Cross-Country Determinants ofMergers and Acquisitions

Stefano Rossi and Paolo Volpin∗

London Business School

First draft: April 2002This version: December 2002

Abstract

This paper studies the determinants of mergers and acquisitions around the world during the 1990s

by focusing on differences in laws and enforcement across countries. We find that the volume of

M&A activity and the premium paid are significantly greater in countries with better investor pro-

tection. This result indicates that an active market for mergers and acquisitions is an important

component of the corporate governance system of countries with high investor protection. We also

show that in cross-border deals the targets are typically from countries with poorer investor pro-

tection than the acquirers. This finding suggests that cross-border transactions play a governance

role by improving the degree of investor protection within firms. Hence, an increase in cross-border

transactions may generate a world-wide convergence in corporate governance regimes.

JEL classification: G21, G28, G32.

Keywords: Mergers and acquisitions, Corporate governance, Investor protection.

∗Institute of Finance and Accounting, London Business School, Regent’s Park, London NW1 4SA. Tel.:+44 20 72625050. Fax: +44 20 77243317. E-mail: srossi@london.edu; pvolpin@london.edu. We thank an

anonymous referee, Richard Brealey, Ian Cooper, Antoine Faure-Grimaud, Denis Gromb, Henri Servaes,

Oren Sussman, David Webb, and participants at seminars at London Business School and London School of

Economics. Paolo Volpin acknowledges support from the JP Morgan Chase Research Fellowship at London

Business School.

1 Introduction

In a perfect world corporate assets would be channelled towards their best possible use.

Mergers and acquisitions help attain this goal by reallocating control over companies. How-

ever, efficient transfers of control may be prevented by frictions like transaction costs, asym-

metries of information and agency conflicts. Recent contributions on corporate governance

have proxied some of these frictions with measures of the quality of the legal and regula-

tory environment within a country, and have shown that differences in laws, regulation and

enforcement correlate with the development of capital markets, the ownership structure of

firms and the cost of capital.1

In this paper we analyze a large sample of all mergers and acquisitions announced in the

1990s and completed by the end of 2001 in 49 major countries and show that differences

in laws and enforcement explain the intensity and the pattern of mergers and acquisitions

(M&A) around the world.

First, we find that the intensity of M&A activity is significantly higher in countries with

better investor protection. M&A activity is alternatively measured by the number of targets

over population, or by the number of targets among traded companies over the total number

of traded companies. This result holds for all measures of investor protection proposed by

La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) and suggests that M&A activity is

an important component of the corporate governance system of countries with high investor

protection.

Similarly, we find that hostile deals are relatively more likely in countries with better

antidirector rights. The intuition is that countries with low antidirector rights typically have

high ownership concentration (La Porta, Lopez-de-Silanes, and Shleifer, 1999), which makes

hostile takeovers rare.

When looking at cross-border deals, we find evidence in favor of the view that the in-

ternational market for corporate control targets firms in countries with weaker corporate

governance practices. First, we show that the probability of a cross-border deal is decreasing

in the investor protection of the target’s country. Second, targets are typically from countries

with poorer investor protection than the acquirers, even after controlling for bilateral trade,

relative GNP per capita, and cultural and geographical differences.

This result suggests that cross-border M&A activity may be an important channel for an

effective world-wide convergence in corporate governance standards.2 The intuition is that

1See La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997), (1998), and Bhattacharya and Daouk

(2002).2A lively debate on the possibility of effective world-wide convergence in corporate governance standards

has recently divided legal scholars. Coffee (1999) argues that differences in corporate governance will persist

but with some degree of functional convergence. Hansmann and Kraakman (2000) believe in formal conver-

gence and argue that “the triumph of the shareholder-oriented model of the corporation over its principal

1

increasing competition may induce firms’ owners in countries with weak investor protection

to sell to companies from countries with stronger investor protection. Selling to a foreign firm

is a form of contractual convergence similar to the decision to cross-listing in countries with

better corporate governance and more developed capital markets. Pagano, Randl, Roell, and

Zechner (2001) and Reese and Weisbach (2002) show that firms from countries with weak

legal protection for minority shareholders list abroad more frequently than firms from other

countries. This paper finds that the probability of a cross-border deal is decreasing in the

investor protection of the target’s country and that targets are typically from countries with

poorer investor protection than the acquirers.

We also analyze the determinants of the takeover premium paid and the means of payment

used in individual transactions. We find that the premium is higher in countries with higher

shareholder protection. This result is consistent with the hypothesis that investor protection

increases the demand for M&A activity. Finally, we find that the probability of an all-cash

bid decreases with the degree of shareholder protection in the target country. This suggests

that acquisitions paid with stocks require an environment with high shareholder protection.

This paper belongs to the growing literature exploring cross-country variation in gover-

nance structures around the world. Specifically, it has been shown that better legal protec-

tion of minority shareholders is associated with more developed stock markets (La Porta,

Lopez-de-Silanes, Shleifer, and Vishny, 1997), higher valuation (La Porta, Lopez-de-Silanes,

Shleifer, and Vishny, 2002), greater dividend payouts (La Porta, Lopez-de-Silanes, Shleifer,

and Vishny, 2000b), lower concentration of ownership and control (La Porta et al., 1999),

lower private benefits of control (Dyck and Zingales, 2002, and Nenova, 2002), lower earnings

management (Leuz, Nanda, and Wysocki, 2002), and higher correlation between investment

opportunities and actual investments (Wurgler, 2000). Our paper documents that better

investor protection is correlated with a more effective market for corporate control.

The structure of the paper is as follows. The testable hypotheses are presented in Section

2. The data are described in Section 3. Section 4 contains the results. Section 5 discusses

the results and compares them with related literature. Section 6 concludes.

2 Hypotheses

To describe the testable hypotheses, it is useful to think about the equilibrium in the mar-

ket for mergers and acquisitions in a target country. The volume of M&A activity and the

premium paid are determined by the demand and supply of M&A activity. The demand is

downward sloping because of decreasing returns from M&A activity. The supply curve is

competitors is now assured.” Bebchuk and Roe (1999) question the idea of a rapid convergence and argue

that political and economic forces will slow down any change. Gilson (2000) argues that convergence will

happen through all three channels.

2

upward sloping because of the increasing cost of capital and relatively flat because interna-

tional capital markets are reasonably integrated. In this setting investor protection affects

the equilibrium level of M&A activity (and the premium) by shifting the demand curve.

Existing literature offers two main hypotheses on the impact of investor protection on

the demand of M&A activity: the “outcome hypothesis” and the “efficiency hypothesis”.

The outcome hypothesis predicts that in countries with better investor protection there

will be a greater demand for mergers and acquisitions. This happens because with low

investor protection, there are large private benefits of control (Nenova, 2002, and Dyck

and Zingales, 2002), ownership will be highly concentrated (Bebchuk, 1999) and therefore

the market for corporate control will not operate freely. Conversely, with high investor

protection, there are low private benefits of control, ownership will be diffuse and there will

be an active market for corporate control (Manne, 1965, and Jensen, 1993). As shown in

Figure 1, a greater demand for M&A implies a greater volume of M&A activity in equilibrium

and a higher premium. The testable predictions of this theory are therefore that both M&A

activity and the premium paid will be greater in countries with higher investor protection.

The opposite predictions derive from the efficiency hypothesis. According to this view,

corporate governance regimes differ in terms of efficiency. This is consistent with the work by

La Porta, Lopez-de-Silanes, Shleifer, and Vishny (2002) and Bhattacharya and Daouk (2002)

who suggest that the valuation of companies and their cost of capital differ systematically

across corporate governance regimes. Specifically, they find higher valuations and lower

cost of capital in countries with higher investor protection. In other terms, they find more

inefficient firms in countries with lower investor protection. The efficiency hypothesis claims

that by targeting these firms, transfers of control generate value. This is particularly relevant

for cross-border deals. The intuition is best described by La Porta, Lopez-de-Silanes, Shleifer,

and Vishny (2000a): “When a British firm fully acquires a Swedish firm, the possibility for

legal expropriation of investor diminish. Because the controlling shareholders of the Swedish

company are compensated in such a friendly deal for the lost private benefits of control, they

are more likely to go along. By replacing the wasteful expropriation with publicly shared

profits and dividends, such acquisitions enhance efficiency.” (p. 23)

The discussion above suggests that the demand of M&A activity should be greater in

countries with lower investor protection. As shown in Figure 2, the testable predictions of

this theory are therefore that both M&A activity and the premium paid will be smaller in

countries with higher investor protection. Moreover, most M&A activity will be cross border

and the acquirers will come from countries with better investor protection than the targets.

The prediction of the efficiency hypothesis (namely, a negative relationship between in-

vestor protection and the demand for M&A activity across countries) is also consistent with

the view that transfers of control are easier and more likely in firms with more concentrated

ownership. The reason, as argued by Grossman and Hart (1980), is that with diffuse own-

3

ership transfers of control require tender offers which push up the price and reduce buyer’s

incentive to launch the takeover. As shown by Shleifer and Vishny (1986), large shareholders

make takeovers more likely because they are aware that they are pivotal for the transfer of

control. Since ownership is more concentrated in countries with lower investor protection

(as shown by La Porta et al., 1997), this hypothesis predicts a negative relationship between

investor protection and M&A activity across countries.

So far, we have assumed that the supply of M&A activity is not affected by investor pro-

tection. However, investor protection may flatten the supply curve because good institutions

increase the availability of funds and reduce the cost of capital for domestic bidders. For

example, in countries with better investor protection bidders can use more easily their com-

pany’s stock as a mean of payment because target shareholders are more willing to accept it.

If so, from the supply side, the volume of M&A activity increases and the premium decreases

as investor protection increases. Hence, the overall effects (including the demand side) de-

pend on the relative movements of the demand and supply schedules. With a relatively flat

supply curve, the outcome hypothesis would predict that investor protection increases both

M&A activity and the premium. The efficiency hypothesis would predict a decrease in both

M&A activity and the premium. With a steeper supply curve, the only prediction of the

outcome hypothesis would be about M&A activity: more M&A activity with higher investor

protection. Conversely, the only prediction of the efficiency hypothesis would be about the

premium: lower premium with higher investor protection.

2.1 Predictions

In this paper we compare the hypotheses described above in several dimensions. Specifically,

we focus on four aspects of mergers and acquisitions around the world: the intensity of the

M&A activity, the nature of the typical domestic deal, the premium paid, and the means of

payment used in individual transactions.

2.1.1 Intensity of M&A activity in target countries

The efficiency hypothesis predicts that the market for corporate control will be more active

in countries with poorer governance. To the extent that poor governance can be proxied

by legal protection, this theory suggests that the intensity of M&A activity will be higher

in countries with lower protection for investors. The inverse prediction emanates from the

outcome hypothesis. According to the latter, the frequency of mergers and acquisitions across

countries will be positively related to the degree of investor protection, because a good legal

environment is a necessary requirement for an active market for corporate control.

