The Geography of Equity Listing:
Why Do European Companies List Abroad?
Marco Pagano
CSEF, University of Salerno and CEPR
Ailsa A. Röell
Princeton University and CEPR
Josef Zechner
University of Vienna and CEPR
First draft: 30 April 1999. This version: 11 December 1999
Abstract
This paper documents the aggregate trends in the foreign listings of companies and analyzes
both their distinctive pre-listing characteristics and their post-listing performance relative to
other companies. In the 1986-97 interval, many European companies listed abroad, but did so
mainly on US exchanges. At the same time, the number of US companies listed in Europe
decreased. The cross-listings of European companies appear to have sharply different
motivations and consequences depending on whether they cross-list in the United States or
within Europe. In the first case, companies pursue a strategy of rapid expansion fuelled by high
leverage before the listing and large equity issues after the listing. They rely increasingly on
export markets both before and after the listing, and tend to belong to high-tech industries. In
the second case, companies do not grow more than the control group, and increase their
leverage after the cross-listing. Also, they fail to increase their foreign sales in the wake of the
cross-listing. The only common features of the two groups are their large size, high foreign sales
before cross-listing and high R&D spending after cross-listing.
Keywords:
JEL classification:
cross-listings, going public, initial public offerings, geography
G15, G30, G39
Authors’ addresses: Marco Pagano, Dipartimento di Scienze Economiche, University of
Salerno, Via Ponte Don Melillo, 84084 Fisciano (Salerno), Italy; ph. +39 081 575-2508, fax +39
081 575-2243, e-mail: [email protected]. Ailsa A. Röell, Department of Economics, Princeton
University, Fisher Hall, Princeton NJ 08544-1021, U.S.A.; ph. +1-609-258-4467, fax +1-609258-6419, e-mail: [email protected]. Josef Zechner, University of Vienna, Brünnerstrasse
72, A-1210 Wien, Austria; ph. +43 1 4277-38071, fax +43 1 4277-38074, e-mail:
[email protected].
Acknowledgements: We thank Asher Blass, Yishay Yafeh, Gilles Chemla and seminar
participants at Aachen, Copenhagen, Konstanz, Tel-Aviv, and the Barcelona CEPR-IAE
Workshop on Banking and Financial Markets for helpful comments. Larissa Lube and Otto
Randl have provided outstanding research assistance. This research has been supported by
grants awarded by INQUIRE, the Fondation Banque de France, and the Italian Ministry of
University and Scientific and Technological Research (MURST). This paper is produced as part
of a CEPR research network on The Industrial Organization of Banking and Financial Markets
in Europe, funded by the European Commission under the TMR Programme (contract No
ERBFMRXCT980222).
1
1. Introduction
Foreign listings are becoming an increasingly important strategic issue for companies
and stock exchanges alike. As companies become global in their product market and
investment strategies, direct access to foreign capital markets via an equity listing can
yield important benefits. At the same time, the international integration of capital
markets has led to unprecedented levels of competition among stock exchanges. In this
competitive struggle, the winners are the exchanges that manage to attract more foreign
listings and the attendant trading volume and business opportunities.
Despite the importance of these issues, still little is known about which exchanges
succeed in capturing more listings from abroad and why. This question is intimately
related with a second issue, namely which advantages companies expect to get from a
foreign listing: securing cheap equity capital for new investment, allowing controlling
shareholders to divest on a liquid market, preparing for foreign acquisitions, or simply
enhancing the company’s reputation. The evidence presented in this paper is relevant
for both issues − the determinants of exchanges’ success and the microeconomic
motives for listing abroad.
We start by providing a broad picture of the geography of cross-listings by European
and US companies, and of its changes in recent years. This aggregate picture shows that
European companies have become more “footloose” in recent years, and that most of
their cross-listings have been directed towards the US exchanges, while US companies
have reduced their cross-listings in Europe. Correspondingly, the ability of European
exchanges to attract listings from the rest of the world has declined, while the reverse
has happened to US exchanges. Interestingly, the European markets with the highest
trading costs, lowest accounting standards and worst shareholder protection have also
fared worst in attracting or retaining foreign listings, and companies from those
countries have been comparatively eager in seeking foreign listings.
We then turn to microeconomic data to gain a better understanding of these shifts in
the geography of cross-listings, by linking companies’ decision to list abroad to their ex
ante characteristics (e.g., size or foreign sales) and their ex post behavior (e.g., their
growth rate after listing abroad). We investigate these relationships by using company-
2
level data for non-financial European companies in 1986-97, drawn from the Global
Vantage and Worldscope databases.
We find that the European companies that list in other European exchanges and those
which list in the US have few common features: they all tend to be larger and more
export-oriented than the companies which not list abroad, and all tend to increase their
R&D spending after cross-listing. The differences between the two groups are far more
numerous and striking. European companies that cross-list in the US pursue a strategy
of rapid expansion fuelled by high leverage before the listing and large equity issues
after the listing. They rely heavily on export markets both before and after the listing,
and tend to belong to high-tech industries. Companies that cross-list elsewhere in
Europe, instead, do not grow more than the control group, and increase their leverage
after the cross-listing. Also, they do not rely on foreign sales to the same extent as firms
cross-listing in the US, and generally do not belong to high-tech sectors.
Thus, cross-listing in the US appears to be driven by the need to raise equity to fund
growth and foreign sales expansion, generally in high-tech sectors. These motives are
less common for European companies that cross-list in other European exchanges.
Therefore, the changing geography of cross-listings across the Atlantic is associated
with a difference in the type of companies that cross-list in the two continents. US
exchanges appear to be especially suited to the needs of high-growth, export-oriented
and high-tech European companies.
In Section 2 we outline the main reasons why companies may wish to list abroad and
draw testable predictions from each hypothesis. In Section 3 we analyze the overall
pattern of cross-listings, studying the geographical origin and destination of firms that
went public on the world’s major equity exchanges in 1986-97. In Section 4 we perform
a first exploration of company-level data using descriptive statistics centered on the year
of cross-listing. Section 5 presents an econometric analysis of the variables that affect
the choice to list abroad for the first time, as well as the choice between listing in the US
or in Europe. In Section 6 we try to gauge if listing abroad affects the subsequent
performance of companies relative to our control sample, and how this differential
performance hinges on cross-listing in the US as opposed to Europe. Finally, Section 7
summarizes the results of the paper, compares them with those of related studies, and
discusses their implications for the comparison between US and European exchanges.
3
2. Hypotheses and Related Literature
In this section we outline the reasons why companies may want to list on an
exchange outside their country of incorporation, either as their first port of entry into the
public equity market or after having already listed on their domestic exchange.1
First of all, companies may list abroad because funding abroad may be cheaper or
more easily available. This can happen for various reasons, detailed below in Section
2.1 jointly with their empirical implications. Second, the publicity that comes with a
listing can enhance a company’s reputation with its suppliers and customers, as
explained in Section 2.2. On the other side of the ledger, the costs of listing abroad may
deter certain companies, as discussed in Section 2.3. Table 1 summarizes the testable
implications of the various motives of the decision to list abroad, relating it both to (i)
the pre-listing company characteristics and to (ii) its likely effect on subsequent
performance.
2.1 Selling Securities on Better Terms
By listing abroad firms may improve the terms on which they can raise capital or on
which their shareholders can sell existing securities. This motive is strongest if the firm
or its existing shareholders need to raise capital and if financial constraints in the home
market are significant. Some of the empirical predictions relevant for this motive have
to do with the reason why capital is needed, and others have to do with why crosslisting makes it cheaper.
The salient reason why a company may need funding is to carry out new investment
programs. The required funding is likely to be especially large for fast-growing
companies, and especially expensive if the company is already highly levered.
Therefore, companies which cross-list in order to raise capital cheaply should have a
1
The decision to list or cross-list on a foreign exchange is related to the more general issue of why firms
go public, which has recently been explored in the finance literature: see Bolton and von Thadden (1998),
Pagano and Röell (1998), Chemmanur and Fulghieri (1999), Mello and Parsons (1996), Pagano (1993),
Röell (1996), Stoughton, Wong and Zechner (1996), and Subrahmanyam and Titman (1999). These
papers emphasize various advantages of going public from the standpoint of controlling shareholders,
such as portfolio diversification, liquidity, information dissemination, ownership dispersion, and product
market spillovers. Some of the insights of this literature can be brought to bear also on the decision to list
abroad.
4
higher leverage, investment and growth rate before cross-listing and engage in a
primary equity offering at the time of the cross-listing or shortly afterwards. Moreover,
such companies would be more likely to cross-list on a deep stock market such as the
US or the UK than on shallow markets such as most exchanges in continental Europe.
Since higher expected growth should translate into higher price-earning ratios (P/E),
one would also expect them to have higher P/E ratios than comparable domestic
companies.
Rather than via organic growth, a company may choose to expand by a merger or
acquisition involving a foreign company. The acquisition of a target company is
facilitated by using the bidder’s shares as a medium of exchange, but the latter are an
acceptable “currency” only if the two companies are listed on the same exchange.2
Even if the firm has no need to finance new investment, its current shareholders may
want to sell out, and listing abroad can increase the market value of their stake.
Privatizations are an important special case, where the government is the divesting
shareholder. Therefore, we would expect newly privatized companies to be more likely
to cross-list than other comparable companies. A more direct test would look at
whether, in general, the main shareholders sell out at the time of cross-listing or shortly
afterwards. An imperfect proxy for such divestment can be an abnormally high
turnover.
We now turn to the reasons why listing abroad can raise a company’s stock market
value.
a) Reducing barriers for foreign investors
Widening the clientele for a firm’s shares improves risk sharing and thus lowers the
cost of capital, as shown by Stulz (1999), Martin and Rey (1999) and Lombardo and
Pagano (1999). The empirical evidence summarized in Karolyi (1998) shows that
indeed cross-listing reduces the cost of capital for firms, whether this is measured by
secondary market returns or by accounting variables such as the price-earnings ratio or
the market to book ratio. The reduction in the cost of capital appears to reflect, at least
2
Listing abroad may also relieve constraints on future growth options by creating the necessary contacts
and reputation in the local financial community (e.g., with investment bankers, regulators, etc.), and by
facilitating the identification of potential target companies.
5
partly, the reduction of the company’s home market beta. This gain should be larger for
riskier firms, which therefore should have greater inducement to cross-list.3
Listing abroad can mitigate market segmentation by reducing barriers to foreign
investors, such as
(i) regulatory barriers: for instance, pension funds may face a ceiling on the proportion
of their assets invested in stocks not listed on their domestic market;
(ii) transaction costs: for instance, dividends of non-US shares held by US residents
must be converted into dollars at the expense of individual investors;
(iii) informational frictions: these range from total ignorance of foreign investment
opportunities as in Merton’s “awareness hypothesis”,4 to an informational
disadvantage when trading foreign stocks, as in Gehrig (1993), Kang and Stulz
(1994) and Brennan and Cao (1997).5 A foreign listing may reduce such frictions,
supplying local investors with more abundant, timely and transparent information.6
Foerster and Karolyi (1999) provide the most direct evidence connecting Merton’s
“awareness hypothesis” to the drop in the cost of capital at the time of cross-listing: they
show that the price of cross-listing companies rise more when they are accompanied by
a greater expansion of the shareholder base.
Related evidence is reported by Kadlec and McConnell (1994) for over-the-counter
shares which listed in the New York Stock Exchange (NYSE): they find that the listing
is accompanied by a 5 percent abnormal return, by an increase in the number of
shareholders and a reduction of the bid-ask spread. Also consistently with the
“awareness hypothesis”, Baker, Nofsinger and Weaver (1999) document that crosslisting on the New York and London exchanges is associated with increased analyst
coverage and media attention.
3
No study so far has examined if cross-listing companies have lower betas with the destination market
and higher home market betas than comparable domestic companies. Consistently with Merton's (1987)
model, such firms would reap the highest risk sharing gains from listing abroad.
4
Merton (1987) derives a simple model of market equilibrium with incomplete information. Listing in a
foreign market can be easily incorporated in this framework by assuming that it involves a cost but
broadens the firm's investor base. In such a framework, risk characteristics should determine which firms
are most likely to incur the cost of broadening its shareholder base by listing in a foreign market.
5
The “home bias” induced by informational frictions may take the form of overconfidence about
domestic shares relative to foreign ones. Kilka and Weber (1997) produces experimental evidence that
potential investors are more optimistic about the expected performance of domestic shares than that of
foreign shares. The publicity associated with a cross-listing could change this perception.
6
Finally, Miller (1999) shows that the stock price reaction to a cross-listing is
positively correlated both to the increase in the shareholder base and to the barriers to
capital flows: abnormal returns are largest when companies list on major US exchanges
such as the NYSE or Nasdaq and when they originate from emerging markets.
b) Capitalizing on product market reputation
Companies which sell popular brands abroad may find it easier to place their shares
in foreign markets because local investors already trust them as consumers. A simple
strategy to test this hypothesis is to look at indicators of the degree of sales
internationalization for companies which cross-list. One would expect a larger fraction
of revenue coming from abroad to encourage eventual cross-listing. Saudagaran (1988)
showed that 104 companies from 9 countries that were already listed abroad in 1981
had a higher proportion of foreign sales than a control sample. This however begs the
question of which came first: the cross-listing or the outward orientation of these
companies. Only the latter would be consistent with the idea that these companies crosslisted to capitalize on their product market reputation.
c) Relying on foreign expertise
The exchange where a company lists may be determined by the location of analysts
with superior technological knowledge of the industry. Especially in high-tech sectors,
the availability of such skills may substantially affect the availability of equity finance
and the terms at which it is available, by reducing informational asymmetries in the
primary market. This hypothesis predicts, for example, that high-tech companies may
be more likely to list in the US where the corresponding industries are well developed.
