have been made to test the competitive effects of trade. The most significant articles in this field are those that test imports as a market discipline hypothesis. Some economists such as Geroski (1982), Chou (1986), De Guellenick et al. (1988) or Levinsohn (1993) among others, showed that there is a negative relationship between imports and domestic producers' price-costs margins. Nevertheless, trade was found to be positively related to domestic mark-ups by Pougalatos and Sorensen (1981), as well as Stahlhammer (1991).

However, these studies respond only partly to the main question raised in this paper, since their sole objective is to investigate whether or not domestic producers suffer as a result of openness. This is what Hertel (1994) and Ianchovichina et al. (2000) explicitly call the *pro-competitive effects* of foreign trade. In this paper, we seek instead to determine the impact of openness on *overall* competition, among domestic and foreign sellers, in national markets. We assume that the relevant market for a typical product is not domestic but national, the boundaries of which are defined by total sales of both domestic and foreign producers. As a result, it would be more accurate to test whether trade affects the mean aggregate mark-up over all the sellers in the market. As Caves (1985) notes (page 378):

> "Foreign and domestic sellers together represent the supply side of the market, and the problem is how to formulate and test hypotheses about their joint influence."

Trade is often thought to affect mark-ups *via* its effect on seller-concentration<fnref n="3" />. A way of evaluating trade's *overall* competitive effect is then to compute the seller concentration index of the market, taking into account foreign exporters' market shares also<fnref n="4" />. Usually however, this

<footnote n="1">¹Markusen (1981) goes one step further by showing that the mere presence of 'potential' competitors has competitive effects.</footnote>

<footnote n="2">²Another argument related to the role of sunk costs is given by Sutton (1991) to explain the persistence of concentration in larger markets.</footnote>

<footnote n="3">³Cowling and Waterson (1976) were the first to show that for a given year (t), industry average mark-up is equal to the Herfindahl index when these two variables are expressed in terms of their counterpart at year (t-1).</footnote>

<footnote n="4">⁴Foreign Direct Investment is not explicitly taken into account in this article. Foreign industries producing in domestic markets are thus considered in this article to be part of domestic producers.</footnote>
*overall* seller-concentration index is impossible to measure without access to the distribution of market shares amongst all of the sellers. Actually, little relevant information is found from either 'supply side' or 'demand side' type databases. For instance, it is rare to find information concerning firms' export structure among different foreign markets<fnref n="5" />. Moreover, few databases in the literature contain market rather than production data<fnref n="6" />. Researchers interested in market issues usually gather information from different sources. Adams' (1980) and Sutton's (1991) work are part of the few attempts to collect market variables relative to all the sellers at the firm level for two specific industries where data was available (respectively Automotive and Food industries in different countries).

With respect to the lack of suitable firm-level data, the first aim of this article is then to provide a new industry-data based method that estimates, rather than computes, a relative seller-concentration index taking into account both domestic and foreign sellers in 14 national markets. As it indicates the degree of Relative *Overall Sellers' Concentration*, we call this index ROSC. The estimation strategy is undertaken in two main steps: First, having access to bilateral trade data, we can estimate a relative bilateral concentration index (RBC) from a joint Structure-Conduct-Performance and Brander-Krugman model<fnref n="7" />. Second, we derive the ROSC index from the sum of the RBC indexes weighted by the squared market share of each export country<fnref n="8" />. Results suggest that *overall* seller-concentration is significantly lower than domestic concentration in only 7 out of 25 ISIC industries, whereas in most other industries these indexes appear to be very similar.

Second, this method makes it possible to estimate foreign sellers' contribution to national market structure determination. We actually assume that market structure and international trade are simultaneously determined<fnref n="9" />. We show that foreign OECD exporters contribute up to 40-50% of European countries' market structure. However, their impact on Japan and the USA markets appear to be significantly lower.

Finally, relying on the determination of relative mark-ups by relative seller concentration

<footnote n="5">⁵Recently however, a database on individual US firms' transactions among different export destinations called PIERS (Port Import/Export Reporting Service) was exploited by Patterson and Abbott (1994).</footnote>

<footnote n="6">⁶The PIMS database, from the Strategic Planning Institute, is a rare example that gives market information. See Caves (1986) and Robinson and Chiang (1986) among others for more insights about this source of data.</footnote>

<footnote n="7">⁷The RBC index is basically a relative export concentration index specific to a country *i* selling to a country *j*.</footnote>

<footnote n="8">⁸We choose deliberately the Hirshmann-Herfindahl index to be a proxy of real market concentration as Golan *et al.* (1996) show that the latter contains more information about the concentration of an industry and thus, is preferable to other concentration measures 'if only one measure [of concentration] is to be used'.</footnote>

<footnote n="9">⁹the 'market structure' concept used here is more relevant to industrial organization than to international trade theory as its definition is closely related to market concentration, not to firms' entry nor to localization matters like in Horstmann and Markusen (1992).</footnote>
in theory, the third contribution of this article is to evaluate trade's competitive effects in the OECD markets between 1971 and 1994. We show that ROSC index did not decrease for most of the highly penetrated industries during the period, suggesting that the degree of competition among OECD countries did not intensify with openness. This result is compatible with Clarke and Davis' theory (1982) applied to an open economy as it is shown in section 3 below.

## 2 Empirical facts

In their structure-performance type models, economists usually consider openness as an exogenous shock that may discipline domestic producers. However, stylized facts seem to point to an endogenous relation between national market structures and foreign producers' behavior relative to these markets.

During the last three decades penetration rates increased significantly in each of the 14 OECD countries included in our sample. As suggested by median values reported in Table 1, most OECD countries were roughly two to three times more penetrated in 1994 than in 1971¹⁰. Note, in addition, that minimum and maximum industry penetration rates were significantly higher in the last decade comparatively to the beginning of the period. Following a country comparison, data show unsurprisingly higher values of penetration rates for smaller countries of the sample. Except for Japan and the United States, penetration values for half of the industries are quite high (over 20%), reaching sometimes more than 50% of the served market in 1994. These facts suggest that international trade might play a large role in determining the market structure of national industries.

In return, national market structures might affect both domestic and foreign producers' behavior. Assuming an oligopolistic framework, one way to see for instance whether market characteristics affect producers' price settings is to compare domestic and CIF export prices. If they are equivalent, this would be compatible with the hypothesis that international markets are integrated. But, if internal and export prices are different then determinants of prices would not be the same which would be consistent with the fact that markets are internationally segmented. Usually domestic and export price absolute values are not available. One can compare instead the evolution of price indexes.

If prices are the same everywhere then the evolution of export and domestic prices should be equivalent. Table 2 presents the maximum, median and minimum estimated response $\rho$ over

<footnote n="10">¹⁰Median, minimum and maximum statistics are computed over 3-4 digit ISIC industries.</footnote>
industries and export countries, of the bilateral export price index to the variation of domestic price index from a simple equation estimated for each couple of partner countries<fnref n="11" />.

$$bil_{k,t} = \rho \cdot dom_{k,t} + \kappa_k + \epsilon_{k,t} \quad (1)$$

The results show that the estimated $\rho$, significant at the 1% level, rarely approaches one. This suggests that the evolution of domestic prices do not match completely the evolution of bilateral export prices. If one assumes that goods that are traded and those that are consumed domestically are similar, then this result is consistent with the market segmentation hypothesis.

## 3 The Clarke and Davis model applied to an open economy

What is the extent of openness competitive effects on national markets? Following Clarke and Davis (1982) who modeled seller concentration for a representative market, we add the hypothesis of an open market where domestic and foreign competitors are distinguishable.

Assuming that the market of a homogeneous good is internationally segmented as in Brander and Krugman (1983), outputs and prices will be determined independently on each open market $j^{12}$. Commonly defined as a Lerner index, the mark-up obtained on that market from the maximization of the profit function of firm $n$ exporting from country $i$ is:

$$L_{ij,n} = \frac{p_j - \frac{c_{i,n}}{1-\tau_{ij}}}{p_j} = [(1 + \alpha_j)/\epsilon_j] * [x_{ij,n}/X_j] \quad (2)$$

where $\alpha_j$ and $\epsilon_j$ stand respectively for the conjectural variation and the price elasticity of demand that prevail in country $j^{13}$. The $1 - \tau_{ij}$ parameter represents the traditional iceberg trade cost. It is positive but lower than unity. Consequently, $\tau_{ij}$ stands for the freight cost rate in value terms or the share of imports that served for paying freight costs ($0 < \tau_{ij} < 1$). The variables $x_{ij,n}$, $p_j$ and $c_{i,n}$ represent respectively the quantity exported by firm $n$ to the market $j$, the price and the firm's marginal cost of production that prevail on that market.

<footnote n="11">¹¹For each couple of countries $(i, j)$, where $i \neq j$, the bilateral price index $bil_{k,t} = p_{ijk,t}/p_{ijk,90}$ and the domestic price index $dom_{k,t} = p_{iik,t}/p_{iik,90}$ both vary with industry $k$ and time $t$. The fixed effect $\kappa_k$ captures the industry heterogeneity while $\epsilon_{k,t}$ is the residual.</footnote>

<footnote n="12">¹²The hypothesis of segmentation between national instead of regional markets is arguable. However, Ottaviano, Tabushi and Thisse (1999) report that the existence of border effects estimated by recent studies is consistent with our assumption. See for instance Mc Callum's (1995) and Head and Mayer's (2000) work conducted respectively on North American and European markets.</footnote>

<footnote n="13">¹³The conjectural variation parameter $\alpha$ varies between -1 and $N_j - 1$ in order to allow for a set of strategic behaviors upon the $N_j$ firms selling in the market. The former value corresponds to a perfectly competitive market while the latter suggests a Cartel behaviour when $N_j > 1$, or a monopoly when only one firm serves the market. A Cournot competition is assumed when $\alpha = 0$.</footnote>
$X_j = \sum_i \sum_n x_{ij,n}$ is the total quantity sold in the market.