In Section 4.1 we compare the two predictions by evaluating the relationship between

M&A activity and several proxies of legal protection using a sample of 49 countries.

4

2.1.2 Nature of the deal

The discussion above refers to the overall level of activity, making no distinction between

friendly and hostile deals, or domestic and cross-border transactions. In this section, we

develop testable predictions on the relative frequency of the different types of deals.

First, as argued by Jensen (1993), the outcome hypothesis should hold more strongly

for hostile takeovers. The reason is that hostile takeovers require control to be contestable,

a feature which is less common in countries with poorer investor protection. Accordingly,

we should expect a greater share of (attempted) hostile takeovers in countries with higher

investor protection.

Second, the efficiency hypothesis is particularly strong for cross-border deals. When the

acquirer comes from a country with better investor protection than the target, the transfer

of control improves the quality of the corporate governance in the target, increasing the

value of the firm. The greater cash flows could compensate management and controlling

shareholders for the lost private benefits of control, facilitating the completion of the deal.

This discussion suggests two testable hypotheses. 1) Cross-border deals will be relatively

more likely in countries with lower investor protection. 2) In cross-border deals, the acquirers

will come from countries with higher investor protection than the targets.

The determinants of the hostile takeover ratio are analyzed in Section 4.2, while the

hypotheses on cross-border deals are tested in Section 4.3.

2.1.3 Premium

The predictions derived above on the intensity of M&A activity translate naturally into

testable hypotheses on the size of the premium, to the extent that the latter reflects the cash

flows generated by the transfer of control. According to the efficiency hypothesis, the pre-

mium received by minority shareholders should be larger in countries with lower shareholder

protection because a disciplining takeover creates more value the poorer the initial corpo-

rate governance. Conversely, the outcome hypothesis predicts that the premium received

by minority shareholders should be larger in countries with stronger shareholder protection

because only a good legal environment generates a competitive market for corporate control

conducive to high takeover premia.

An important assumption in deriving the predictions above is that the premium reflects

mainly the cash flows generated by the transfer of control. However, Barclay and Holderness

(1989) and Dyck and Zingales (2002) argue convincingly that the premium in block trades

may also proxy for the private benefits of controls. If so, the premium should be greater

in countries with poorer investor protection as a reflection of the higher private benefits of

control.

In Section 4.4 we present evidence on the cross-country determinants of the takeover

5

premium, and return to this issue.

2.1.4 Means of payment

Legal protection of investors also affects the form of payment used in mergers and acqui-

sitions. The outcome hypothesis suggests that high shareholder protection increases the

availability of equity financing for bidders. Moreover, in a country with low investor protec-

tion target shareholders are likely to prefer cash rather than that bidder’s shares as merger

compensation. Both factors predict that, in completed deals, we should observe less equity

financing and more cash financing in countries with lower shareholder protection.

In Section 4.4 we present evidence on the determinants of the means of payment in

international mergers and acquisitions.

3 Data

Our sample contains all mergers and acquisitions, as reported by Thomson Financial Secu-

rities Data, announced between January 1, 1990 and December 31, 1999 and completed as

of December 31, 2001. We focus on mergers (business combinations where the number of

companies decreases after the transaction) and acquisitions of majority interests (all cases

in which the acquirer owns less than 50 percent of the target-company’s stock before the

deal, and more than 50 percent after the deal) because we want to focus on transactions

clearly motivated by changes in control. The availability of empirical measures of investor

protection limits the set of countries to the 49 countries examined by La Porta et al. (1998).

Excluded deals represent about six percent of the total of deals in the original data-set in

number and one percent in value.

Table 1 describes all variables used in the paper and indicates their sources. These

variables can be classified into three broad categories corresponding to three different levels

of analysis.

The first set of variables is at the country level. It includes measures of M&A activity

from the target’s perspective, broad macroeconomic conditions, and proxies of the legal and

regulatory environment of the 49 countries. These variables will be used in the cross-country

analysis of the determinants of the intensity and the nature of international mergers and

acquisitions.

The second set of variables focuses on cross-border deals. It comprises data on M&A

activity between any ordered pair of countries (there are 49×48, that is, 2,352 orderedpairs),3 and on cultural differences and similarities between target and acquirer for any

pair of countries. In the analysis of cross-border activity, we also compute measures of the

3We use ordered pairs to distinguish between acquirer and target countries.

6

differential macroeconomic, legal and regulatory conditions for each ordered pair of countries

using the variables defined above at country level.

The third set of variables is at individual-deal level. It includes data on the premium

paid, the value of the deal and the means of payment. These data are used in the analysis

of the determinants of the premium and the means of payment, together with country-level

variables defined above.

3.1 M&A activity

The data on M&A activity are presented in Table 2 sorted by target country. The sample

includes 45,536 deals. We define as M&A Activity the ratio of the total number of targets

over the average population (in millions) in the Nineties. As apparent from Table 2, the

market for corporate control plays a different role in different countries. For example, in

Japan M&A activity is very low (our index equals 6.01, that is, in the all 1990s there have

been only 752 deals compared with an average population of 125 millions) while in the UK

M&A activity is very high (the index equals 73.39, that is, in the 1990s there have been 4,294

deals and the average population of 58.5 millions). In column (2) we present an alternative

measure of M&A activity defined as the percentage of traded firms which were the target of

successful mergers or acquisitions. This alternative index describes a similar pattern across

countries. During the 1990s, 53.5 percent of British traded companies were the target of a

completed deal, compared with only 6.4 percent in Japan.

Of all mergers and acquisitions, we focus specifically on hostile and cross-border deals,

since they are likely to play an important governance role. We define as Hostile Takeovers

the number of attempted hostile takeovers as a percentage of the total number of traded

companies. The intuition is that the disciplinary role of hostile takeovers is related to the

threat they represent to incumbent managers. In other words, it is likely that attempted

(but failed) hostile takeovers play as important a role in disciplining management as hostile

takeovers that are eventually completed.4 In all countries hostile takeovers represent a very

small phenomenon compared with overall M&A activity. In fact, they are completely absent

in 21 out of 49 countries, and when present they never exceed the 6.46 percent observed in

the US. While the lack of attempted hostile takeover bids in Japan is hardly surprising, it

is perhaps less well known how few are attempted in countries such as the US and the UK.

We define as Cross-Border Ratio the percentage of completed deals where the acquirer

comes from a different country than the target.5 The cross-border mergers and acquisitions

4As a robustness check, we performed the same analysis on completed hostile deals (not reported) with

similar qualitative results.5In the case of mergers, the distintion between acquirers and targets is arbitrary. We follow the classi-

fication done by our data source, Thomson Financial Securities Data. For example, in the merger between

Daimler and Chrylser, Thomson considers Daimler the acquirer and Chrysler the target.

7

are 11,638, i.e. 26 percent of the total. Table 2 shows that different countries play different

roles in the cross-border M&A market. Indeed, while 51 percent of the acquisition of Mexico

firms are done by a foreign acquirer, only 8.7 percent of the acquirers in US deals come from

abroad.

3.2 Investor protection

To try to explain the measures of M&A activity by target country, we will employ several

indices of investor protection developed by La Porta, et al. (1998): an index of the quality of

the accounting standards, two indices of the quality of law enforcement (Judicial Efficiency

and Rule of Law), a measure of the rights that shareholders have with respect to management

(Antidirector Rights), and an index of the rights that creditors have in liquidation (Creditor

Rights).

These indices are highly correlated (their pairwise correlation ranges between 30 and

60 percent) because they all reflect to some degree the underlying quality of the investor

protection in one country. However, they measure different institutional characteristics.

Specifically, Accounting Standards measures the quality of the disclosure of accounting

information. It was created by examining and rating companies’ 1990 annual reports on their

inclusion or omission of 90 items. Since good disclosure requirements are a necessary condi-

tion for a well developed stock market, we interpret Accounting Standards as an exogenous

proxy for stock market development.

Judicial Efficiency is an assessment of the efficiency and the integrity of the legal environ-

ment. Following La Porta et al. (1998), this index is typically interpreted as the investors’

assessment of general business conditions in a country. In a country with low Judicial Effi-

ciency, doing business is more difficult because contracts are more difficult to enforce.

Next, we follow the work of Johnson, Boone, Breach and Friedman (2000) and define

Shareholder Protection as the product between Antidirector Rights and Rule of Law. Share-

holder Protection measures the rights that minority shareholders effectively hold after con-

trolling for their enforcement by the legal system. In countries with lower shareholder pro-

tection, minority shareholders are expropriated more. Thus, Shareholder Protection is a

proxy for the quality of the corporate governance.

Similarly, we define as Creditor Protection the product between Creditor Rights and

Rule of Law. Creditor Protection measures the rights that creditors effectively hold in a

bankruptcy procedure. We interpret this variable as a proxy for the ease of access to credit

markets.

Finally, it is important to note that the number of observation in our empirical analysis

varies with the measure of investor protection used. This is because only Judicial Efficiency

and Shareholder Protection are available for all 49 countries. Creditor Protection is not

8

available for Jordan and Venezuela. Accounting Standards is not available for Ecuador,

Indonesia, Ireland, Jordan, Kenya, Pakistan, Sri Lanka, and Zimbabwe.

4 Methodology and Results

Before proceeding to a direct test of the predictions outlined in Section 2.3, we start by

comparing the data presented in Table 2 across legal origins. This follows La Porta et al.

(1998), who argue that legal origin is a broad indicator of investor protection.

Table 3 shows that French civil law countries have the lowest level of M&A activity of

all. The difference is significant at 10 percent with respect to German, and at 1 percent

with respect to English and Scandinavian legal origins. Moreover, countries of French legal

origin have also the highest cross-border ratio of 50.57 percent, which is significantly greater

than English and Scandinavian countries at 1 percent, and 5 percent level, respectively.

Finally, Scandinavian and English legal origins are characterized by more hostile takeovers

than French civil law countries. The results for M&A in Traded Companies are weaker. Only

countries with Scandinavian Legal Origin have a significantly higher volume of mergers and

acquisitions.

These results show that M&A activity differs significantly across legal origins. Moreover,

to the extent that French legal origin is associated with low investor protection (as suggested

by La Porta et al., 1998), these results suggest that investor protection has a strong impact

on M&A activity. Indeed, the results hint that the level of M&A activity and the frequency

of hostile deals are positively correlated, while the incidence of cross-border transactions is

negatively correlated with investor protection.

In the following we analyze the relationship between investor protection and legal origin

in greater depth by disaggregating legal origin into the four specific measures of investor

protection defined in Section 3.2.