Evidence in this direction is already provided by Blass and Yafeh (1999), who show
that Israeli and Dutch firms which list in the US (bypassing their respective home
markets) are relatively high-tech and fast growing.
6
Access to cheaper capital not only through the stock market but also through the bond market and trade
credit through suppliers. This may be due to a reduction in adverse selection since strictly more
information is available about the company.
7
d) Committing to disclosure and corporate governance standards
The listing location may also be affected by differences in regulatory regimes,
especially concerning shareholder protection. By selecting a more tightly regulated
foreign exchange, a firm precommits to adhere to higher standards of corporate
governance and/or disclosure. Exchanges compete to attract cross-listings by designing
a regulatory environment that is expected to lower the cost of capital of their companies.
Huddart, Hughes and Brunnermeier (1998) present a model in which exchanges
competing for trading volume engage in a “race to the top” regarding disclosure
requirements.7 Ashbaugh (1997) documents firms’ decisions to voluntarily adopt the
tighter US accounting standards. Fuerst (1998) argues that firms signal quality by listing
on strictly regulated markets.8 Similarly, Stulz (1999) argues that companies from
countries with poor legal standards can secure a lower cost of capital by subjecting
themselves to the tighter regulatory standards of other markets and thereby reducing the
agency costs of external finance.
At the empirical level, this suggests that companies located in countries with
particularly inadequate supervision and disclosure standards would be more likely to
cross-list abroad. However, so far the evidence on this point is mixed. On one hand,
Biddle and Saudagaran (1989) and Saudagaran and Biddle (1992) find that stringent
disclosure requirements deter the listing of foreign companies. On the other, Reese and
Weisbach (1999) report that firms from countries with French Civil Law systems, which
give weak protection to minority shareholders, list abroad more frequently than firms
from other countries, and cross-list more often on organized exchanges such as NYSE
or Nasdaq rather than on the Over the Counter (OTC) market. Of course, to the extent
that exchanges compete for new listings by adjusting their regulatory standards, this
motive for cross-listing may diminish over time. For example, Fanto and Karmel (1997)
suggest that current improvements in European regulatory standards are attracting US
institutional investors to stocks exclusively listed in Europe.
If foreign accounting standards and disclosure requirements act as a better guarantee
for minority shareholders than corresponding home-market regulations, companies
which cross-list can also be expected to feature a larger decrease in the concentration of
ownership relative to their domestic counterparts.
7
8
On this point see also Chemmanur and Fulghieri (1998).
See also Cantale (1996).
8
In addition, the signaling hypothesis predicts that the post-listing profitability of
companies which cross-list on a more demanding exchange should be better than that of
companies which cross-list on other exchanges. This should also be reflected in a
positive stock price reaction to the cross-listing announcement. This prediction is
supported by several studies surveyed in Karolyi (1998), which show that the price
reaction is significant for non-US companies listing in the US, which has the tightest
disclosure standards, whereas it is negligible otherwise.
e) Liquidity
Some markets may be better positioned than others in the production of liquidity, for
instance because of a better design of their microstructure or because of a larger number
of competing market makers. The beneficial effects of competitive pressure from
another exchange can also narrow the spreads on the domestic market and thereby raise
its trading activity, as found by Kadlec and McConnell (1994), Noronha, Sarin and
Saudagaran (1996), Foerster and Karolyi (1996) and Smith and Sofianos (1997).
However, cross-listing may not always lead to a greater liquidity for one’s shares,
due to the potentially offsetting impact of market fragmentation. Domowitz, Glen and
Madhavan (1998) show that opening trade on the foreign market may reduce liquidity in
both the domestic and the foreign market, if intermarket information linkages are poor,
and support this point with evidence concerning Mexican companies issuing American
Depository Receipts (ADRs).
To test if liquidity might be a reason for cross-listing, one can compare indicators of
trading for cross-listed shares, such as turnover ratios or bid-ask spreads. For instance,
the liquidity of the US market is unlikely to be an important motive for cross-listing for
European cross-listed companies if their turnover in the US turns out to be small
relative to their domestic turnover.9
f) Relative mispricing
Firms may decide to list abroad to take advantage of a temporarily high price for
their shares abroad relative to their home market. This could be due either to an
overvaluation in the foreign market or to an undervaluation in the domestic market. This
9
A related issue is whether foreign trading volume of cross-listed stocks tends to remain permanently
high after the foreign listing or gravitates back towards the home market over time.
9
can be tested by including the price indices of the two exchanges (or the relevant
sectoral indices) in regressions explaining the probability of a foreign listing.
2.2. Product Market Spillovers
Firms’ decisions about where to list may be affected by the location of their product
market, because the cross-listing may generate a positive feedback on the company’s
demand from consumers and its relationships with suppliers, owing to its perceived
reliability, managerial and product quality.10 Stoughton, Wong and Zechner (1998)
provide a model where a company lists in order to signal to consumers its high product
quality, and as a result captures a larger share of the market and increases its profits. In
this model, a listing is not associated with the need to raise capital or with the intention
to sell out by existing shareholders. The product market spillover hypothesis predicts
that cross-listed companies increase their fraction of foreign sales. It is also consistent
with higher overall sales growth and profits after the cross-listing. Furthermore, it is
only potentially relevant for industries in which the firm’s product market reputation is
of particular significance.
As far as profitability is concerned, caution is in order because this effect is also
consistent with other hypotheses. Furthermore, the corresponding test suffers from the
problem that the timing of cross-listing may be endogenous. Companies may choose to
list when their earnings performance is abnormally good, so mean reversion may induce
a drop in profitability after listing. This bias could be partially corrected by including
the change in profitability before listing as one of the explanatory variables in a
regression context. This would still, however, fail to control for accounting
manipulation, as found, for instance, by Teoh, Welch and Wong (1997a, 1997b). The
latter may be corrected only by using as control sample a set of companies that go
public in the domestic market, since these would presumably attempt the same kind of
accounting manipulation.11
10
It may also improve the quality of its managerial decisions since, after the foreign listing, its stock price
incorporates information which otherwise managers may have overlooked.
11
A post-listing improvement in profitability may also show up in the stock price performance, but we
shall not analyze this aspect, which has already been thoroughly researched. For instance, Foerster and
Karolyi (1999) analyse companies that went public in their domestic markets and subsequently listed in
one of the main US markets, and document abnormal positive performance before but abnormal negative
performance subsequent to the foreign listing.
10
2.3 Cost of Listing Abroad
Listing abroad also involves a variety of costs. There are direct costs, such as listing
charges and fees for professional advice. But the main costs cited in survey evidence
regarding potential cross-listings in the US (see Fanto and Karmel (1997)) are the cost
of complying with US GAAP accounting standards and the risk of lawsuits.
Presumably, shareholders’ power to interfere in managerial decisions increases with a
US listing. This survey evidence agrees with the results of the above-quoted studies by
Biddle and Saudagaran (1989) and Saudagaran and Biddle (1992).
Since the costs of cross-listing include a large fixed cost element, they bear most
heavily on small companies. Thus, we expect larger companies to be more likely to
cross-list. This prediction is borne out by Saudagaran’s (1988) study.
3. The Changing Geography of Equity Listings
This section provides a description of the cross-listing behavior of European and US
companies in the last decade. First, we document the “geography” of cross-listings, by
gauging regional clusters in cross-listing behavior. Second, we inquire if these patterns
have changed over time, and how. In particular, we investigate if there have been
substantial changes in “transatlantic listings”, that is, in the tendency of European
companies to list in the US and of US corporations to list in Europe. Third, we try to
relate these changes to the characteristics of the European and US exchanges concerned.
The sources of the cross-listing data used in the tables and figures of this section are
described in Appendix A.
3.1 Geographical Pattern of Cross-Listings
Table 2 summarizes the pattern of foreign listings in 1986-97 on the following stock
exchanges: Amsterdam, Brussels, Frankfurt, Milan, London, Madrid, Paris, Stockholm,
Vienna, Easdaq, Amex, Nasdaq, and NYSE.12 Panel A displays a matrix of foreign
12
The figures in the table refer to the stock of foreign listings on a given market, not the flow of new
listings in a given year.
11
listings, with the country of incorporation appearing in the columns and the destination
stock exchange along the rows. Each cell of the table contains three values: the top one
refers to 1986, the middle to 1991, and the bottom one to 1997. For each stock
exchange, the table displays only the foreign listings originating in the countries of our
sample: Netherlands, Belgium, Germany, Italy, UK, Spain, France, Sweden, Austria
(henceforth shortened to EU9 countries) and US. For instance, Japanese, Australian or
Canadian companies are excluded (evidence on these is deferred to Panel C of Table 2).
Looking at the column of a given country, one learns where the companies
originating from that country tend to cross-list. The column EU9 indicates in which
exchanges European (EU9) companies as a whole tend to cross-list. The last column
shows how the total number of cross-listing companies from the EU9 and US area have
distributed themselves within the area. The table indicates that European companies list
abroad more frequently within Europe than in the US. For instance, only 4 out of 32
foreign listings by Belgian companies were on the three US exchanges. This is true for
all EU9 countries’ companies (with the exception of the UK in 1997).
The table also suggests that common language and similar institutions foster crosslistings. For example, the Vienna stock exchange is the single largest destination for
German companies and vice versa. The same is true for the US and the UK.
This “clustering” indicates that companies tend to cross-list in countries
geographically or culturally close to their country of incorporation. This parallels the
findings by Portes and Rey (1999) and by Tesar and Werner (1995) that geographical
proximity and cultural homogeneity (especially language) greatly enhance cross-border
securities transaction flows. Geography and culture seem to matter also in cross-listing
behavior, presumably for the same reason: as a proxy for informational asymmetries.
For a US investor, for instance, the accounting data and the behavioral patterns of a
British company are easier to decipher than those of a French or Spanish company.
Looking at each row across columns, we learn what has been the contribution of each
country to the total number of foreign listings in a given market. Comparing the figures
in the US column with the others, American companies turn out to be the single largest
source of foreign listings on European exchanges. This is particularly evident for
Amsterdam and London, where in 1997 American corporations still are over two thirds
of the total number of foreign listings, despite the considerable decline in their number.
12
3.2 Changes in the Geography of Cross-Listings
The information in Panel A of Table 2 also gives a picture of how the geography of
European and US cross-listings has changed between 1986 and 1997. The two bottom
lines give an overall view of the change in the cross-listings pattern. The row “Total
Listings” displays the number of listings that companies from a given country have in
the foreign exchanges included in our sample. The bottom row “Total Companies”
eliminates double counting by reporting the number of companies from a given country
with at least one foreign listing. The number of foreign listings originated by a given
country is greater (or at most equal) to the corresponding number of companies listed
abroad, because the same company can be listed in several foreign exchanges.
The numbers in these two rows reveal that European companies have become more
outward looking in their search for investors: the number of EU9 companies listed
abroad doubled (from 177 to 337) and the total number of their foreign listings
increased by 61 percent (from 320 to 516).
In contrast with European companies, European stock exchanges do not appear to
have become equally outward oriented. Foreign listings on most European exchanges
exhibit an inverse U-shaped time pattern over time. In the European exchanges as a
whole, the total number of foreign listings increased very slightly from 732 in 1986 to
757 in 1991, and then declined to 625 in 1997 (see the last cell in the row “European
exchanges”). So these exchanges lost over one hundred foreign listings in a decade.
The opposite picture emerges when one considers American companies and
exchanges. US companies have become less eager to list in Europe, with their number
decreasing from 284 to 184. In contrast, US exchanges (especially the Nasdaq and the
NYSE) have captured an increasing share of foreign listings by European companies:
the listings of EU9 companies in the US went from 53 in 1986 to 207 in 1997, while in
the same interval their listings within Europe went from 267 to 309.
The contrast between these two opposite flows of “transatlantic listings” emerges
very clearly in panel B of Table 2. While European listings in the US almost
quadrupled, the number of US companies listed in Europe fell by over a third. In 1986
the US firms listed in Europe were more than five times as many as the European firms
13
listed in the US. In 1997, the latter outnumber the former. This suggests that the relative
attractiveness of European equity markets declined in this time window.
Panels A and B of Table 2 do not provide a full account of the outward orientation of
each exchange, because they neglect the listings originating outside our sample of
countries. Panel C completes the picture, by reporting cross-listings originating from the
rest of the world. Canadian, Latin American and Israeli companies are major sources of
listings in US exchanges, while they list much less frequently in Europe. In contrast,
South African and Asian companies list predominantly in London - with the exception
of Japanese corporations, which gravitate primarily towards Frankfurt. Considering
instead how the overall pattern changed over time, one sees again that the US exchanges
have captured the lion’s share of the increase in cross-listings from the rest of the world,
especially those from Australia, Canada, Latin America and Israel. In contrast, in most
cases, European exchanges have lost cross-listings originating from these regions.
The data in Table 2 raise two questions. First, is the decline of foreign listings on
European exchanges part of a more general decline in their ability to attract new
listings, including domestic ones? Second, are the three data points reported in Table 2
representative of the overall history of cross-listings between 1986 and 1997? Figures
1a and 1b address both questions.
Figure 1a (on the left) displays the time pattern of domestic and foreign companies
listed on each exchange, as well as their total number. The general impression is that the
European exchanges’ inability to attract new listings is not confined to foreign listings
alone. Most of them have not attracted a large number of new domestic listings either,
especially in the 1990s, with the exception of Frankfurt and, to some extent, of London.
