swf&politics

8/9/2019 SWF&Politics

1/39

Bilateral Political Relations and the Impact of Sovereign Wealth Fund

Investment: A Study of Causality

April Knill Bong-Soo Lee Nathan Mauck254 RBB 251 RBB 336D RBB821 Academic Way 821 Academic Way 821 Academic Way

Tallahassee, FL 32306-1110 Tallahassee, FL 32306-1110 Tallahassee, FL 32306-1110(850) 644-2047 phone (850) 644-4713 phone (850) 644-1861 phone(850) 644-4225 fax (850) 644-4225 fax (850) 644-4225 [email protected] [email protected] [email protected]

Abstract:

We test the role of bilateral political relations in sovereign wealth fund (SWF) investment decisions. Our

empirical results suggest that political relations do play a role in SWF decision making. We find, counter

to predictions based on the trade and political relations literature, that SWFs prefer to invest in nations

with which they have relative weaker political relations. This indicates that SWFs behave differently than

other economic agents. Despite this observed difference, we find that, consistent with the trade and

political relations literature, SWF investment has a positive (negative) impact for relatively closed (open)

countries. Our results indicate SWFs use, at least partially, non-financial motives in investment

decisions.


2/39

I. Introduction

Sovereign wealth fund (SWF) investment has generated controversy both in the popular press and

in the political realm. The controversy surrounds the motives and impact of SWFs. One side of the

debate suggests that SWFs may be a stabilizing force in markets and perhaps also lead to improved

political relations. The other side suggests that SWFs may be motivated by non-financial concerns in

their investments and that this could lead to destabilization in both a financial and political sense. In

particular, many country leaders are uncomfortable with this potential destabilization and have voiced

concern both anecdotally and in a survey (see the Government Accountability Office Report where six

out of ten countries surveyed expressed discomfort with this form of investment). The contribution of

this paper is to offer empirical evidence as to whether we can rule out the latter belief. To that end, we

examine the relation between political relations and SWFs. Specifically, we examine whether there is a

significant relation between the two and what direction it takes.

Recent studies explore SWFs, specifically with regard to the performance of SWF investment. In

several working papers, Knill, Lee, and Mauck (2009), Chhaochharia and Laeven (2009), Bortolotti,

Fotak, Megginson, and Miracky (2009), Dewenter, Han, and Malatesta (2009), and Kotter and Lel (2009)

find the initial reaction of the purchased asset to SWF investment is positive. Fernandes (2009) shows that

SWFs lead to greater firm value. Four studies, however, find a negative reaction to SWF investment after

one year. The first three ultimately conclude that the impact of SWF investment is generally negative.

Knill, Lee, and Mauck (2009) further show that the Sharpe ratios significantly worsen following SWF

investment. This effect is especially strong for SWFs from oil producing nations. Consistent with these

results is the hypothesis that the relation between the SWF host nation and target nation may play a role in

the impact of the investments The observed poor performance of SWF investment provides anecdotal


3/39

There was immediate concern about this pending acquisition by some Americans due to perceived

national security risks. The issue was highly public, highly politicized, and ultimately resulted in Dubai

Ports selling the portion of their acquisition related to the US ports (White (2006)).

In another example, the Libyan SWF purchased a stake in the publicly traded Italian soccer team,

Juventus, in 2003. The purchase was widely considered a public relations ploy by the Italian media as the

Gaddafi family sought to gain respectability in the West through their investment (Owen (2002)).

In some cases, SWF investment has spurred protectionist responses. For example, Germany

rushed to enact laws that would allow its government to block SWF investment in certain cases after

Chancellor Angela Merkel struck down deals involving the Russian SWF and aerospace and defense

manufacturer EADS (Evans-Pritchard (2007)). US officials have stepped in multiple times based on

political concerns. Former Defense Secretary Caspar Weinberger disallowed a contract with FIAT based

on the fact that the Libyan government owned 15% of the firm and that political relations between the US

and Libya were less than amicable. In another episode, the US heightened scrutiny of SWF Investments

after Moscows military action against its neighboring country Georgia. Collectively, these stories

suggest that political relations are viewed in the minds of some SWFs, as well as policymakers, as a factor

in SWF investment decisions. However, SWFs are also often noted as long-term investors that often do

not take board seats or even voting shares. Thus, whether or not SWFs are politically motivated remains

to be verified empirically.

To our knowledge, extant literature has not yet explored the relation between political relations

and SWF investment. There has, however, been some examination into the determinants of SWF

investment. Chhaochharia and Laeven (2009) find that SWFs exhibit a home bias and tend to invest in

countries with similar cultures to their own although SWFs tend to invest less in countries in which they


4/39

concludes that there is no evidence of non-financial motives in SWF holdings. He additionally advocates

examining SWFs on a case by case basis given the differences between SWFs. Finally, Karolyi and Liao

(2009) find some evidence that government related cross border acquisitions have differing determinants

from corporate cross border mergers, although SWF motives do not differ from other government

acquisitions. The paper notes their overall results suggest no evidence for resource misallocation due to

political bargaining . We add to the literature on the determinants of SWF investing by examining one

hypothesized motivator in SWF investment: political relations.

Despite the lack of research linking SWFs and political relations, there is a substantial body of

work regarding trade and political relations. In fact, the issue dates back to Smith (1776), Mill (1848),

and Keynes (1919). Although the work regarding trade and political relations does not comment on

SWFs, we rely on the literature nonetheless because SWF investment likewise reflects economic flows

across nations and thus is likely governed by similar forces. Forbes (2009) finds empirical evidence that

trade between nations is positively related to foreign investment. Polacheck, Seiglie, and Xiang

(2007) find that foreign direct investment reduces conflict. These findings provide a link between

the trade and investment (similar to SWF investment) literature.

Reuveny (2000) surveys recent work on trade and political relations. He notes that prior studies

theoretically suggest that trade may cause political relations (both an improvement and deterioration in

political relations have been predicted in various models). Conversely, theory has suggested that political

relations may cause trade.

Numerous empirical tests have also been conducted to identify the relation between political

relations and trade. Different proxies are used for political relations, including the number of military

fli t b t t ti UN ti d d t t l i i ti l t id tif


5/39

reconcile these contradictory findings showing both theoretically and empirically that bilateral trade

reduces conflict between the two countries. They also, however, show that the general openness to trade

(i.e. many trade partners that individually do not represent a large share of total trade) increases the

probability of conflict (i.e., there is potentially a nonlinear relation). The reason for this is that more

open nations are less reliant on any one trade partner, which consequently reduces the costs of conflict

and increases the probability of conflict.

The political relations trade literature has also been extended to other economic flows, such as

foreign investment. Gupta and Yu (2007) focus on bilateral political relations between the United States

and other nations. They find that when political relations between two nations suffer, the flow of foreign

investment between the two countries is reduced. Particularly important to our study, the authors note

that their results, suggest that both foreign and U.S. investors take into account a change in bilateral

political relations in their investment strategies. The authors note the importance of understanding the

role that political relations play in determining various economic trade flows. Specifically, they cite a

large body of work in finance focusing on the impact of various restrictions and barriers (which are

related to political relations) to trade. In particular, Rajan and Zingales (2003), and Bekaert, Harvey, and

Lundblad (2005) address the issue of various restrictions and trade and generally report a positive impact

associated with market openness.

This issue is especially relevant to SWF investment, as some nations have already taken steps to

prevent or limit SWF investment (see the Germany example discussed previously) and others have begun

discussions to do so. Much of the concern surrounding SWFs relates to the funds lack of disclosure.

Fernandes, Lel, and Miller (2009) examine market reactions around the 2007 passage of SEC Rule 12h-6,

which makes it easier for foreign firms to deregister with the SEC They find that investors value


6/39

We begin a unique strand of research by examining the relation between SWFs and political

relations. We extend the growing literature on SWFs, and the literature examining the role of political

relations in economic activity. We attempt to identify the causal relation between political relations and

SWF investment. Specifically, we address the question of whether SWF investment Granger-causes a

change in political relations, or if changes in bilateral political relations Granger-cause SWF investment.

Through our analysis, we add to the understanding of both the economic and political implications of

SWFs.