4.1 Determinants of M&A activity

To study the cross-country relationship between M&A activity and investor protection, we

estimate the following specification:

M&A Activity = α+ βX + γ Investor Protection+ . (1)

The dependent variable in (1) is M&A Activity, i.e. the ratio of the total number of

completed deals in the 1990s and the average population (in millions). Investor Protection is

proxied by the variables described in Section 3.2: Accounting Standards, Judicial Efficiency,

Shareholder Protection, and Creditor Protection. Control factors, X, are GDP Growth, as a

9

proxy for the change in economic conditions, and the logarithm of the 1995 per capita GNP,

as a proxy of the country’s wealth. All variables are at target-country level.

Table 4 reports the estimates obtained by maximum likelihood of five tobit models derived

from specification (1). We estimate tobit models because the variable M&A Activity cannot

be negative by construction. In each of the first four columns, we include only one of the

proxies for investor protection listed above. In column 5, we report the results of a full

specification, where we use all the measures of investor protection together.

The results show that all measures of investor protection are positively and significantly

correlated with M&A Activity. These legal and regulatory measures are also economically

significant. Raising Accounting Standards by 13.4 points (one standard deviation) increases

M&A Activity by about 15.3 points. An improvement of Judicial Efficiency by one standard

deviation (about 2 points) increases M&A Activity by about 17 points. An increase in

Shareholder Protection by 1.29 raises M&A Activity by 14 points. Finally, raising Creditor

Protection by one point increases M&A Activity by about 8.5 points. Judging by the joint

regression 5, Judicial Efficiency and Accounting Standards are the measures of investor

protection with more explanatory power.

These results strongly characterize M&A Activity as being driven by a good legal envi-

ronment. Hence, they provide evidence in favor of the outcome hypothesis and against the

efficiency hypothesis.

To evaluate the robustness of the results, in Table 5 we produce the same regressions

as in Table 4 using M&A of Traded Companies as the dependent variable. The results

are similar although weaker: only Accounting Standards and Shareholder Protection are

positively correlated with M&A activity. One possible explanation for this difference is that

the size of the stock market (the denominator of M&A in Traded Companies) is positively

related to investor protection, as shown by La Porta et al. (1997). Since both numerator

and denominator are increasing in investor protection, the two effects partially cancel out.

The coefficients are economically significant: raising Accounting Standards by 13.4 points

increases M&A Activity by about 6 percent, while an increase in Shareholder Protection by

1.29 raises M&A Activity by 5.5 percent.

4.2 Hostile takeovers

We now turn to the analysis of cross-country differences in the nature of the M&A activity,

starting with hostile takeovers. To this purpose, we adapt specification (1) as follows:

Hostile Takeovers = α+ βX + γ Investor Protection+ . (2)

In (2) the dependent variable (Hostile Takeovers) is the number of attempted hostile

takeovers in the 1990s as a percentage of the number of traded companies. As before,

10

investor protection is proxied by the four variables described in Section 3.2, and we include

GDP Growth and the logarithm of GNP per Capita as control factors.

The results are reported in Table 6, where we present the output of five tobit regressions.

In each of the first four regressions, we include only one of the proxies for investor protection.

The fifth regression contains the full specification. We find that Accounting Standards and

Shareholder Protection are statistically significant. Consistent with the outcome hypothesis,

countries with higher Shareholder Protection have more hostile takeovers. The intuition is

that countries with low Shareholder Protection typically have high ownership concentration

(La Porta et al., 1999) that impedes the success of hostile takeovers and therefore lessens

their likelihood.

4.3 Cross-border mergers and acquisitions

We now turn to cross-border deals. First, we apply the same analysis used for hostile deals

to cross-border deals. Then, we estimate an alternative specification more appropriate for

cross-border deals.

4.3.1 Target-country analysis

Specification (1) is now adapted to cross-border deals:

Cross-Border Ratio = α+ βX + γ Investor Protection+ . (3)

The dependent variable (Cross-Border Ratio) is the number of cross-border deals as a

percentage of all deals by target country. Investor Protection is proxied by the four variables

described in Section 3.2. As before, we control for the logarithm of GNP per Capita, as a

measure of country’s wealth, and GDP Growth as a proxy of the change in macroeconomic

conditions.

Table 7 shows that cross-border deals are more frequent in countries with lower investor

protection. Specifically, the coefficients on Accounting Standards and Shareholder Protec-

tion are negative and significant both in the univariate regression (at 1 percent level), and in

the full specification (at 1 percent and 5 percent, respectively). In economic terms, raising

Accounting Standards by one standard deviation (13.4 points) decreases the cross-border

ratio by 9 percent. An increase of Shareholder Protection by one standard deviation (1.29)

decreases the cross-border ratio by about 7.8 percent. All other measures of investor protec-

tion are negatively correlated with the cross-border ratio but are not statistically significant.

This result is consistent with the efficiency hypothesis. According to this theory, compa-

nies in countries with lower investor protection are relatively more likely to be targeted by

foreign buyers because of their poor governance standards (as measured by low Shareholder

11

Protection). Similarly, companies in countries with low investor protection are more likely

to face underdeveloped capital markets (as measured by low Accounting Standards).

To confirm fully the efficiency hypothesis one needs to check whether the investor pro-

tection of the acquirer is significantly higher than the target’s. We will address this issue in

the next section.

4.3.2 Ordered-pair analysis

The results in Table 7 suggest that cross-border mergers and acquisitions play a governance

role by targeting firms in countries with lower investor protection. To explore this hypothesis

fully, we arrange our data-set to produce a “worldwide matrix” of mergers and acquisitions,

where each entry ms,b is defined as the number of deals where the acquirer comes from

country b (for buyer) and the target is in country s (for seller), as a percentage of the total

number of deals in country s. Such a matrix is reported in Table A2 of the Appendix.

With the newly-arranged data-set, we can study the pattern of cross-border mergers and

acquisitions by controlling at the same time for the characteristics of target and acquirer

countries. An appropriate specification to address this issue is:

Cross-Border Dealss,b = βXs,b + γInvestor Protections + δInvestor Protectionb + s,b. (4)

The dependent variable in (4) is the number of cross-border deals where the acquirer

comes from country b and the target is in country s (b 6= s) as a percentage of the total

number of M&A deals (domestic and cross-border) in country s. The specification includes

the investor protection of both acquirer and target. The control variables, Xs,b, are: the

logarithm of GNP per Capita of acquirer and target country as a measure of the economic

development of the two countries; and three dummy variables equal to one if acquirer and

target share the same cultural background, that is, if they come from the same linguistic,

religious and geographical area.6 We estimate a tobit model to control for the fact that the

dependent variable is truncated from below at 0 and from above at 100 by construction.

The results, which are reported in Table 8, show that only the quality of the investor

protection in the acquirer country is significant. Specifically, the volume of deals between two

countries increases with the investor protection of the acquirer. The results are particularly

clear for Accounting Standards and Shareholder Protection. The findings on Shareholder

Protection suggest that countries with better governance standards (as measured by higher

Shareholder Protection) export their standards to other countries via cross-border deals.

6Language is the main official language spoken in a country according to the World Atlas 1995. Geo-

graphical area is one of the following four: i) Africa, ii) Asia and Oceania, iii) Europe, and iv) North and

South America. Religion is the main official religion in a country according to the World Atlas 1995.

12

This finding is consistent with the efficiency hypothesis. The result on Accounting Standards

shows that companies in countries with a more developed capital market (as measured by

higher Accounting Standards) use their lower cost of capital for cross-border acquisitions.

This is consistent with the efficiency hypothesis.

In Table 8, we find that the investor protection of the target country is never significant.

From the results in Table 7 we would have expected a negative coefficient. A more interesting

anomaly is the sign on Creditor Protection: the higher the Creditor Protection in the acquirer

country, the smaller the volume of cross-border deals. This finding is difficult to reconcile

with the outcome hypothesis. Indeed, in this respect better Creditor Protection implies

more a developed credit market and more available resources to engage in M&A activity.

Comparing this finding with the result for Accounting Standards, we may conclude that

M&A activity is favored by a developed stock market (high Accounting Standards) but

damaged by a developed banking sector (high Creditor Protection). This hypothesis requires

further research.

Among the control factors, we find that richer countries are more likely to be acquirers

and all dummy variables on cultural similarities between target and acquirer are significant

and positive, that is, acquirer and target typically share the same language, religion, and

come from the same geographical area.

One shortcoming of Table 8 is that controls for cultural and institutional characteristics of

target and acquirer countries may be inadequate. For example, controlling for bilateral trade

may be important because companies exporting in one given country may engage in M&A

activity in that country for reasons that have nothing to do with governance. Therefore, in

Table 9 we augment specification (4) with fixed effects for target and acquirer countries and

with Bilateral Trade as a new control variable, where we define Bilateral Trade as imports

from country b to country s, as a percentage of total imports of country s.7

Cross-borders,b = βXs,b + γ∆ (Shareholder protection)b−s + δb + ζs + s,b. (5)

As we are controlling for individual characteristics of the countries with dummy variables,

we now use the difference in investor protection and economic development between acquirer

and target rather than the levels.

The results in Table 9 confirm those in Table 8. We find that the acquirer typically has

stronger investor protection than the target. The coefficient on the difference in Shareholder

Protection is significant (and positive) across all alternative specifications. Bilateral Trade

is also positive and significant confirming that trade is an important motive for cross-border

mergers and acquisitions.

7Bilateral Trade is not available for six countries: Belgium, Brazil, Israel, Nigeria, Switzerland and

Zimbabwe. The number of observations in Table 9 will change accordingly.

13

Overall, our findings are consistent with the efficiency hypothesis of cross-border mergers

and acquisitions.

4.4 Analysis of individual deals

The final step in our analysis is the study of the cross-country variation in the premium and

means of payment. For this purpose, we have to turn to transaction-level data. We compute

the premium as the ratio between the price paid by the acquirer and the closing price of

the target four weeks before the announcement. After excluding missing observations and

countries with less than 3 deals (to reduce the impact of outliers), we are left with 3,963

observations from 28 countries. Observations are highly concentrated: 60 percent are US

firms, 15 percent are UK, 5 are Australian, and 4 percent are Canadian firms.

4.4.1 Premium

In this section, we use the sample of individual transactions to analyze the cross-country

determinants of the takeover premium. We estimate the specification:

Log(Premium) = α+ βX + γShareholder Protection+ (6)

where Shareholder Protection is measured at target-country level and X are control factors.

The reason why we consider only Shareholder Protection as a proxy for investor protection is

that the premium paid to shareholders should mainly reflect the degree of protection offered

to minority shareholders.

Table 10 reports the results of three regressions based on specification (6). In all regres-

sions, the standard errors reported in parenthesis are adjusted for heteroskedasticity using

White’s (1980) correction and for clustering at country level. We also include year dummies

but we do not report their coefficients.