The opposite is true of US exchanges, where both domestic and foreign listings
increased over the sample period: domestic listings rose from 6,168 in 1986 to 7,950 in
1997 (a 29% increase), while foreign listings increased from 350 to 873 (a staggering
150% increment, mostly accounted for by the NYSE).
Figure 1b (on the right) shows how cross-listings from our EU9 countries and the US
evolved in each exchange. It is based on the same data as Table 2, except that it reports
figures for all the years of our sample. The dotted line is the number of foreign
companies (from the rest of EU9 and the US) listed on a given domestic exchange,
whereas the solid line is the number of domestic companies listed in other EU9 and US
14
exchanges. For almost all the European exchanges, the dotted lines are declining and the
solid lines are rising, especially toward the end of the sample period, whereas the
opposite is true for US exchanges. This confirms the finding of Table 2.
A possible measure of the “outward orientation” of these markets is the ratio between
its foreign listings and its total listings. This measure is shown in Figure 2. The ratio
declined in Amsterdam, Frankfurt, London, and Vienna, was roughly stable in the other
European exchanges, Amex and Nasdaq, and increased substantially in the NYSE.
Conversely, Figure 3 shows a measure of the tendency of domestic companies to
cross-list in foreign exchanges, by reporting the ratio of cross-listed companies to the
total of domestically listed ones. We call this the “diaspora” index. For most European
countries, the diaspora index has increased substantially, with the smaller countries
recording the strongest diaspora.
3.3 Relationship with Characteristics of Stock Exchanges
The changes in the geography of equity listings documented in Table 2 and in
Figures 1 to 3 raise the question if they are related to some characteristics of the
exchanges and countries concerned. Since our data include only twelve exchanges and
ten countries, we cannot hope to draw reliable inferences from the data about such
correlations. Nevertheless, it is intriguing to see if the markets whose companies are
more eager to list abroad and which attracted less foreign listings differ systematically
from those with the opposite experience. In Table 3 we provide some information on the
market characteristics that may be related to the cross-listing decisions of companies,
based on the hypotheses outlined in Section 2 and summarized in the last column of
Table 1: accounting standards, degree of investor protection, size of the market (as
measured both by market capitalization and turnover) and trading costs.
In the first three columns we report information on the gross and net change in crosslistings of each exchange, drawn from the same data used for Table 2, Panel A.
Consistently with the results so far illustrated, most EU9 markets are net losers of
listings, Sweden being the only exception, while the US market experiences a net gain.
The normalized net change in the fourth column of the table indicates that the net loss
has been particularly large in the Netherlands, followed by Great Britain, Belgium and
Austria (in this order).
15
With the glaring exception of Great Britain, the normalized net change in crosslistings appears to correlate positively with accounting standards and investor
protection, in agreement with the evidence produced by Reese and Weinbach (1999).
The Netherlands, Belgium and Austria have relatively low values for both variables,
even within Europe. Sweden and the US have instead the highest rating on accounting
standards; Sweden fares a fair degree of investor protection and the US scores very
highly on this front.
In general, Europe − again except for the UK − compares
negatively with the US on both accounts.
Market capitalization and turnover hold less promise as a potentially explanatory
variable. Some of the markets that lost relatively more listings are small (the
Netherlands, Belgium and Austria), while the NYSE and NASDAQ are obviously the
largest. However, in this case the exceptions are not confined to the UK: relatively small
markets like Sweden are doing relatively well, and others like Italy and Spain are not
losing comparatively as much as larger markets like France, Germany and the UK.
Of all five variables, trading costs is the indicator which appears to have the closest
correlation with the normalized net change in cross-listings. The four markets with the
highest trading costs − Great Britain, Austria, the Netherlands and Belgium, in that
order − are precisely those featuring the largest net outflow of cross-listings. The
NYSE, which attracts most cross-listings, has the lowest trading costs (together with
Spain, and immediately followed by Germany, France, Italy, Nasdaq and Sweden).
In conclusion, the data in Table 3 are consistent with the view that low trading costs,
good accounting standards and strong investor protection may play a role in the crosslisting decisions of European and US companies. They also help to explain the one-way
net flow of transatlantic cross-listings in the 1986-97 interval (with the partial exception
of the UK, which has high trading costs but excellent accounting and investor protection
standards). Of course, the paucity of the data points in Table 3 is such that this
conclusion should be regarded as purely tentative. To make further progress in our
understanding of the cross-listing decision, we now leave the big picture and turn to
microeconomic evidence.
16
4. Company-Level Data: Descriptive Statistics
In the rest of this paper, we investigate the characteristics and performance of the
companies that cross-list, using companies that do not as our control sample. The
sample includes all the companies incorporated in our nine European countries for
which balance sheet information is available in the Global Vantage data base for the
1986-1997 interval. We exclude from the sample financial companies and investment
funds, as well as companies not listed in their country of incorporation.13
Summary statistics for the entire sample are provided in the Panel A of Table 4. The
total number of companies is 1,823. The median company has assets of US $ 372
million, sales of US $ 400 million and 3,022 employees. The median growth rate of
assets is 7.6 percent, property plant and equipment 6.7 percent, sales 8.2 percent, and
employees 1.2 percent. The median company has leverage of 19 percent, market to book
ratio of 1.81 and earns one third of its revenue from foreign sales.
There is huge variation in the values of some of these variables, even though we
eliminated economically meaningless outliers, such as negative sales figures (see the
appendix for details). For instance, total assets range from US $ 170 thousand to 113
billion, and the growth rate of plant property and equipment ranges from –100 percent
to over 1 million percent. This points to the need for robust statistical analysis in our
hypothesis testing.
R&D data is only provided for a very small proportion of the companies in the
sample; these spend 1.72 percent of revenue on R&D on average. To remedy the
paucity of observations on R&D, we construct an alternative “high-tech intensity”
indicator, based on the company’s SIC 4-digit classification code (see the appendix for
details). This dummy classifies 10.15 percent of the sample as high-tech companies.
Panel B of Table 4 illustrates the composition of the sample in terms of country of
incorporation and proportion of companies cross-listed, distinguishing those that were
already cross-listed in 1986 from those that cross-listed during the sample period. For
all countries of origin only a small proportion of sample companies, about 12.5 per cent,
list abroad at all. In terms of the country composition of our sample, the United
17
Kingdom is very heavily represented: nearly half of all companies studied, and over half
of the companies that first list abroad during our sample period, are British.
We now turn to a first comparison of the companies that list abroad with those that
do not, mainly focusing on balance sheet variables (such as total assets and sales) and
ratios (such as leverage and market to book value). Panel A of Table 5 reports the
difference between the median values of these variables for the cross-listed companies
and the companies listed only domestically, controlling for calendar year and country of
incorporation. More precisely, the values reported in the table are obtained by fitting a
median regression on a constant, a cross-listing dummy variable as well as control
dummies for calendar year and country. There are eight cross-listing dummy variables:
each one represents a particular year relative to the year of cross-listing, ranging from
year –3 (three years before) to year 3 (three years after) and a “permanent” dummy (4
or more years after).
The results concerning company size show that cross-listing companies are
significantly larger than companies that are only listed domestically. This is the case for
all the years relative to the listing period and for every size measure considered: total
assets, market value of common stock, revenue and number of employees. The
coefficient of the dummy variable for the permanent difference in median size is by far
the largest. This reflects the fact that this dummy captures mostly companies that were
historically the first ones to cross-list, and these are the largest companies in their home
country.14 The relatively large size of cross-listed companies agrees with the view that
there are economies of scale in cross-listing, reflecting fixed costs combined with
benefits that increase with company size.
Turning to the relationship between cross-listing and company growth, the table
displays growth in total assets, employment, sales and plant-and-equipment. For all
these variables, there is a marked peak in growth in the four years surrounding the
cross-listing date. In that period, the growth rates for cross-listing firms exceed the
growth rates of the control sample by about three to six percent, peaking in year zero
and reverting to normal two years later. That cross-listing is associated with a period of
13
Within the data base, we select only the companies in the sections “industrial active” or “industrial
research” and listed on their home-country stock exchange. To exclude financial companies, all
companies with SIC-codes starting with 6 have been dropped.
14
This finding does not imply that companies which cross-list grow faster in the long run.
18
exceptional growth, is consistent with the notion that new capital needs to be raised.
However, it is striking that the growth differential is not sustained in the long run.
As an indicator of international orientation, the table includes foreign sales as a
proportion of total sales. This variable is significantly greater for the cross-listing
companies in all the years considered, but particularly so after the cross-listing date. So
the data suggest that a foreign listing is more likely to be pursued by export-oriented
companies and at the same time is part of a strategy of expansion on foreign markets.
Surprisingly, the relatively high leverage of cross-listing firms increases upon crosslisting. Before, leverage is about seven percent above that of the control group, rising to
about twelve percent in the three years after listing. This is mirrored by the marked and
permanent decline of the capitalization ratio (stockholders’ equity divided by total
assets) after the cross-listing. Thus, reducing leverage does not seem to be the main
motive for cross-listing.
There is also some weak evidence that cross-listing firms are R&D-intensive (the
ratio of R&D expense to total sales is larger in all the years around the cross-listing but
not always significantly so). They also pay significantly higher average wages in the
years before the cross-listing date and up to two years after. Thus, they seem to be skillintensive firms.
Trading activity on the home exchange – as measured by the number of common
shares traded divided by their total number outstanding – is larger for companies which
cross-list, both before and after the time of cross-listing. This is consistent with the
already mentioned fact that these are large companies in their home market, with
accordingly high turnover ratios. So far, the data do not appear to support the hypothesis
that cross-listing enhances home market liquidity, but it does not seem to divert trading
away from the home market either.
Finally, the market-to-book ratio of cross-listing firms is not significantly different
from that of other firms, suggesting that market timing holds little promise as a potential
explanation for cross-listing. Also the return on assets (ROA) does not differ
significantly from that of the control group around the cross-listing date, except for a
drop three years after the cross-listing and thereafter.
In Panel B of Table 5 we repeat the comparison separately for companies which
cross-list for the first time in the US and for those that do so within Europe. Compared
19
with the control group, the companies that cross-list in Europe tend to be larger than
those that cross-list in the US in terms of total assets and number of employees, both
before and after the cross-listing date. But the most visible differences between the two
groups concern their R&D intensity before cross-listing and their long-run profitability
relative to the control group. First, the companies which cross-list in the US spend more
on R&D than the control sample, using the three measures of Table 5, whereas this is
not true of the companies that cross-list within Europe. The high-tech nature of the
companies listing in the US is also mirrored by their higher labor cost per employee.
Second, the companies that cross-list in the US appear to have a larger ROA in the longrun, compared to those that cross-list in Europe. This may reflect a different quality of
the two groups − a finding consistent with a signalling model, in the presence of higher
listing costs in the US.
5. Predicting Cross-Listing from Company Characteristics
The descriptive statistics discussed in the previous section provide some exploratory
evidence concerning the reasons why European companies list abroad. However, to
compare the explanatory power of the competing hypotheses and filter out spurious
correlations, we must turn to regression analysis. In the next subsection, we use logit
and duration analysis to investigate which company characteristics predict listing
abroad, and multinomial logit analysis to predict where they cross-list.
In Table 6 we analyze the determinants of the probability of listing abroad for the
first time in any given year. The set of determinants includes beginning-of-year values
of the logarithm of total assets, leverage, the domestic exchange’s market-to-book ratio,
the average of the three highest foreign market-to-book ratios, the previous year’s total
asset growth, proportion of sales abroad, and ROA.15 The regression also includes a
15
The foreign sales variable in our data set is missing for roughly one third of all companies. We impute
these missing values via regressions which generate predicted values of the percentage of foreign sales
based on the following regressors: the company mean value of the fraction of foreign sales (for the
companies where at least one data point is available), the logarithm of total assets, the growth rates of
total assets and sales, dummies for SIC codes at the 1-digit level, country of incorporation, calendar year,
and the high-tech dummy. The regression results reported in Tables 6 and 7 use the data obtained with
this imputation method. Since a regional breakdown of sales may be missing more frequently in
companies with no foreign sales, we perform a robustness check via an alternative imputation method
whereby the percentage of foreign sales is set equal to zero wherever it is missing. The estimates of the
coefficients in Table 6 and 7 are practically unaffected, and so are their estimated standard errors.
20
privatization dummy16, the “high-tech” dummy defined above, calendar year dummies
and regional origin dummies for each company: South (France, Italy and Spain), East
(Austria and Germany), North (Sweden, Belgium and Netherlands) and the default
(United Kingdom).
Since we are using repeated observations on individual companies to estimate a
logistic regression, we must take into account that the errors can be correlated across
observations referring to the same company. For this reason we use a Huber-Whitesandwich estimator of the variance-covariance matrix, which allows for dependence
within clusters of data concerning the same company.
The two most significant predictors of the decision to list abroad are the proportion
of sales abroad and the size of the company (as measured by the log of total assets). To
interpret the magnitude of their effect, observe that the estimates in the table are the
multiplicative impact of a unit increase in the independent variable on the odds ratio.
Therefore, a 1 percent point increase in the proportion of sales abroad increases the odds
ratio by a factor of 1.02, i.e. by 2 percent. A possible interpretation is that listing abroad
is partly a means of capitalizing on the reputation acquired through a presence on
foreign output markets. Conversely, companies that depend on foreign sales value the
positive publicity associated with a foreign listing – as suggested by Stoughton, Wong
and Zechner (1998). Size also raises the probability of listing abroad: the elasticity of
the odds of listing abroad with respect to total assets is 0.71 (the logarithm of 2.041).