We find evidence that political relations play a role in SWF investment decisions. Counter to

predictions based on trade and political relations literature, we find that SWFs prefer to invest in nations

with which they have relatively weaker political relations. This evidence may be due to SWF nations

using the investment to improve relations, or to SWF nations seeking another channel (i.e. economic) for

conflict. Either way, the results suggest that SWFs do not behave in the same manner as other economic

agents. Despite this difference in behavior, we observe that SWF investment impacts political relations

similarly to other economic flows. Specifically, we find that SWF investment improves (hinders)

political relations when the investment takes place in nations that are relatively closed (open). Our results

may be of interest to policymakers considering whether or not to limit or block SWF investment based on

concerns regarding the funds motives. To the extent that they would like to avoid misinterpretation of

their investment involvement, the results may also be of interest to those that manage SWFs.

II. DataA. Sovereign Wealth Fund Investments

We obtain data on SWF investments from two sources. First, we conduct a search of all known


7/39

acquisitions of public and private targets by SWFs over the period 1984-2009, which is comparable in

size to that used in other SWF studies.

1

B. Political Relations

We use two proxies for political relations. The first proxy is based on United Nations voting

records.2 A comprehensive list of all UN General Assembly votes from 1946 to 2008 is provided by Erik

Voetens website.3 The motivation for this proxy is that nations with more (less) closely related votes in

the UN General Assembly are likely to have stronger (weaker) political relations. We quantify the degree

to which countries votes are similar following Gartzkes S measure (Gartzke (1998)), where S is the

proxy for bilateral political relations. Specifically we calculate S using the equation:

S = 1 [2 * d / dmax] (1)

where d is the sum of the distance between votes for a given bilateral pair and year and dmax is the

maximum possible distance between votes for a given bilateral pair and year. The distance between votes

is calculated by first classifying Yes votes equal to one and No votes equal to zero.4 Then for each

vote the distance is calculated as the absolute value of the difference in votes. Thus, if both nations vote

the same (opposite) way, the distance is zero (one) for that vote. This distance measure is then cumulated

over the year for each bilateral pair. Thus, our S measure ranges from -1 (all votes are different) to +1

(all votes are the same), which represents weak and strong political relations respectively. A political

relations proxy based on UN voting is desirable due to the continuous nature of the measure and because

1 For instance, Fotak, Bortolloti, and Megginson (2008) have a sample of 182 investments in their analysis of oneyear return performance Chhaochharia and Laeven (2008) use a large sample of holdings for determinants analysis


8/39

it is based on official government action. We provide the time series of political relations for several

countries with the United States, based on the S measure, in Appendix A (Panel A).

Our second proxy for political relations is based on event data provided by Gary Kings website.5

The data consists of Reuters news articles involving political events between nations from 1990 to 2004.6

Overall, the sample consists of over 10 million dyadic events. The event codes are provide by Virtual

Research Associates (VRA) and are in the Integrated Data for Event Analysis (IDEA) framework.

Goldstein (1992)7 uses a conflict-cooperation scale to convert the IDEA event code into a numerical score

for political relations where large scores correspond to more favorable political events and

smaller/negative scores correspond to less positive or negative political events. We use the scale, which

ranges from -10 to +10, to arrive at political relations scores as in King and Lowe (2003b). Examples of

event types and their corresponding scores are: extend military aid (+8.3), ease sanctions (+2.3), accuse (-

2.8), and military occupation (-10). We apply the Goldstein score to each event from the King sample.

We form three measures of political relations using these scores. Each measure reflects a different

method of aggregating the individual events into a meaningful, yearly measure. The first is the average of

all event scores for a given dyad (i.e. bilateral pair) and year. The second is the standard deviation8 of

the event scores for a given dyad and year. The third is the average political relations divided by the

volatility of political relations. Our time series of average and standard deviation scores used in the paper

can be found in Appendix A (Panel B). This proxy, based on IDEA events for political relations, is

preferable to others (i.e. militarized conflicts) as it provides a continuous variable for which we can

construct a time series of political relations. This proxy also has the advantage of including non-military

political events. The disadvantage of this method relative to the UN voting data is that the data is

available only through 2004 We report the bilateral political relations for two dyads the United


9/39

States/United Kingdom, and the United States/Iraq, in Appendix A (Panel C). These country pairs are not

in our sample, but we include them to demonstrate the intuitive appeal of our political relations proxy.

The United States/United Kingdom (United States/Iraq) average political relations are consistently

positive (negative) over the time series. The Gulf War spans 1990-1991 and the United States/Iraq

average political relations are especially negative over this period. The start of the Iraq War in 2003

again provides evidence as to the success of the political relations proxy. In this year, political relations

become increasingly negative with Iraq while political relations improve with the United Kingdom (a US

ally in the Iraq War).

C. Other data

Lastly, we gather data on other variables likely related to SWF investment. We create a proxy for

bilateral trade flows using important trade partners as identified by the CIA World Factbook. We also

use the CIA World Factbook to gather data on the dominant religion and language of each country in our

sample and the membership of each country in the World Trade Organization (WTO). Macro level

variables come from the World Banks World Development Indicators (WDI) database. These variables

include GDP per capita, GDP per capita growth, tourism levels, the market capitalization of publicly

traded firms, and net FDI flows. Our proxy for financial openness is a measure of average liberalization

intensity from Bekaert, Harvey, and Lundblad (2005).9 The measure ranges from zero (closed) to one

(open). We define open (closed) nations as those with a liberalization intensity greater than or equal to

(less than) 0.50. Splitting on the median liberalization measure provides an intuitive break point for our

definition of open and closed nations, however, most nations that are classified as open are equal to one,

or nearly so. We include a proxy related to the political system of a given nation based on a scale of


10/39

III. Methodology

A.Theoretical Basis and Empirical Hypotheses

Reuveny (2000) notes in his survey of political relations research that there are two paradigms

through which trade and political relations are typically viewed. The first is liberalism, which suggests

that trade will reduce conflict. The mechanism by which this occurs is the knowledge that conflict will

result in economic costs for both parties. This knowledge results in both parties avoiding conflict in order

to avoid associated costs. In the case of SWF investment, the involved economic parties are the

governments of the respective countries. These governments would also seek to avoid economic costs

associated with conflict and would also have more direct influence over political relations than economic

actors used in other settings.

Using similar logic to that of liberalism, some also examine the extent to which political relations

cause trade. Bergeijk (1994) develops the expectation that deterioration (improvement) in political

relations will lead to less (more) trade. He finds empirical evidence supporting this hypothesis. Using

SWF as an analog, we expect this form of investment or economic flow to increase (decrease) following

political relation improvements (deterioration). Note that theory suggests that causality may be in either

direction (i.e. a change in trade may cause a change in political relations or a change in political relations

may cause a change in trade). This suggests that both directions of causality must be examined. Also,

recent work in political relations recognizes its dynamic nature and typically focuses on identifying the

direction of this dynamic relation, rather than focusing on a purely cross-sectional relation.

The second paradigm, realism, predicts that trade will either have a neutral or negative impact on

political relations. The negative outcome results when trade between two countries means more to one


11/39

significant impact of political relations on trade, the two theories both suggest that trade is likely to

increase (decrease) following improving (deteriorating) political relations. Reuveny and Kang (1996)

show that the direction of causality between trade and political relations can be different across various

dyads. Thus, it may be necessary to examine each dyad individually in addition to a pooled examination.

The authors further show that the causality frequently goes in both directions. Thus, formally, we may

expect that the SWF analog can be stated as follows:

H1 SWF investment will increase (decrease) following an improvement (deterioration) in political

relations.