The result of the basic regression is reported in column 1, where only Target Size is used

as control variable. We expect larger deals to be associated with lower premia because of

reduced competition among potential bidders. We find that Shareholder Protection signifi-

cantly increases the takeover premium. In particular, an increase in the level of Shareholder

Protection by one point leads to a 5-percent increase in the premium. This result is consistent

with the outcome hypothesis because higher Shareholder Protection increases the financial

resources available to domestic bidders and thus enhance competition among bidders.

In columns 2 and 3, we evaluate the robustness of this finding. First, in column 2, we

control for several characteristics of the deal by augmenting the regression with the following

independent variables: Tender Offer (in a tender offer the acquirer is likely to pay an higher

premium), Hostile Bid (the premium may be higher in hostile bids), and Cross-Border Deal

(the premium may be higher in cross-border deals). We find that the results on Shareholder

14

Protection are unchanged. Of the new control factors, only Tender Offer is significant: tender

offers are associated with higher premia.

One concern with the regression presented in column 1 is that in an OLS estimation

all observations have the same weight. Since the observations are highly concentrated in a

few countries (US and UK, in particular), the results may be driven by these countries. To

address this concern, in column 3, we estimate specification (6) weighting observations differ-

ently across countries. Specifically, the weights are the inverse of the number of observations

by country, so that all countries have effectively the same impact on the final results. With

these adjustments, we find that Shareholder Protection continues to be significant, although

at a 10 percent level.

From Table 10, we can safely conclude that higher Shareholder Protection induces bidders

to pay higher premia, as predicted by the outcome hypothesis. This result is easy to interpret

if we believe that premia mainly reflect the cash flows generated by the deal. Indeed, with

higher Shareholder Protection there is more M&A activity (as shown in section 4.1) and

therefore more competition among bidders. However, Barclay and Holderness (1989) and

Dyck and Zingales (2002) argue that block premia also reflect the private benefits of control.

From this perspective the result in Table 10 appears puzzling because private benefits of

control are higher in countries with lower Shareholder Protection. How can we explain this

puzzle?

Whether the takeover premium reflects the cash flows for all shareholders (dividends and

capital gains) rather than the private benefits for the controlling party depends critically on

the size of the block being traded and the stake owned by the bidder after the deal. The

size of the block (and the stake owned by the bidder after the deal) has a non-monotonic

relation with the private benefits. For a small block (well below 50 percent) the larger the

block, the larger the private benefits that the ownership of the block may confer. However,

when the block is large (above 50 percent) a larger block simply reduces the private benefits

deriving from expropriation of minority shareholders, as shown by Burkart, Gromb, and

Panunzi (1998).

Our sample focuses on clear cases of changes of control in which an acquirer (holding less

than 50 percent before the transaction) ends up controlling at least 50 percent of the shares

upon completion of the deal. Hence, we believe that in our sample the takeover premium

mainly reflects the cash flows for all shareholders.

Moreover, the results are not affected by controlling for the stake traded or the stake

held by the bidder after the deal (not reported). We believe that this is the case because in

the sample used in this section the average stake owned by the acquirer after the completion

of the deal is 94 percent of the common shares and in 84 percent of the observations the

acquirer stake after the deal is 100 percent. With such a large stake, the acquirer is unlikely

to engage in wasteful extraction of private benefits of control.

15

4.4.2 Means of payment

A regression similar to (6) is also estimated for the means of payment:

Prob(All-Cash Bid) = π0 + π1X + π2 Shareholder Protection+ η (7)

According to the outcome hypothesis, higher shareholder protection should be correlated

with fewer cash bids since the acquirer can issue shares more easily and target shareholders

are more willing to accept stocks in exchange for their shares.

Table 11 reports the results of three regressions based on specification (7). In all regres-

sions, the standard errors reported in parenthesis are adjusted for heteroskedasticity using

White’s (1980) correction, and for clustering at country level. We also include year dummies

but we do not report their coefficients.

The result of the basic regression is reported in column 1, where only Target Size is used

as control variable. We expect that larger deals may be more likely to be financed with stock.

We find that Shareholder Protection significantly decreases the use of cash in takeovers as

suggested by the outcome hypothesis. In particular, an increase in the level of Shareholder

Protection by one point leads to a 40-percent reduction in the probability of using cash.

In columns 2 and 3, we evaluate the robustness of this finding. In column 2, we control

for several characteristics of the deal by augmenting the regression with the following inde-

pendent variables: Tender Offer (if there is a tender offer the means of payment is likely to

be cash), Hostile Bid (we expect them to be cash offers), and Cross-Border Deal (given the

home-bias puzzle, target shareholders may prefer cash over foreign stocks). We find that the

results on Shareholder Protection are unchanged. All the control factors are significant and

positive: tender offers, hostile bids and cross-border deals are more likely to be in cash.

In column 3, we estimate specification (7) with weights equal to the inverse of the number

of observations by country. This implies that all countries have effectively the same impact on

the final results. Following these adjustments, we find that Shareholder Protection continues

to be significant, although at a 10 percent level.

4.4.3 System of equations

One caveat in the analysis above is that the control variables used in regression (6) and

(7), Tender Offer, Hostile Bid, and Cross-Border Deal are themselves endogenous variables.

As a result, our estimates could be inconsistent. To address this concern we estimate the

following system of equations as a set of seemingly unrelated regressions with weights equal

to the inverse of the number of observations for each country to give the same weight to each

country.8

The results are as follows:8The system is recursive to allow the estimation.

16

Target Size Shareholder Protection Cross-Border Hostile Bid Tender Offer All-Cash Bid

Log(Premium) = 0.00 +0.03a +0.07a +0.02 +0.13a +0.10a

(0.00) (0.01) (0.02) (0.04) (0.02) (0.02)

All-Cash Bid = −0.02a −0.06a +0.16a +0.06 +0.16a

(0.00) (0.01) (0.01) (0.04) (0.01)

Tender Offer = 0.03a +0.06a +0.19a +0.37a

(0.01) (0.01) (0.02) (0.04)

Hostile Bid = 0.01a +0.01a +0.02a

(0.00) (0.00) (0.01)

Cross-Border = 0.04a +0.00

(0.00) (0.05)

Cross-border deals and tender offers are associated with higher premiums and more cash.

Cross-border and hostile deals are also more likely to be tender offers. Shareholder Protection

is almost always statistically significant. We confirm the results in Table 10, finding that

the premium is higher in countries with better shareholder protection. As shown in Table

11, deals are more likely to be financed with stocks in countries with better shareholder

protection. Countries with better shareholder protection exhibit a higher probability of a

tender offer and of a hostile bid. The latter finding is consistent with the results of Table 5.

Overall, the results in this section on the premium and means of payment are consistent

with the outcome hypothesis and are a mirror image of the results on the intensity of M&A

activity presented in Table 3.

5 Discussion

Our paper belongs to the growing literature exploring cross-country variation in governance

structures around the world. It documents that better investor protection is correlated with a

more effective external mechanism of corporate governance, namely mergers and acquisitions.

In this section we compare our results with existing literature and explore possible exten-

sions. The first concern in our analysis is that both the governance and efficiency hypotheses

focus on targets. Section 5.1 evaluates whether also looking at the acquirer side changes our

cross-country predictions. The second issue, which is dealt with in Section 5.2, is that there

are large differences in corporate tax rates around the world and these may impact cross-

border deals. In Section 5.3 we compare our results on the takeover premium with Dyck

and Zingales (2002) explaining the sources of differences. Finally, in Section 5.4, we suggest

possible ways to extend our analysis.

5.1 Acquirer perspective

While the case for target shareholders to sell out to bidders with higher governance standards

is clear, it is not yet fully understood why acquirers seek to take over a poorly governed

company. The efficiency hypothesis suggests that in doing so they create value by improving

the corporate governance in the target.

17

A possible concern with this view is that the acquirer firms may, to some extent, import

the poorer governance of their targets (poor accounting and disclosure practices, board

structures and so on). However, this does not seem likely. Anecdotal evidence of cross-

border deals with high press coverage seems to suggest that the targets almost always adopt

the governance standards of the acquirers.9 Thus, if convergence is to happen, it is towards

the acquirers’ governance standards.

A second issue in relation to the efficiency hypothesis is that the deal may be motivated by

agency and hubris problems of the acquirer and not by the desire to improve the governance

regime in the target company. If so, the deal may not create value. This question demands

a study of the price reaction of the target and acquirer, which cannot be done with our large

sample. An indirect test of this hypothesis is possible instead. Indeed, if countries with

poorer investor protection (in particular, lower governance standards as measured by lower

Shareholder Protection) have more severe agency problems, the hypothesis suggests more

acquisitions by companies in countries with lower Shareholder Protection. This is not what

we observe. Rather we find the opposite: more acquisitions by companies in countries with

higher Shareholder Protection.

5.2 Taxes

An alternative determinant of international mergers and acquisitions is tax competition

across countries. One reason why taxes may affect M&A activity is that the tax law may

favor domestic firms over foreign firms by allowing investment tax credits and accelerated

depreciation which can be enjoyed more readily by domestic firms. An example is the 1986

Tax Reform Act, which reduced the implicit advantage of domestic firms over foreign firms

by eliminating the investment tax credits and reducing accelerated depreciation. As shown

by Scholes and Wolfson (1990) and Servaes and Zenner (1994) this change of tax regime

increased the level of foreign takeovers and the return to US targets acquired by foreign

companies. In this paper, we do not test this prediction because we are not using time-series

data.

Taxes may also affect cross-border M&A activity through the treatment of foreign income

in the acquirer’s tax regime. Accordingly, tax regimes can be classified into two broad

categories: worldwide and territorial systems. In worldwide systems companies are taxed on

their worldwide income and allow for tax credit for foreign taxes.10 Under this regime, the

tax rate in the target country should not affect the acquirer’s decision. In territorial systems,

companies are taxed only on the income generated domestically and exempt foreign income.11

9For instance in the acquisition of Chrysler by Daimler, the resulting company has adopted a two-tier

board structure, as required by German law.10Greece, Italy, Japan, Norway, the UK, and the US fall into this group.11The tax regime in Canada, Denmark, France, Germany, Japan, Norway, Pakistan share this feature.

18

Under this regime, the tax rate in the target country should be an important determinant

of the acquirer’s decision.

However, no tax regime precisely fits either of the above, and generalizing is not straight-

forward. For example, in the US (which is classified as a worldwide system) companies can

occasionally defer tax liabilities on foreign income. This is a feature of territorial systems.

Moreover, when the foreign tax rate is higher than the US one, US companies do not receive

money back from the US government, but can offset the excess foreign tax paid against fu-

ture foreign income. Hence, in this example the effective tax rate for a US acquirer is higher

than the US tax rate. On top of this, international investments generate large opportunities

for tax avoidance, as shown in Desai, Foley and Hines (2002).