The fact that the probability of listing abroad increases with company size suggests that
there are substantial fixed costs involved and that benefits are increasing in size: for
instance, a large company places larger demands on equity markets, thus benefiting
more from a wider shareholder base.
Several other variables are significant at the 1 percent level: the high-tech dummy
variable, the asset growth rate and the leverage ratio.
The odds of listing abroad are 2.70 times as large for high-tech companies as for
traditional companies. This agrees with the idea that high-tech companies turn to
foreign equity markets for capital because foreign investors and intermediaries know
more about the company’s business than their domestic counterparts, and thus can better
evaluate the stock.
16
This dummy equals 1 when the government makes a public offering of shares in the company.
21
There is also support for the view that companies list abroad after experiencing a
spurt in growth and investment, as found for domestic Italian initial public offerings
(IPOs) by Pagano, Panetta and Zingales (1998). Past growth of assets plays a significant
role in the regression: a 1 percent point increase in the growth rate is associated with a
0.1 percent increase in the odds of listing abroad. In addition, companies seem to list
abroad after accumulating debt, possibly the financial consequence of rapid asset
growth: a 1 percent point increase in leverage raises the odds ratio by 2 percent.
The privatization dummy is statistically significant at the 5 percent level. The odds of
listing abroad for a privatization issue are 4.41 times as large as for non-privatization
issues. Privatization issues tend to be very large, so that the depth of the international
equity market is likely to be needed to obtain a good price.
The market-to-book ratios on both the domestic and the foreign markets and the
ROA have no significant effects on the probability of listing abroad. So we do not find
evidence of companies trying to exploit “windows of opportunity” in the pricing of their
country’s stock or in the pricing on foreign stock markets, or to time their cross-listing
after an exceptional profit performance. This contrasts with the findings of the literature
on the timing of domestic IPOs (see Loughran, Ritter and Rydqvist, 1994, and Pagano,
Panetta and Zingales, 1998).
Lastly, there is evidence (at the 5% significance level) that companies from the
Southern region (France, Spain and Italy) are less likely to seek a listing abroad than
British companies: the odds of listing for a Southern company are only 0.33 times as
high as those for a British company.
Table 7 repeats the estimates using a Cox regression. This method measures the
impact of the various determinants on the hazard ratio, which in our case is the
probability of listing abroad for the first time. This method is particularly suited to the
prediction of discrete events in a panel setting. The results are very similar to those of
the logit regression of Table 6, both in magnitude and statistical significance.
We next investigate where companies cross-list. We wish to predict whether a
company is more likely to cross-list in Europe, in the US (possibly at the same time as
in Europe), or not at all. This is done in the multinomial regression shown in Table 8.17
17
The sample used to estimate this regression is about 10 percent larger than in the previous two tables,
because now we also use the observations for companies which were already listed abroad in 1986 (either
in a foreign European country or in the US). Another difference with the logit and Cox regression
22
All the regressors are lagged except for the High Tech dummy, for which no
endogeneity problems arise. As in the logit regression, standard errors are adjusted to
allow for dependence within clusters of data concerning the same company. The
estimates confirm that large size and high proportion of foreign sales are good
predictors of cross-listing – be it in the US or in Europe. High leverage and high-tech
industry classification are significant predictors of a cross-listing in US, but not in
Europe. Instead, the privatization dummy and the ROA are significant predictors only
for Europe.
Therefore, the overall picture is that a US listing is a more natural choice for
companies that have embarked on ambitious expansion programs and therefore have
accumulated a substantial amount of debt, and for high-tech companies. European stock
exchanges have instead been chosen more often by governments in the implementation
of their privatization programs. They have also been chosen by companies with a
stronger record of past profitability, but this may reflect the tighter listing requirements
of European exchanges (where only companies with some years of accounting profits
are eligible for listing) compared to US ones.
Interestingly, the companies which cross-list in the US are very unlikely to seek a
subsequent listing elsewhere in Europe, while a previous listing in Europe has no
significant effect on listing in the US.18 A previous listing in the US implies a sixfold
reduction in the relative odds ratio of cross-listing within Europe. The decision to access
US equity markets appears to be a one-way trip, which accords with the growing
imbalance in transatlantic cross-listings noted in Section 3.
The choice of cross-listing location also differs considerably by country of origin,
other factors being equal. British companies (the default regional dummy) are more
likely to list in the US than German and Austrian companies. Again, this agrees with the
greater tendency of UK companies to list in the US noted in the aggregate statistics of
Section 3.
estimates is that the observations for companies which already cross-listed within Europe are used in
estimating the probability of cross-listing in the US and vice versa, since these two events are not
mutually exclusive. In fact, the list of regressors used in Tables 6 and 7 is now expanded to include the
number of previous foreign listings of each company in the other continent.
18
In our sample, two thirds of the companies that cross-listed in the US had not previously cross-listed
elsewhere in Europe. 88 firms that cross-listed in the US had no prior EU cross-listing, 17 had one, 9 had
two, 7 had three, 4 had four and 7 had five cross-listings in Europe prior to listing in the US.
23
6. Ex Post Evidence on Cross-Listed Companies
In this section we assess the effects of listing abroad on the subsequent performance
of companies. In the model to be estimated each variable is modeled as depending on
the dummies (first introduced in Table 5) to indicate the year of listing, the three years
after the listing and the subsequent years:
yit = α 0 + α1 f i + α 2d it0 + α 3d it1− 3 + α 4d itp + ε it ,
where yit is the dependent variable (e.g., the logarithm of total assets of company i at
time t), f i denotes a company fixed effect, d it0 is a dummy intended to capture the
impact effect of the first cross-listing of company i, dit1−3 is a dummy corresponding to
the three subsequent years and d itp captures the permanent effect of the cross-listing. To
limit the effect of influential observations, we use median regression estimation, and to
eliminate fixed effects we difference both sides of the equation, so that the specification
becomes:
∆yit = α 2 ∆d it0 + α 3∆d it1− 3 + α 4 ∆ditp + ηit ,
where ηit ≡ ∆ε it . In Table 9 we report the results of the estimation of this differenced
model. Very few coefficients appear to be precisely estimated. The turnover rate on the
domestic market appears to be negatively affected by the foreign listing, both upon
impact and in the three subsequent years, in contrast with the findings of Noronha, Sarin
and Saudagaran (1996) and Foerster and Karolyi (1996). The fraction of foreign sales
appears to increase upon cross-listing. Research per employee rises strongly both upon
impact and keeps increasing afterwards, witnessing the rising R&D intensity of these
companies, while the labor cost per employee changes in the opposite direction.
A possible reason why most of the estimated coefficients in Table 9 are small and
imprecise is that this specification unduly restricts the effects of cross-listing on the US
and European exchanges to be the same. This is confirmed by Table 10, where we
introduce separate dummy variables for the listings in the two continents.19
19
In Table 10 there are two cross-listing variables: one captures the first cross-listing in Europe and the
other captures the first cross-listing in the US Thus, unlike in Table 9 (where only the first cross-listing
anywhere was considered), in Table 10 the same company can go through two cross-listing events.
24
What happens after a cross-listing could hardly differ more sharply in the two cases.
The companies that cross-list in the US experience a 6.5 percent permanent increase in
total assets and a 4.62 percent long-run increase in the growth rate of employees. In
contrast, companies that cross-list within Europe end up with a 3.8 percent permanent
reduction of total assets and a 5 percent long-run decrease in the growth rate of sales,
relative to the control sample. The estimates for the capitalization and leverage ratios
show that the expansion of total assets for the companies which cross-list in the US is
funded by an increased amount of equity and no significant leveraging (actually some
deleveraging in the cross-listing year). The opposite is true for companies cross-listing
within Europe. The opposite changes in the capital structure of the two types of
companies are also mirrored in the opposing time patterns of the market value of their
outstanding stock after the cross-listing.20
In short, the overall picture is that cross-listings in the US are prompted by the need
to fuel rapid expansion via new equity issues, while those within Europe are at best used
to increase the debt capacity of the company and are hardly followed by rapid growth.
This striking difference is consistent with the results of Table 8, where cross-listings in
the US – but not in Europe – are shown to follow rapid expansion of the asset base.
Another difference concerns the degree of export orientation of the two group’s sales
strategies: companies cross-listed in the US experience a small (0.2 percent) increase in
the fraction of foreign sales upon impact, leveling off to 0.18 percent in subsequent
years, relative to the control group. Companies cross-listed within Europe temporarily
lose ground on foreign markets upon impact and in the subsequent three years, although
eventually they experience a foreign sales expansion comparable to the other group.
The turnover ratio on the home market also behaves differently in the two cases.
After a listing in the US the change in turnover is not statistically significant, whereas
after a cross-listing in Europe there is an 8 percent drop in the domestic turnover ratio
(significant at the 10 percent level) upon impact, followed by comparable reductions in
later years, though imprecisely estimated. This is consistent with the “time zone”
hypothesis proposed by Pulatkonak and Sofianos (1999), who show that NYSE trading
in non-US stocks tends to decrease with the time zone difference.21
20
It is less clear why the market-to-book ratio does not change appreciably for the companies that crosslist in the US but rises considerably (though not permanently) after a listing in Europe.
21
In Table 9 (p. 47) of their study they show that for European, non-UK stocks the share of NYSE trading
is considerably lower than that of London trading: for the cross-listed stocks of the Netherlands, Spain,
25
R&D intensity is the only variable that appears to move in the same direction in both
cases, rising throughout the post-listing period. On a per employee basis and a percent
of sales, it increases more after European cross-listings, whereas as a percentage of total
labor expenses it rises more after US listings. But in any event the increasing reliance
on R&D that already emerges in Table 9 is confirmed by the results of Table 10.
7. Conclusions
We can now bring together the results in the two parts of this paper: the account of
the aggregate trends in the geography of listings in Europe and the US in 1986-97 and
the analysis of a panel of European companies in the same time interval. In particular, it
is worthwhile asking if our findings about the individual cross-listing decisions help us
explain the changes in the geography of equity listings.
Our aggregate figures show that the number of European companies cross-listing
their shares increased considerably, but most of the increase went to US exchanges (of
which the NYSE absorbed more than half). At the same time, the number of US
companies cross-listing in Europe fell by a third. The end result has been a decline of
foreign listings in Europe and a large increase in European listings in the US.
The decline of foreign listings on European exchanges appears to be part of a more
general decline in their ability to attract new listings. Most of them have not attracted a
large number of new domestic listings either, especially in the 1990s, with the exception
of Frankfurt and, to some extent, of London. The opposite is true of US exchanges,
where both domestic and foreign listings increased over the sample period.
Interestingly, the European countries whose companies have been more eager in
seeking foreign listings and whose exchanges have been least able to attract or retain
foreign listings are those with the highest trading costs and - with the exception of the
UK - with the lowest accounting standards and worst shareholder protection. Viceversa,
the US feature lower trading costs, tighter accounting standards and better shareholder
protection than most European countries.
Germany, Italy, France, and Sweden, the NYSE share of total trading is on average 12 percent, while the
UK share is 28 percent. Being effected on a closer marketplace, a cross-listing within Europe tends to
“eat” into domestic turnover much more than a listing effected in the US.
26
The microeconomic analysis of the characteristics and behavior of European
companies helps to understand better the motives of their cross-listing decisions, and
thus the reasons behind the one-way flow of cross-listings from Europe to the US. Apart
from a few common features, European companies that cross-list in Europe and in the
U.S. appear to have sharply different characteristics and performances.
The few common features are size, high foreign sales and high R&D spending before
listing abroad. The importance of size suggests that the cross-listing decision involves
non-negligible fixed costs and economies of scale, consistently with the findings of
studies of the decision to list in domestic market such as Pagano, Panetta and Zingales
(1998). The role of the fraction of foreign sales underscores the importance of
international orientation. Firms are able to take advantage of foreign investors’
familiarity with the firm and its products in order to find a market for their shares; or
conversely, the purpose of the cross-listing may be to enhance the firm’s profile and
reputation abroad so as to aid its product marketing efforts. The role of R&D spending
may indicate that firms seek a foreign listing to access a pool of investors who are more
knowledgeable about their prospects than domestic investors.
Apart from these common features, European companies that cross-list in the United
States differ considerably from those which do so within Europe. In the first case,
companies pursue a strategy of rapid expansion fuelled by high leverage before the
listing and large equity issues after the listing. They feature increasing reliance on
export markets not only before but also immediately after the listing, and tend to belong
to high-tech industries. Companies which cross-list in Europe, instead, do not grow
more than the control group and increase their leverage after the foreign listing.
Moreover, they do not rely on foreign sales to the same extent as firms cross-listing in
the US, and generally do not belong to high-tech sectors. Finally, they are more likely to
be newly privatized firms.22
Therefore, on the whole the motivation for a US listing appears to be the need of an
equity infusion by rapidly expanding, highly levered companies that plan to expand
their sales internationally and/or belong to high-tech industries. The latter finding is
consistent with Blass and Yafeh (1999), who report that Israeli and Dutch firms which
22
In addition, transatlantic listing seems to be a one-way street for European companies. A European
company that is already listed abroad within Europe is not discouraged from seeking a US listing; but
once a company is cross-listed in the US, it is significantly less likely to cross-list abroad within Europe.
27
choose Nasdaq for the first listing are overwhelmingly high-tech oriented. The
motivations for cross-listing within Europe are not equally clear, but the companies that
take this route are definitely less dynamic, less outward-oriented and in more mature
sectors than those of the other group.