For sake of clarity, domestic producers who serve the local market $j$ will be considered as 'exporters' to their own market so that in this case we have $i = j, \forall i, j \in (1...J)$. Moreover, marginal costs of exporting from $i$ to $j$ will be expressed as

$$c_{ij,n} = \frac{c_{i,n}}{1 - \tau_{ij}} \qquad (3)$$

From equation 2, the expression of the exporter's market share is:

$$\left(\frac{x_{ij,n}}{X_j}\right) = \left(\frac{\epsilon_j}{1 + \alpha_j}\right) \left(1 - \frac{c_{ij,n}}{p_j}\right) \qquad (4)$$

When summing this expression over all the competitors $N_j = \sum_i N_{ij}$, where $N_{ij}$ expresses the number of exporters from $i$ to $j$, one obtains the price expression at equilibrium:

$$p_j = \epsilon_j \frac{\sum_i \sum_n c_{ij,n}}{\epsilon_j N_j - (1 + \alpha_j)} \qquad (5)$$

If we assume that the number of firms is sufficiently high then the price expression at equilibrium would be close to the mean exporting costs over domestic and foreign producers. This implies that a new foreign firm's entry is associated with a decrease in price equilibrium when its marginal cost to export is lower than the mean costs of incumbents.

As in Clarke and Davis article, these two prior equations 4 and 5 provide the Herfindahl expression at equilibrium:

$$H_j = \sum_{i=1}^{I} \sum_{n=1}^{N} \left( \frac{x_{ij,n}}{X_j} \right)^2 \qquad (6)$$

$$= - \left( \frac{\epsilon_j}{1 + \alpha_j} \right)^2 N_j + 2 \left( \frac{\epsilon_j}{1 + \alpha_j} \right) + \left( 1 - N_j \frac{\epsilon_j}{1 + \alpha_j} \right)^2 \frac{\sum_i \sum_n c_{ij,n}^2}{(\sum_i \sum_n c_{ij,n})^2} \qquad (7)$$

Assuming that firm specific costs are normally distributed in a given country, the marginal cost function of production takes the form:

$$c_{i,n} = mc_i + \zeta_{i,n} \qquad (8)$$

where $mc_i = E(c_{i,n}) = \sum_n \frac{c_{i,n}}{N_{ii}}$, and $Var(c_{i,n}) = \sigma_{mc_i}^2 = \frac{\sum_n \zeta_{i,n}^2}{N_{ii}} = \frac{\sum_n c_{i,n}^2}{N_{ii}} - \bar{c}_i^2$. Hence, we can express the overall average and variance of costs in the $j$ market respectively by:
$$ \bar{c}_j = \sum_i \frac{N_{ii} mc_i}{N_j (1 - \tau_{ij})} \qquad (9) $$

and

$$ \begin{aligned} \sigma_{c_j}^2 &= \frac{\sum_i \sum_n c_{ij,n}^2}{N_j} - \bar{c}_j^2 \\ &= \left[ \sum_i \frac{N_{ii}}{N_j (1 - \tau_{ij})^2} \sigma_{mc_i}^2 \right] + \left[ \sum_i \frac{N_{ii}}{N_j (1 - \tau_{ij})^2} mc_i^2 - \bar{c}_j^2 \right] \\ &= (firm)var_{c_j} + (cty)var_{c_j} \end{aligned} \qquad (10) $$

The overall variance of costs from equation 10 above is thus a linear combination of an inter-firm dimension variance and a between-country dimension variance of mean costs. Hence, the inter-firm variance can be interpreted as firms' competitive advantage variance whereas the between-country variance can be thought to be countries' cost advantage variance.

Replacing the above expression in the 'Herfindahl index of costs' term $\frac{\sum_i \sum_n c_{ij,n}^2}{(\sum_i \sum_n c_{ij,n})^2}$, and substituting into equation 7 one can find the Clarke and Davis Herfindahl expression, but now applied to an open market:

$$ H_j = \frac{1}{N_j} + \frac{1}{N_j} \left(1 - N_j \frac{\epsilon_j}{1 + \alpha_j}\right)^2 \left[ \frac{(firm)var_{c_j} + (cty)var_{c_j}}{c_j^2} \right] \qquad (11) $$

The market concentration is thus jointly determined by the number of competitors, their behavior via $(\alpha_j)$, the price-elasticity of demand as well as the coefficients of variation of costs $\frac{(firm)var_{c_j}}{c_j}$ and $\frac{(cty)var_{c_j}}{c_j}$. The latter equation suggests that in an open economy the costs differentials among firms as well as the differentials of mean costs between countries affect both positively the concentration index. Hence following openness and keeping all other things equal, the concentration effect of costs differentials could prevail on the competitive effect represented by the increased number of firms serving the market. In addition, the more average cost varies between partner countries, the more national markets would be concentrated. On the opposite, when costs of partner countries are similar, these markets should be more fragmented.

Finally, keeping in mind the price and concentration equations 5 and 11, one of the most interesting features of this model is its prediction that lower prices consecutive to openness could be consistent with higher concentration levels. Actually, the negative effect of entry on prices could be due to lower cost or higher performance of new firms that enter the market rather than higher competition.
# 4 Estimation strategy of the seller-concentration index

Empirical economists do not usually observe costs distribution among all the foreign and domestic sellers, their number, their behavior as well as the price elasticity of demand to estimate the seller concentration index ($H_j$). We develop in what follows an alternative strategy of estimation in the following steps. First, the concentration index need to be expressed in terms of bilateral concentrations:

$$ 
\begin{align*}
H_j &= \sum_{i=1}^{I} \sum_{n=1}^{N} \left( \frac{x_{ij,n}}{X_j} \right)^2 \\
&= \sum_{i=1}^{I} \left[ \sum_{n=1}^{N} \left( \frac{x_{ij,n}}{X_{ij}} \right)^2 \left( \frac{X_{ij}}{X_j} \right)^2 \right] \\
&= \sum_{i=1}^{I} H_{ij} \left( \frac{X_{ij}}{X_j} \right)^2 \tag{12}
\end{align*}
$$

where $H_{ij} = \sum_n (x_{ij,n}/X_{ij})^2$ stands for the bilateral concentration or bilateral Herfindahl index and $X_{ij}$ are total firms exports from country $i$ to $j$. Equation 12 shows that in an open economy, the seller concentration of the market can be expressed by the mean value over all the bilateral export concentrations weighted by the corresponding squared country's market share. Put differently, the overall industry Herfindahl index expresses the weighted mean of the bilateral Herfindahl indexes.

As we observe market shares, an alternative method to estimate the seller concentration index would be then to estimate only the bilateral Herfindahl indexes. Hence, reconsidering equations 2 and 8, summing over all the firms exporting from $i$ to $j$, and weighting by $x_{ij,n}/X_{ij}$, gives the bilateral Lerner index expression at the industry level:

$$ L_{ij} = \frac{p_{ij}X_{ij} - [mc_iX_{ij} + \sum_n (\zeta_{ij,n}x_{ij,n})]/(1 - \tau_{ij})}{p_{ij}X_{ij}} = \frac{1 + \alpha_j}{\epsilon_j} H_{ij} \frac{X_{ij}}{X_j} \quad (13) $$

This expression shows that industry export mark-ups are related to industry export concentration, elasticity of demand, conjectural variation and the export country's market share. Patterson and Abott (1994) obtain a similar export mark-up formulation. However, they do not include freight costs when computing mark-ups, since they use FAS (free alongside ship) unit values as export price proxies and average domestic price as a marginal cost proxy. Since one of the factors that may lead to international market segmentation is precisely the existence of
transport costs we account for these specific trade costs in our export mark-up relation (Green-
hut, Ohta, and Sailors 1985). Dividing this expression by its counterpart relative to domestic
sellers and log-linearizing gives:

$$
\log(L_{ij}/L_{jj}) = \log[H_{ij}/H_{jj}] + \log[X_{ij}/X_{jj}] \quad (14)
$$

This expression sweeps out the elasticity of demand and the conjectural variation variables.
The empirical counterpart of equation 14 that accounts for time variation would be:

$$
\log \left( \frac{L_{ij,t}}{L_{jj,t}} \right) = \psi_{i/j} + \log \left( \frac{X_{ij,t}}{X_{jj,t}} \right) + u_{ij,t} \quad (15)
$$

We have noted log[H<sub>ij,t</sub>/H<sub>jj,t</sub>] = ψ<sub>i/j</sub> + u<sub>ij,t</sub> with u<sub>ij,t</sub> following a normal distribution with mean 0 and variance σ<sub>u</sub>. Hence, exp ψ<sub>i/j</sub> can be interpreted as the average of the relative bilateral concentration over the considered period and can thus be estimated by an export country specific effect i relative to domestic sellers in j.

This equation reminds us of the structure-conduct-performance tests that are usually con-
ducted by industrial economists. However, most of the related literature was interested in es-
timating the conduct and the price-elasticity parameters while seller concentration was usually
approximated by concentration ratios, or Herfindahl indexes that do not account for foreign
sellers. Being aware of the joint determination of mark-ups and concentration at equilibrium
pointed out by Clarke and Davis, some economists use simultaneous equations to solve the prob-
lem. Here instead, while equation 15 sweeps out the ϵ<sub>j</sub> and α<sub>j</sub> parameters, it is the log of relative
bilateral concentration index that is estimated jointly with the dependent variable (log of rela-
tive mark-ups) without the necessity of using simultaneous equations techniques. Expression 14
is a structural-form equation. Consequently, we preferred to constrain to unity the parameter
related to the market share variable log[X<sub>ij,t</sub>/X<sub>jj,t</sub>].

Once $\psi_{i/j}$ is estimated, the Relative Bilateral Concentration index (RBC) is inferred by
computing:

$$
\left( \frac{H_{ij}}{H_{jj}} \right) = \exp(\psi_{i/j}) \qquad (16)
$$

It is to be noted that econometrics are not capable to estimate the absolute level of bilateral
concentration *H*<sub>ij</sub>. However, by estimating RBC indexes we are able to infer the relative *overall*
seller concentration index (ROSC) as:
$$[H_{j,t}/\widetilde{H}_{jj}, t] = \sum_{i=1}^{I} [H_{ij}/\widetilde{H}_{jj}] * (X_{ij,t}/X_{j,t})^2 \quad (17)$$

Note that $(X_{ij,t}/X_{j,t})$ vary in time. Hence, one can follow the ROSC evolution during the considered period in order to see whether it declines with increased penetration.