Martin, Mayer, and Thoenig (2008), who reconcile the differing results in the trade/PR literature,

show theoretically and empirically that when viewing a bilateral pair in isolation, trade between nations is

associated with improved political relations. However, nations that have many trading partners will not

place as much value on a given bilateral relation and will be less apt to reconcile with a given trading

partner, thus potentially leading trade to have a negative impact on political relations. Essentially,

openness is the determining factor in whether or not trade will have a positive or negative impact on

political relations, where openness is related to a given countrys trade inflows (in terms of size of inflows

and the portfolio of involved nations). To the extent that SWFs will respond to issues of economic

incentives and political relations in the same manner as other economic agents, we expect:

H2 - In bilateral pairs in which the involved nations are closed (open) we will see an improvement

(deterioration) in political relations following SWF investment


12/39

difference in means and medians tests as well as a tobit model to examine this relation. We seek to

explain what determines a SWFs decision to invest in a given country in a given year. We examine the

role of bilateral political relations in the investment decisions of SWFs, controlling for other related

variables, in the tobit model:11

,,,2,1,0, titiit

ii

i

t eXPRSWFINV +++= (2)

wherei

tSWFINV is the dollar amount of SWF investment in country i in year t. The vectorX consists

of our control variables. Specifically, we use country-level control variables that are likely related to

SWF investment, for country i in year t, similar to those in Gupta and Yu (2007). Chhaochharia and

Laeven (2009) include bilateral investment between the two countries involved in the transaction, as well

as cultural variables including proxies based on religion and language. Thus, we include PARTNER, a

dummy variable equal to one if the target nation is identified as an important trade partner (as defined

in the data section), and equal to zero otherwise. We predict this variable is positively related to

SWFINV as SWF nations are more likely to invest in countries with which they have other economic ties.

Our cultural proxies are RELIGION DIFF, which is a dummy variable equal to one if the country pair

does not share the same major religion, and equals zero otherwise, and LANGUAGE DIFF, which is a

dummy variable equal to one if the country pair does not share the same major language and equals zero

otherwise. We anticipate, based on prior research, that the cultural variables will be positively related to

SWFINV. TOURISM is the tourism inflow level of the target nation and is also predicted to be positively

related to SWFINV as tourist levels are likely related to economic ties. We also use control variables from

Martin Mayer Thoenig (2008) Thus we include CLOSE a dummy variable for countries that are close


13/39

of each other.12 CLOSE equals one if the countries are within 500 miles of each other and zero

otherwise.13 Given the home bias found in Chhaochharia and Laeven (2009), we expect CLOSE to be

positively related to SWF investment. Finally, we include DEMOCRACY DIFF which is the absolute

value of the difference in the democracy index between the two nations. The democracy index is from

the Polity IV database and ranges from -10 (least democratic) to +10 (most democratic). We expect

DEMOCRACY DIFF to be negatively related to SWFINV.

Additionally, we include variables related to the size and economic activity of the target nation

that may be related to SWFINV. GDP is the target nation GDP per capita. GDP GROWTH is the GDP

growth of the target nation, MKT CAP is the aggregate market capitalization of the publicly traded firms

in the target country. NET FDI is the net foreign direct investment (FDI) flows for a given target nation.14

TRADE is the net trade (i.e. balance of payments) scaled by GDP. WTO is a dummy variable equal to

one if the target nation is a member of the World Trade Organization and zero otherwise. Finally, G10 is

a dummy variable equal to one if the target nation is a member of the G10 and zero otherwise. If SWFs

prefer to invest in relatively larger (smaller) and more (less) active economies, then GDP, MKT CAP,

FDI IN, FDI OUT, TRADE, and G10 should be positively (negatively) related to SWFINV.

PR is the level of political relations between the target and acquiring nations measured as either

S from equation (1), or PR RATIO, the ratio of PR AVERAGE to PR VOL. PR AVERAGE is the

average political relations event score for a given year, and PR VOL is the volatility of political relations

over the year as measured by the standard deviation of each event over the year. By scaling PR

AVERAGE by PR VOL, this last measure represents a risk-adjusted measure of political relations.15 If

political relations are positively related to the likelihood of SWF investment in a given country during a


14/39

given year, as predicted by our hypothesis, then we expect a positive coefficient 1,i for S and PR

RATIO. This is because S and PR RATIO both increase as political relations between countries improve.

Table 1 displays the summary statistics (Panel A) and correlations (Panel B) of the variables. The

proportion of country-years with SWF investment is around 1%; thus, 99% of the dependent variable

observations equal zero.16 The proportion of zeros in our dependent variable motivates the use of the

tobit model. The proportion of country-years involving G10 target nations is fairly low using UN voting

data as the political relations proxy compared to the Goldstein Scale proxy (12% compared to 60%) .

This is because G10 nations tend to be involved in more political relations events reported in Reuters

news articles and thus have relatively more populated political relations measures for that proxy. Thus,

the UN voting proxy provides a much broader sample of countries. Approximately 8% of the country-

years involve target nations that are significant trade partners (based on CIA data) with the SWF nation.

S and PR RATIO, the two political relations proxies, both have positive means. This suggests that the

average county pair in our sample has relatively favorable political relations. However, there is

significant dispersion in both political relations proxies.

[Insert Table 1 Here]

We estimate equation (2) using all possible country pairs based on the availability of political

relations data. When S is the political relations proxy, our SWF nation sample includes: the

United States, Canada, Mauritania, Kuwait, Qatar, United Arab Emirates, China, South Korea,

Japan, Malaysia, Singapore, Brunei, Indonesia, Australia, Papua New Guinea, and New Zealand.

Wh PR RATIO i h li i l l i i l i l d i d


15/39

both measures (around 60% for UN data and 90% for Goldstein Scale data), so we weight the tobit results

accordingly.17 We estimate a pooled (across countries) random effects tobit model and provide

specifications using year dummies.18 Including random effects is appropriate as it allows us to control for

omitted variable problems.

We are also interested in determining whether the relation between SWF investment and political

relations is a dynamic causal relation. The tobit approach allows us to examine whether or not political

relations is contemporaneously related to SWF investment, while a causal framework allows us to see if

one variable leads the other. A causal framework is consistent with our hypotheses, which suggest that

political relations are linked to SWF investment and that SWF investment influences political relations.

We examine both directions of Granger causality for each country dyad. In order to evaluate the dynamic

causal relation between SWF investment and bilateral political relations by estimating the following

vector autoregressive regression (VAR) for each dyad:

=

=

++=

k

j

jtj

k

j

jtjt SWFINVPRPR1

,2

1

,10,1 , (3)

where PR is defined as before. SWFINVt is the same as before and measured in two different ways: 1)

the number of investments19 by a given SWF in the country of interest in year t - j, and 2) the investment

in terms of $US20 in country i and year t j (the same measurement used in the tobit framework). We use

length k equals two.21

Equation (2) examines if PR is affected by lagged PR and SWFINV. SWF investment is said to

Granger-cause bilateral political relations if we reject:


16/39

H0: 2,j = 0, for all j. (4)

If lagged SWF investment can predict current political relations, controlling for past political relations,

SWF investment Granger-causes bilateral political relations. This specification also allows us to test the

cumulative (net) effect of lagged SWF investment. Specifically, we test:

H0 : = 0. (5)

This allows us to test for the direction of the causal relation. If the target nation is open (closed), we

expect the cumulative impact of SWF investment on political relations to be negative (positive). If SWFs

invest in a manner consistent with our first hypothesis, we expect a positive cumulative effect of political

relations on SWF investment.

We also examine whether lagged political relations, after controlling for lagged SWFINV,

Granger-causes SWF investment, using the VAR:

=

=

++=

2

1

,2

2

1

,10,1 ,j

jtj

j

jtjt PRSWFINVSWFINV (6)

In this case, political relations are said to Granger-cause SWF investment if we reject:

H0: j,2 = 0, for j = 1, 2. (7)

It is necessary that a SWF make an acquisition in a given nation in more than one year to be

included in our tests. We have a sample of seven SWFs and are able to examine 43 country pairs. Our


17/39

IV. Results

A. Difference in means and medians tests

We examine differences in means and medians between country-years that receive (do not

receive) SWF investment. This provides preliminary information about political relations levels during

years of SWF investment as we examine our hypothesis that suggests SWF investment may be linked to

higher levels of political relations. We perform t-tests of differences in means and non-parametric tests of

differences in medians using a chi-squared statistic. Country-years receiving (not receiving) SWF

investment equal one (zero). Thus, a negative difference in mean or median indicates that the given

variable is higher in country-years of SWF investment. Results are reported in Table 2 for the two

measures of political relations.

In Panel A, both the mean and median S are lower (significant at the 1% level) during SWF

investment years. This result is inconsistent with our hypothesis as it suggests that SWF investment is

associated with lower levels of political relations. In Panel B, the mean and median PR RATIO are

significantly lower, at the 5% and 1% levels, respectively, during SWF investment years. Consistent with

Panel A, risk-adjusted political relations are lower in SWF investment years. Both panels in Table 2

provide preliminary evidence that, contrary to our hypothesis based on the PR/trade literature, SWFs are

more likely to invest during times of relatively weaker political relations. See Table 2 for results.