The contrast between these two groups is reminiscent of the contrast between
European and US companies’ domestic IPOs, documented by Pagano, Panetta and
Zingales (1998), Planell (1995), Rydqvist and Högholm (1995) and Mikkelson, Partch
and Shah (1997). These studies, respectively conducted on Italian, Spanish, Swedish
and US panel data, investigate the characteristics and behavior that distinguish
companies listing for the first time (on their domestic market) from those that decide to
stay private. In Italy, Spain and Sweden, domestic IPOs do not appear to finance
subsequent investment and growth while in the US they feature phenomenal growth.
Moreover, European IPOs are on average much older then their US counterparts.
These studies on domestic IPOs therefore suggest that in European countries the
stock market mainly caters to large, mature companies with little need to finance
investment, while the opposite is true of the United States. In the present paper we find
that this applies equally to cross-listing decisions: when it comes to cross-listing, the
most dynamic and outward-oriented European companies self-select in US exchanges.
The main remaining puzzle is why European exchanges are judged to be less attractive
by this group of companies. Probably the answer has several pieces to itself.
First, the high-tech nature of the European companies listing in the US suggests that
a key advantage of the US market is the presence of skilled analysts and institutional
investors specializing in evaluating these companies. This agrees with the finding by
Baker, Nofsinger and Weaver (1999) that listing on the NYSE induces higher analyst
coverage than listing in London. This comparative advantage of the US market may
partly reflect its sheer size, combined with the fixed costs of expertise in high-tech
industries. The costly investments in human capital required to evaluate high-tech
companies in their respective industries are worthwhile only if many such companies
are already listed, and this is true of a large continental market such as the US, but not
of European markets.
Second, as already stated, American exchanges are more liquid than most European
exchanges, and the US feature better accounting standards and shareholder rights’
28
protection than most European countries. Insofar as these comparative advantages
translate in a lower cost of equity capital, they may be particularly important to
companies who need to raise large amounts of fresh equity.
Last, but not least, the US economy has not only a large capital market but also a
huge product market − and one which has grown at a consistently higher pace than
European markets in the last decade. Therefore, it has been the natural springboard for
foreign companies with a strong export orientation, since it has allowed them to
capitalize on their product market reputation and expand their foreign sales rapidly,
possibly via acquisitions in the US.
If these are the main factors of comparative advantage of US exchanges relative to
European ones, they may attenuate gradually as the process of integration of European
capital markets proceeds. The removal of capital controls and the more homogeneous
regulatory framework of European directives is likely to lead to the birth of a truly
continental equity market and to increasing integration of markets for good and services
in Europe. If many of the factors of comparative advantage discussed above depend on
sheer market size, European companies may become less interested in cross-listing on
US exchanges. But this will not apply to companies from many non-European
countries, for which the US market is likely to retain its attraction.
29
Appendix: Data Sources and Definitions
(A) Market segments used and data sources
Stock exchange
market segment
control group
-
Data sources used
Amsterdam
market segment
(foreign companies)
Foreign and Canadian
Issues
Aandelen Buitenland
Aandelen Binnenland,
(excl. parallel market)
Brussels
Easdaq
Premier Marche
EASDAQ market
Premier Marché
-
Frankfurt
Amtlicher Handel
Amtlicher Handel
Milan
Stock Exchange’s Equity
Market and Mercato
Ristretto
Overseas Listings
(excl. Ireland)
[Official List]
Stock Exchange’s Equity
Market and Mercato
Ristretto
Companies of the
F.T. All Shares Index
Het Financieele Dagblad;
Officiele Prijscourant;
Stock exchange
Stock exchange
Financial Times 27. 11. 1997
and FT Information
Amtliches Kursblatt der
Frankfurter Wertpapier-börse,
1986-1997
Stock exchange
Continuous and Floor
International Listings
Non-US corporate issuers
Premier, Second and
Nouveau Marche
A, O und OTC-list
Amtlicher Handel and
Geregelter Freiverkehr
Primero Mercado
Premier, Second and
Nouveau Marche
A, O und OTC-list
Amtlicher Handel and
Geregelter Freiverkehr
Amex
London
Madrid
Nasdaq
NYSE
Paris
Stockholm
Vienna
Stock exchange
Official price list, factbooks
from the Financial Times
Business Research Centre,
LSE Quarterly, Risk
Measurement Service, 19861997 from the LBS
Stock exchange
Stock exchange
Stock exchange
Stock exchange
Stock exchange
Stock exchange
Note: The number of domestic companies used for Figures 1 to 3 are obtained by adjusting FIBV data on
main and parallel markets in various ways. First, since the FIBV 1986-88 figures include investment
funds, 1986-88 figures are adjusted by the proportion of investment funds over companies in 1989.
Second, we had to make a number of market-specific adjustments.
For the Paris Stock Exchange the FIBV numbers before 1997 do not include the Second Marché. We
therefore use FIBV data only for 1997, and before 1997 drawn our data from the SBF 1997 factbook.
For the Frankfurt Stock Exchange we restrict ourselves to the Amtlicher Handel. We did not include
foreign listings in the Freiverkehr. This segment features an inflated number of foreign listings, since it
trades foreign firm’s shares even without the firm’s application. We were could not obtain data on the
Geregelter Markt, but only very few companies in this segment would qualify to be in our sample.
For the Nasdaq data before 1997, the number of domestic firms was provided by Nasdaq and the total
number of listings was obtained from the Nasdaq Factbook 1997. The number of foreign firms was
calculated as the difference between total and domestic listings.
For the Stockholm Stock Exchange, we obtained the total number of listings and the number of foreign
listings from the factbooks 1997 and 1998. The number of domestic listings was obtained by calculating
the difference between the two.
For the Amsterdam stock exchange, the FIBV data for 1993 and 1994 are the number of shares, not
companies. To obtain a proxy for the number of domestic companies, we calculated the proportion of
domestic companies relative to domestic shares for 1993 by the ratio of the number of domestic
companies (OM) to the number of domestic shares (OM) reported in the factbook "The Amsterdam Stock
Exchange in 1993". We then multiplied the FIBV numbers by this ratio.
30
(B) Variable Definitions and Sources
Variable
Calendar Year
Dummies
Capitalization Ratio
Common Shares
Outstanding
Common Shares
Traded
Common Shares
Traded / Outstanding
Countries’ PBV
Country Dummies
Employees
(in 1000)
Employees Growth
(in percent)
Foreign Sales
Proportion
(in percent)
Source / formula
Strategy used to purge the
data from measurement
error
The calendar year dummy for 1986 equals 1 in
1986 and 0 in all other years. Analogous dummies
are defined for all calendar years from 1986 to
1997.
Global Vantage; the average of the most current Set not available whenever
two years’ of stockholder’s equity divided by total smaller than or equal to
assets, multiplied by 100.
zero;
Set not available if
shareholders’ equity is
smaller than zero.
Global Vantage; issue item; represents the net
Set not available whenever
number of common / ordinary shares outstanding smaller than or equal to zero
as of the company’s fiscal year-end.
Global Vantage; issue item; represents the number Set not available whenever
of shares traded in the calendar month for an issue; smaller than zero
December values were used.
Calculated from common shares traded divided by
common shares outstanding
Morgan Stanley Capital International; yearend
price to book ratios for the countries investigated
Takes on the value of one for the country where
the company is incorporated or legally registered.
The information on the country of incorporation is
obained from Global Vantage
Global Vantage; number of company workers as
reported to shareholders. It is reported as an
average number of employees by some companies
and as the number of employees at yearend by
others. These different bases of reporting are not
differentiated.
Calculated from employees – employees(-1)
divided by employees(-1) multiplied by 100
Worldscope
Negative sign of number of
employees has been changed
into a positive sign for the
companies (name, year):
Greenall Whitley, 1994
Rugby Cement, 1991
Spring Ram Corp PLC,
1991
Bluebird Toys, 1996
Additionally set not
available whenever smaller
than zero or employees
growth rate smaller than –99
%
Set not available whenever
smaller than zero or larger
than 100
31
Variable
Hightech Sector
Dummy
Source / formula
The dummy equals 1 for the following list of SICcodes, and 0 otherwise:
2830 drugs
2833 medicinal chemicals, botanical products
2834 pharmaceutical preparations
2835 in vitro, in vivo diagnostics
2836 biological products, ex diagnostics
3570 computer and office equipment
3571 electronic computers
3572 computer storage devices
3575 computer terminals
3576 computer communication equipment
3577 computer peripheral equipment
3651 household audio and video equipment
3660 communication equipment
3661 telephone and telegraph apparatus
3663 radio, tv broadcast, communication
equipment
3669 communiations equipment
3670 electronic components and accessories
3671 electron tubes
3672 printed circuit boards
3674 semiconductor and related device
3760 guided missiles, space vehicles
3761 guided missiles, space vehicles
3764 guided missiles, space vehicles propulsion
3769 guided missiles, space vehicles parts
3810 search, detection, naval, guided, aero
systems
3812 search, detection, naval, guided, aero
systems
3820 laboratory apparatus, optical, measure,
control instruments
3821 laboratory apparatus and furniture
3822 automatic regulating controls
3823 industrial measurement instruments
3826 laboratory analytical instruments
3840 surgical, medical, dental instruments
3841 surgical, medical instruments, apparatus
4800 communications
4810 telephone communications
4812 radiotelephone communications
4813 phone comm ex radiotelephone
4820 telegraph and other mess. communication
4822 telegraph and other mess. communication
4830 radio, tv broadcasting stations
4832 radio broadcasting stations
4833 television broadcasting stations
4840 cable and other pay tv services
4841 cable and other pay tv services
4890 communication services
4899 communication services
7370 cmp programming, data processing
7371 computer programming service
7372 prepackaged software
7373 component integrated system design
Strategy used to purge the
data from measurement
error
32
Variable
Issue Market to Book
Ratio
Issue Market Value
(in billion USD)
Labor & Related
Expense
(in million USD)
Labor Cost / Employee
(in 1000 USD)
Leverage
(in percent)
Privatisation Dummy
Source / formula
Strategy used to purge the
data from measurement
error
Set not available whenever
Global Vantage; issue item; represents pricesmaller than or equal to
monthly-close divided by the result of
zero;
common/ordinary equity divided by common
shares outstanding (converted to the corresponding Set not available if total
shareholders’ equity is
month). If the most recent observation for
common shares outstanding is not available, the smaller than zero.
prior year’s value is used
Global Vantage; issue item; is the price-monthly- Set not available whenever
close mulitplied by common shares outstanding. If smaller than or equal to zero
the most recent observation for common shares
outstanding (converted to the corresponding
month) is not available, the prior year’s value is
used.
Global Vantage; represents direct payments to,
Set not available whenever
and indirect payments on behalf of, all employees. smaller than zero
Calculated as labor and related expense divided by
employees
Calculated from total debt divided by total assets,
multiplied by 100
Dummy is set equal to one in the year of a
privatisation (or seasoned offering) and zero in
other years; the database used for assigning the
values has been kindly provided by Bernardo
Bortolotti, Fondazione ENI Enrico Mattei
Property Plant
Calculated as property plant equipment net –
Equipment Growth
property plant equipment net (-1) divided by
property plant equipment net (-1), multiplied by
100.
Property Plant
Global Vantage; net cost or valuation of tangible
Equipment Net
fixed property used in the production of revenue.
Calculated by Global Vantage as: Fixed Assets Total (gross) less Depreciation, Depletion,
Amortisation (Accumulated), less Investment
Grants and Other Deductions
Regional Dummy
Takes on the value of one if country of
North
incorporation is Netherlands, Sweden or Belgium,
zero elsewhere
Regional Dummy
Takes on the value of one if country of
East
incorporation is Germany or Austria, zero
elsewhere
Regional Dummy
Takes on the value of one if country of
South
incorporation is France, Italy or Spain, zero
elsewhere
Regional Dummy UK Takes on the value of one if country of
incorporation is Great Britain, zero elsewhere
Research / Labor
Calculated as research and development expenses
Expense
divided by labor and related expense
Research / Revenue
Calculated as research and development expenses
(in percent)
divided by (total revenue*10)
Research and
Global Vantage; represents all costs incurred
Development Expenses relating to development of new products or
(in million USD)
services
Set not available if larger
than 100
Set not available whenever
smaller than zero
Set not available whenever
smaller than zero
33
Variable
Source / formula
Research per Employee
(in 1000 USD)
Return on Assets
(in percent)
Calculated by research and development expenses
divided by employees
Global Vantage; income before extraordinary
items divided by the average of the most recent
two years of assets-total multiplied by 100
Global Vantage; 4 digit Standard Industry
Classification code
Global Vantage; total value of assets reported on
the Balance Sheet
SIC Codes
Total Assets
(in billion USD)
Strategy used to purge the
data from measurement
error
Set not available whenever
return on assets is less than –
100%.
Set not available whenever
total assets is smaller than or
equal to zero or total assets
growth is smaller than –95
%.
Total Assets Growth
(in percent)
Calculated from total assets – total assets(-1)
divided by total assets(-1) multiplied by 100
Total Debt
Global Vantage; sum of long term debt – total plus Set not available whenever
(in billion USD)
debt in current liabilities
smaller than zero
Total Revenue
Global Vantage; represents Sales/Turnover (net) Set not available if smaller
(in billion USD)
than zero
Total Revenue Growth Global Vantage; represents (total revenue divided Set not available if smaller
(in percent)
by total revenue(-1))-1, multiplied by 100.
than
–99 %
Total Shareholders
Global Vantage; represents common/ordinary and
Equity
preferred/preference shareholders’ interest in the
company and any reserves reported in the
Shareholders’ Equity section
variables concerning
Data constructed using data sources summarized
Foreign Listings
in the appendix on market segments.