An additional interesting feature of this method is that we can infer the contribution of foreign exporters to the market seller concentration. Let $FC_{j,t}$ represents the foreign contribution to market structure determination. One can easily estimate this variable from the preceding estimations as:

$$\begin{aligned} FC_{j,t} &= \frac{\sum_{h \neq j} H_{hj} (X_{hj,t}/X_{j,t})^2}{H_{j,t}} \\ &\approx \frac{\sum_{h \neq j} (\widetilde{H}_{hj}/\widetilde{H}_{jj}) (X_{hj,t}/X_{j,t})^2}{(H_{j,t}/\widetilde{H}_{jj}, t)} \end{aligned} \quad (18)$$

Alternatively, domestic contribution to seller concentration can be obtained by computing $DC_{j,t} = 1 - FC_{j,t}$.

# 5 Data availability and estimation

Activity as well as bilateral trade data are needed for the equation 15 to be tested, and expressions 17 and 18 to be inferred. I constitute a panel of 14 countries that are also trading partners, 25 industries and 24 years from the STAN (OECD) and FLUBIL (INSEE) databases. The STAN annual database from the OECD has provided us with manufacturing activity data such as values of production, value added in current and constant prices, total imports and exports, wages and salaries as well as the number of employees from 1971 to 1994. The domestic sales are expressed as trade with self variable $X_{jj}$ and are computed as the difference between output and total exports. As a matter of fact, only countries and industries that did not show apparent problems when calculating the trade with self indicator were kept in this study<fnref n="14" />.

FLUBIL database from the French Statistical Institute INSEE provides trade data for about 5,000 products classified in the SITC product nomenclature. Conversion tables has been made between SITC (product) and ISIC (industry) nomenclatures to get bilateral trade values and

<footnote n="14">¹⁴For instance, Belgium and the Netherlands known as important re-exporters, were both removed from the sample since their trade with self variable appeared frequently with a negative sign.</footnote>
prices for the STAN industries and countries used for this study<fnref n="15" />. Calculations were conducted on imports and unit value indexes on the basis of import declarations rather than on that of export declarations. This implies that imports are valued at prices including insurance and freight costs<fnref n="16" />.

Although having access to the above cited variables for the relation 15 to be tested, some remaining factors that enter the cost function of exports are not observable. Typically, assuming constant return to scale production functions, marginal costs of production at the industry level can be represented by observable average costs<fnref n="17" />. We assume moreover that capital is a fixed cost variable that do not enter the mark-up equation. This enable us to define average costs of production $mc_{i,t}$ as the ratio of labour costs $w_{ij,t}L_{ij,t}$ to value added data denoted by $Y_{i,t}$. Now obviously, value added is observed in value rather than in volume terms in the data, which is not rigorously compatible with theory. In order to match the mark-up empirical expression with its theoretical counterpart we note $V_{i,t} = p_{i,t}Y_{i,t}$, the variable that designates value added as it is observed in the data. Hence, considering equation 13 and assuming from the latter that $(\frac{\sum_n \zeta_j x_{ijn,t}}{(1-\tau_{ij})p_{j,t}X_{ij,t}})$ is sufficiently small to be neglected hereafter, then the Lerner equation can be written:

$$L_{ij,t} = 1 - \frac{mc_{i,t}}{(1 - \tau_{ij})p_{j,t}} = 1 - \frac{w_i L_i}{V_{i,t}} \frac{1}{1 - \tau_{ij}} \frac{p_{i,t}}{p_{j,t}} \quad (19)$$

Three components constitute the bilateral price cost margins expression: average production costs of exporters, transport costs and relative prices. As STAN database provides industry average costs rather than exporters ones, we assume that they are equivalent<fnref n="18" />.

The second component of the bilateral mark-up $(1 - \tau_{ij})$, is not directly provided by the data.

<footnote n="15">¹⁵The sum of bilateral values proved to be quasi identical to STAN total trade values (imports as well as exports).</footnote>

<footnote n="16">¹⁶Prices observed may also include tariff barriers that were neglected by the theoretical model. This is not a strong assumption since we work on a sample including European countries in majority (11 out of 14) with no or little tariffs between them during the given period. In reality some tariffs are applied for few lines of products only, observable at very disaggregated industry levels, between the three considered blocks: Europe, North America and Japan. However, we consider that these tariffs would be rather small regarding the relatively high aggregated industries we observe.</footnote>

<footnote n="17">¹⁷Several authors estimate cost and price-type equations simultaneously (see Bresnahan (1989) for a review of this literature). An identical cost function implies however identical technology between the competitors that serve the market. Here however, it would be too risky to follow the same method as technology might not be the same between the countries of our sample (see Helpman (1998) for a brief review of the literature on inter-country technology differences).</footnote>

<footnote n="18">¹⁸It is to be noted that some studies at the firm level show that exporters outperform non exporters in terms of mean costs and productivity (Bernard and Jensen 1996). However, provided with the 1990 French Enquête Annuelle d'Entreprises (EAE) firm-level database, we find similar values for exporters' and overall industry average costs. The reason is that exporters outweighs significantly non-exporters within industries in France and it is not a strong assumption to consider that it is the case for the remaining countries of our sample. Comparison data are available upon request.</footnote>
However, some simple regressions enable to infer this variable. Having access to a weighted freight cost variable relative to imports for the USA ($\tau_{i,usa}$) 19 and of weighted geographical distances between the USA and its partners ($d_{i,usa}$) 20 , the first step is to run the following fixed effects regression for 1990 pooled on the export country $i$ and the industry $k$:

$$ \operatorname{Log}(\tau_{i,usa,k}) = \delta\operatorname{Log}(d_{i,usa}) + \alpha_k + \xi_{i,usa,k} \quad (20) $$

then to estimate freight costs share $\tau_{ij,k}$ as

$$ \tilde{\tau}_{ij,k} = \exp\left(\tilde{\delta}\operatorname{Log}(d_{ij}) + \tilde{\alpha}_k\right) \quad (21) $$

From equation 21, estimations of both international ($\tau_{ij,k}$) and within country freight costs ($\tau_{ii,k}$) are driven, since within country weighted distance is also computed as in Head and Mayer (2000) article. Table 3 presents the mean freight costs weighted across industries for the 14 countries in our sample$^{21}$. The import distribution is not the same for each partner among industries which causes the weights to differ between countries when computing weighted average freight costs. This explains why the results shown in the table are not symmetric within couple of partners.

Finally, accounting for the price expression at equilibrium (equation 5) as well as the bilateral cost expression (equation 3), and assuming $\epsilon_i = \epsilon_j = \epsilon$, $\alpha_i = \alpha_j = \alpha$, and $N_i = N_j, \forall i$ and $j$, the relative price component of the export-mark-up determination may be written as:

<footnote n="19">¹⁹Data is provided by Hummels (1998) who computes freight costs at the 2-digit SITC industry level. Whereas some of the industries that are 2-digit SITC match directly with some at 3 or 4-digit ISIC nomenclature that we use, we were constrained to compute simple means estimates to match SITC data with the ISIC-OECD for the remaining industries.</footnote>

<footnote n="20">²⁰see Head and Mayer (2000) for method of calculation.</footnote>

<footnote n="21">²¹Estimated freight costs seem to be rather low between trading partners but they show to be very close to the ones computed by Hummels (1998) for the USA and its partners for comparable distances. For instance, freight costs between Canada and its European partners are very close to those relative to USA vis-à-vis the same partners. Moreover, within country freight costs are particularly significant in the US and Canada, most of times reaching higher values than between European countries freight costs which is very plausible due to very large internal distances between main cities in the north American countries. Note that one of the few shortcomings of this freight costs estimations is that the effect of distance on transport costs is assumed to be the same whatever the two trading partners considered and thus do not depend on the type of transport between countries. For instance, most of merchandise transportation is done via land type transporters such as trucks or trains within European countries whereas it is obviously not the case between a European country and a north American partner where most of commodities are shipped by sea. However, if freight costs are evaluated with some error, it appears to be very small relative to total costs that are supported as estimated freight costs relative to imports account for 1 to 2% on average within regions (North America, Europe) and up to 8% between regions.</footnote>
$$ \frac{p_{j,t}}{p_{i,t}} = \frac{\sum_h \sum_n \frac{c_{h,n}}{1-\tau_{hj}}}{\sum_h \sum_n \frac{c_{h,n}}{1-\tau_{hi}}} = \frac{\sum_h \frac{N_{h,j}}{N_j} \frac{mc_h}{1-\tau_{hj}}}{\sum_h \frac{N_{h,j}}{N_i} \frac{mc_h}{1-\tau_{hi}}} \quad (22) $$

with $h \in (1...J)$ and $mc_h = \sum_n \frac{c_{h,n}}{N_h}$ representing the average cost of production in the exporting country $h$. Hence, relative prices can be evaluated as the relative sum of costs weighted by the size and freight of each exporting country. This expression is quite similar to a relative market potential equation since it says that prices must be relatively low in countries located near central regions, both in terms of geography and economic size$^{22}$. Assuming that the relative size $N_{h,j}/N_j$ can be approximated by relative exports to domestic sales, relative price proxy can then be easily computed.