B. Tobit Results

Table 3 Panel A displays the results of the tobit regressions using S as the political relations


18/39

explanations of this result are that either SWFs are seeking to improve political relations with the target

country or they are seeking additional ways to pursue conflict (i.e. by destabilizing markets as surmised

by Knill, Lee, and Mauck (2009)).


The coefficients for G10 are significant, but not consistently positive or negative. Consistent with

Chhaochharia and Laeven (2009), bilateral trade is positively associated with SWF investment as the

coefficient on PARTNER is positive and significant in specifications (1) through (4). Also consistent

with Chhaochharia and Laeven (2009), the coefficients on the cultural controls (DEMOCRACY DIFF,

LANGUAGE DIFF, RELIGION DIFF) are negative, suggesting that SWFs prefer to invest in nations

with which they are culturally similar. NETFDI is negative and significant in all specifications,

indicating that SWFs prefer to invest in countries with less net foreign direct investment.

The coefficients for GDP, GDP GROWTH, and MKT CAP are generally positive and significant,

which indicates that SWFs prefer to invest in relatively larger markets. To our knowledge, this result has

not been documented previously. We find negative and significant coefficients for TRADE in all

specifications. This suggests that, on average, SWFs prefer to invest in markets that generally conduct a

less trade. The coefficient on TOURISM is not significant based on the full model in specifications (5)

and (6). The coefficient on CLOSE is positive and significant in all of the specifications in which it is

included, which is consistent with Chhaochharia and Laeven (2009) who find a home bias in SWF

investment.

In Panel B, the set of regressions uses PR RATIO as the political relations proxy; its coefficient is


19/39

Collectively, the tobit results offer evidence that controlling for other factors that may be related

to SWF investment decisions, SWFs prefer to invest in nations with which they have relatively weaker

political relations. This result is robust to different specifications and measures of political relations.

Although we are unable to observe the true intentions of SWF, these results suggest that their investment

decisions differ from other economic agents (as documented in the PR/trade literature and that their

decisions depend at least partly on political relations.

B. Granger Causality

B.1. Political relations Granger cause SWF investment

We now examine the dynamic causal relation between political relations and SWF investment.

The results of our tests if political relations jointly or cumulatively Granger-causes SWF investment are in

the first three columns of Table 4. The joint tests address if political relations play a role in SWF

investment decisions, while the cumulative tests assign direction to this relation. Our results in Tables 2

and 3 are counter to our predictions based on the PR/trade literature. They suggest that political relations

are negatively related to SWF investment. It is worth noting that our results in Tables 2 and 3 pool all

target and SWF nations together, and examine contemporaneous correlations. The Granger causality

framework, in contrast, examines each dyad separately in a dynamic framework. Thus, our causality

results should not be viewed as exactly comparable to the previous results and should instead be viewed

as a different approach to addressing our hypotheses. We find that separating the analysis into dyads

provides a finer examination that allows us to isolate the role of political relations as a determinant of

SWF investment for particular target countries.

Overall, we examine 29 dyads (13 dyads), involving seven (one) SWF nations using UN Voting


20/39

The first political relations proxy is S, based on UN Voting data. The results, in Panel A, show

that when Mauritania is the SWF nation, political relations jointly Granger-cause SWF investment for

India and Pakistan (significant at the 1% level). When the United Arab Emirates is the SWF nation,

political relations Granger-cause SWF investment for the United States, United Kingdom, Australia

(significant at the 10% level), and France (significant at the 1% level). When Singapore is the SWF

nation, political relations joint Granger-cause SWF investment for India, the Netherlands, Switzerland

and South Korea (the first two significant at the 5% level and the second two at the 1% level).

The second political relations proxy is PR RATIO, based on the Goldstein Scale measure. The

results, in Panel B, show that when Singapore is the SWF nation, political relations jointly Granger-cause

SWF investment for the United Kingdom, South Korea, and Vietnam, New Zealand, Taiwan, and

Thailand (the first country significant at the 10% level, the second and third at the 5% level and the fourth

and fifth at the 1% level). Overall, we find that political relations jointly Granger-cause SWF investment

in sixteen target countries involving four different SWF nations.

[Insert Table 4]

We also test the cumulative effect of the prior two years political relation scores on current SWF

investment, controlling for past SWF investment. This assigns a direction to the Granger causality (i.e.

do political relations have a positive or negative impact on SWF investment). Trade/ political relations

theory and evidence suggest increased trade flows following an improvement in political relations.

However, Tables 2 and 3 show that SWFs are more likely to invest during periods of weaker political

relations. The mixed direction of results suggests that SWF motives vary by country. Thus, our findings

indicate consistent with the suggestions of Balding (2009) that analyzing each fund individually


21/39

our hypothesis, but consistent with Table 3 and suggests that for this country pair deteriorating political

relations lead to more SWF investment. When the SWF nation is Mauritania, political relations exhibit a

negative Granger-causal relation with Pakistan (significant at the 1% level). When Kuwait is the SWF

nation, political relations exhibit a negative Granger-causal relation with the United States (significant at

the 10% level). When Qatar is the SWF nation, political relations exhibit a negative Granger-causal

relation with the United States (significant at the 10% level). When the United Arab Emirates is the SWF

nation, political relations exhibit a negative Granger-causal relation with the United States (significant at

the 5% level) and France (significant at the 1% level). When China is the SWF nation, political relations

exhibit a negative Granger-causal relation with the Australia (significant at the 10% level). When

Singapore is the SWF nation, political relations exhibit a positive Granger-causal relation with the

Switzerland (significant at the 1% level). Thus, for all cases but the pair of Singapore and Sweden, the

Granger-causality results using S as the political relations proxy are consistent with Table 3 and indicate

that political relations are negatively related to SWF investment.

The cumulative tests in Panel B use the Goldstein Scale to proxy for political relations. The

results show that when Singapore is the SWF nation, Taiwan (significant at the 10% level), South Korea

(significant at the 5% level), New Zealand and Thailand (significant at the 1% level) all exhibit a positive

Granger-causal relation. The positive causal relation is consistent with our hypothesis that improved

political relations lead to greater SWF investment. A negative Granger-causal relation is found for the

Philippines (significant at the 10% level), the United Kingdom and Vietnam (significant at the 5% level).

Collectively, our joint results suggest that political relations are a significant determinant of SWF

investment for many dyads. Further, we find that the direction that political relations Granger cause SWF

investment depends on the dyad examined


22/39

define political relations using two different measures and conduct both joint and cumulative tests. These

results examine our second hypothesis, which suggests that SWF investment may cause political relations

and that the direction of causality depends on market openness.

Our hypotheses predict that relatively open country pairs would see deterioration in political

relations following SWF investment while relative closed countries would see an improvement in

political relations following SWF investment. Using the measure of openness from Bekaert, Harvey, and

Lundblad (2005), we identify Australia, Belgium, France, Italy, Japan, Netherlands, New Zealand, Spain,

Sweden, Switzerland, the United Kingdom, and the United States as open target nations in our sample.22

Open SWF nations include Singapore and the United States. Open SWF nations should lead to

investment in other open nations being especially negatively associated with political relations. It may

also lead to a negative relation between SWF investment and political relations for relatively closed target

nations.

The first political relations proxy is the S measure, based on UN Voting data. In Panel A, the

joint tests are significant when Mauritania is the SWF nation and Pakistan is the target (significant at the

1% level). The direction of this causal relation is negative (significant at the 10% level). Both nations are

considered closed, therefore, the results are not consistent with our hypothesis. The joint tests are

significant when the United Arab Emirates is the SWF nation and Spain (significant at the 5% level) or

India (significant at the 1% level) is the target nation. The direction of causality when Spain is the target

is positive (significant at the 5% level). Since the United Arab Emirates is considered closed, this result is

consistent with our hypothesis. The joint tests are significant when China is the SWF nation and

Australia is the target nation (significant at the 1% level). The direction of the relation is negative

(significant at the 1% level) and as Australia is an open nation is consistent with our hypothesis


23/39

for Switzerland (Australia) and is not (is) consistent with our hypothesis (both significant at the 1% level).