If we include the marginal corporate tax rate of acquirer and target in the regressions

reproduced in Table 8, the coefficients of the measures of investor protection do not change

and we find that only the tax rate of the acquirer country matters: countries with higher tax

rates are more likely to do cross-border mergers and acquisitions. This result is consistent

with the view that companies in countries with high tax rates try to reduce their tax liabilities

by acquiring activities abroad.

However, when we explore the tax hypothesis more closely, we do not find a lower impact

of the tax differential between foreign and domestic country on the volume of deals when

the acquiring country taxes corporations on the basis of their worldwide income. This is a

puzzle, as the tax hypothesis argues that such countries should not be affected by the tax

differential between countries. We believe that this puzzle is explained by the fact that tax

regimes cannot easily be classified with a simple binary variable.

5.3 Takeover premium

Our section on the determinants of the takeover premium is closely related to Dyck and

Zingales (2002), who study 393 private control transactions between 1990 and 2000. They

find that larger block premia are associated with less developed capital markets and more

concentrated ownership. We find substantially the opposite: larger premia in countries

with higher Shareholder Protection. The different results can be attributed to the different

approaches used in the two papers.

Dyck and Zingales (2002) use information on the block prices to compute an indirect

measure of the private benefits of control, defined as the difference between the price paid

per share and the market price after the change in control. Instead, we simply compute

the premium as the percentage difference between the price paid by the buyer and the

closing price four weeks before the announcement of the deal, as a measure of the minority

shareholders’ benefits from the deal.

Their sample is also very different from ours: we have 3,963 deals while they have only

19

393 deals. This is because they restrict their attention to private purchases of blocks while we

also include public offers and acquisition paid with stocks. Moreover, they need to observe

both the price per share of the control block and the exchange price after the market has

incorporated the identity of the new acquirer in its expectation of future cash flow, while we

do not impose this restriction.

Finally, they focus on transfer of blocks larger than or equal to 10 percent of the stock,

where the acquirers hold less than 20 percent of the shares before the deal, and more than

20 percent of the shares after the deal. In contrast, we define control as the ownership of the

majority (50 percent or more) of the voting shares, and therefore we focus on all mergers

(business combinations where the number of companies decreases after the transaction) and

acquisitions of majority interests (all cases in which the acquirer owns less than 50 percent

of the target-company’s stock before the deal, and more than 50 percent after the deal).

As already discussed in section 4.4.1, because of these differences in sample selection, our

measure of takeover premium captures the cash flows generated by the deal rather than the

private benefits of control.

5.4 Possible extensions

The phenomenon of cross-border mergers and acquisitions is recent and still largely un-

explored by finance literature. The paper suggests several research opportunities for the

future.

As shown byMitchell andMulherin (1996), industry shocks are an important determinant

of M&A activity. The importance of cross-border deals is also likely to vary across industries.

For example, according to the efficiency hypothesis we should observe more cross-border deals

in industries that need more external capital. This prediction is tested in Rossi and Volpin

(2002).

Moreover, the efficiency hypothesis suggests that cross-border deals should reduce the

cost of capital in the target companies when the acquirer’s investor protection is higher than

the target’s. To verify this prediction, an analysis of post acquisition performance may be

performed for cross-border deals, following Healy, Palepu, and Ruback (1992).

Finally, there are several macroeconomic predictions related to the frequency of cross-

border M&A that could become testable in the future. First, an increase of foreign acquisi-

tions may prompt governments in countries with low investor protection to improve laws and

institutions, motivated by the desire to keep domestic companies in domestic hands. Sec-

ond, an increase in cross-border M&A financed with stocks may increase market integration,

alleviating the home bias in equity portfolio. Indeed, if the foreign bidder pays with stock,

target shareholders face the problem of disposing of a new investment domiciled abroad.

As a result, they may choose to keep the foreign shares. In aggregate, this would imply a

20

reduction of the home bias in equity portfolio in target countries, which in turn would also

reduce the cost of capital at country level, because investors would be able to diversify their

portfolio more (Stulz, 1996).

6 Conclusion

In this paper, we have shown that the legal and regulatory environment is an important

determinant of the pattern of mergers and acquisitions around the world.

We have proposed two alternative theories on the relationship between investor protection

and M&A activity. The first theory (the outcome hypothesis) argues that more legal protec-

tion for investors is likely to generate more deals and breed more competition among bidders.

The intuition is that investor protection reduces ownership concentration and weakens the

barriers to the market for corporate control. The second theory (the efficiency hypothesis)

predicts a negative relationship between investor protection and M&A activity across coun-

tries. The intuition is that companies in countries with low investor protection are plagued

by inefficiency and they stand to gain a lot from a transfer of control to a more efficient

owner. This is particularly true when the buyer comes from a country with higher investor

protection.

Using a large sample of deals announced in the 1990s and completed by the end of 2001

in 49 major countries, we find that both the level of M&A activity and the premium paid

are significantly higher in countries with better investor protection.

These findings suggest that good domestic investor protection is a requirement for M&A

and the international market for corporate control is not able to overcome the barriers to

entry in countries with poor investor protection. Although this result rejects the stronger

version of the efficiency hypothesis, a weaker test of this theory focuses on cross-border deals.

In cross-border deals, the efficiency hypothesis predicts that the targets should have weaker

investor protection than the acquirers.

We find evidence in favor of the weaker version of the efficiency hypothesis. First, the

probability of a cross-border deal decreases in the investor protection of the target’s country.

Second, when looking at the identity of targets and acquirers, cross-border deals seem to

increase the effective investor protection within target firms, as predicted by the efficiency

hypothesis: acquirers have higher investor protection than targets.

Thus, we can conclude that the mechanism suggested by the efficiency hypothesis is oper-

ational but not strong enough to overcome the impact of domestic mergers and acquisitions.

However, this may change in the future. Cross-border deals are also becoming more common:

in the 1980s only 15 percent of the deals were cross-border, compared with 26 percent in the

1990s. This paper suggests that a further increase in the number of cross-border mergers

and acquisitions may generate a convergence in governance standards across countries, of

21

the type suggested by Coffee (1999) who argues that companies from countries with better

protection of investors will end up buying companies from countries with weaker protection.

22

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25

Table 1. Description of the variables used in the analysis

Country level variables

M&A Activity Number of completed deals in the 1990s by target country divided by the average popu1ation (in millions). Sources: Thompson Financial Securities Data and the International Data Base of the US Census Bureau.

M&A Activity in Traded Companies

Percentage of domestic traded companies target in a complete deal during the 90s sorted by target country. Sources: Thompson Financial Securities Data and the World Development Indicators.

Hostile Takeovers Percentage of attempted hostile takeovers of domestic traded companies sorted by target country. Sources: Thompson Financial Securities Data and the World Development Indicators.

Cross-Border Ratio Percentage of completed deals where the acquirer country is different from the target country, sorted by target country. Source: Thompson Financial Securities Data.

GDP Growth Average rate of growth of the Gross Domestic Product in the 1990s. Source: World Development Report.

GNP per Capita Gross National Product in 1995 (in US$) divided by the population. Source: World Development Report.

Accounting Standards Index created by the Center for International Financial Analysis and Research to rate the quality of 1990 annual reports on their disclosure of accounting information. Source: La Porta et al. (1998).

Judicial Efficiency Assessment of the “efficiency and integrity of the legal environment as it affects business, particularly foreign firms” produced by the country risk-rating agency Business International Corp. Average between 1980 and 1983. Source: La Porta et al. (1998).

Rule of Law Assessment of the law and order tradition in the country produced by the risk-rating agency International Country Risk (ICR). Average of the months of April and October of the monthly index between 1982 and 1995. Source: La Porta et al. (1998).

Antidirector Rights The index is formed by adding 1 when (1) the country allows shareholders to mail their proxy vote to the firm, (2) shareholders are not required to deposit their shares prior to the general shareholders’ meeting, (3) cumulative voting or proportional representation of minorities in the board of directors is allowed, (4) an oppressed minorities mechanism is in place, (5) the minimum percentage of share capital that entitles a shareholder to call for an extraordinary shareholders’ meeting is less than or equal to 10 percent (the sample median), or (6) shareholders have preemptive rights that can be waived only by a shareholders’ vote. Source: La Porta et al. (1998).

Shareholder Protection Measure of the effective rights of minority shareholders computed as the product of Rule of Law and Antidirector Rights.

Creditor Rights The index is formed by adding 1 when (1) the country imposes restrictions, such as creditors’ consent or minimum dividends to file for reorganization; (2) secured creditors are able to gain possession of their security once the reorganization petition has been approved (no automatic stay); (3) secured creditors are ranked first in the distribution of the proceeds that result from the disposition of the assets of a bankrupt firm; and (4) the debtor does not retain the administration of its property pending the resolution of the reorganization. Source: La Porta et al. (1998).

Creditor Protection Measure of the effective rights of creditors in bankruptcy computed as the product of Rule of Law and Creditor Rights.

Legal Origin Classification of the company law into four legal families (English, French, German and Scandinavian). Source: La Porta et al. (1998).

Cross-border variables

Cross-Border Deals s,b Percentage of cross-border deals where the target is from country s and the acquirer is from country b over the total number of deals with target in country s. Source: Thompson Financial Securities Data.

Same Language It equals 1 when target and acquirer's countries share the same main language and 0 otherwise. Source: World Atlas 1995.

Same Geographical Area It equals 1 when target and acquirer's countries are from the same broadly defined continent and 0 otherwise. All countries are classified into four areas (Africa, America, Asia, and Europe). Source: World Atlas 1995.

Same Religion It equals 1 when target and acquirer's countries share the same main religion and 0 otherwise. Source: World Atlas 1995.

Bilateral Trade Value of imports by country s from country b as a percentage of total import by country s. Source: World Bank Trade and Production Database.

Transaction-level variables

Premium Ratio of the price paid per share by the acquirer over the closing price of the target shares 4 weeks before the announcement. Source: Thompson Financial Securities Data.

All-Cash Bid It equals 1 if no stock is used in the transaction and 0 otherwise. Source: Thompson Financial Securities Data.

Target Size Market capitalization of the target 4 weeks before the announcement of the deal in US$ million. Source: Thompson Financial Securities Data.

Tender Offer It equals 1 if the acquisition is done through a tender offer and 0 otherwise. Source: Thompson Financial Securities Data.

Cross-Border It equals 1 if the target country differs from the acquirer country and 0 otherwise. Source: Thompson Financial Securities Data.

Hostile Bid It equals 1 if our source classifies the bid as unsolicited and 0 otherwise. Source: Thompson Financial Securities Data.

26

Table 2. International M&A activity: Summary statisticsM&A Activity is Number of Deals divided by the average population (in millions). M&A in Traded Companies is the percentage

of traded companies target in a complete deal during the 90s. Hostile Takeovers is the number of attempted hostile takeovers

as a percentage of Number of of traded firms . Cross-Border Ratio is the percentage of completed deals where the acquirer is

from a different country than the target.