# 6 Econometric specification

Bilateral price cost margins that we compute, appear to be negative for about 5% of the observations. However, this is mainly due to the measurement of mean production costs since they are approximated by $w_iL_i/V_i$. Actually, STAN documentation reports that labour costs could exceed value added in some cases when the industry makes losses or when it receives significant subsidies which overestimates the above term$^{23}$. Moreover, some countries do not trade with each other in some particular industries$^{24}$. In the former and the latter cases, we shall consider that trade must not be profitable and hence assume that export mark-ups approach zero on average. As we treat with log functions, we approximate zero values by 0.0001$^{25}$. Taking this constraint into account, the fixed effect Tobit model appears to be the most natural method to estimate equation 15 that is convenient to re-express as$^{26}$:

<footnote>$^{22}$See Krugman (1992) for more development on the market potential concept.</footnote>

<footnote>$^{23}$Variables relative to bilateral trade features such as relative prices or freight costs could be overestimated and hence lead to negative export mark-ups. We computed however traditional production mark-ups and found negative signs as well for most of the same questionable observations which tends to prove that the main biases are due to mean production rather than export costs measurement.</footnote>

<footnote>$^{24}$This concerns however less than 1% of the data.</footnote>

<footnote>$^{25}$Note that the regressions' parameters are not sensitive to changes in this zero proxy value.</footnote>

<footnote>$^{26}$Since we pool data over a relatively long period (T=24 years), we consider that $T \to \infty$ which makes the selected model produce consistent fixed effects estimators $\psi_{i/j}$. See Baltagi (1995) for discussion on fixed effects Tobit models and Hsiao (1986) for a proof of the consistency of the estimators from the fixed effects probit-type models when T tends to infinity.</footnote>
$$ \text{Log} \frac{L_{ij,t}}{L_{jj,t}} = \begin{cases} \psi_{i/j} + \log\left(\frac{X_{ij,t}}{X_{jj,t}}\right) + u_{ij,t} & \text{if } L_{ij,t} > 0, \forall i \neq j \\ \text{Log}(0.0001) & \text{otherwise} \end{cases} \quad (23) $$

It is to be noted that the observations where the denominator was negative were removed from the sample $L_{jj,t} < 0^{27}$. We found it economically more relevant to present results for one tailed rather than two tailed bounded distributions otherwise the upper-bound value would have been applied arbitrarily.

Tables 4 and 5 present minimum and maximum estimates of the log of RBC indexes, expressed as $\psi_{i/j}$, in the seven main national markets of our sample$^{28}$. Since the estimated $\psi_{i/j}$ is higher than zero for most of the minimum, and a fortiori maximum values, then the results suggest that estimated bilateral export concentrations are much higher than domestic ones ($\exp \overline{\psi_{i/j}} > 1$). For some cases however, the log of RBC is negative, and thus $\exp \overline{\psi_{i/j}} < 1$, suggesting that export concentration relative to some countries is smaller than the domestic concentration of the host country (see USA exporters vis-a-vis Canada in the Industrial Chemicals, Pottery and China, Machinery Equipment and Vehicles).

RBC estimates are not so important to analyze from the theory point of view, as the main purpose on which one might focus should be the overall concentration. However, a finer look on these estimates, and especially the minimum and maximum values, can provide us with some information on the joint country-comparative and firms-competitive advantages in a particular industry. Consider for instance an export country such as Greece to make this point clearer. Results show that in most capital-intensive or high-technology industries and independently from the host country, Greece's relative export concentration is the highest (see the Machinery and Equipment, Radio-TV and Telecom, Electrical Machinery as well as the Motor Vehicles industries results). This is to say that when Greece succeed in exporting to an important OECD country within the cited industries, this is because of a few performing firms that are capable to compete in foreign markets and which competitive advantages overstates their country's comparative disadvantage. As the number of these Greek firms must be small relative to the one of the domestic firms in the host countries then this ends up with a log of relative concentration $\psi_{i/j}$ that is a lot higher than zero. Notice moreover, that the RBC index seems to be negatively

<footnote>$^{27}$For some industries in Austria, Finland, Greece, Norway, Portugal and Sweden, we could not obtain estimates for the relative seller concentration index as domestic mark-ups computed from the STAN database turned out to be negative.</footnote>

<footnote>$^{28}$Most of these estimates are significant at the 1% level. When they are not, then this suggests that export concentration relative to an export country is similar to the one of the import country on its domestic market as $\exp \overline{\psi_{i/j}}$ equals 1.</footnote>
correlated with geographical distance between two countries (Greece's export concentration is lower when exporting to Italy then when exporting to Japan). This is not surprising since distance is a barrier to trade keeping only few firms, the most performing ones, to reach the most distant countries.

# 7 Seller concentration and foreign contribution

Accounting for RBC estimates and export countries' observed market shares, we can infer an estimation for the relative overall seller concentration (ROSC) from:

$$[H_{j,t}/\widetilde{H}_{jj}, t] = \sum_{i=1}^{I} [H_{ij}/\widetilde{H}_{jj}] * (X_{ij,t}/X_{j,t})^2 \quad (24)$$

Table 6 presents median values of ROSC index over the 1971-1994 period. At a first view, seller concentration seem to be lower than domestic concentration ($H_{j}/\widetilde{H}_{jj} < 1$). This tends to confirm the traditional view that not controlling for foreign sellers biases upward the computation of industry concentration indexes. Mainly, in seven industries (Industrial Chemicals, Glass, Drugs and medecines, Machinery, Radio, TV and telocom., Electrical machinery and Vehicles), the estimated ratio of seller to domestic concentration is significantly lower than unity for most countries. In the four last cited industries, seller concentration is sometimes five times lower than that of domestic concentration which suggests that market structure in these industries is mostly determined by foreign exporters. On the opposite however, and in some particular industries such as Pottery and China in Germany, Spain and UK, it appears that concentration indexes are two to five times higher when accounting for openness.

Apart from some extreme cases cited above, and although usually lower than unity, estimated ROSC indexes remain very close to one. Two hypothesis can explain such a persistent result: This could be due to the relative closeness of the observed country when foreign market shares are very small relative to domestic ones. In this case, market structure is mostly determined by domestic sellers. However, if we refer to section 3, the additional firms entering the market might be more efficient or simply, belong to countries where costs are lower which is an alternative explanation for why one could obtain high ratios of seller to domestic concentration.

In order to see which of the two hypothesis is true to explain the ROSC tendency to unity, we computed the relative contribution of foreign exporters to seller concentration determination in each of the 14 countries from equation 18. From Table 7, representing median values of foreign contribution to market structure determination over the period, one can see that for most of the
countries the contribution of foreign exporters is very high. Some basic statistics may show that in open small countries such as Sweden or Norway, foreign contribution exceeds 50% on average. Moreover, exporters account for nearly 45% of seller concentration in the United Kingdom and Canada, while in France and Germany the average contribution is around 35% which is still very high. However, market structures in countries like Japan do not seem to be altered by foreign exporters. Hence, domestic concentration in the latter is representative of overall concentration. This reinforces the well known idea that Japan is relatively hermetic to other OECD countries' goods (Harrigan 1996). Finally, in some countries such as the USA or Spain, foreign contribution seems to depend specifically on industry characteristics.

## 8 Evolution of relative seller concentration

At one point in time and in an open OECD economy, overall concentration shows up to be usually but not systematically lower than the domestic one in most industries.

This finding does not inform us however on the dynamic effects of trade. As import penetration increased substantially in each of the OECD countries during the period, then following the standard intuition that trade induces more competition, the estimated ROSC index is expected to decrease over time.

In order to check the validity of this assumption, we dressed country figures for eight of the most penetrated industries in the OECD countries during the 1971-1994 period<fnref n="29" />. Figures 1, 2 and 3 report the concentration evolution for these industries within which we find Footwear (324), Textiles (321), Radio, TV and Telecommunications (3832) but also Motor Vehicles (3843). Apart from the United Kingdom, most values of relative seller concentration were below or equal to unity during the whole considered period. However, it is surprising to see that for each given country the ROSC index did not decline for most of the highly penetrated industries between the beginning and the end of the period.

However, in most countries the industry ROSC curves seem to be decreasing in the seventees. But between the eightees and the ninetees two types of curves can be distinguished. The first type draws a hat-curve where the maximum is reached in the middle of the eightees and attending again initial levels at the end of the period. This is typically the case of most of the industries in Germany, France, Norway, Portugal, Sweden and UK. As for the second type, the estimated relative concentration remain constant or grow very slowly between 1980 and 1994. This is

<footnote n="29">²⁹Penetration rates growth in these industries reached in most OECD countries values around 300 to 500% between 1971 and 1994. Results are available upon request.</footnote>
mainly the case of industries in Finland, USA or Japan. Notice that in the latter country, relative seller concentration remained very close to unity during the whole period.

Do these results suggest that growing openness was not associated to more competition in the OECD markets? Actually, one has to be careful about this conclusion and keep in mind that the ROSC index expresses industry *overall* concentration in terms of the domestic one. Hence, it could be possible that openness have driven some inefficient domestic firms out of the market, increasing in consequence domestic concentration more than the decrease in *overall* concentration. This is however a strong assumption as the firms that are swept out from the market are typically those that have weak market shares and thus do not really contribute to domestic concentration determination.

## 9 Conclusion

In this article, a new method has been developed to estimate the industry seller-concentration among domestic and foreign sellers in each of 14 OECD countries. Results have suggested that seller-concentration is usually although not necessarily lower than domestic concentration as relative seller concentration often approached unity. However, in most national markets foreign exporters were found to contribute significantly to market structure determination (up to 40-50% of national market structure). Moreover, the evolution of the estimated seller concentration index did not decline for most of the highly penetrated industries in the OECD countries.

These results seem to be compatible with the Clarke and Davis adapted theoretical model as we have showed that the negative impact of trade on concentration, *via* an increase in the number of competitors, may be compensated by a positive effect due to foreign firms' competitive and countries' cost advantages.