Collectively, the results suggest that SWF investment plays a role in political relations and the results are

generally, although not uniformly, consistent with our hypothesis.

Our second causal analysis uses PR RATIO, based on the Goldstein Scale, as the political

relations proxy and as a result Singapore is the only SWF nation in this sample. In Panel B, the joint tests

are significant for the United Kingdom (significant at the 10% level), the United States (significant at the

5% level), and Australia, Japan, China, Malaysia, and South Korea (significant at the 1% level). The

causal relation is positive for the Philippines (significant at the 10% level), and Japan and Malaysia

(significant at the 1% level). The result for Japan is not consistent with our hypothesis regarding the role

of market openness and the direction of causality, while the results for Philippines and Malaysia are

consistent with our hypothesis. The causal relation is negative for China, South Korea, and Vietnam

(significant at the 1% level). Given that Singapore is an open nation, these results are also consistent with

our hypothesis. Collectively, the results in Table 4, Panel B provide evidence that SWF investment

Granger-causes a change in political relations. Further, with the exception of Japan, the direction of

causality is generally consistent with our hypotheses.

B.3. Summary of Granger Causality Results

Table 5 summarizes the Granger causality cumulative results. Each nation and political relations

proxy combination is first labeled (J), indicating joint insignificance, or (I) indicating an insignificant

joint test. Then each dyad is labeled (+), indicating a positive causal relation, (-) indicating a negative

causal relation, or (I) indicating an insignificant cumulative relation. There are 43 country pair/political

relations proxy combinations and we find either significant joint or directional Granger-causality for 32 of


24/39

In general, our empirical predictions hold in the sense that SWF investment is linked to reduced

(improved) political relations for open (closed) nations. Therefore, although SWF investment decisions

may be based on different motives than other economic actors, the impact of those decisions appears to be

somewhat similar.

V. ConclusionsSWF motivations have been called into question by policymakers, politicians and the popular

press. Current working papers on SWFs find evidence which would imply that these motivations may be

non-financial (one of them, Chhaochharia and Laeven, 2009, directly). To our knowledge, we are the

first to empirically test the role of political relations in SWF investment decisions. The trade political

relations literature suggests that if SWFs act in the same manner as other economic agents, they will

choose to invest in nations with which they have relative better political relations. To the extent that

SWFs act like other economic agents, we would expect SWF investment to influence political relations

and the direction of influence will depend on the openness of the target nation.

Our empirical evidence provides evidence contrary to the PR/trade literature predictions with

regard to SWF investment decisions. We find that SWFs are more likely to invest in nations with which

they have relatively weaker political relations (although this result is shown to depend on the specific

bilateral pair). That is to say, SWFs do not appear to make investment decisions similarly to other

economic agents. This could be due to SWF nations seeking to improve relations or, alternatively, SWF

nations seeking another channel of conflict through government economic involvement. Despite the

apparent different motivations for SWFs, we find that the impact of the investments is consistent with

other economic flows Specifically we find that SWF investment leads to improvement (deterioration) in


25/39

limit or block SWF investment. Results may also be of interest to SWF managers to the extent that they

would like their motivations to be understood.


26/39

References

Akaike, Hirotugu, 1974, A new look at the statistical model identification, IEEE Transactions AutomaticControl, AC-19, 716723.

Balding, Christopher, 2008, A portfolio analysis of sovereign wealth funds, Working Paper, University ofCalifornia, Irvine.

Barbieri, Katherine, 1996, Economic interdependence: A path to peace or a source of interstate conflict?,

Journal of Peace Research 33(1), 29-49.

Barbieri, Katherine, 2002, The liberal illusion, does trade promote peace? The University of MichiganPress.

Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad, 2005, Does financial liberalization spurgrowth?,Journal of Financial Economics 77, 3-56.

Bortolotti, Bernardo, Veljko Fotak, William Megginson, and William Miracky, 2009, The financial

impact of sovereign wealth fund investments in listed companies, Working Paper, University ofOklahoma, Monitor Group.

Chhaochharia, Vidhi, and Luc Laeven, 2009, Sovereign wealth funds: Their investment strategies andperformance, Working Paper, University of Miami and International Monetary Fund.

Dewenter, Kathryn, Xi Han, and Paul Malatesta, 2008, Firm values and sovereign wealth fundinvestments, Working Paper, University of Washington and Tsinghua University.

Evans-Pritchard, Amrose, 2007, EC to rule on sovereign wealth funds, The Daily Telegraph, November29, 2007.

Fernandes, Nuno, 2009, Sovereign wealth funds: Investment choices and implications around theworld, Working Paper, IMD International.

Fernandes, Nuno, Ugur Lel, and Darius P. Miller, 2009, Escape from New York: The market impact ofloosening disclosure requirements,forthcoming, Journal of Financial Economics.

Forbes, Kristin J., 2009, Why do foreigners invest in the US?,Journal of International Economics

doi:10.1016/j.physletb.2003.10.071.

Friedman, Thomas L., 1999, The lexus and the olive tree, Farrar.


27/39

Gupta, Nandini, and Xiaoyum Yu, 2007, Does money follow the flag?, Working Paper, IndianaUniversity.

Karolyi, G. Andrew, and Rose C. Liao, 2009, What is different about government-controlled acquirers incross-border acquisitions?, Cornell University and Rutgers University.

Keynes, John Maynard (1919), The economic consequences of peace, London, Macmillan.

King, Gary, and Will Lowe, 2003a, 10 Million International Dyadic Events, hdl:1902.1/FYXLAWZRIAUNF:3:dSE0bsQK2o6xXlxeaDEhcg== Murray Research Archive [Distributor].

King, Gary, and Will Lowe, 2003b, An automated information extraction tool for international conflictdata with performance as good as human coders: A rare events evaluation design,InternationalOrganization 57, 617-642.

Kirchgaessner, Stephanie. US set to tighten scrutiny of Russian investment August 23 2008.

Knill, April, Bong-Soo Lee, and Nathan Mauck, 2009, Sleeping with the enemy or An ounce ofprevention: Sovereign wealth fund investments and market destabilization, Working Paper, Florida StateUniversity.

Kotter, Jason and Lel, Ugur, Friends or foes? The stock price impact of sovereign wealth fundinvestments and the price of keeping secrets, 2009, FRB International Finance Discussion Paper No. 940.

Liu, Laura Xiaolei, Ann E. Sherman, and Yong Zhang, 2008, The role of the media in initial publicofferings, Working Paper.

Mansfield, Edward D., Helen V. Milner, and Jon C. Pevehouse, 2008, Democracy, veto players, and the

depth of regional integration, The World Economy , 67-96.

Martin, Philippe, Thierry Mayer, and Mathias Thoenig, 2008, Make trade not war?, The Review ofEconomic Studies 75, 1-38.

Mill, John Stuart, 1970 [1848], Principles of political economy, Baltimore: Penguin Books.

Oneal, John, and Bruce Russett, 1999, Assessing the liberal peace with alternative specifications: Tradestill reduces conflict,Journal of Peace Research 36, 423-442.

Owen, Richard, 2002, Gaddafi buys Pounds 14 million stake in Italian football glory, The Times, January11, 2002.

Polachek, Solomon, 1980, Conflict and trade,Journal of Conflict Resolution 24(1), 57-78.


28/39

Reuveny, Rafael, 2000, The trade and conflict debate: A survey of theory, evidence and future research,Peace Economics, Peace Science and Public Policy 6, 23-49.

Reuveny, Rafael and Heejoon Kang, 1996, International trade, political conflict/cooperation, and Grangercausality,American Journal of Political Science 40, 943-970.

Schneider, Gerald, and Vera E. Troeger, 2006, War and the world economy: Stock market reactions tointernational conflicts,Journal of Conflict Resolution 50, 623-645.

Smith, Adam, 1937 [1776], An inquiry into the nature and causes of the wealth of nations, New York,

Modern Library.

Voeten, Erik and Adis Merdzanovic, United Nations General Assembly Voting Data, hdl:1902.1/12379UNF:3:Hpf6qOkDdzzvXF9m66yLTg==.

Weinberger Thinks Boycotts Work (New York Times, 5/15/86).

White, Ben, 2006, Uproar surprised Dubai firm, Washington Post, February 24, 2006.