Country M&A Activity M&A in Traded Companies (%) Hostile Takeovers (%) Cross-Border Ratio (%)

Argentina 11.39 27.45 0.65 54.31

Australia 63.35 33.91 4.60 27.26

Austria 36.50 38.14 1.03 51.55

Belgium 36.50 33.33 0.56 44.99

Brazil 2.74 22.88 0 51.92

Canada 69.97 29.85 2.73 22.72

Chile 10.08 10.57 0.42 64.79

Colombia 2.25 19.42 0 66.67

Denmark 72.52 24.03 0.81 38.26

Ecuador 2.53 10.53 0 68.97

Egypt 1.02 1.46 0 47.62

Finland 147.26 45.45 0.91 22.70

France 38.20 56.66 1.68 33.72

Germany 48.45 35.07 0.30 25.97

Greece 14.11 12.66 0 23.13

Hong Kong 84.48 34.12 0.41 38.52

India 0.23 1.96 0.02 56.81

Indonesia 0.66 10.60 0.48 61.03

Ireland 45.69 28.90 4.62 52.41

Israel 28.22 9.43 0.23 46.94

Italy 19.92 55.97 3.04 36.32

Japan 6.01 6.39 0 13.16

Jordan 2.17 0.00 0 55.56

Kenya 0.26 1.80 0 28.57

Malaysia 62.20 15.23 0.19 11.27

Mexico 2.67 27.51 0 51.02

Netherlands 48.19 25.83 1.32 43.13

New Zealand 62.17 49.82 0.70 46.61

Nigeria 0.11 0.61 0 58.33

Norway 93.39 61.24 5.86 36.70

Pakistan 0.14 0.48 0 55.56

Peru 4.45 12.21 0 56.48

Philippines 2.16 21.41 0 38.46

Portugal 23.10 31.37 1.96 40.43

Singapore 125.29 34.46 0.40 31.11

South Africa 13.82 23.89 0.45 24.69

South Korea 2.60 4.81 0 53.85

Spain 23.51 16.07 0.17 37.45

Sri Lanka 2.70 4.83 0 42.86

Sweden 84.67 60.93 3.74 35.44

Switzerland 77.19 38.95 1.43 43.69

Taiwan 3.73 0.89 0 49.37

Thailand 3.21 17.14 0 43.55

Turkey 1.27 6.12 0 45.45

United Kingdom 73.39 53.54 4.39 23.36

United States 59.33 65.61 6.46 8.68

Uruguay 6.24 7.55 0 85.00

Venezuela 5.02 14.91 0 57.01

Zimbabwe 1.20 4.76 0 46.15

27

Table 3. M&A activity by legal origin.Panel A presents the average M&A Activity, M&A in Traded Companies, Hostile Takeovers and Cross-Border Ratio sorted

by different legal origins, as defined in La Porta et al. (1998). M&A Activity is the number of M&A deals, scaled by the

average population (in millions), by target country. M&A in Traded Companies is the percentage of traded companies target

in a complete deal during the 90s. Hostile Takeovers is the number of attempted hostile takeovers as a percentage of all traded

companies. Cross-Border Ratio is the percentage of cross-border deals out of the total number of deals (both domestic and

cross-border). Panel B presents pairwise tests on the differences of means for all three variables across legal origins. T-statistics

are reported. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

Panel A: Means M&A Activity M&A in Traded Companies Hostile Takeovers Cross-Border Ratio

English origin 38.60 22.80 1.40 36.95

French origin 12.20 20.21 0.49 50.57

German origin 29.06 20.71 0.46 39.58

Scandinavian origin 99.55 47.91 2.83 33.33

Panel B: Tests M&A Activity M&A in Traded Companies Hostile Takeovers Cross-Border Ratio

English vs. French 2.96a 0.46 1.81c −2.88aEnglish vs. German 0.56 0.23 1.05 −0.36English vs. Scandinavian −2.96a −2.32b −1.19 0.45

French vs. German −1.96c −0.07 0.09 1.62

French vs. Scandinavian −9.00a −3.27a −3.66a 2.35b

German vs. Scandinavian −3.48a −2.33b −2.34b 0.71

28

Table 4. Determinants of the M&A activity across countries.The coefficients of the five Tobit models are estimated by maximum likelihood. The dependent variable is M&A Activity.

M&A Activity is Number of Deals divided by the average population (in millions). The independent variables are Accounting

Standards, Judicial Efficiency, Shareholder Protection, and Creditor Protection as defined in Table 1. The logarithm of GNP

per capita and GDP Growth are included in all regressions as control variables. Standard errors are reported in parenthesis.

Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

(1) (2) (3) (4) (5)

Log (GNP per capita) 13.92a 9.88a 11.91a 15.39a 6.80c

(3.62) (2.92) (2.87) (2.58) (3.78)

GDP Growth 4.09c 2.57 1.91 3.34 3.08

(2.43) (1.93) (2.07) (2.20) (2.36)

Accounting Standards 1.14a 0.71b

(0.34) (0.33)

Judicial Efficiency 8.29a 7.55a

(2.10) (2.49)

Shareholder Protection 10.85a 3.17

(3.29) (3.43)

Creditor Protection 9.02b 0.81

(4.05) (4.21)

Constant −171.6a −126.4a −100.1a −125.8a −146.8a(30.7) (21.4) (24.3) (24.0) (32.7)

Pseudo R2 0.09 0.10 0.09 0.08 0.12

N. Observations 41 49 49 47 40

29

Table 5: Robustness Check: M&A activity of traded companies.The coefficients of the five Tobit models are estimated by maximum likelihood. The dependent variable is M&A of Traded

Companies. M&A in Traded Companies is the percentage of traded companies target in a complete deal during the 90s.

The independent variables are Accounting Standards, Judicial Efficiency, Shareholder Protection, and Creditor Protection as

defined in Table 1. The logarithm of GNP per capita and GDP Growth are included in all regressions as control variables. The

standard errors are reported in parenthesis. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level,

respectively.

(1) (2) (3) (4) (5)

Log (GNP per capita) 5.64a 7.75a 6.43a 8.51a 4.60c

(1.94) (1.66) (1.47) (1.33) (2.29)

GDP Growth −2.53c −1.86c −2.37b −1.62 −2.76c(1.31) (1.10) (1.06) (1.14) (1.43)

Accounting Standards 0.46b 0.34c

(0.18) (0.20)

Judicial Efficiency 0.96 0.62

(1.19) (1.50)

Shareholder Protection 4.27b 2.82

(1.69) (2.07)

Creditor Protection −0.64 −1.74(2.09) (2.54)

Constant −43.2b −44.0a −32.2b −42.5a −34.1c(16.5) (12.1) (12.5) (12.4) (19.8)

Pseudo R2 0.08 0.09 0.10 0.08 0.09

N. observations 41 49 49 47 40

30

Table 6. Incidence of hostile takeovers.The coefficients of the five Tobit models are estimated by maximum likelihood. The dependent variable is Hostile Takeovers.

Hostile Takeovers is the number of attempted hostile takeovers as a percentage of Number of of traded firms. The independent

variables are Accounting Standards, Judicial Efficiency, Shareholder Protection, and Creditor Protection as defined in Table 1.

The logarithm of GNP per Capita and GDP Growth are included in all regressions as control variables. Standard errors are

reported in parenthesis. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

(1) (2) (3) (4) (5)

Log (GNP per Capita) 0.93b 1.13a 0.75a 1.33a 0.50

(0.35) (0.36) (0.27) (0.29) (0.35)

GDP Growth 0.04 0.22 0.06 0.27 −0.09(0.21) (0.19) (0.17) (0.19) (0.19)

Accounting Standards 0.07b 0.01

(0.03) (0.03)

Judicial Efficiency 0.18 0.25

(.21) (0.24)

Shareholder Protection 0.88a 0.78a

(0.25) (0.26)

Creditor Protection −0.16 −0.32(0.35) (0.34)

Constant −12.2a −11.8a −8.34a −12.0a −7.73b(3.32) (2.90) (2.53) (2.92) (3.14)

Pseudo R2 0.17 0.17 0.24 0.16 0.23

N. Observations 41 49 49 47 40

31

Table 7. Frequency of cross-border M&A.The coefficients of the five Tobit models are estimated by maximum likelihood. The dependent variable is the Cross-Border

Ratio. Cross-Border Ratio is the percentage of completed deals where the acquirer is from a different country than the target.

The independent variables are Accounting Standards, Judicial Efficiency, Shareholder Protection, and Creditor Protection as

defined in Table 1. The logarithm of GNP per capita and GDP Growth are included in all regressions as control variables.

Standard errors are reported in parenthesis. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level,

respectively.

(1) (2) (3) (4) (5)

Log (GNP per Capita) −2.03 −2.99c −1.50 −4.13a −1.14(1.74) (1.75) (1.50) (1.44) (2.02)

GDP Growth 0.91 0.81 1.45 0.83 1.55

(1.18) (1.16) (1.08) (1.23) (1.27)

Accounting Standards −0.67a −0.55a(0.16) (0.18)

Judicial Efficiency −1.78 0.89

(1.26) (1.33)

Shareholder Protection −6.02a −3.83b(1.71) (1.83)

Creditor Protection −1.65 −0.35(2.26) (2.24)

Constant 96.8a 79.3a 62.8a 77.5a 81.1a

(14.8) (12.8) (12.7) (13.4) (17.4)

Pseudo R2 0.07 0.03 0.05 0.03 0.08

N. Observations 41 49 49 47 40

32

Table 8. The governance motive in cross-border deals.The coefficients of the five Tobit models are estimated by maximum likelihood. The dependent variable is Cross-Border Dealss,b,

defined as the percentage of cross-border deals where the target is from country s and the acquirer is from country b (s 6= b) out

of Number of Deals in country s. The independent variables are levels of the quality of investor protection of the target (s) and

acquirer country (b). As in previous tables, investor protection is measured by several proxies (Accounting Standards, Judicial

Efficiency, Shareholder Protection, and Creditor Protection). The language dummy takes a value of one if target and acquirer

come from countries with the same official language. The geographical area dummy takes a value of one if target and acquirer

come from the same geographical area. The religion dummy takes a value of one if target and acquirer come from countries

with the same official religion. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

(1) (2) (3) (4) (5)

Accounting Standardss−0.01(0.01)

−0.01(0.01)

Accounting Standardsb0.10a

(0.01)

0.06a

(0.01)

Judicial Efficiencys−0.09(0.07)

−0.15(0.08)

Judicial Efficiencyb0.20a

(0.07)

0.04

(0.08)

Shareholder Protections0.07

(0.09)

0.08

(0.10)

Shareholder Protectionb1.00a

(0.09)

0.60a

(0.10)

Creditor Protections−0.06(0.11)

−0.03(0.12)

Creditor Protectionb−0.12(0.11)

−0.43a(0.12)