If one believes that relative aggregate mark-ups over domestic producers and foreign exporters are literally explained by relative seller concentration indexes following the structure-conduct-performance paradigm, then international trade does not seem to have reduced market power in most of the industries in the OECD countries.
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<table><caption>Table 1: Statistics on industry penetration rates in 14 OECD countries</caption><thead><tr><th rowspan="2">country</th><th colspan="3">1971</th><th colspan="3">1982</th><th colspan="3">1994</th></tr><tr><th>MAX</th><th>MEDIAN</th><th>MIN</th><th>MAX</th><th>MEDIAN</th><th>MIN</th><th>MAX</th><th>MEDIAN</th><th>MIN</th></tr></thead><tbody><tr><td>AUT</td><td>79.1</td><td>18.81</td><td>2.56</td><td>80.06</td><td>29.12</td><td>3.3</td><td>79.06</td><td>47.65</td><td>4.5</td></tr><tr><td>CAN</td><td>63.21</td><td>17.25</td><td>4.33</td><td>84.42</td><td>20.74</td><td>6.56</td><td>86.02</td><td>45.02</td><td>13.47</td></tr><tr><td>DEU</td><td>39.45</td><td>12.13</td><td>3.56</td><td>49.95</td><td>18.62</td><td>5.97</td><td>255.05</td><td>26.47</td><td>7.21</td></tr><tr><td>ESP</td><td>55.26</td><td>6.53</td><td>0.43</td><td>27.62</td><td>7.08</td><td>1.79</td><td>60.74</td><td>25.94</td><td>3.57</td></tr><tr><td>FIN</td><td>68.76</td><td>28.17</td><td>2.56</td><td>63.2</td><td>35.16</td><td>3.09</td><td>81.35</td><td>41.9</td><td>4.76</td></tr><tr><td>FRA</td><td>30.95</td><td>13.66</td><td>5.81</td><td>44.74</td><td>24.05</td><td>2.86</td><td>71.3</td><td>32.82</td><td>5.48</td></tr><tr><td>GBR</td><td>34.72</td><td>9.75</td><td>2.31</td><td>43.76</td><td>23.69</td><td>3.25</td><td>61.08</td><td>34.05</td><td>5.5</td></tr><tr><td>GRC</td><td>86.16</td><td>18.77</td><td>0.12</td><td>76.46</td><td>23.75</td><td>4.54</td><td>94.28</td><td>43.86</td><td>13.61</td></tr><tr><td>ITA</td><td>63.97</td><td>10.72</td><td>0.61</td><td>60.73</td><td>15.58</td><td>1.37</td><td>79.25</td><td>19.33</td><td>2.83</td></tr><tr><td>JPN</td><td>11.73</td><td>2.25</td><td>0.22</td><td>13.01</td><td>3.2</td><td>0.62</td><td>23.84</td><td>5.08</td><td>0.72</td></tr><tr><td>NOR</td><td>79.3</td><td>48.82</td><td>6.31</td><td>81.01</td><td>47.87</td><td>8.01</td><td>93.51</td><td>56.96</td><td>9.3</td></tr><tr><td>PRT</td><td>79.4</td><td>13.69</td><td>0.77</td><td>56.95</td><td>15.7</td><td>0.76</td><td>97.79</td><td>42.08</td><td>7.38</td></tr><tr><td>SWE</td><td>54.21</td><td>29.03</td><td>3.38</td><td>77.35</td><td>44.96</td><td>4.57</td><td>98.47</td><td>54.42</td><td>7.42</td></tr><tr><td>USA</td><td>19.08</td><td>5.36</td><td>0.59</td><td>36</td><td>8.6</td><td>0.84</td><td>73.08</td><td>14.73</td><td>1.46</td></tr></tbody></table>
<table><caption>Table 2: Response of bilateral export price to the variation of domestic price</caption><thead><tr><th>CTY</th><th>MAX</th><th>MEDIAN</th><th>MIN</th></tr></thead><tbody><tr><td>AUT</td><td>0.863</td><td>0.743</td><td>0.685</td></tr><tr><td>CAN</td><td>0.969</td><td>0.834</td><td>0.777</td></tr><tr><td>DEU</td><td>0.939</td><td>0.732</td><td>0.57</td></tr><tr><td>ESP</td><td>1.198</td><td>0.7</td><td>0.615</td></tr><tr><td>FIN</td><td>0.958</td><td>0.875</td><td>0.81</td></tr><tr><td>FRA</td><td>0.896</td><td>0.804</td><td>0.49</td></tr><tr><td>GBR</td><td>1.145</td><td>0.707</td><td>0.607</td></tr><tr><td>GRC</td><td>1.279</td><td>0.845</td><td>0.711</td></tr><tr><td>ITA</td><td>0.999</td><td>0.778</td><td>0.622</td></tr><tr><td>JPN</td><td>1.107</td><td>0.604</td><td>0.432</td></tr><tr><td>NOR</td><td>0.999</td><td>0.822</td><td>0.627</td></tr><tr><td>PRT</td><td>0.735</td><td>0.564</td><td>0.502</td></tr><tr><td>SWE</td><td>0.935</td><td>0.757</td><td>0.702</td></tr><tr><td>USA</td><td>0.939</td><td>0.857</td><td>0.62</td></tr></tbody></table>
<table><caption>Table 3: Estimated freight costs as % of imports (weighted mean costs across industries)</caption><thead><tr><th rowspan="2">Import Country</th><th colspan="12">Export Country</th></tr><tr><th>AUT</th><th>CAN</th><th>DEU</th><th>ESP</th><th>FIN</th><th>FRA</th><th>GBR</th><th>GRC</th><th>ITA</th><th>JPN</th><th>NOR</th><th>PRT</th><th>SWE</th><th>USA</th></tr></thead><tbody><tr><td>AUT</td><td>0.4</td><td>7.32</td><td>0.88</td><td>2.02</td><td>2.1</td><td>1.2</td><td>1.4</td><td>2.13</td><td>1.03</td><td>4.46</td><td>2.16</td><td>2.38</td><td>1.53</td><td>5.61</td></tr><tr><td>CAN</td><td>3.95</td><td>1.49</td><td>3.97</td><td>5.2</td><td>4.02</td><td>5.15</td><td>4.09</td><td>7.6</td><td>4.76</td><td>5.6</td><td>3.6</td><td>5.75</td><td>3.63</td><td>1.77</td></tr><tr><td>DEU</td><td>1.15</td><td>6.31</td><td>0.72</td><td>2</td><td>3.08</td><td>1.11</td><td>1.05</td><td>1.97</td><td>1.15</td><td>5.57</td><td>1.69</td><td>2.63</td><td>2.09</td><td>5.3</td></tr><tr><td>ESP</td><td>2.27</td><td>5.98</td><td>1.58</td><td>1.03</td><td>4.98</td><td>1.42</td><td>1.7</td><td>3.27</td><td>1.37</td><td>6.74</td><td>3.57</td><td>1.59</td><td>3.11</td><td>4.77</td></tr><tr><td>FIN</td><td>1.67</td><td>4.89</td><td>1.61</td><td>3.56</td><td>0.48</td><td>2.46</td><td>1.88</td><td>2.46</td><td>1.97</td><td>4.66</td><td>1.31</td><td>2.81</td><td>0.77</td><td>4.69</td></tr><tr><td>FRA</td><td>1.65</td><td>5.76</td><td>1.02</td><td>1.59</td><td>3.9</td><td>0.87</td><td>0.99</td><td>2.05</td><td>1.28</td><td>5.89</td><td>2.51</td><td>2.7</td><td>2.7</td><td>4.83</td></tr><tr><td>GBR</td><td>1.74</td><td>5.17</td><td>1.02</td><td>2.37</td><td>3.44</td><td>1.14</td><td>0.53</td><td>3.41</td><td>1.67</td><td>6.34</td><td>2.01</td><td>2.17</td><td>2.35</td><td>4.91</td></tr><tr><td>GRC</td><td>1.98</td><td>8.51</td><td>1.59</td><td>2.69</td><td>4.18</td><td>2.28</td><td>2.19</td><td>0.65</td><td>1.22</td><td>6.05</td><td>4.15</td><td>3.32</td><td>3.51</td><td>5.54</td></tr><tr><td>ITA</td><td>1.42</td><td>6.75</td><td>1.15</td><td>2.4</td><td>4.09</td><td>1.44</td><td>1.5</td><td>1.86</td><td>0.99</td><td>7.01</td><td>2.78</td><td>3.16</td><td>2.95</td><td>5.41</td></tr><tr><td>JPN</td><td>5.14</td><td>8.59</td><td>4.41</td><td>8.07</td><td>8.15</td><td>5.99</td><td>5.1</td><td>7.49</td><td>4.96</td><td>0.75</td><td>5.74</td><td>8.25</td><td>4.52</td><td>6.08</td></tr><tr><td>NOR</td><td>1.47</td><td>3.36</td><td>1.35</td><td>3.08</td><td>1.38</td><td>2.08</td><td>1.48</td><td>2.16</td><td>1.69</td><td>6</td><td>0.44</td><td>2.65</td><td>0.81</td><td>4.53</td></tr><tr><td>PRT</td><td>2.02</td><td>4.46</td><td>1.82</td><td>1.24</td><td>4.12</td><td>1.57</td><td>1.64</td><td>3.16</td><td>1.5</td><td>6.22</td><td>4.18</td><td>0.27</td><td>3.1</td><td>4.66</td></tr><tr><td>SWE</td><td>1.54</td><td>5.45</td><td>1.23</td><td>2.97</td><td>0.93</td><td>1.97</td><td>1.56</td><td>2.23</td><td>1.75</td><td>5.38</td><td>0.82</td><td>2.94</td><td>0.4</td><td>4.83</td></tr><tr><td>USA</td><td>5.05</td><td>2.26</td><td>4.26</td><td>5.78</td><td>8.13</td><td>5.99</td><td>5.07</td><td>7.44</td><td>5.2</td><td>5.7</td><td>5.23</td><td>5.56</td><td>4.46</td><td>1.86</td></tr></tbody></table>