29/39

Table 1 Data characteristicsThis table provides the summary statistics for variables used in our tobit analysis. SWFINV is a dummy variable that takes the value of one ifthere is an SWF investment in country i and time t and zero otherwise. G10 is a dummy variable that takes the value of one if the potential targetnation is a member of the G10 and zero otherwise. PARTNER is a dummy that takes the value of one if the potential target is identified as a

major trade partner with the SWF nation. DEMOC DIFF is the absolute value of the difference between the target and SWF nations democracyscore. LANGUAGE DIFF is a dummy variable that is equal to one if the two nations do not share the same major language and is equal to zerootherwise. RELIGION DIFF is a dummy variable that is equal to one if the two nations do not share the same major religion and is equal to zerootherwise. DEMOCRACY is democracy level of the target as defined by the Polity IV database. NET FDI is a measure of FDI inflows net ofoutflows for the potential target nation. GDP is the GDP per capita for the potential target nation. GDP GROWTH is the GDP growth rate forthe potential target nation. TRADE is the total trade level for the potential target nation. MKT CAP is the dollar amount of all traded securitiesfor the potential target nation. TOURISM is the number of tourist arrivals for the potential target nation. CLOSE is a dummy that takes the valueof one if the potential target nation is within 500 miles of the SWF nation. WTO is a dummy variable equal to one if the target nation is amember of the WTO. S is the distance between UN voting records for a given bilateral pair. Specifically, S = 1 [2 * d / dmax] ,where d is thesum of the distance between votes for a given bilateral pair and year and dmax is the maximum possible distance between votes for a given

bilateral pair and year. PR RATIO is PR AVERAGE divided by PR VOL. PR AVERAGE is the average political relations event score for a

given year, and PR VOL is the volatility of political relations over the year as measured by the standard deviation of each event over the year.

Panel A: Summary statistics

Mean Median Min Max Std Dev N

SWFINV 0.009 0 0 1 0.096 12808

S 0.703 0.835 -1 1 0.394 12808

G10 0.120 0.000 0 1 0.325 12808

PARTNER 0.077 0 0 1 0.267 12808

DEMOCRACY DIFF 8.030 6.000 0 20 6.664 12808

LANGUAGE DIFF 0.934 1.000 0 1 0.248 12808

RELIGION DIFF 0.715 1.000 0 1 0.451 12808

DEMOCRACY 6.090 8 -10 10 5.615 12808

NET FDI 2.205 1.531 -22.364 23.810 3.921 12808

GDP 0.009 0.004 0 0.041 0.010 12808

GDP GROWTH 3.048 2.844 -11.77 16.236 16.236 12808

TRADE 83.967 74.587 14.932 473.510 47.633 12808

MKT CAP 45.568 29.683 0.019 298.45 47.706 12808

TOURISM 7.401 2.714 0.014 79.083 12.626 12808

CLOSE 0.038 0 0 1 0.191 12808

WTO 0.959 1 0 1 5.615 12808

PR RATIO 0.738 1.501 -4.747 13.568 1.334 700


30/39

29

Panel B: Correlations using S as political relations proxy

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

SWFINV (1) 1.00S (2) -0.03 1.00

G10 (3) 0.05 -0.21 1.00

PARTNER (4) 0.15 -0.10 0.28 1.00DEMOCRACY DIFF(5) 0.03 -0.07 0.01 0.09 1.00

LANGUAGE DIFF (6) 0.02 0.03 -0.05 -0.07 0.17 1.00

RELIGION DIFF (7) 0.02 0.06 -0.01 0.05 0.42 0.42 1.00

DEMOCRACY (8) 0.00 -0.09 0.25 -0.02 -0.07 0.18 0.01 1.00

NET FDI (9) -0.03 0.07 -0.30 -0.11 -0.01 0.00 0.00 -0.10 1.00

GDP (10) 0.05 -0.21 0.62 0.25 0.03 -0.06 0.00 0.34 -0.35 1.00

GDP GROWTH (11) 0.02 0.00 -0.12 0.02 -0.01 0.05 0.03 -0.03 0.21 -0.13 1.00

TRADE (12) -0.01 0.05 -0.09 -0.11 0.00 -0.03 0.02 -0.09 0.23 0.02 0.12 1.00

MKT CAP (13) 0.08 -0.13 0.39 0.20 0.02 -0.11 0.01 0.09 -0.20 0.52 -0.04 0.18 1.00

TOURISM (14) 0.06 -0.17 0.50 0.29 0.01 0.00 -0.01 0.12 -0.26 0.36 -0.01 -0.18 0.28 1.00

CLOSE (15) 0.07 0.06 -0.02 0.18 -0.04 -0.20 -0.09 -0.10 -0.03 -0.03 0.00 0.01 0.05 0.01 1.00

WTO (16) 0.02 -0.02 0.08 0.06 0.00 0.05 0.02 0.05 -0.07 0.06 -0.03 -0.03 0.10 0.04 -0.02 1.00

*Bold indicates significant at 5% level


31/39

Table 2 Difference of political relations means and mediansThis table reports results of difference in means and difference in medians tests. SWF = 0 refers to country-years in which there is no SWFinvestment. SWF = 1 refers to country-years in which there is SWF investment. We use two different proxies for political relations which areidentified in the panel labels. Panel A uses UN voting records to calculate the political relations proxy as the distance between votes for a given

country pair and year, labeled as S. Panel B uses PR RATIO, which is PR AVERAGE divided by PR VOL and is based on the Goldstein Scaleproxy for political relations. PR AVERAGE is the average political relations event score for a given year, and PR VOL is the volatility ofpolitical relations over the year as measured by the standard deviation of each event over the year. We report the mean and median for eachproxy for both SWF investment years and non-SWF investment years. T-tests (non-parametric equality tests using chi-squared statistics) areconducted to compare the difference in means (medians) for our political relations proxies between SWF investment years and non-SWFinvestment years. We examine all possible country pairs based on data availability.

Panel A: Political relations proxy based on UN voting distance (S)

Mean Median N

SWF = 0 0.74 0.93 53008

SWF = 1 0.54 0.77 222

Difference .20*** .16***

Panel B: PR RATIO based on Goldstein Scale proxy for political relations

Mean Median N

SWF = 0 0.84 0.54 1654

SWF = 1 0.41 0.36 84

Difference 0.43** 0.18**** significant at 10%; ** significant at 5%; *** significant at 1%.


32/39

Table 3 Tobit results

We specify the following tobit model: ,,1,

3,1,2,111,10,1 te

tm

PRtiXi

tSWFINVi

tSWFINV ++++= (1). The dependent

variable, SWFINV is the amount of SWF investment in country i and time t. G10 is a dummy variable that takes the value of one if the potentialtarget nation is a member of the G10 and zero otherwise. PARTNER is a dummy that takes the value of one if the potential target is identified asa major trade partner with the SWF nation. DEMOC DIFF is the absolute value of the difference between the target and SWF nations democracyscore. LANGUAGE DIFF is a dummy variable that is equal to one if the two nations do not share the same major language and is equal to zerootherwise. RELIGION DIFF is a dummy variable that is equal to one if the two nations do not share the same major religion and is equal to zerootherwise. NET FDI is a measure of FDI inflows net of outflows for the potential target nation. GDP is the GDP per capita for the potentialtarget nation. GDP GROWTH is the GDP growth rate for the potential target nation. TRADE is the total trade level for the potential targetnation. MKTCAP is the dollar amount of all traded securities for the potential target nation. TOURISM is the number of tourist arrivals for the

potential target nation. CLOSE is a dummy that takes the value of one if the potential target nation is within 500 miles of the SWF nation.WTO is a dummy variable equal to one if the target nation is a member of the WTO. Panel A uses UN voting records to calculate the politicalrelations proxy as the distance between votes for a given country pair and year, labeled S. Panel B uses PR RATIO, which is PR AVERAGE

divided by PR VOL and is based on the Goldstein Scale proxy for political relations. PR AVERAGE is the average political relations event scorefor a given year, and PR VOL is the volatility of political relations over the year as measured by the standard deviation of each event over theyear. YEAR DUMMIES is Yes (No) when we estimate a panel model using (not using) year dummies. All specifications are weighted by the

proportion of non-zero dependent variable observations for a given SWF nation. We examine all possible country pairs based on data availabilityand estimate the model using random effects. Coefficients are presented with robust standard errors in brackets.