Log(GNP per Capita)s−0.03(0.09)

0.23b

(0.10)

0.09

(0.09)

0.10

(0.08)

0.05

(0.11)

Log(GNP per Capita)b1.19a

(0.11)

1.64a

(0.12)

1.25a

(0.10)

1.79a

(0.10)

1.06a

(0.13)

Same Language2.81a

(0.36)

3.23a

(0.36)

2.52a

(0.35)

3.31a

(0.36)

2.46a

(0.37)

Same Geographical Area2.50a

(0.22)

2.66a

(0.22)

2.73a

(0.21)

2.64a

(0.22)

2.49a

(0.21)

Same Religion0.91a

(0.24)

0.39

(0.25)

0.70a

(0.24)

0.25

(0.25)

0.74a

(0.24)

Constant−18.4a(1.27)

−20.4a(1.13)

−17.1a(1.10)

−19.2a(1.12)

−15.4a(1.36)

Pseudo R2 0.12 0.12 0.14 0.12 0.13

N. observations 1640 2352 2352 2162 1560

33

Table 9. The efficiency hypothesis: Robustness checks.This table reports the results of five OLS regressions with fixed effects for target and acquiring country. The dependent variable

is Cross-border dealss,b, defined as the percentage of cross-border deals where the target is from country s and the acquirer is

from country b (s 6= b) out of the total number of completed deals in country s. The independent variables are differences in

the quality of investor protection between acquirer country’s (b) and target country’s (s). Investor protection is measured by

Shareholder Protection. We include as control variables: the differences between the two countries in the logarithm of GNP per

capita; Bilateral Trade, which is the value of imports by country s from country b as a percentage of total import by country

s; and several dummies. The language dummy takes a value of one if target and acquirer come from countries with the same

official language. The geographical area dummy takes a value of one if target and acquirer come from the same geographical

area. The regressions contain fixed effects both for target and acquirer country (not reported). The standard errors reported in

parenthesis are adjusted for heteroskedasticity using White’s (1980) correction. Superscript letters a, b, c indicate significance

at 1 percent, 5 and 10 percent level, respectively.

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

∆(Shareholder Protection)b−s 1.97a 1.94a 0.90a 1.21a

(0.20) (0.20) (0.21) (0.23)

∆(GNP per capita)b−s 0.98a 0.06

(0.10) (0.07)

Bilateral Trade 0.71a 0.67a

(0.09) (0.09)

Same Language 0.97a 0.07

(0.30) (0.22)

Same Geographical Area 1.12a 0.36b

(0.12) (0.15)

Adjusted R2 0.45 0.50 0.67 0.67

N. observations 2352 2352 1677 1677

34

Table 10. Determinants of the takeover premium.The dependent variable is the logarithm of the Premium, which is computed as the logarithm of the ratio of the price paid and

the closing price four weeks before the announcement of the deal. Independent variables are Target Size, Shareholder Protection,

Cross-Border, Hostile Bid, and Tender Offer as defined in Table 1. In all regressions, we also include year dummies and the

standard errors reported in parenthesis are adjusted for heteroskedasticity using White’s (1980) correction and corrected for

clustering at country level. Column (3) presents the results of a weighted least square regression, are the inverse of the number

of observations per country. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

(1) (2) (3)

Target Size −0.01c −0.01c 0.01

(0.01) (0.01) (0.03)

Shareholder Protection 0.05a 0.06a 0.03c

(0.01) (0.02) (0.02)

Cross-Border 0.03

(0.02)

Hostile Bid 0.05

(0.04)

Tender Offer 0.06a

(0.01)

Estimation method OLS OLS Weigthed LS

R2 0.02 0.03 0.03

N. Observations 3949 3949 3949

35

Table 11. Means of payment.This table reports the results of two probit regressions. The dependent variable is All-Cash Bid. The independent variables

are Target Size, Shareholder Protection, Cross-Border, Hostile Bid and Tender Offer. In all regressions, we also included year

dummies and the standard errors reported in parenthesis are adjusted for heteroskedasticity using White’s (1980) correction

and corrected for clustering at country level. Column (3) presents the result of a weighted probit, where the weights are the

inverse of the number of observations per country. Reported coefficients are the change in probability for an infinitesimal change

in the independent variables. Superscript letters a, b, c indicate significance at 1 percent, 5 and 10 percent level, respectively.

(1) (1) (2)

Target Size −0.16a −0.08a −0.01(0.03) (0.01) (0.02)

Shareholder Protection −0.46a −0.11a −0.05c(0.07) (0.02) (0.03)

Cross-Border 0.19a

(0.03)

Hostile Bid 0.10a

(0.02)

Tender Offer 0.32a

(0.09)

Estimation method OLS OLS Weigthed LS

Pseudo R2 0.08 0.18 0.04

N. Observations 3949 3949 3949

36

Demand (High investor protection)

M&A Activity

Premium

Supply

Figure 1. The Outcome Hypothesis

Demand (Low investor protection)

M(Low) M(High)

P(Low)

P(High)

37

P(High)

M&A Activity

Premium

Supply

Figure 2. The Efficiency Hypothesis

Demand (Low investor protection)

Demand (High investor protection)

M(High) M(Low)

P(Low)

38

Table A1. M&A activity: Raw numbersFor each country, Number of Deals is the number of completed deals announced during the 1990s and sorted by target country.

Number of Traded Companies is the average number of domestic companies listed on the domestic stock exchanges during the

90’s. Number of Deals in Traded Companies is the number of traded companies that were target in a completed deal during

the 1990s.

Country Number of Deals Number of Traded Companies Number of Deals in Traded Companies

Argentina 394 153 42

Australia 1141 1153 391

Austria 291 97 37

Belgium 369 177 59

Brazil 443 520 119

Canada 2025 2455 733

Chile 142 236 25

Colombia 81 103 20

Denmark 379 245 59

Ecuador 29 47 5

Egypt 63 824 12

Finland 749 110 50

France 2215 773 438

Germany 3939 673 236

Greece 147 237 30

Hong Kong 527 489 167

India 213 4186 82

Indonesia 136 207 22

Ireland 166 86 25

Israel 147 435 41

Italy 1140 230 129

Japan 752 2270 145

Jordan 9 134 0

Kenya 7 55 1

Malaysia 1207 538 82

Mexico 245 189 52

Netherlands 742 302 78

New Zealand 221 142 71

Nigeria 12 163 1

Norway 406 153 94

Pakistan 18 624 3

Peru 108 262 32

Philippines 156 191 41

Portugal 230 153 48

Singapore 434 252 87

South Africa 567 674 161

South Korea 117 686 33

Spain 932 572 92

Sri Lanka 49 207 10

Sweden 742 267 163

Switzerland 547 210 82

Taiwan 79 338 3

Thailand 186 297 51

Turkey 77 212 13

United Kingdom 4294 1823 976

United States 15527 7125 4675

Uruguay 20 26 2

Venezuela 107 80 12

Zimbabwe 13 63 3

39

Table A2: Matrix of cross-border mergers and acquisitionsA generic element of the matrix ms,b represents the number of deals in which a company from country s is acquired by a

company from country b, as a percentage of the total number of deals (domestic and cross-border) involving a target from

country s. Target countries are reported by row and acquirer countries by column. Countries are sorted by geographical area

(Africa, America, Asia, and Europe) and alphabetically, within geographical area.

Target \ Acquirer

Egy

pt

Keny

a

Nig

eria

Sou

th A

fric

Zim

babw

e

Arge

ntin

a

Bra

zil

Can

ada

Chi

le

Col

ombi

a

Ecua

dor

Mex

ico

Per

u

Uni

ted

Sta

Uru

guay

Ven

ezue

l a

Aus

tralia

Hon

g Ko

ng

Indi

a

Indo

nesi

a

Isra

el

Japa

n

Jord

an

Egypt 0.0 0.0 0.0 0.0 0.0 0.0 3.5 0.0 0.0 0.0 1.8 0.0 10.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Kenya 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Nigeria 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0South Africa 0.0 0.0 0.0 0.2 0.0 0.0 2.2 0.0 0.0 0.0 0.0 0.0 5.2 0.0 0.0 0.7 0.4 0.0 0.2 0.0 0.4 0.0Zimbabwe 0.0 0.0 0.0 16.7 0.0 0.0 8.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Argentina 0.0 0.0 0.0 0.5 0.0 2.8 2.1 3.6 0.3 0.0 0.5 0.0 21.9 0.3 0.8 0.5 0.0 0.0 0.0 0.0 0.0 0.0Brazil 0.0 0.0 0.0 0.0 0.0 1.6 2.8 1.2 0.0 0.0 0.5 0.2 19.1 0.0 0.2 0.0 0.2 0.0 0.0 0.2 1.4 0.0Canada 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.6 0.0 0.0 0.5 0.3 0.0 0.0 0.1 0.2 0.0Chile 0.0 0.0 0.0 0.0 0.0 1.4 1.4 8.6 0.0 0.0 2.9 0.0 22.9 0.7 1.4 2.1 0.0 0.0 0.0 0.0 0.0 0.0Colombia 0.0 0.0 0.0 1.3 0.0 1.3 1.3 8.9 3.8 3.8 5.1 0.0 17.7 0.0 3.8 1.3 0.0 0.0 0.0 0.0 0.0 0.0Ecuador 0.0 0.0 0.0 0.0 0.0 0.0 3.7 7.4 0.0 11.1 0.0 0.0 25.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Mexico 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.4 0.0 0.0 0.0 29.9 0.0 0.0 0.4 0.4 0.0 0.0 0.0 0.0 0.0Peru 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.9 9.7 1.9 0.0 2.9 16.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0United States 0.0 0.0 0.0 0.1 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.6 0.0Uruguay 0.0 0.0 0.0 0.0 0.0 30.0 5.0 0.0 10.0 0.0 0.0 0.0 0.0 15.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Venezuela 0.0 0.0 0.0 0.0 0.0 1.9 1.9 3.9 1.0 3.9 1.0 4.9 0.0 18.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Australia 0.0 0.0 0.0 1.2 0.0 0.0 0.0 1.4 0.0 0.0 0.0 0.0 0.0 7.3 0.0 0.0 1.2 0.1 0.3 0.1 1.0 0.0Hong Kong 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.0 0.0 0.0 0.0 0.0 9.3 0.0 0.0 1.0 0.0 0.2 0.0 2.8 0.0India 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.9 0.0 0.0 0.0 0.0 0.0 19.5 0.0 0.0 1.0 1.0 0.0 1.0 2.4 0.0Indonesia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.8 0.0 0.0 0.0 0.0 0.0 6.9 0.0 0.0 6.1 0.8 0.8 0.0 7.6 0.0Israel 0.0 0.0 0.0 1.4 0.0 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 25.2 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0Japan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 5.1 0.0 0.0 0.1 0.4 0.0 0.1 0.3 0.0Jordan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 28.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Malaysia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.9 0.0 0.0 0.7 1.3 0.1 0.0 0.0 0.3 0.0New Zealand 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.8 0.0 0.0 0.0 0.0 0.0 9.0 0.0 0.0 15.6 1.4 0.0 0.0 0.0 0.9 0.0Pakistan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.5 0.0 0.0 0.0 0.0 6.3 0.0 0.0 12.5 0.0Philippines 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 1.3 0.0 4.6 0.0 0.0 0.7 3.9 0.0 0.0 0.0 3.3 0.0Singapore 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 3.6 0.0 0.0 1.9 4.1 0.0 1.2 0.0 1.9 0.0South Korea 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.6 0.0 0.0 0.0 0.0 0.0 14.9 0.0 0.0 0.9 0.0 0.0 0.0 0.0 7.9 0.0Sri Lanka 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 2.3 4.7 0.0 0.0 2.3 0.0Taiwan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.3 0.0 0.0 2.7 8.2 0.0 0.0 0.0 5.5 0.0Thailand 0.0 0.0 0.0 1.1 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 7.7 0.0 0.0 0.5 1.6 0.0 0.0 0.0 9.3 0.0Austria 0.0 0.0 0.0 1.1 0.0 0.0 0.0 1.8 0.0 0.0 0.0 0.0 0.0 3.9 0.0 0.4 0.7 0.0 0.0 0.4 0.0 0.0 0.0Belgium 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 6.1 0.0 0.0 0.6 0.0 0.0 0.0 0.3 0.0 0.0Denmark 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 6.1 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.5 0.0Finland 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0France 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.0 7.8 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.8 0.0Germany 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.1 0.0 6.6 0.0 0.0 0.3 0.2 0.0 0.0 0.1 0.4 0.0Greece 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Ireland 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 11.9 0.0 0.0 1.9 0.0 0.0 0.0 0.0 0.6 0.0Italy 0.0 0.0 0.0 0.2 0.0 0.0 0.2 0.5 0.0 0.0 0.0 0.0 0.0 5.8 0.0 0.0 0.4 0.1 0.0 0.2 0.0 1.2 0.0Netherlands 0.0 0.0 0.0 0.3 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 8.2 0.0 0.0 0.1 0.0 0.0 0.1 0.5 1.0 0.0Norway 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 5.6 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0Portugal 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.4 0.0 0.0 0.0 0.0 0.0 3.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0Spain 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.7 0.0 0.1 0.0 0.2 0.0 5.3 0.0 0.0 0.2 0.1 0.0 0.0 0.1 0.8 0.0Sweden 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 6.2 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.3 0.0Switzerland 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.1 0.0 0.0 0.0 0.0 0.0 6.5 0.0 0.0 0.6 0.4 0.0 0.0 0.2 0.4 0.0Turkey 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0United Kingdom 0.0 0.0 0.0 0.6 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 10.3 0.0 0.0 0.6 0.3 0.0 0.1 0.0 0.6 0.0