<table><caption>Table 4: Minimum and maximum estimated values of log of RBC (Relative Bilateral Concentration index)</caption><thead><tr><th>INDUSTRY</th><th>MIN</th><th>CAN</th><th>MAX</th><th>MIN</th><th>DEU</th><th>MAX</th><th>MIN</th><th>FRA</th><th>MAX</th><th>MIN</th><th>UK</th><th>MAX</th></tr></thead><tbody><tr><td>Beverages</td><td>FRA (3.49)</td><td>SWE (12.55)</td><td></td><td>FRA (3.24)</td><td>SWE (11.63)</td><td></td><td>UK (2.71)</td><td>NOR (13.29)</td><td></td><td>FRA (3.26)</td><td>NOR (10.55)</td><td></td></tr><tr><td>Textiles</td><td>USA (1.54)</td><td>GRC (8.27)</td><td></td><td>ITA (2.07)</td><td>CAN (7.08)</td><td></td><td>ITA (3.09)</td><td>FIN (7.92)</td><td></td><td>USA (3.85)</td><td>FIN (7)</td><td></td></tr><tr><td>Apparel</td><td>USA (3.85)</td><td>NOR (8.67)</td><td></td><td>ITA (2.83)</td><td>NOR (7.84)</td><td></td><td>ITA (2.39)</td><td>NOR (8.61)</td><td></td><td>ITA (3.85)</td><td>ESP (7.21)</td><td></td></tr><tr><td>Leather</td><td>USA (1.35)</td><td>PRT (9.81)</td><td></td><td>ITA (1.05)</td><td>PRT (7.18)</td><td></td><td>ITA (2.14)</td><td>PRT (7.26)</td><td></td><td>ITA (3)</td><td>GRC (6.98)</td><td></td></tr><tr><td>Footwear</td><td>ITA (1.99)</td><td>NOR (11.06)</td><td></td><td>ITA (0.64)</td><td>NOR (8.92)</td><td></td><td>ITA (0.83)</td><td>FIN (9.88)</td><td></td><td>ITA (1.86)</td><td>NOR (9.01)</td><td></td></tr><tr><td>Wood</td><td>USA (2.7)</td><td>GRC (14.11)</td><td></td><td>FIN (3.56)</td><td>GRC (8.92)</td><td></td><td>CAN (3.31)</td><td>GRC (12.12)</td><td></td><td>CAN (2.14)</td><td>GRC (10.89)</td><td></td></tr><tr><td>Furniture</td><td>USA (2.23)</td><td>GRC (14.49)</td><td></td><td>ITA (4.31)</td><td>GRC (12.21)</td><td></td><td>DEU (2.85)</td><td>GRC (12.45)</td><td></td><td>DEU (4.82)</td><td>GRC (12.98)</td><td></td></tr><tr><td>Paper</td><td>USA (2.15)</td><td>GRC (22.32)</td><td></td><td>USA (2.15)</td><td>GRC (11.44)</td><td></td><td>USA (2.84)</td><td>GRC (10.41)</td><td></td><td>FIN (2.94)</td><td>GRC (11.7)</td><td></td></tr><tr><td>Printing</td><td>USA (1.67)</td><td>FIN (10.81)</td><td></td><td>USA (5)</td><td>PRT (10.7)</td><td></td><td>DEU (3.47)</td><td>GRC (11.75)</td><td></td><td>DEU (4.62)</td><td>GRC (11.36)</td><td></td></tr><tr><td>Ind.chem</td><td>USA (-0.2)</td><td>PRT (8.84)</td><td></td><td>FRA (2.68)</td><td>GRC (7.9)</td><td></td><td>DEU (1.37)</td><td>GRC (6.83)</td><td></td><td>DEU (2.62)</td><td>GRC (8.41)</td><td></td></tr><tr><td>Rubber</td><td>USA (1.55)</td><td>GRC (18.74)</td><td></td><td>FRA (2.92)</td><td>PRT (10.04)</td><td></td><td>DEU (1.69)</td><td>GRC (9.67)</td><td></td><td>DEU (3.39)</td><td>GRC (9.72)</td><td></td></tr><tr><td>Plastic</td><td>USA (1.59)</td><td>GRC (10.95)</td><td></td><td>ITA (4.2)</td><td>CAN (9.35)</td><td></td><td>DEU (2.85)</td><td>GRC (8.67)</td><td></td><td>DEU (3.98)</td><td>GRC (9.8)</td><td></td></tr><tr><td>Pottery/China</td><td>UK (-0.96)</td><td>GRC (8.32)</td><td></td><td>JPN (4.77)</td><td>CAN (11.01)</td><td></td><td>DEU (0.91)</td><td>FIN (8.76)</td><td></td><td>DEU (4.44)</td><td>GRC (12.45)</td><td></td></tr><tr><td>Glass</td><td>USA (0.45)</td><td>GRC (13.71)</td><td></td><td>UK (2.74)</td><td>GRC (11.94)</td><td></td><td>DEU (2.4)</td><td>GRC (14.77)</td><td></td><td>USA (4.5)</td><td>GRC (12.14)</td><td></td></tr><tr><td>Non-Metallic</td><td>USA (2.49)</td><td>GRC (12.04)</td><td></td><td>ITA (3.61)</td><td>CAN (9.33)</td><td></td><td>DEU (3.44)</td><td>FIN (10.29)</td><td></td><td>DEU (5.3)</td><td>GRC (9.88)</td><td></td></tr><tr><td>Iron/Steel</td><td>USA (2.41)</td><td>PRT (12.98)</td><td></td><td>FRA (3.08)</td><td>PRT (8.65)</td><td></td><td>DEU (2.72)</td><td>PRT (9.13)</td><td></td><td>DEU (3.57)</td><td>PRT (8.4)</td><td></td></tr><tr><td>Non-Fer. Metals</td><td>USA (1.98)</td><td>PRT (18.61)</td><td></td><td>UK (3.03)</td><td>PRT (10.21)</td><td></td><td>DEU (2.01)</td><td>PRT (8.77)</td><td></td><td>CAN (2.59)</td><td>PRT (9.21)</td><td></td></tr><tr><td>Metal</td><td>USA (2.49)</td><td>GRC (11.74)</td><td></td><td>FRA (3.85)</td><td>GRC (8.87)</td><td></td><td>DEU (3.2)</td><td>GRC (8.86)</td><td></td><td>DEU (4.68)</td><td>GRC (10.43)</td><td></td></tr><tr><td>Food</td><td>USA (3.04)</td><td>FIN (9.52)</td><td></td><td>FRA (4.23)</td><td>FIN (8.52)</td><td></td><td>DEU (4.05)</td><td>FIN (9.64)</td><td></td><td>NOR (4.32)</td><td>FIN (8.35)</td><td></td></tr><tr><td>Drugs/Med</td><td>USA (2.31)</td><td>GRC (20.55)</td><td></td><td>FRA (2.54)</td><td>PRT (8.98)</td><td></td><td>ITA (4.04)</td><td>GRC (12.28)</td><td></td><td>FRA (3.39)</td><td>PRT (8.48)</td><td></td></tr><tr><td>Chemical</td><td>USA (2.01)</td><td>GRC (16.44)</td><td></td><td>FRA (3.57)</td><td>PRT (9.13)</td><td></td><td>DEU (2.62)</td><td>GRC (9.95)</td><td></td><td>FRA (3.01)</td><td>GRC (8.58)</td><td></td></tr><tr><td>Machinery</td><td>USA (-0.58)</td><td>GRC (12.7)</td><td></td><td>UK (3.87)</td><td>GRC (9.82)</td><td></td><td>DEU (1.42)</td><td>GRC (10.11)</td><td></td><td>DEU (3.14)</td><td>GRC (9.74)</td><td></td></tr><tr><td>R.,TV</td><td>USA (0.15)</td><td>GRC (11.11)</td><td></td><td>JPN (2.23)</td><td>GRC (7.95)</td><td></td><td>USA (3.17)</td><td>GRC (11.24)</td><td></td><td>JPN (2.8)</td><td>GRC (9.93)</td><td></td></tr><tr><td>Electric</td><td>USA (1.55)</td><td>GRC (16.89)</td><td></td><td>ITA (4.72)</td><td>GRC (10.61)</td><td></td><td>DEU (-0.14)</td><td>GRC (8.75)</td><td></td><td>DEU (1.53)</td><td>GRC (10.1)</td><td></td></tr><tr><td>Vehicles</td><td>USA (-1)</td><td>GRC (15.41)</td><td></td><td>FRA (3)</td><td>GRC (11.79)</td><td></td><td>DEU (1.67)</td><td>GRC (12.76)</td><td></td><td>DEU (2.1)</td><td>GRC (11.9)</td><td></td></tr></tbody></table>