Panel A: Political relations proxy based on UN voting distance

(1) (2) (3) (4) (5) (6)

S -0.046*** -0.027*** -0.102*** -8.178*** -9.152*** -8.554***

[0.001] [0.001] [0.002] [0.379] [0.390] [0.378]

G10 0.056*** 0.060*** -0.056*** -3.050*** -2.677*** -1.720***[0.002] [0.002] [0.003] [0.538] [0.524] [0.515]

PARTNER 0.200*** 0.202*** 0.123*** 1.360*** -3.016*** -2.262***

[0.002] [0.002] [0.003] [0.392] [0.450] [0.440]

DEMOCRACY DIFF -0.008*** -0.008*** -0.005*** -0.047* -0.053** -0.038

[0.000] [0.000] [0.000] [0.025] [0.025] [0.025]

LANGUAGE DIFF -0.011*** -0.011*** -0.046*** -4.480*** -2.345*** -2.244***

[0.002] [0.002] [0.003] [0.475] [0.486] [0.484]

RELIGION DIFF -0.016*** -0.019*** -0.065*** -0.967** -0.720* -0.645*

[0.001] [0.001] [0.002] [0.385] [0.377] [0.372]

WTO -0.015*** -0.013***

[0.001] [0.001]

NETFDI -0.005*** -0.064* -0.051 -0.063*

[0.000] [0.035] [0.037] [0.036]

GDP -0.033*** 1.495*** 1.770*** 1.652***

[0.001] [0.180] [0.180] [0.179]

GDP GROWTH -0.005*** 0.547*** 0.673*** 0.577***

[0.000] [0.060] [0.058] [0.060]TRADE -0.001*** -0.028*** -0.030*** -0.029***

[0.000] [0.003] [0.003] [0.003]

MKT CAP -0.000*** 0.052*** 0.059*** 0.051***

[0.000] [0.003] [0.003] [0.003]

TOURISM -0.001*** 0.023** 0.017 0.010

[0 000] [0 011] [0 011] [0 011]


33/39

Table 3 Ctd.

Panel B: PR RATIO based on Goldstein Scale proxy for political relations(1) (2) (3) (4) (5) (6)

PR Ratio -0.664** -0.421 -0.803* -1.052** -0.756* -1.028**

[0.334] [0.297] [0.458] [0.467] [0.457] [0.467]

G10 -4.958*** -4.785*** -3.582*** -3.728** -4.172*** -4.545***

[0.901] [0.912] [1.374] [1.490] [1.410] [1.579]

PARTNER 12.108*** 13.279*** 6.903*** 7.610*** 7.601*** 8.509***

[1.316] [1.225] [1.292] [1.455] [1.394] [1.624]

DEMOCRACY DIFF -0.107 -0.030 0.067* 0.093** 0.057 0.084**

[0.092] [0.064] [0.039] [0.040] [0.039] [0.041]

LANGUAGE DIFF -7.276*** -8.105*** -8.226*** -7.534*** -8.100*** -7.477***

[1.257] [1.182] [1.650] [1.902] [1.610] [1.866]

RELIGION DIFF -1.548 -1.984 -1.475 -1.891 -1.861 -2.434

[1.167] [1.228] [1.426] [1.498] [1.459] [1.564]

WTO 40.275 33.078

[10,917.272] [984.053]

NETFDI -0.040 -0.013 -0.018 0.002

[0.108] [0.123] [0.108] [0.123]GDP -2.423*** -2.669*** -2.018*** -2.211***

[0.613] [0.669] [0.636] [0.698]

GDP GROWTH 0.042 0.155 -0.001 0.097

[0.125] [0.142] [0.127] [0.144]

TRADE 0.046*** 0.049*** 0.048*** 0.053***

[0.007] [0.007] [0.007] [0.008]

MKT CAP 0.069*** 0.074*** 0.066*** 0.071***

[0.010] [0.012] [0.010] [0.012]

TOURISM 0.146*** 0.153*** 0.145*** 0.155***[0.031] [0.035] [0.031] [0.035]

CLOSE -2.123* -2.491*

[1.271] [1.392]

YEAR DUMMIES NO YES NO YES NO YES

CONSTANT -57.001 -85.585 -18.802*** -22.619*** -18.543*** -22.416***

[10,917.272] [1,959.787] [2.252] [2.962] [2.237] [2.928]

OBSERVATIONS 1340 1340 673 673 673 673

MODEL 2 107*** 243*** 89*** 104*** 92*** 104***

* significant at 10%; ** significant at 5%; *** significant at 1%


34/39

Table 4 Granger causality results

We specify the following VAR models: =

+=

+=k

jjtPRj

k

jjtSWFjtSWF

1,,2

1,10,1 (5), and

=

+=

+=k

jjtSWFj

k

jjtPRjtPR

1,,2

1,10,1 (2). The results from equation (5) are in the first three columns and the results from

equation (2) are in the last three columns. tSWF is the level of SWF investment in year t where level is measured as either the total dollar amount

(first row) or the number of investments (second row). tPR is the bilateral political relations score for year t. Panel A uses UN voting records to

calculate the political relations proxy as the distance between votes for a give country pair and year. Panel B uses PR RATIO, which is PRAVERAGE divided by PR VOL and is based on the Goldstein Scale proxy for political relations. Joint 2 refers to the chi-square test statisticfrom a test of the joint significance of the causal relation. Cumulative is the sum of the coefficients from the two lags of PR, and the significanceof the test is based on a chi-square test.

Panel A: Political relations proxy based on UN Voting S measure

PR Granger-Causes SWF SWF Granger-Causes PR

(Acquirer)/Target Joint 2 Cumulative R2 Joint 2 Cumulative R2

(US)

Sweden 2.23 -3.37 0.32 3.10 -0.040 0.67

2.80 -0.70* 0.41 3.12 -0.201* 0.67

(Mauritania)India 10.49*** -3.98 0.53 0.80 0.001 0.23

5.38* -8.67 0.38 2.93 -0.003 0.33

Pakistan 107.55*** -67.07*** 0.90 8.98** -0.016 0.59

45.31*** -15.06*** 0.81 18.91*** -0.057* 0.72

(Kuwait)

United States

2.82 -0.47* 0.54 0.03 0.023 0.71

(Qatar)

United States

3.09 -0.57* 0.55 0.02 0.015 0.70

United Kingdom 1.40 2.56 0.08 0.76 -0.013 0.18

1.40 0.81 0.87 0.76 -0.042 0.18

(United Arab Emirates)

United States 5.06* -11.07** 0.50 0.39 -0.003 0.63

5.54* -2.65** 0.86 0.45 -0.004 0.64

United Kingdom 5.32* -7.97 0.56 0.46 -0.004 0.12


35/39

Table 4 Ctd.


(Acquirer)/Target Joint 2 Cumulative R2 Joint 2 Cumulative R2


Spain

0.34 0.46 0.04 6.01** 0.223** 0.65

Italy 2.17 -4.70 0.12 2.25 -0.007 0.25

2.39 -2.13 0.27 0.63 -0.025 0.19

India 0.36 -1.09 0.22 12.50*** 0.002 0.56

1.45 0.28 0.37 4.97* 0.016 0.41Australia 3.68 1.29 0.31 1.68 0.027 0.83

5.67* 0.27 0.49 2.13 0.137 0.84

(Singapore)

United States 0.74 -5.20 0.09 0.22 -0.001 0.69

0.42 -0.98 0.03 0.32 0.001 0.69

United Kingdom 1.69 11.38 0.61 0.75 -0.003 0.14

2.88 2.76 0.47 1.68 -0.032 0.19

Netherlands 6.39* 0.39 0.29 3.03 0.009 0.17

6.77** 0.23 0.30 3.03 0.037 0.17

Belgium 1.18 6.03 0.23 0.49 -0.001 0.03

0.84 1.398 0.11 2.57 -0.003 0.13

Switzerland

13.51*** 15.71*** 0.75 89.17*** 0.045*** 0.98

China 0.57 31.23 0.30 1.16 0.003 0.342.99 9.93 0.56 2.00 0.006 0.37

South Korea 0.55 4.98 0.47 3.11 -0.006* 0.51

12.14*** -0.89 0.45 2.73 -0.024 0.50

Japan 2.07 3.93 0.16 1.31 -0.003 0.15

1.80 2.62 0.29 0.74 -0.021 0.12

India 6.39** -61.75 0.33 4.20 0.001 0.35

3.88 -14.30 0.47 3.90 0.003 0.34

Vietnam 1.24 10.15 0.08 0.36 0.004 0.59

4.39 7.68 0.38 0.56 0.012 0.60

Malaysia 1.45 83.59 0.19 1.02 0.001 0.37

2.49 46.43 0.16 1.98 0.004 0.40

Philippines 0 45 48 56 0 14 4 32 0 003** 0 51


36/39

Table 4 Ctd.