The variables are defined in different ways for the
different methodologies used and explained in the
notes preceding the tables.
Note: Variables are always calculated using the “purged” data. The variables marked as “issue item”
variables concern a selected issue of the company only. Where available, we have selected common /
ordinary shares; otherwise, we have selected an issue as close as possible to common shares.
Additional adjustments:
-
Several variables of Fiat 1988, DAF 1989, 1992, Heidelberger 1989 and ENI 1986-88 have been set
not available due to unrealistic magnitudes of the data in those years.
If total revenue is actually zero or the ratio of total revenue to total assets is smaller than 0.01, this
fact is used as indicator of a holding company. All accounting variables are set not available for these
companies.
34
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38
Table 1
Motives for Cross-Listing and Their Empirical Implications
Hypothesis about
motive for crosslisting
1. Raising capital
for investment
2.
3.
4.
5.
6.
7.
Predicting crosslisting (ex ante
evidence)
High leverage
Consequences of
cross-listing (ex post
evidence)
High growth, P/E and
real investment
High growth, P/E and
real investment
Stock sales by Newly privatized
High share turnover
firms
existing
shareholders
High risk firms
More foreign
Broadening
investors and high
shareholders’
foreign turnover
base
Capitalizing on High fraction of
product market foreign sales
reputation
High-tech sector,
Foreign
large R&D spending
expertise
Decrease in
Commitment to Low domestic
regulatory
standards
ownership
disclosure &
concentration,
governance
no worsening in
standards
profitability
Higher share turnover
Liquidity
8. Relative
mispricing
Stock Market
characteristics
Deep and liquid stock
market
Deep and liquid stock
market
Large stock market
Stock market located
where company’s
foreign sales are high
Knowledgeable
investors and analysts
High regulatory
standards
Stock market with
low spreads, low
brokerage fees and
high volume
Low domestic E/P
ratio relative to
foreign E/P ratio
9. Strengthen the
company’s
output market
10. Listing costs are Large size
low relative to
benefits
Higher foreign sales
and profits, without
necessarily raising
more capital
Market located where
company’s foreign
sales have large
growth potential
Low listing fees
and disclosure
requirements
39
Table 2. Number of Cross-Listings in 1986, 1991 and 1997 (End-of-Year Values)
Panel A: EU9-USA Cross-Listings Matrix
Country of origin
Stock Exchange
Amsterdam
Stock Exchange
Brussels
Stock Exchange
Frankfurt
Stock Exchange
NetherBelgium Germany Italy
lands
7
8
7
15
15
14
12
16
19
2
4
4
Italian
Stock Exchange
London
Stock Exchange
7
10
11
2
1
2
2
3
8
11
11
12
11
9
3
3
12
15
13
Madrid
Stock Exchange
Paris
Stock Exchange
10
9
8
Stockholm
Stock Exchange
Vienna
Stock Exchange
12
11
10
10
9
8
1
4
5
5
3
3
1
5
4
2
4
6
5
UK
14
20
11
14
17
11
14
21
13
1
1
Spain
France Sweden Austria
EU9
USA
Total
Comp.
2
38
44
31
54
60
49
48
74
65
129
108
83
36
36
34
51
58
42
167
152
114
90
96
83
99
132
107
193
159
111
2
4
234
206
158
52
52
37
1
1
5
3
4
2
3
4
116
128
98
1
4
9
25
34
28
6
4
4
2
2
8
13
12
5
10
8
4
4
4
1
4
7
5
2
1
1
3
4
4
1
1
2
9
8
2
4
41
47
47
15
13
14
1
6
6
3
1
1
17
21
20
3
1
59
73
69
2
19
23
14
14
24
17
5
5
4
5
5
5
1
2
27
23
24
2
2
11
13
2
2
1
1
3
4
64
76
61
0
3
4
22
30
26
Easdaq
European
exchanges
(EU9 & Easdaq)
Amex
Nasdaq
NYSE
Total Listings
Total
Companies
48
55
58
6
5
17
4
6
16
58
66
91
27
32
48
6
23
24
28
1
1
1
2
1
23
24
32
7
60
74
77
4
11
19
27
27
3
57
83
55
3
4
4
18
25
55
11
26
46
89
138
160
17
15
24
26
29
31
10
11
19
54
89
130
3
2
2
8
7
6
10
1
7
9
16
20
21
3
14
19
40
58
3
34
29
37
2
11
13
18
267
339
309
3
5
4
34
39
96
16
46
107
320
429
516
8
9
10
15
22
43
18
15
21
2
9
11
177
231
337
15
13
12
5
17
34
36
1
2
465
418
316
465
418
316
20
732
757
625
3
5
4
34
39
96
16
46
107
785
847
832
284
234
184
461
465
521
40
[Table 2, continued]
Panel B: Summary of Transatlantic Listings
Stock Exchange
EU9
Exchanges
U.S.
Exchanges
Country of origin
EU9-Countries
Foreign Listings
Foreign Companies
267
147
339
182
309
180
53
52
90
89
207
206
Foreign Listings
465
418
316
U.S.
Foreign Companies
284
234
184
41
[Table 2, continued]
Panel C: Listings on EU9-USA Exchanges from the Rest of World,
by Country or Region of Origin
Country of origin
Stock Exchange
Amsterdam
Stock Exchange
Brussels
Stock Exchange
Frankfurt
Stock Exchange
Australia,
New Guinea,
New
Canada
Zealand,
Vanuatu
2
5
Central
Central
and
and South
Eastern
America
Europe
6
3
13
8
4
9
11
9
2
4
2
18
19
14
25
29
22
1
1
14
7
16
19
1
3
1
15
13
7
1
1
3
4
3
1
Israel
1
1
1
Rest of
Africa
Rest of
Europe
Rest of
Asia
South
Africa
West
Indies
23
24
21
6
6
5
57
60
56
1
1
1
2
2
6
6
4
4
5
3
7
9
6
12
23
18
2
1
1
16
16
18
5
5
6
1
8
27
29
8
7
6
14
24
18
22
15
50
90
94
55
4
15
5
16
37
32
11
12
10
8
8
8
6
7
4
1
5
2
2
1
22
22
17
1
1
1
3
8
4
2
1
14
2
3
32
30
32
103
1
1
1
17
17
15
1
1
1
154
157
114
1
2
3
9
9
8
4
4
13
19
32
30
30
32
101
102
105
67
16
29
28
Japan
Italian
Stock Exchange
London
Stock Exchange
3
3
2
Madrid
Stock Exchange
Paris
Stock Exchange
Stockholm Stock
Exchange
1
1
Vienna
Stock Exchange
2
2
1
Easdaq
1
12
10
22
1
9
15
34
53
56
1
34
44
40
119
125
165
21
27
65
238
262
316
32
36
47
198
221
285
Amex
Nasdaq
NYSE
Total Listings
Total Companies
2
2
2
4
18
1
1
4
11
8
26
3
4
93
25
35
146
5
5
5
16
23
71
1
1
6
25
32
84
16
15
16
8
9
11
134
178
170
26
26
20
8
6
23
2
3
21
62
92
105
1
3
18
21
26
139
23
31
84
81
99
100
24
24
19
44
63
78
2
42
Table 3. Foreign Listings, Market and Country Characteristics
This table merges information on cross-listings within the EU9 and US area with market and country characteristics.
Change in Cross-Listings into Market is the difference between the number of cross-listings of EU9 and US
companies into a given market 1997 and 1986. Change in Cross-Listings Out of Market is the difference between the
number of listings by domestic companies in other EU9 and US markets between 1997 and 1986. Net Change is
difference between Change in Cross-Listings into Market and Change in Cross Listings Out of Market. Normalized
Net Change is the ratio of Net Change to the total number of EU9 and US companies listed in 1991, multiplied by
100. Accounting Standards is the rating reported by La Porta et al. (1998) on the basis of 1990 accounting
information. Investor Protection is the Antidirector Rights Index from LaPorta et al. (1998). Market Capitalization is
measured in billions of US dollars as of 1991 (source: International Federation of Stock Exchanges). Turnover is the
value of share trading (main and parallel markets) as of 1991, measured in billions of US dollars (source:
International Federation of Stock Exchanges). Note that comparison between exchanges is problematic due to
different definitions and calculation methods used by different stock exchanges. T figures only count transactions
that pass through the trading systems of an exchange, while R figures include all transactions, without making a
difference between on- and off-market. Trading Cost is measured in basis points as of the 3rd quarter of 1998. It is the
average sum of commission, fees and market impact based on trade data on all global trades done by 135
institutional investors in a given market (source: Elkins/McSherry Co., Inc.).
Market
Change in Change in
CrossCrossNet
Listings Listings
Change
into
out of
Market
Market
NormalAccountMarket
ized
Investor
ing StandCapitalNet
Protection
ards
ization
Change
Turnover
Trading
Costs
(Basis
Points)
Netherlands
-53
+33
-86
-24.2
64
2
135.98
38.9 R
34.56
Belgium
-7
+9
-16
-5.7
61
0
71.11
8.2 T
33.21
Germany
+8
+17
-9
-2.2
62
1
392.47
404.6 R
29.70
Italy
+4
+8
-4
-1.6
62
1
158.81
23.4 T
29.84
Great Britain
-76
+71
-147
-6.6
78
5
986.11
553.9 R
51.88
Spain
+4
+5
-1
-0.2
64
4
127.30
35.3 T
24.57
France
-18
+39
-21
-2.2
69
3
373.36
116.6 T
27.63
Sweden
+8
+3
+5
+2.3
83
3
97.06
20.6 R
32.26
Austria
+3
+11
-8
-5.8
54
2
26.04
7.2 T
51.29
Amex
+1
124.45
40.9 T
N.A.
Nasdaq
+62
490.68
693.9 R
30.64
NYSE
+91
3484.34 1520.2 T
24.57
-149
+303
+4.6
71
5
43
Table 4. The Global Vantage Sample
Panel A: Summary Statistics
Mean
Total Assets
Total Assets Growth
Capitalization Ratio
Com. Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Prop. Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
High Tech Dummy
Return on Assets
2.06
14.56
40.01
322.87
38.75
12.39
34.78
20.58
385.59
1.41
113.14
14.70
5.92
16.96
1.93
22.20
87.96
0.10
4.61
Std.
No. of No. of
Min
Max
Median
Dev.
Obs. comp.
6.05
0.00
113.12
0.37 14929
1813
59.29 -92.24
3638.01
7.61 13047
1797
17.32
0.40
99.68
38.80 12867
1787
6144.64
0.00 212610.40
24.13
5413
1370
1727.22 -98.24 136633.30
1.16 11607
1706
34.41
0.00
1851.00
3.02 13533
1751
29.20
0.00
100.00
33.02 11066
1273
15.07
0.00
100.00
18.95 14886
1813
4338.11
0.00 398001.30 181.48 11737
1719
5.73
0.00
297.77
0.26 11926
1729
9953.55 -100.00 1134913.00
6.67 13022
1793
141.38
0.01
3432.89
2.60
2635
483
60.49
0.00
2961.89
1.72
2695
491
240.18
0.00
3724.52
0.10
1692
313
5.06
0.00
71.96
0.40 14918
1812
328.78 -98.57
31883.44
8.21 13029
1797
1321.30
0.00
33975.84
22.15
6458
878
0.30
0.00
1.00
0.00 21876
1823
8.77 -91.92
391.82
4.62 13073
1796
Panel B: Number of Companies by Country of Incorporation
No. of
companies
Total no. of
Country of incorporation
cross-listed
companies
already in
1986
Austria
57
2
Belgium
67
8
Germany
204
16
Spain
80
1
France
333
15
United Kingdom
812
28
Italy
79
6
Netherlands
111
16
Sweden
80
15
Total
1823
107
No. of
companies
that crosslist during
1987-1997
7
2
11
2
13
68
5
9
4
121
Proportion
of sample
with crosslistings
0.158
0.149
0.132
0.038
0.084
0.118
0.139
0.225
0.238
0.125
44
Table 5. Descriptive Statistics
Panel A: Difference in Medians between Cross-Listing Companies and Companies
that Do Not Cross-List, by Year Relative to First Cross-Listing Date
This table reports the differences in medians between companies that do cross-list and those that do not.
Columns give the differences in medians in the years -3, -2, etc. relative to the event of cross-listing. The
control sample consists of companies that are not cross-listed at all during the whole sample period from
1986 to 1997. Computation is implemented as median regression, where the variable of interest (e.g. total
assets) is the dependent variable, and a relative-listing-year dummy and dummies controlling for calendar
year and country of incorporation effects serve as explanatory variables; the relative-listing-year dummy
for year +n (-n) takes the value 1 for observations taken n years after (before) the year in which the
company is first cross-listed abroad. A separate median regression is run for each cell in the table. The
value reported is the coefficient of the relative-listing-year dummy. Standard computation formulas (the
method of Koenker and Basett (1982) and Rogers (1993) are used for obtaining standard errors and hence
levels of significance, where *** reports significance at the 1 % level, ** 5 %, * 10 %. This computation
does not take into account clustering effects (errors might be dependent within companies observed for
subsequent years). However, computation of the standard errors by bootstrapping does not change the
significance levels substantially (results not reported).