<table><caption>Table 5: Minimum and maximum estimated values of log of RBC (Relative Bilateral Concentration index)</caption><thead><tr><th>INDUSTRY</th><th>MIN</th><th>ITA</th><th>MAX</th><th>JPN</th><th>MIN</th><th>USA</th></tr></thead><tbody><tr><td>Beverages</td><td>UK (3.72)</td><td>NOR (15.72)</td><td></td><td>UK (4.02)</td><td>SWE (12.57)</td><td>UK (4.19)</td></tr><tr><td>Textiles</td><td>FRA (3.44)</td><td>CAN (9.07)</td><td></td><td>USA (4.14)</td><td>GRC (11.07)</td><td>JPN (4.73)</td></tr><tr><td>Apparel</td><td>FRA (4.36)</td><td>NOR (9.73)</td><td></td><td>ITA (5.17)</td><td>NOR (11.82)</td><td>ITA (4.82)</td></tr><tr><td>Leather</td><td>ESP (3.44)</td><td>PRT (8.85)</td><td></td><td>ITA (3.98)</td><td>PRT (13.16)</td><td>CAN (3.75)</td></tr><tr><td>Footwear</td><td>ESP (5.21)</td><td>FIN (11.67)</td><td></td><td>ITA (4.44)</td><td>NOR (26.32)</td><td>ITA (1.94)</td></tr><tr><td>Wood</td><td>USA (2.36)</td><td>GRC (10.02)</td><td></td><td>CAN (3.72)</td><td>GRC (21.41)</td><td>CAN (2.74)</td></tr><tr><td>Furniture</td><td>DEU (4.99)</td><td>GRC (13.13)</td><td></td><td>USA (6.21)</td><td>GRC (21.64)</td><td>CAN (4.01)</td></tr><tr><td>Paper</td><td>USA (2.74)</td><td>GRC (9.43)</td><td></td><td>USA (3.46)</td><td>GRC (26.16)</td><td>CAN (2.73)</td></tr><tr><td>Printing</td><td>DEU (4.39)</td><td>GRC (12.15)</td><td></td><td>USA (5.57)</td><td>GRC (15.38)</td><td>JPN (6.05)</td></tr><tr><td>Ind.chem</td><td>DEU (1.48)</td><td>FIN (6.84)</td><td></td><td>USA (2.78)</td><td>GRC (14.24)</td><td>CAN (3.43)</td></tr><tr><td>Rubber</td><td>FRA (2.36)</td><td>FIN (10.01)</td><td></td><td>USA (4.18)</td><td>PRT (28.07)</td><td>CAN (3.31)</td></tr><tr><td>Plastic</td><td>DEU (3.41)</td><td>PRT (9.61)</td><td></td><td>USA (5.95)</td><td>PRT (15.2)</td><td>DEU (4.87)</td></tr><tr><td>Pottery/China</td><td>DEU (3.69)</td><td>CAN (10.55)</td><td></td><td>UK (3.14)</td><td>GRC (11.64)</td><td>JPN (1.45)</td></tr><tr><td>Glass</td><td>UK (2.74)</td><td>GRC (10.63)</td><td></td><td>USA (3.83)</td><td>GRC (16.31)</td><td>UK (3.9)</td></tr><tr><td>Non-Metallic</td><td>DEU (4.2)</td><td>FIN (10.78)</td><td></td><td>USA (6.27)</td><td>GRC (12.87)</td><td>CAN (4.99)</td></tr><tr><td>Iron/Steel</td><td>FRA (2.69)</td><td>PRT (8.36)</td><td></td><td>SWE (6.8)</td><td>GRC (16.38)</td><td>CAN (3.14)</td></tr><tr><td>Non-Fer. Metals</td><td>DEU (1.66)</td><td>PRT (9.18)</td><td></td><td>USA (3.22)</td><td>PRT (18.35)</td><td>CAN (2.58)</td></tr><tr><td>Metal</td><td>DEU (2.8)</td><td>FIN (8.14)</td><td></td><td>USA (4.36)</td><td>GRC (12.47)</td><td>CAN (4.14)</td></tr><tr><td>Food</td><td>DEU (2.91)</td><td>JPN (8.39)</td><td></td><td>USA (3.62)</td><td>GRC (10.94)</td><td>CAN (5.34)</td></tr><tr><td>Drugs/Med</td><td>DEU (3.39)</td><td>GRC (12.42)</td><td></td><td>USA (3.08)</td><td>GRC (16.01)</td><td>ITA (4.67)</td></tr><tr><td>Chemical</td><td>DEU (1.48)</td><td>CAN (7.94)</td><td></td><td>USA (3.37)</td><td>GRC (14.74)</td><td>FRA (4.23)</td></tr><tr><td>Machinery</td><td>DEU (1.64)</td><td>GRC (8.43)</td><td></td><td>USA (4.26)</td><td>GRC (14.8)</td><td>DEU (2.85)</td></tr><tr><td>R..TV</td><td>DEU (1.26)</td><td>GRC (7.17)</td><td></td><td>USA (3.57)</td><td>GRC (17.97)</td><td>JPN (1.16)</td></tr><tr><td>Electrica</td><td>DEU (1.25)</td><td>GRC (9.59)</td><td></td><td>DEU (3.95)</td><td>GRC (21.96)</td><td>CAN (2.31)</td></tr><tr><td>Vehicles</td><td>DEU (1.49)</td><td>GRC (10.95)</td><td></td><td>DEU (3.89)</td><td>GRC (19.34)</td><td>UK (1.8)</td></tr></tbody></table>
<table><caption>Table 6: Estimated Relative Overall Seller Concentration indexes (ROSC median values over 1970-1994)</caption><thead><tr><th>Industry</th><th>AUT</th><th>CAN</th><th>DEU</th><th>ESP</th><th>FIN</th><th>FRA</th><th>UK</th><th>GRC</th><th>ITA</th><th>JPN</th><th>NOR</th><th>PRT</th><th>SWE</th><th>USA</th></tr></thead><tbody><tr><td>Beverages</td><td>1.00</td><td>1.03</td><td>0.95</td><td>0.99</td><td>1.09</td><td>0.95</td><td>0.99</td><td>0.93</td><td>0.96</td><td>0.97</td><td>0.97</td><td>1.02</td><td>1.35</td><td>1.00</td></tr><tr><td>Textiles</td><td>0.82</td><td>0.81</td><td>0.80</td><td>0.93</td><td>0.75</td><td>1.07</td><td>1.30</td><td>0.81</td><td>0.93</td><td>0.98</td><td>0.92</td><td>0.94</td><td>0.86</td><td>0.99</td></tr><tr><td>Apparel</td><td>0.70</td><td>0.99</td><td>1.05</td><td>0.96</td><td></td><td>0.86</td><td>1.08</td><td></td><td>0.99</td><td>1.00</td><td>0.27</td><td>1.11</td><td></td><td>0.99</td></tr><tr><td>Leather</td><td>0.69</td><td>1.07</td><td>0.61</td><td>0.96</td><td>0.99</td><td>1.04</td><td>1.04</td><td>0.54</td><td>0.98</td><td>0.99</td><td>0.49</td><td>1.18</td><td>0.99</td><td>1.28</td></tr><tr><td>Footwear</td><td>0.53</td><td>0.97</td><td>0.67</td><td>0.99</td><td>0.83</td><td>0.71</td><td>1.04</td><td>1.34</td><td>1.02</td><td>1.01</td><td>0.58</td><td>1.10</td><td>0.47</td><td>1.02</td></tr><tr><td>Wood</td><td>0.87</td><td>0.99</td><td>0.95</td><td>0.98</td><td>1.01</td><td>0.91</td><td>1.00</td><td>0.84</td><td>0.89</td><td>0.99</td><td>0.89</td><td>1.03</td><td>0.98</td><td>0.94</td></tr><tr><td>Furniture</td><td>0.79</td><td>0.93</td><td>1.11</td><td>0.98</td><td>1.05</td><td>0.94</td><td>1.43</td><td>0.96</td><td>0.99</td><td>1.00</td><td>0.94</td><td>1.41</td><td>1.09</td><td>1.03</td></tr><tr><td>Paper</td><td>0.97</td><td>0.96</td><td>0.74</td><td>0.82</td><td>1.04</td><td>0.90</td><td>1.12</td><td>0.59</td><td>0.83</td><td>0.95</td><td>0.76</td><td>0.78</td><td>0.94</td><td>0.97</td></tr><tr><td>Printing</td><td>0.78</td><td>0.84</td><td>1.04</td><td>0.96</td><td>0.97</td><td>0.96</td><td>0.99</td><td>0.91</td><td>0.98</td><td>0.99</td><td>1.03</td><td>0.94</td><td>0.98</td><td>1.00</td></tr><tr><td>Ind.chem</td><td>0.67</td><td>0.42</td><td>0.90</td><td>0.67</td><td>0.68</td><td>0.74</td><td>0.92</td><td>0.47</td><td>0.71</td><td>0.91</td><td>0.37</td><td>0.40</td><td>0.58</td><td>0.92</td></tr><tr><td>Rubber</td><td>0.52</td><td>1.39</td><td>1.04</td><td>0.85</td><td>0.51</td><td>0.84</td><td>1.08</td><td>0.51</td><td>0.83</td><td>0.98</td><td>0.40</td><td>0.64</td><td>0.67</td><td>1.01</td></tr><tr><td>Plastic</td><td>0.69</td><td>0.82</td><td>1.05</td><td>0.98</td><td>0.79</td><td>0.93</td><td>1.07</td><td>0.91</td><td>1.00</td><td>1.00</td><td>1.02</td><td>0.92</td><td>0.79</td><td>0.99</td></tr><tr><td>Pottery/China</td><td>0.61</td><td>0.32</td><td>3.60</td><td>4.73</td><td>0.59</td><td>0.80</td><td>2.35</td><td>0.77</td><td>0.98</td><td>0.97</td><td>1.03</td><td>1.06</td><td>0.68</td><td>0.98</td></tr><tr><td>Glass</td><td>0.64</td><td>0.61</td><td>0.89</td><td>0.96</td><td>0.73</td><td>0.88</td><td>2.26</td><td>0.58</td><td>0.95</td><td>0.97</td><td>0.79</td><td>1.08</td><td>0.86</td><td>1.02</td></tr><tr><td>Non-Metallic</td><td>0.99</td><td>0.94</td><td>0.96</td><td>0.99</td><td>1.05</td><td>0.95</td><td>1.04</td><td>0.96</td><td>0.98</td><td>1.00</td><td>1.01</td><td>0.96</td><td>1.16</td><td>1.00</td></tr><tr><td>Iron/Steel</td><td>2.43</td><td>1.06</td><td>0.92</td><td>0.91</td><td>1.19</td><td>0.78</td><td>1.10</td><td>0.89</td><td>0.81</td><td>0.95</td><td>0.32</td><td>0.52</td><td>0.76</td><td>0.93</td></tr><tr><td>Non-Fer. Metals</td><td>0.91</td><td>1.12</td><td>0.92</td><td>0.91</td><td>1.19</td><td>0.78</td><td>1.10</td><td>0.89</td><td>0.81</td><td>0.95</td><td>0.32</td><td>0.52</td><td>0.76</td><td>0.93</td></tr><tr><td>Metal</td><td>0.87</td><td>1.25</td><td>0.95</td><td>0.93</td><td>0.74</td><td>1.00</td><td>1.30</td><td>0.84</td><td>0.87</td><td>0.98</td><td>0.87</td><td>0.90</td><td>0.94</td><td>0.97</td></tr><tr><td>Food</td><td>0.95</td><td>0.97</td><td>1.01</td><td>0.98</td><td>0.98</td><td>0.97</td><td>1.03</td><td>0.92</td><td>0.9</td><td>0.97</td><td>1.24</td><td>1.00</td><td>1.01</td><td>1.00</td></tr><tr><td>Drugs/Med</td><td>0.74</td><td>0.96</td><td>0.91</td><td>0.98</td><td>0.66</td><td>1.04</td><td>0.96</td><td>0.87</td><td>0.97</td><td>0.94</td><td>0.80</td><td>0.80</td><td>0.76</td><td>0.98</td></tr><tr><td>Chemical</td><td>0.89</td><td>0.95</td><td>1.05</td><td>0.94</td><td>0.76</td><td>0.99</td><td>1.02</td><td>0.89</td><td>0.70</td><td>0.95</td><td>0.87</td><td>0.81</td><td>1.05</td><td>0.98</td></tr><tr><td>Machinery</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R.,TV</td><td>0.63</td><td>0.53</td><td>0.81</td><td>0.67</td><td>0.47</td><td>1.01</td><td>1.13</td><td>0.41</td><td>0.61</td><td>0.97</td><td>1.04</td><td>0.77</td><td>0.67</td><td>0.73</td></tr><tr><td>Electrica</td><td>0.65</td><td>1.15</td><td>2.86</td><td>0.80</td><td>0.41</td><td>0.66</td><td>1.12</td><td>0.49</td><td>0.93</td><td>0.98</td><td>0.44</td><td>0.52</td><td>0.51</td><td>0.91</td></tr><tr><td>Vehicles</td><td>0.22</td><td>0.25</td><td>1.11</td><td>0.82</td><td></td><td>0.88</td><td>1.4</td><td>0.12</td><td>0.76</td><td>0.97</td><td></td><td>0.54</td><td>0.48</td><td>0.71</td></tr></tbody></table>
<table><caption>Table 7: Foreign contribution to seller concentration (FC median values over 1970-1994)</caption><thead><tr><th>Industry</th><th>AUT</th><th>CAN</th><th>DEU</th><th>ESP</th><th>FIN</th><th>FRA</th><th>UK</th><th>GRC</th><th>ITA</th><th>JPN</th><th>NOR</th><th>PRT</th><th>SWE</th><th>USA</th></tr></thead><tbody><tr><td>Beverages</td><td>0.12</td><td>0.26</td><td>0.15</td><td>0.08</td><td>0.19</td><td>0.31</td><td>0.21</td><td>0.09</td><td>0.10</td><td>0.06</td><td>0.22</td><td>0.14</td><td>0.54</td><td>0.17</td></tr><tr><td>Textiles</td><td>0.69</td><td>0.34</td><td>0.42</td><td>0.08</td><td>0.66</td><td>0.46</td><td>0.57</td><td>0.16</td><td>0.14</td><td>0.06</td><td>0.82</td><td>0.19</td><td>0.77</td><td>0.06</td></tr><tr><td>Apparel</td><td>0.60</td><td>0.13</td><td>0.56</td><td>0.04</td><td></td><td>0.27</td><td>0.37</td><td></td><td>0.08</td><td>0.05</td><td>0.78</td><td>0.20</td><td></td><td>0.06</td></tr><tr><td>Leather</td><td>0.87</td><td>0.77</td><td>0.53</td><td>0.10</td><td>0.73</td><td>0.61</td><td>0.42</td><td>0.71</td><td>0.21</td><td>0.14</td><td>0.89</td><td>0.46</td><td>0.86</td><td>0.43</td></tr><tr><td>Footwear</td><td>0.59</td><td>0.51</td><td>0.67</td><td>0.04</td><td>0.55</td><td>0.46</td><td>0.55</td><td>0.39</td><td>0.09</td><td>0.07</td><td>0.97</td><td>0.13</td><td>0.95</td><td>0.54</td></tr><tr><td>Wood</td><td>0.20</td><td>0.21</td><td>0.25</td><td>0.22</td><td>0.12</td><td>0.18</td><td>0.46</td><td>0.13</td><td>0.19</td><td>0.13</td><td>0.28</td><td>0.12</td><td>0.12</td><td>0.04</td></tr><tr><td>Furniture</td><td>0.20</td><td>0.19</td><td>0.26</td><td>0.05</td><td>0.22</td><td>0.26</td><td>0.43</td><td>0.09</td><td>0.02</td><td>0.01</td><td>0.54</td><td>0.41</td><td>0.57</td><td>0.16</td></tr><tr><td>Paper</td><td>0.53</td><td>0.28</td><td>0.34</td><td>0.20</td><td>0.16</td><td>0.44</td><td>0.63</td><td>0.32</td><td>0.25</td><td>0.05</td><td>0.43</td><td>0.27</td><td>0.18</td><td>0.14</td></tr><tr><td>Printing</td><td>0.34</td><td>0.20</td><td>0.13</td><td>0.06</td><td>0.05</td><td>0.09</td><td>0.06</td><td>0.11</td><td>0.03</td><td>0.01</td><td>0.20</td><td>0.07</td><td>0.08</td><td>0.02</td></tr><tr><td>Ind.chem</td><td>0.62</td><td>0.75</td><td>0.40</td><td>0.34</td><td>0.58</td><td>0.65</td><td>0.37</td><td>0.75</td><td>0.53</td><td>0.11</td><td>0.60</td><td>0.69</td><td>0.70</td><td>0.10</td></tr><tr><td>Rubber</td><td>0.30</td><td>0.68</td><td>0.41</td><td>0.05</td><td>0.68</td><td>0.41</td><td>0.39</td><td>0.37</td><td>0.30</td><td>0.02</td><td>0.82</td><td>0.11</td><td>0.60</td><td>0.16</td></tr><tr><td>Plastic</td><td>0.36</td><td>0.30</td><td>0.23</td><td>0.03</td><td>0.43</td><td>0.23</td><td>0.28</td><td>0.12</td><td>0.20</td><td>0.01</td><td>0.53</td><td>0.08</td><td>0.43</td><td>0.06</td></tr><tr><td>Pottery/China</td><td>0.75</td><td>0.36</td><td>0.83</td><td>0.82</td><td>0.33</td><td>0.22</td><td>0.68</td><td>0.22</td><td>0.04</td><td>0.04</td><td>0.86</td><td>0.15</td><td>0.63</td><td>0.53</td></tr><tr><td>Glass</td><td>0.36</td><td>0.53</td><td>0.17</td><td>0.25</td><td>0.64</td><td>0.37</td><td>0.72</td><td>0.37</td><td>0.20</td><td>0.04</td><td>0.73</td><td>0.47</td><td>0.76</td><td>0.16</td></tr><tr><td>Non-Metallic</td><td>0.28</td><td>0.20</td><td>0.13</td><td>0.05</td><td>0.25</td><td>0.21</td><td>0.12</td><td>0.10</td><td>0.09</td><td>0.01</td><td>0.28</td><td>0.09</td><td>0.37</td><td>0.07</td></tr><tr><td>Iron/Steel</td><td>0.80</td><td>0.46</td><td>0.19</td><td>0.14</td><td>0.59</td><td>0.36</td><td>0.36</td><td>0.39</td><td>0.15</td><td>0.02</td><td>0.75</td><td>0.53</td><td>0.58</td><td>0.13</td></tr><tr><td>Non-Fer. Metals</td><td>0.66</td><td>0.46</td><td>0.32</td><td>0.05</td><td>0.64</td><td>0.25</td><td>0.64</td><td>0.29</td><td>0.70</td><td>0.05</td><td>0.68</td><td>0.48</td><td>0.62</td><td>0.13</td></tr><tr><td>Metal</td><td>0.41</td><td>0.56</td><td>0.14</td><td>0.15</td><td>0.41</td><td>0.30</td><td>0.42</td><td>0.36</td><td>0.13</td><td>0.03</td><td>0.64</td><td>0.21</td><td>0.35</td><td>0.05</td></tr><tr><td>Food</td><td>0.13</td><td>0.11</td><td>0.13</td><td>0.07</td><td>0.07</td><td>0.09</td><td>0.15</td><td>0.23</td><td>0.14</td><td>0.04</td><td>0.29</td><td>0.14</td><td>0.16</td><td>0.02</td></tr><tr><td>Drugs/Med</td><td>0.72</td><td>0.40</td><td>0.21</td><td>0.20</td><td>0.52</td><td>0.22</td><td>0.17</td><td>0.60</td><td>0.25</td><td>0.13</td><td>0.70</td><td>0.56</td><td>0.54</td><td>0.04</td></tr><tr><td>Chemical</td><td>0.42</td><td>0.36</td><td>0.24</td><td>0.20</td><td>0.58</td><td>0.41</td><td>0.46</td><td>0.39</td><td>0.30</td><td>0.07</td><td>0.64</td><td>0.22</td><td>0.65</td><td>0.06</td></tr><tr><td>Machinery</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R..TV</td><td>0.78</td><td>0.80</td><td>0.23</td><td>0.38</td><td>0.77</td><td>0.43</td><td>0.60</td><td>0.72</td><td>0.35</td><td>0.02</td><td>0.84</td><td>0.73</td><td>0.64</td><td>0.18</td></tr><tr><td>Electrica</td><td>0.14</td><td>0.75</td><td>0.73</td><td>0.26</td><td>0.47</td><td>0.22</td><td>0.48</td><td>0.24</td><td>0.24</td><td>0.01</td><td>0.58</td><td>0.45</td><td>0.63</td><td>0.06</td></tr><tr><td>Vehicles</td><td>0.88</td><td>0.89</td><td>0.45</td><td>0.29</td><td></td><td>0.60</td><td>0.76</td><td>0.82</td><td>0.50</td><td>0.02</td><td></td><td>0.86</td><td>0.68</td><td>0.30</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: Evolution of the estimated Relative *Overall* Seller-Concentration index</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: Evolution of the estimated Relative *Overall* Seller-Concentration index</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3: Evolution of the estimated Relative *Overall* Seller-Concentration index</figcaption></figure>