(Acquirer)/Target Joint 2 Cumulative R2 Joint 2 Cumulative R

2

(Singapore)

Australia 2.44 -1.54 0.55 2.37 -0.12 0.28

0.87 -4736.40 0.07 19.77*** 0.00 0.68

India 1.60 -2.07 0.23 1.02 -0.14 0.16

1.60 -67.05 0.18 1.02 0.00 0.16

Indonesia 3.71 0.01 0.62 4.57 -0.24 0.30

2.98 -194.12 0.66 4.23 0.00 0.29

Japan 1.13 -0.03 0.25 11.86*** 0.67*** 0.55

0.38 -189.27 0.05 0.37 0.00 0.12

New Zealand 31.70*** 0.36*** 0.77 1.25 -0.48 0.14

74.18*** 38.57*** 0.88 1.26 -0.01 0.14

Philippines 3.07 -0.97* 0.20 2.95 0.54 0.26

0.16 -3.78 0.04 0.72 0.01 0.13

China 3.32 -0.51 0.27 19.78*** -0.21*** 0.83

3.76 -159.80 0.26 0.40 0.00 0.58

Malaysia 2.23 -1.32 0.46 7.22** 0.25** 0.44

0.38 -89.38 0.33 23.91*** 0.02*** 0.70

South Korea 6.74** 2.81** 0.63 11.23*** -0.68*** 0.57

2.82 305.83 0.46 7.00** -0.01** 0.47

Taiwan 0.54 0.29 0.36 2.85 0.04 0.3417.20*** 74.34* 0.71 0.54 0.00 0.19

Thailand 35.20*** 8.78*** 0.80 0.29 -0.03 0.19

16.39*** 757.39*** 0.58 0.72 0.00 0.21

United Kingdom 5.73* -1.38** 0.48 6.14* 0.14 0.69

4.43 -779.28** 0.30 1.31 0.00 0.58

United States 4.04 7.84 0.27 4.81* -0.05 0.48

1.05 -107.23 0.20 6.38** 0.00 0.52Vietnam 6.01** -1.00** 0.44 2.93 -0.37 0.41

0.98 -7.05 0.10 3.60 -0.02* 0.44* significant at 10%; ** significant at 5%; *** significant at 1%


37/39

Table 5 Summary of Granger causality resultsThis table summarizes the Granger causality results for cumulative tests from Tables 4 and 5. NS corresponds to no significant relation for thegiven country and cumulative causality test. + (-) indicates a positive (negative) and significant (at the 10% level or greater) cumulative effect.Panel A uses UN voting records to calculate the political relations proxy as the distance between votes for a give country pair and year. Panel B

uses PR RATIO, which is PR AVERAGE divided by PR VOL and is based on the Goldstein Scale proxy for political relations.

Panel A: Political relations proxy based on UN Voting S measure

PR Causes SWF SWF Causes PR

(Acquirer)/Target S S

(US)

Sweden I/- I/-

(Mauritania)

India J/I I/I

Pakistan J/- J/-

(Kuwait)

United States I/- I/I

(Qatar)

United States I/- I/I

United KingdomI/I I/I


United States J/- I/I

United Kingdom J/I I/I

France J/- I/I

Spain I/I J/+

Italy I/I I/I

India I/I J/I

Australia J/I I/I

(China)

Australia J/- J/-

(Singapore)

United States I/I I/I

United Kingdom I/I I/I

Netherlands J/I I/I

Belgium I/I I/I

Switzerland J/+ J/+

China J/I I/I

South Korea J/I I/-

Japan I/I I/I

India J/I I/I


38/39

Table 5 Ctd.


PR Causes SWF SWF Causes PR

(Acquirer)/Target PR RATIO PR RATIO

(Singapore)

Australia I/I J/I

United Kingdom J/- J/I

United States I/I J/I

Japan I/I J/+

New Zealand J/+ I/I

Malaysia I/I J/+

Philippines I/- I/+

Indonesia I/I I/I

Thailand J/+ I/I

India I/I I/I

South Korea J/+ J/-

Taiwan J/+ I/IChina I/I J/-

Vietnam I/- I/-

Bold country names indicate open countries.


39/39

38

Appendix A Time series of political relationsThis table provides the calculated PR AVERAGE scores for country pairs involving Singapore. PR AVERAGE is the average political relations event score between two nations.

Panel A: UN Voting Records (S)

Country 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Australia 0.18 0.20 0.21 0.27 0.38 0.05 0.36 0.19 0.14 0.16 0.16 0.02 -0.06 0.00 -0.02 -0.07 -0.07 0.09 0.19

Cuba -0.81 -0.88 -0.77 -0.85 -0.60 -0.81 -0.71 -0.78 -0.69 -0.71 -0.65 -0.83 -0.75 -0.83 -0.90 -0.93 -0.86 -0.97 -0.91

UK 0.58 0.46 0.41 0.49 0.58 0.68 0.47 0.36 0.30 0.33 0.29 0.18 0.10 0.20 0.08 0.05 -0.10 -0.07 0.06

Panel B: Goldstein Scale (in sample)

Country 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Australia 0.13 0.64 0.96 1.11 0.95 1.24 0.51 0.69 0.68 0.47 0.61 0.46 -0.36 0.73UnitedKingdom 0.60 1.39 0.76 0.90 0.99 0.62 0.91 0.29 0.34 -0.08 0.84 0.00 2.14 -0.79

United States 0.25 0.55 0.80 0.38 -0.04 0.33 0.66 0.28 0.62 0.67 0.40 0.40 0.73 0.37

Japan 1.10 1.43 0.07 0.89 0.50 0.82 0.59 0.84 0.64 1.06 0.95 0.52 1.30 1.18

New Zealand -0.05 -0.03 1.57 -0.03 1.47 -0.04 -0.03 0.65 2.48 0.46 0.09 0.51 0.67 -0.10

Malaysia 1.08 0.95 1.26 0.73 0.37 -0.58 -0.01 -0.23 0.29 0.17 0.75 0.79 1.21 0.20

Philippines 0.37 0.92 0.67 1.49 1.10 -0.04 0.78 0.68 1.56 1.21 0.65 0.97 0.86 -0.80

Indonesia 1.45 -0.49 2.11 1.08 2.10 1.26 0.66 1.18 0.32 0.47 0.72 1.29 -0.18 0.59

Thailand -0.05 1.28 -0.58 0.51 0.88 0.88 0.14 1.04 1.00 0.13 0.60 -0.22 0.27 0.34

India 0.00 0.00 0.16 0.99 1.95 0.71 1.05 1.05 0.49 0.23 0.74 0.31 0.84 0.00

South Korea 0.00 0.82 -0.09 1.55 1.11 0.09 0.80 1.24 1.09 0.88 -0.03 0.62 0.72 -0.01

Taiwan -0.10 0.00 1.56 -0.04 -0.28 0.92 0.33 1.33 1.17 0.35 0.76 0.64 0.87 0.78

China 1.71 1.47 2.18 1.70 1.62 1.60 0.88 1.18 0.58 0.84 0.42 0.47 0.76 -0.15

Vietnam 2.90 1.51 1.79 2.00 1.12 0.22 1.27 1.57 0.73 0.73 1.45 0.42 0.42 1.23

Panel C: Goldstein Scale (out of sample)

Country Pair 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

US/Iraq -1.24 -1.41 -1.22 -1.20 -1.42 -0.94 -1.05 -0.97 -1.15 -1.25 -0.77 -1.06 -1.10 -1.83US/UK 0.29 0.48 0.51 0.66 0.53 0.42 0.54 0.56 0.77 0.62 0.52 0.24 0.39 0.70

swf&politics

Documents