Total Assets
Total Assets Growth
Capitalization Ratio
Com. Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Prop. Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
Return on Assets
-3
1.887 ***
2.864
-2.038
19.150 ***
-1.799
7.463 ***
22.140 ***
7.035 ***
22.081
2.352 ***
1.070
1.834 ***
0.308
0.238 ***
1.485 ***
0.935
3.585 **
0.531
-2
1.560 ***
1.260
-2.500
11.415 *
2.983 **
7.642 ***
20.580 ***
7.572 ***
27.253
1.308 ***
0.713
1.977 ***
1.039 **
0.029
1.194 ***
2.578
6.405 ***
-0.143
-1
1.307 ***
5.070 **
-3.458
19.130 ***
-0.370
7.449 ***
23.400 ***
5.797 ***
21.025
1.322 ***
3.683
1.527 ***
0.146
0.104 ***
1.364 ***
5.429 ***
5.332 ***
0.142
0
1
1.851 *** 1.749 ***
5.231 *** 4.878 **
-4.027 *
-5.530 ***
***
12.825
9.916 **
***
3.304
2.551 **
***
9.027
10.302 ***
22.550 *** 26.810 ***
9.710 *** 9.289 ***
15.178
9.899
1.120 *** 1.138 ***
6.244 *** 4.016 *
2.285 *** 1.289 ***
0.130
0.010
0.022
0.281 ***
1.683 *** 1.784 ***
3.851 **
3.030
4.043 *** 2.922 **
0.542
0.360
2
2.379 ***
1.641
-7.404 ***
13.586 **
-0.508
10.944 ***
36.530 ***
12.794 ***
-0.173
1.105 ***
3.188
1.734 ***
0.394
0.299 ***
2.268 ***
-1.402
4.212 ***
-0.834
3
1.794 ***
-0.771
-8.391 ***
9.931 **
-1.689
10.683 ***
35.700 ***
12.786 ***
8.388
1.229 ***
-0.490
3.131 ***
0.618
0.174 ***
2.461 ***
3.267
2.014
-1.032 *
>3
4.839 ***
-0.882
-3.890 ***
16.467 ***
-1.734 ***
24.468 ***
29.260 ***
4.636 ***
-6.115
2.705 ***
-0.699
1.846 ***
0.299 ***
0.106 ***
4.403 ***
-1.622 ***
0.272
-0.391 **
45
[Table 5, continued]
Panel B: Differences in Medians between Cross-Listing Companies and Companies
that Do Not Cross-List, by Year Relative to First Foreign Listing Date and by
Continent of First Foreign Listing
This table reports differences in medians of companies that cross-list in the US with the control sample
and companies that cross-list within EU9 with the control sample. Only first foreign listings within the
total geographical area (EU9 and US) are considered, then the sample is divided into sub-groups
depending on where this first foreign listing takes place. Companies that cross-list in the same calendar
year in the US and in EU9 are excluded. For columns -3 to +3, subsequent cross-listings in the other
geographical area are ignored. The permanent effect column excludes observations for companies that are
listed abroad for more than three years in both continents. The sample period is from 1986 to 1997.
Calculation is in the form of a median regression. The dependent variable is regressed on a US-relativelisting-year dummy and a EU9-relative-listing-year dummy, and dummies controlling for country of
incorporation and calendar year effects. The coefficients of the relative-listing-year dummies are the
differences in medians. The setup of this calculation ensures that the effect of controlling for country and
calendar year effects is the same for the US and the EU9 subsample and allows a simple test for equality
of the coefficients. The P-values of these equality tests are also reported. Note that observations that
cannot be assigned to a specific subsample are not used for estimation in the relevant regression. Because
of different samples, this table cannot be compared directly to Panel A of Table 4; in particular the >3
column, where a relatively large number of observations is excluded.
US
Total Assets
Total Assets Growth
Capitalization Ratio
Com. Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Prop. Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
Return on Assets
-3
1.770 ***
2.042
-2.670
19.283 **
-1.729
5.560 ***
22.410 **
5.820 *
1.555
1.398 ***
0.632
2.614 ***
1.562 ***
0.238 ***
1.753 ***
2.550
3.585 *
0.719
-2
1.560 ***
1.944
-4.550
11.396
0.595
4.348 ***
20.690 **
7.572 ***
48.438 *
1.269 ***
0.627
2.790 ***
2.015 ***
0.114 ***
1.160 ***
-1.823
6.396 ***
-0.419
-1
1.307 ***
2.316
-8.056 **
19.340 ***
-1.457
3.670 ***
19.190 ***
7.725 ***
60.536 **
1.088 ***
2.387
1.930 ***
0.317
0.149 ***
1.334 ***
5.340 **
6.723 ***
-0.095
0
1.850 ***
6.184 **
-8.372 ***
14.282 ***
4.281 ***
6.287 ***
24.320 ***
10.955 ***
74.191 ***
0.960 ***
6.776 **
3.398 ***
0.295
0.109 ***
1.372 ***
2.055
3.379 **
-0.202
1
1.993 ***
5.730 **
-7.526 **
13.146 **
3.243 *
8.749 ***
35.100 ***
13.834 ***
33.967
1.739 ***
6.206 **
2.067 ***
0.321
0.281 ***
2.098 ***
6.666 ***
0.170
0.680
2
2.284 ***
-1.555
-9.123 ***
8.430
-0.688
10.466 ***
40.860 ***
13.812 ***
88.925 ***
2.210 ***
2.222
1.754 **
0.394
0.297 ***
2.678 ***
-1.831
5.885 ***
-1.080
3
1.480 ***
-1.295
-10.194 ***
8.177
-3.163
10.599 ***
42.520 ***
12.466 ***
69.582 **
1.562 ***
-3.556
1.921 ***
0.593
0.174 ***
2.461 ***
4.170
7.676 ***
-0.267
>3
3.318 ***
3.074 **
-4.643 ***
18.000 ***
0.506
18.288 ***
32.930 ***
5.541 ***
92.784 ***
4.600 ***
2.074
1.871 ***
0.588 ***
0.035 *
4.299 ***
0.100
6.752 ***
0.675 *
EU9
Total Assets
Total Assets Growth
Capitalization Ratio
Com. Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Prop. Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
Return on Assets
-3
2.707 ***
5.539
0.134
30.022 **
-2.699
14.157 ***
19.660 *
8.865 **
27.968
2.696 ***
1.654
-0.100
-0.370 ***
-0.028 ***
1.334 ***
-7.301 *
2.069
-0.021
-2
1.543 ***
1.038
-2.356
11.913
3.140
12.140 ***
13.900
8.228 **
-17.571
1.950 ***
0.908
-0.543
-0.714
-0.039
1.194 ***
4.579
4.704
-0.055
-1
1.331 ***
6.494 *
0.274
19.027 **
0.220
14.310 ***
32.650 ***
5.704 **
-17.382
1.581 ***
5.166
-0.083
-0.672
-0.034
1.299 ***
6.401 **
-0.487
0.282
0
1.890 ***
5.665 *
-1.225
12.179
0.390
14.828 ***
19.610 **
7.297 **
-8.435
1.147 ***
3.247
-0.351
-0.722
-0.043
1.716 ***
3.861
3.135
0.584
1
1.749 ***
1.164
-2.816
7.028
0.920
10.302 ***
20.780 **
7.395 ***
-27.296
0.694 ***
2.707
-0.683
-0.801
-0.042
1.742 ***
0.843
2.842
-0.084
2
2.380 ***
3.472
-5.464 *
18.763 **
0.324
10.368 ***
26.630 ***
12.097 ***
-28.097
0.713 ***
5.183
0.450
-0.584
-0.047
1.894 ***
-1.402
3.113
-0.759
3
2.552 ***
-0.351
-6.363 **
9.931
-1.218
10.819 ***
29.810 ***
12.780 ***
-25.659
1.066 ***
1.102
0.098
2.110 ***
no obs.
2.430 ***
1.820
0.000
-1.233
>3
4.441 ***
-1.413 *
-2.621 ***
15.842 ***
-1.949 ***
22.450 ***
26.660 ***
3.366 ***
-16.165 ***
1.888 ***
-1.105
1.086 ***
0.093
0.101 ***
4.012 ***
-2.103 ***
0.000
-0.345 *
46
[Table 5, Panel B, continued]
Difference US - EU9
Total Assets
Total Assets Growth
Capitalization Ratio
Com. Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Prop. Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
Return on Assets
-3
-0.937 ***
-3.497
-2.804
-10.739
0.970
-8.597 ***
2.750
-3.045
-26.413
-1.298 ***
-1.022
2.714 **
1.932 ***
0.266 ***
0.419 ***
9.851 **
1.516
0.740
-2
0.017
0.906
-2.194
-0.517
-2.545
-7.792 ***
6.790
-0.656
66.009
-0.681 ***
-0.281
3.333 ***
2.729 **
0.153 **
-0.034
-6.402
1.692
-0.364
-1
-0.024
-4.178
-8.330 *
0.313
-1.677
-10.640 ***
-13.460
2.021
77.918 *
-0.493 ***
-2.779
2.013 *
0.989
0.183 **
0.035
-1.061
7.210 **
-0.377
0
-0.040
0.519
-7.147 *
2.103
3.891
-8.541 ***
4.710
3.658
82.626 **
-0.187 **
3.529
3.749 ***
1.017
0.152 **
-0.344 ***
-1.806
0.244
-0.786
1
2
0.244 ***
-0.096
4.566
-5.027
-4.710
-3.659
6.118
-10.333
2.323
-1.012
-1.553 *
0.098
14.320
14.230
6.439 *
1.715
61.263 *
117.022 ***
1.045 ***
1.497 ***
3.499
-2.961
2.750 ***
1.304
1.122
0.978
0.323 ***
0.344 ***
0.356 ***
0.784 ***
5.823
-0.429
-2.672
2.772
0.764
-0.321
3
>3
-1.072 ***
-1.123 ***
-0.944
4.487 ***
-3.831
-2.022
-1.754
2.158
-1.945
2.455 ***
-0.220
-4.162 ***
12.710
6.270
-0.314
2.175
95.241 ** 108.949 ***
0.496 ***
2.712 ***
-4.658
3.179 *
1.823 *
0.785 *
-1.517 *
0.495 **
no obs.
-0.066 ***
0.031
0.287 ***
2.350
2.203
7.676 ***
6.752 ***
0.966
1.020 **
47
Table 6. Predicting Cross-Listing: Logit Analysis
This table reports the odds ratios of the logit estimation of the probability of a foreign listing. The listing
year dummy takes the value one in the year of the first foreign listing in the EU9 countries or in the US
and zero otherwise. Subsequently to the event of cross-listing, a company is excluded from estimation.
Standard errors and resulting p-values are adjusted for clustering on companies. All explanatory variables
are lagged, with the exception of the high tech dummy, where there is no danger of endogeneity. The
Mean of 3 Highest Countries’ PBV is the arithmetic mean of the three highest values of the Price/Book
ratio in each year within the countries of our sample. Calendar year dummies have been used in the
estimation, but their coefficients are not reported for brevity.
Number of obs.
χ²(20)
Prob > χ²
Pseudo R²
10786
269.34
0.000
0.2029
Log Likelihood -332.685
Listing year dummy (adjusted)
Odds Ratio
Leverage (lagged)
1.022
Foreign Sales Percentage (imputed, lagged)
1.020
Total Assets Growth (lagged)
1.001
Privatization dummy (lagged)
4.414
Log of Total Assets (lagged)
2.041
Return on Assets (lagged)
0.997
High Tech Dummy
2.697
Mean of 3 Highest Countries’ PBV (lagged)
0.997
Domestic PBV (lagged)
1.001
Regional dummy (North)
0.811
Regional dummy (South)
0.332
Regional dummy (East)
0.566
z
2.290
3.449
2.602
2.268
6.392
-0.067
2.614
-0.513
0.260
-0.562
-2.236
-1.236
P>|z|
0.022
0.001
0.009
0.023
0.000
0.947
0.009
0.608
0.795
0.574
0.025
0.216
48
Table 7. Cox Regression (Survival Time Analysis)
This table reports the hazard ratios of the Cox estimation of a foreign listing. The dependent variable is
the foreign listing dummy, which takes the value one in the year of the first foreign listing in the EU9
countries or in the US and zero otherwise. Observations are excluded from the estimation sample after the
event of a foreign listing took place. Standard errors and resulting p-values are adjusted for clustering on
companies. All explanatory variables are lagged, with the exception of the high tech dummy, where there
is no danger of endogeneity.
No. of subjects: 1642
No. of failures: 69
Time at risk: 10786
Leverage (lagged)
Foreign Sales Percentage (imp., lagged)
Total Assets Growth (lagged)
Privatization dummy (lagged)
Log of Total Assets (lagged)
Return on Assets (lagged)
High Tech Dummy
Domestic Price/Book Value (lagged)
Regional dummy (North)
Regional dummy (South)
Regional dummy (East)
Log likelihood
χ² (11)
Prob > χ²
Hazard
Ratio
1.021
1.019
1.001
3.741
1.993
0.997
2.480
1.001
0.825
0.343
0.571
z
2.356
3.603
2.793
2.404
6.575
-0.077
2.543
0.271
-0.548
-2.285
-1.288
-409.530
238.05
0.000
P>|z|
0.018
0.000
0.005
0.016
0.000
0.939
0.011
0.786
0.584
0.022
0.198
49
Table 8. Predicting the Location of Cross-Listing by Multinomial Logit
This table reports the results of the multinomial logit estimation. The dependent variable is the region of
the foreign listing. Considered are all within-region new foreign listings. Companies that have foreign
listings in both regions in a given year are excluded from the sample in the subsequent year. Two
companies have new foreign listings in both the US and EU9 in a given year; these observations are used
in the US sample only. We control for whether a company is already cross-listed in the other continent by
using the lagged number of foreign listings as regressor. All explanatory variables are lagged, with the
exception of the High Tech dummy. The mean of 3 highest EU – Domestic PBV is the difference
between the arithmetic mean of the 3 highest EU9 Price/Book Values and the country of incorporations
PBV. US – Domestic PBV is the difference between the US and the domestic Price/Book ratio. Calendar
year dummies have been used in the estimation, but their coefficients are not reported for brevity.