40

Table A2 (Continues)

Mal

aysi

a

New

Zea

land

Pak

ista

n

Phi

lippi

nes

Sing

apor

e

Sout

h Ko

rea

Sri

Lank

a

Taiw

an

Thai

land

Aus

tria

Belg

ium

Den

mar

k

Finl

and

Fran

ce

Ger

man

y

Gre

ece

Irela

nd

Italy

Net

herla

nds

Nor

way

Portu

gal

Spai

n

Sw

eden

Switz

erla

nd

Turk

ey

Uni

ted

King

dom

0.0 0.0 0.0 0.0 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 1.8 0.0 0.0 1.8 1.8 0.0 0.0 1.8 0.0 1.8 0.0 8.80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 28.60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.1 0.0 0.0 22.21.8 0.0 0.0 0.2 0.4 0.2 0.0 0.0 0.0 0.4 0.2 0.0 0.2 1.3 2.5 0.0 0.2 0.2 0.5 0.2 0.0 0.0 0.7 0.7 0.0 4.50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.70.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.5 0.0 0.0 4.6 1.8 0.0 0.5 3.9 1.3 0.0 0.0 4.1 0.5 0.5 0.0 2.60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.2 0.2 4.2 3.5 0.0 0.2 2.1 1.2 0.0 3.0 2.6 1.2 0.9 0.0 3.00.1 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.7 0.4 0.0 0.1 0.2 0.2 0.3 0.0 0.1 0.2 0.2 0.0 1.50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.3 2.1 0.0 0.7 0.7 1.4 0.7 0.7 10.0 0.0 0.7 0.0 1.40.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.3 0.0 0.0 0.0 0.0 1.3 0.0 0.0 6.3 1.3 0.0 0.0 2.50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 3.7 0.0 0.0 7.4 0.0 0.0 0.0 3.70.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.8 0.8 1.2 0.8 0.4 2.5 2.1 0.0 0.0 2.1 0.0 0.4 0.0 3.71.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.9 0.0 0.0 0.0 1.9 1.9 0.0 1.0 6.8 0.0 1.9 0.0 1.90.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.5 0.4 0.0 0.1 0.1 0.4 0.1 0.0 0.1 0.2 0.2 0.0 1.80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 5.00.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.9 0.0 0.0 0.0 0.0 1.0 0.0 0.0 3.9 0.0 1.9 0.0 5.81.8 1.7 0.0 0.0 1.9 0.1 0.0 0.0 0.0 0.1 0.1 0.3 0.1 0.8 1.0 0.0 0.1 0.0 0.5 0.1 0.0 0.0 0.4 0.8 0.0 4.05.1 0.2 0.0 0.6 4.7 0.2 0.0 0.4 0.4 0.0 0.0 0.0 0.2 0.6 1.2 0.0 0.0 0.0 0.4 0.0 0.0 0.2 0.4 0.8 0.0 3.71.0 0.0 0.0 0.5 1.0 0.5 0.0 0.0 0.0 0.5 0.0 0.5 1.0 4.3 2.9 0.0 0.0 1.0 1.9 0.0 0.0 0.0 1.4 3.8 0.0 9.06.1 0.0 0.0 0.0 11.5 0.8 0.0 0.0 1.5 0.0 0.0 0.0 0.8 2.3 1.5 0.0 0.0 0.0 1.5 0.8 0.0 0.0 0.8 2.3 0.0 3.80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.7 0.0 3.5 1.4 0.0 0.0 0.7 1.4 0.0 0.0 0.0 1.4 0.0 0.7 4.90.1 0.0 0.0 0.0 0.4 0.4 0.0 0.5 0.0 0.0 0.3 0.3 0.0 0.8 1.3 0.0 0.0 0.1 0.7 0.0 0.0 0.0 0.3 0.1 0.0 1.60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.2 3.1 0.0 0.0 0.0 0.1 0.0 0.0 0.2 0.0 0.2 0.3 0.0 0.0 0.0 0.3 0.1 0.0 0.2 0.0 0.1 0.0 0.50.9 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.5 0.5 0.0 0.0 0.5 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.0 0.0 7.10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.3 0.0 0.0 0.0 0.0 12.5 0.0 0.0 0.0 0.0 0.0 0.0 0.07.2 0.0 0.0 5.9 0.0 0.0 0.7 0.7 0.0 1.3 0.0 0.0 0.7 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.7 0.0 0.7 0.0 3.94.6 0.2 0.0 0.2 0.0 0.2 0.2 0.5 0.0 0.0 0.0 0.0 1.2 1.7 0.0 0.0 0.0 0.5 0.7 0.0 0.2 0.7 0.5 0.0 2.90.0 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 3.5 0.0 0.9 6.1 4.4 0.0 0.0 0.0 1.8 0.9 0.0 0.0 0.9 0.9 0.0 6.10.0 0.0 0.0 0.0 7.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 2.3 0.0 0.0 0.0 2.3 0.0 0.0 4.71.4 0.0 0.0 0.0 4.1 0.0 0.0 1.4 0.0 0.0 0.0 0.0 0.0 1.4 0.0 0.0 0.0 1.4 0.0 0.0 0.0 0.0 4.1 0.0 2.70.5 0.0 0.0 0.0 5.5 0.0 0.0 1.6 0.0 1.1 0.0 0.0 2.7 1.6 0.0 0.0 0.0 2.7 0.5 0.0 0.0 0.0 0.5 0.0 3.80.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.7 0.7 1.1 0.7 27.0 0.4 0.0 1.1 1.4 0.4 0.0 0.4 2.8 2.1 0.0 2.50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.3 0.3 8.6 3.6 0.0 1.1 1.1 9.4 0.0 0.3 0.0 1.9 0.8 0.0 8.30.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.5 1.3 1.3 4.0 0.0 0.8 0.3 2.4 4.8 0.0 0.3 8.3 1.1 0.0 4.80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.6 0.8 1.1 0.0 0.0 0.3 0.7 1.8 0.0 0.1 9.1 0.8 0.0 1.90.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.6 0.4 0.3 4.6 0.1 0.3 2.0 1.7 0.2 0.1 0.5 0.9 1.5 0.1 6.60.1 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 1.3 0.6 0.4 0.5 3.1 0.1 0.2 0.4 2.3 0.2 0.1 0.1 0.8 2.1 0.0 3.70.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 1.4 0.0 0.0 3.5 0.7 0.0 3.5 2.8 0.0 0.0 0.0 1.4 0.7 0.0 2.80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.6 0.0 0.0 1.9 1.9 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.6 0.0 28.90.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.6 0.3 0.6 0.4 6.6 5.7 0.1 0.1 1.8 0.1 0.1 0.3 0.6 2.1 0.1 5.10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.2 1.1 0.4 2.2 9.1 0.0 0.8 0.8 0.1 0.0 0.1 1.1 1.4 0.1 11.70.5 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.3 0.3 4.3 1.8 0.5 1.3 0.0 0.3 0.5 1.5 0.0 0.3 9.1 1.5 0.0 6.60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.9 0.0 0.0 7.2 4.0 0.0 0.4 1.8 1.3 0.0 12.1 0.0 0.9 0.0 3.60.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.9 0.3 0.1 6.4 3.9 0.3 0.2 2.2 3.9 0.2 2.1 0.9 1.2 0.0 5.70.1 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.3 3.4 4.1 2.1 3.0 0.0 0.1 0.0 1.5 4.3 0.0 0.0 1.2 0.0 6.20.4 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 1.1 1.3 1.5 0.4 3.5 14.0 0.0 0.9 2.4 1.7 0.0 0.0 0.4 1.1 0.4 3.50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.6 2.6 0.0 2.6 9.2 5.3 0.0 1.3 2.6 3.9 0.0 0.0 2.6 0.0 1.3 6.60.2 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.2 0.3 0.3 0.2 1.8 1.4 0.1 1.3 0.1 0.7 0.3 0.0 0.1 0.5 0.4 0.0

41