Standard errors and resulting p-values are adjusted for clustering on companies.
Number of obs.
χ² (42)
Prob > χ²
Pseudo R²
Log Likelihood: -450.539
Region of foreign listing
U.S.A.
Number of foreign listings (lagged)
Leverage (lagged)
Foreign Sales Percentage (lagged)
Total Assets Growth (lagged)
Privatization dummy (lagged)
Log of Total Assets (lagged)
Return on Assets (lagged)
High Tech Dummy
US - Domestic Price/Book Value (lagged)
Mean of 3 highest EU - Domestic PBV (lagged)
Domestic PBV value (lagged)
Regional dummy (North)
Regional dummy (South)
Regional dummy (East)
relative
risk ratio
1.117
1.020
1.014
1.001
1.723
1.858
0.991
3.670
1.004
0.999
1.006
0.734
0.447
0.255
z
11894
435.60
0.000
0.2112
P>|z|
0.931
1.763
2.455
2.721
0.633
5.419
-0.193
3.945
0.701
-0.131
1.189
-0.707
-1.509
-2.719
0.352
0.078
0.014
0.007
0.527
0.000
0.847
0.000
0.483
0.895
0.234
0.480
0.131
0.007
Europe
Number of foreign listings (lagged)
0.160
-2.628
Leverage (lagged)
1.018
1.548
Foreign Sales Percentage (lagged)
1.028
3.130
Total Assets Growth (lagged)
1.002
2.690
Privatization dummy (lagged)
5.249
2.118
Log of Total Assets (lagged)
2.505
5.284
Return on Assets (lagged)
1.084
3.192
High Tech Dummy
0.218
-1.303
US - Domestic Price/Book Value (lagged)
0.992
-0.986
Mean of 3 highest EU - Domestic PBV (lagged)
1.015
1.179
Domestic PBV value (lagged)
1.002
0.157
Regional dummy (North)
1.735
1.146
Regional dummy (South)
0.434
-1.045
Regional dummy (East)
1.931
1.186
(Outcome no cross-listing is the comparison group)
0.009
0.122
0.002
0.007
0.034
0.000
0.001
0.192
0.324
0.238
0.875
0.252
0.296
0.236
50
Table 9. Effects of Cross-Listing: Ex-Post Median Regressions
This table reports estimates of the ex-post effects of listing. Each row in the table gives the results of a
median regression, for a dependent variable (e.g. Total Assets). The explanatory variables are an impact
dummy (1 in the year of cross-listing and 0 elsewhere), a three-year impact dummy (1 in the three years
subsequent to cross-listing) and a permanent effect dummy (1 in year 4 from cross-listing and later). We
take first differences of all variables in order to eliminate fixed effects. The following dependent variables
have been used in logarithmic form: total assets, employees, issue market value and total revenue. A
constant and additional control dummies are included in non-differenced form: calendar year dummies in
all regressions; country of incorporation dummies only in the regressions explaining total assets,
employees, issue market value and total revenue. The coefficients of these variables are not reported for
brevity. Only data points from 1990 or later are used, so as to use the observations for companies already
cross-listed in 1986 (For these companies, the permanent effect dummy is 1).
Impact effect Three year Permanent Pseudoeffect
effect
R²
0.003
0.006
-0.016
0.139
Total Assets
3.673
-0.579
0.509
0.101
Total Assets Growth
-0.059
-0.467
-0.527
0.001
Capitalization Ratio
-4.785
-4.102*
-6.256*
0.002
Common Shares Traded / Outstanding
0.575
0.633
0.055
0.001
Employees Growth
-0.001
0.010
-0.009
0.011
Employees
0.130***
0.110***
0.110***
0.001
Foreign Sales Percentage
-0.129
0.239
0.004
0.003
Leverage
10.820
-1.397
3.772
0.016
Issue Market to Book Ratio
0.078*
0.065
0.053
0.050
Issue Market Value
1.280
1.315
0.643
0.014
Property Plant Equipment Growth
0.491***
0.383***
0.560***
0.008
Research per Employee
0.051
0.066
0.096***
0.001
Research / Revenue
0.003
0.003
0.003
0.001
Research / Labor Expense
0.006
0.005
-0.016
0.116
Total Revenue
-2.824
-3.281
-3.842*
0.100
Total Revenue Growth
-0.760*
-1.493***
-0.825**
0.022
Labor Cost / Employee
0.388
0.429
0.300
0.014
Return on Assets
No. of
obs.
10936
10089
9901
3941
9031
10004
8102
10888
9147
9331
10074
1956
2003
1285
10913
10057
4819
10094
51
Table 10. Effect of Where Companies List: Ex-Post Median Regressions
Distinguishing Cross-Listings in Europe and US
This table reports estimates of the ex-post effects of cross-listing, distinguishing US cross-listings and
EU9 cross-listings. The methodology is the same as for the estimates reported in Table 9, with the
difference that the impact, three year effect and permanent effect dummies are defined for first regional
cross-listings. Note that therefore these dummies do not sum up exactly to the dummies of Table 9, as in
that table only the first cross-listing anywhere (either in Europe or the US) is considered, while here first
cross-listings both in the US and in Europe are counted. The additional dummies used are the same as in
Table 9; their coefficients are not reported for brevity.
US
US
US
EU9
impact three year permanent impact
effect
effect
Total Assets
Total Assets Growth
Capitalization Ratio
Common Shares Traded / Outst.
Employees Growth
Employees
Foreign Sales Percentage
Leverage
Issue Market to Book Ratio
Issue Market Value
Property Plant Equipm. Growth
Research per Employee
Research / Revenue
Research / Labor Expense
Total Revenue
Total Revenue Growth
Labor Cost / Employee
Return on Assets
0.007
0.040
0.944
-0.506
0.065 **
0.001
-0.038**
0.139
10936
10089
3.868
0.258
-1.785
0.102
0.755
1.438 *
1.577 *
-0.167
-0.910
-0.954*
0.001
9901
4.774
1.520
0.099
-8.740 *
-7.303
-9.413
0.002
3941
0.962
2.672
4.622 *
-0.185
-0.994
-2.014
0.001
9031
0.028 *
0.009
0.000
0.011
-0.016
0.011
10004
0.180 ***
0.180 ***
0.001
8102
-0.002
0.200 ***
3.826
0.009
EU9
EU9
Pseudo No.
three year permanent R²
of obs.
effect
effect
-0.090 ***
-0.306*
-0.015
0.112
0.475 **
2.013
-13.487
6.153
24.754 **
0.116 **
-0.988
-0.140 ***
0.638 ***
0.160 ***
0.010
0.003
10888
19.029 *
2.601
0.016
9147
0.110 *
0.126 *
0.079
-0.020
-0.082*
0.050
9331
-2.507
1.787
6.963
1.777
2.021
0.014
10074
0.056
0.201 **
0.202 **
0.659 ***
0.509 ***
0.632 ***
0.008
1956
0.038
0.077
0.094 *
0.057
0.082
0.112 ***
0.002
2003
0.000
0.017 ***
0.015 ***
0.002 **
0.003
0.001
1285
0.014
0.032
0.014
-0.006
-0.010
-0.030**
0.116
10913
-2.453
0.365
3.183
-2.815
-4.665
-5.111**
0.100
10057
-2.184 ***
-0.064
-0.825*
0.022
4819
0.152
0.296
0.014
10094
-0.605
0.521 *
-1.599 ***
-0.613
-0.106
-0.030
0.111
-0.004
52
Figure 1a. History of companies
listed on each exchange:
domestic, foreign and total
Figure 1b. History of cross-listings
among sample countries
(domestic companies listed abroad in
EU9-US exchanges, and EU9-US
companies on domestic exchanges)
Amsterdam Stock Exchange
Amsterdam Stock Exchange
450
200
400
175
350
150
300
125
250
100
200
75
150
100
50
50
25
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
Foreign Companies
Brussels Stock Exchange
Brussels Stock Exchange
350
120
300
100
250
80
200
60
150
40
100
20
50
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
Foreign Companies
Frankfurt Stock Exchange
Frankfurt Stock Exchange
200
600
175
500
150
400
125
300
100
75
200
50
100
25
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
Foreign Companies
Italian Stock Exchange
Italian Stock Exchange
70
300
60
250
50
200
40
150
30
100
20
50
10
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
Foreign Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
53
Figure 1a (continued).
Figure 1b (continued).
London Stock Exchange
London Stock Exchange
300
2500
250
2000
200
1500
150
1000
100
500
50
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
Foreign Companies
Madrid Stock Exchange
Madrid Stock Exchange
70
500
60
400
50
300
40
200
30
20
100
10
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
Foreign Companies
Paris Stock Exchange
Paris Stock Exchange
150
1250
125
1000
100
750
75
500
50
250
25
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
Foreign Companies
Stockholm Stock Exchange
Stockholm Stock Exchange
70
300
60
250
50
200
40
150
30
100
20
50
10
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
Foreign Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
54
Figure 1a (continued).
Figure 1b (continued).
Vienna Stock Exchange
Vienna Stock Exchange
70
200
60
150
50
40
100
30
50
20
10
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
Foreign Companies
Amex, Nasdaq, Nyse
Amex, Nasdaq, Nyse
500
9000
8000
400
7000
6000
300
5000
4000
200
3000
2000
100
1000
0
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Total
Domestic Companies
Foreign Companies
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Foreign Companies (EU9+US)
domestic companies listed abroad (EU9+US)
55
Figure 2. Proportion of Foreign Companies
(No. of foreign companies listed on domestic exchange / total no. of companies
listed on domestic exchange)
0.60
0.60
0.60
0.50
0.50
0.50
0.40
0.40
0.40
0.30
0.30
0.30
0.20
0.20
0.20
0.10
0.10
0.10
0.00
0.00
86
87
88
89
90
91
92
93
94
95
96
97
0.00
86
87
Amsterdam Stock Exchange
88
89
90
91
92
93
94
95
96
97
86
0.60
0.60
0.50
0.50
0.50
0.40
0.40
0.40
0.30
0.30
0.30
0.20
0.20
0.20
0.10
0.10
0.10
0.00
0.00
87
88
89
90
91
92
93
94
95
96
97
87
88
89
90
91
92
93
94
95
96
97
86
0.60
0.50
0.50
0.50
0.40
0.40
0.40
0.30
0.30
0.30
0.20
0.20
0.20
0.10
0.10
0.10
0.00
88
89
90
91
92
93
94
95
96
87
88
89
90
91
92
93
94
95
96
97
86
0.60
0.60
0.50
0.50
0.50
0.40
0.40
0.40
0.30
0.30
0.30
0.20
0.20
0.20
0.10
0.10
0.10
88
89
90
91
92
Amex
93
94
95
96
97
93
94
95
96
97
89
90
91
92
93
94
95
96
97
87
88
89
90
91
92
93
94
95
96
97
96
97
0.00
0.00
87
92
Vienna Stock Exchange
0.60
86
88
Stockholm Stock Exchange
Paris Stock Exchange
0.00
91
0.00
86
97
90
Madrid Stock Exchange
0.60
87
87
London Stock Exchange
0.60
86
89
0.00
86
Italian Stock Exchange
0.00
88
Frankfurt Stock Exchange
0.60
86
87
Brussels Stock Exchange
86
87
88
89
90
91
92
Nasdaq
93
94
95
96
97
86
87
88
89
90
91
92
NYSE
93
94
95
56
Figure 3. Diaspora Indices
(No. of domestic companies listed abroad / no. of domestic companies listed on
domestic exchange)
0.30
0.30
0.30
0.25
0.25
0.25
0.20
0.20
0.20
0.15
0.15
0.15
0.10
0.10
0.10
0.05
0.05
0.05
0.00
0.00
86
87
88
89
90
91
92
93
94
95
96
97
0.00
86
87
Amsterdam Stock Exchange
88
89
90
91
92
93
94
95
96
97
86
0.30
0.30
0.25
0.25
0.25
0.20
0.20
0.20
0.15
0.15
0.15
0.10
0.10
0.10
0.05
0.05
0.05
0.00
0.00
87
88
89
90
91
92
93
94
95
96
97
87
88
89
90
91
92
93
94
95
96
97
86
0.30
0.30
0.25
0.25
0.20
0.20
0.20
0.15
0.15
0.15
0.10
0.10
0.10
0.05
0.05
0.05
0.00
88
89
90
91
92
93
94
95
96
91
92
93
94
95
96
97
88
89
90
91
92
93
94
95
96
97
0.00
86
97
90
Madrid Stock Exchange
0.25
87
87
London Stock Exchange
0.30
86
89
0.00
86
Italian Stock Exchange
0.00
88
Frankfurt Stock Exchange
0.30
86
87
Brussels Stock Exchange
87
88
89
90
91
92
93
94
95
96
97
Stockholm Stock Exchange
Paris Stock Exchange
0.25
0.20
0.15
0.10
0.05
0.00
87
88
89
90
91
92
93
94
Amex, Nasdaq, NYSE
95
87
88
89
90
91
92
93
94
95
Vienna Stock Exchange
0.30
86
86
96
97
96
97