Are Sovereign Wealth Fund Investment Decisions based

Are Sovereign Wealth Fund Investment Decisions based on Country
Factors?I
J. Amara , B. Candelonb , C. Lecourtc,⇤
a Aix-Marseille
University, CERGAM EA 4225, 13540, Puyricard, France
b IPAG Business School, Paris.
c Aix-Marseille University, CERGAM EA 4225, 13540, Puyricard, France.
Abstract
In this paper, we assess whether macroeconomic factors significantly influence observed sovereign
wealth fund (SWF) investment decisions. Using both a novel dataset of SWF investments and a
two-tiered dynamic panel tobit model, we evaluate the role of structural macroeconomic factors
(as governance, democracy,..) in the decision to invest between SWFs and targeted countries.
Considering a large panel (75 countries over the recent period 2000-2013), we find that indeed
SWFs take into account macroeconomic characteristics to decision wether or not they should
invest in a particular country. It is also interesting to observe that ... .
JEL classification: F310; F31; G15
Keywords: Sovereign Wealth Funds; Targeted Countries ;Macroeconomic country Factors ; twotiered dynamic panel model
⇤ Corresponding
author.
Email addresses: [email protected] (J. Amar), [email protected] (B. Candelon),
[email protected] (C. Lecourt )
Preprint submitted to Elsevier
November 5, 2015
1. Introduction
Sovereign wealth funds (SWFs), defined as public investment agencies which manage part of
the assets of national states, have recently attracted considerable public attention. Many countries
have set up government-owned SWFs for di↵erent macroeconomic purposes, such as stabilisation,
saving fur future generations and investments in socio-economic projects. The resources controlled
by these funds, estimated to be USD 7 trillion by the Sovereign Wealth Funds Institute in March
2015, have grown sharply over the past decade, thanks to high oil prices, financial globalisation
and sustained global large imbalances and this amount is projected to grow as much as USD13
trillion in the next ten years1 . While the size and rapid growth of SWFs suggest that they have
become major players in the world, buying large stakes in companies and giving government’s
exposure to sectors they may otherwise be unable to achieve, their objectives and behavior are
not well understood. In particular, the opaqueness surrounding their structure and activities is
a major concern in host countries, as it is unclear whether SWFs behave like governments or
institutional investors.
Existing empirical research on SWFs o↵ers evidence about whether and how they create value
by investing in publicly traded companies. A first group of studies use event study methodology
to analyze the short and long-term impact of SWF investments on the target firm’s stock price
(Knill et al. (2009) [31], Sun and Hesse (2009) [48], Dewenter et al. (2010) [21], Bortolotti et
al. (2010a,b) [25, 11], Karolyi and Liao (2011) [? ], Kotter and Lel (2011) [34], Bortolotti, Fotak
and Megginson (2014) [12], while the second group analyzes how SWF investments a↵ect the
performance of target firm (Dewenter et al. (2010) [21], Knill et al. (2012a) [32], Fernandes (2014)
[23]). These studies conclude to the poor performance of SWF target firms in the long run, which
implies that SWF motives go beyond profit maximisation.
Another stream of research documents and analyzes how SWFs should invest their assets into
di↵erent asset classes through normative, theoretical or empirical studies (see among others Sa
and Viani (2011) [46], Bodie and Briere (2013) [9] or Bertoni and Lugo (2013) [8]) and how they
actually do invest2 . This second piece of researches examines in particular what drive SWFs to
invest in firms and what role these investors play in it. It turns out that the main motivation
for SWF investments is, like other fund managers, a risk-return objective and the primary source
of variation in SWF investment choices concerns the financial characteristics of the firm (Balding
(2008)[5], Aizemann and Glick (2008) [1], Fernandez and Eschweiler (2008) [24], Dyck and Morse
(2011) [22], Karolyi and Liao (2011) [29], Avendano (2012) [3] and Bernstein et al. (2013) [7]).
Some papers find that SWF investments are influenced not only by financial but also by geographic
or political considerations. Bernstein et al. (2013) [7] show that politician-influenced funds tend to
invest in the highest P/E industries. In the same way, Knill et al. (2012b) [33] suggest that SWFs
are more likely to invest in countries with which they have weaker political relations. Lyons (2007)
[37] suggests that SWF investments should be focused in the West and in the emerging countries
which are likely to see stronger rate of growth than OECD countries. At last, Chhaochharia and
Leuven (2009) [18] find that SWFs tend to invest to diversify away from industries at home but
that they do so predominantly in countries that share the same culture, suggesting their investment
rules are not entirely driven by profit maximizing objectives.
In this paper we examine the driving factors of SWF foreign investments. In particular we
analyze whether macroeconomic factors influence SWF asset allocation policies. For that, we
consider the determinants of SWF investments other than financial and more precisely measures
of economic, institutional and political development of the targeted country. Contrary to the
existing literature stressing the firm characteristics as main determinant of SWF strategy, we try
to explain that SWFs tend to invest abroad by considering the macroeconomic characteristics
1 According to the Sovereign wealth Fund Institute, the assets managed by these funds were estimated to be
USD 3,2 trillion in September 2007, which means that the size of these funds hase more than doubled since the
beginning of the financial crisis (source: www.swfinstitute.org).
2 For an exhaustive list of papers on this stream of research, see the excellent survey of Megginson and Fotak
(2014) [38].
2
of the target country. Specifically, we address the question of whether SWFs tend to invest in
countries which share the same macroeconomic characteristics as theirs.
Using a large database over the recent period 2000 2013, we examine 992 foreign equity
investments done by 50 SWFs in 75 target countries. Based on the recent paper of Zhou and
Lubrano (2015) [51], we estimate a two-tiered dynamic panel tobit model in order to disentangle
the SWF decisions of where and how much to invest. 3 Considering the dynamics in the panel
model allows to test the e↵ect of learning of SWFs in their investment decision-making.
Anticipating on our findings, we find that macroeconomic factors are important influences on
SWF investment decision and that SWFs largely invest to diversify away from industries at home,
but do so mostly in countries with economic and institutional stability...
The paper is organized as follows. The next section introduces the theoretical framework as
well as the hypotheses for analyzing SWF investment decision abroad. Section 3 provides some
details regarding the data. Section 4 presents the two-tiered dynamic panel tobit model used and
Section 5 reports our empirical findings. Finally, Section 6 concludes.
2. Theoretical framework and empirical hypotheses
Are SWF investment decisions influenced by financial and/or non-financial motives? In other
words, are SWFs di↵erent from other large, internationally active institutional investors in the
way they invest? The literature finds mixed evidence with respect to whether or not SWFs
behave like other institutional investors. Balding (2008)[5] finds that there is no evidence of nonfinancial motives in SWF acquisitions. In the same way, Dewenter et al. (2010) [21], Kotter
and Lel (2011) [34], Knill et al. (2012a) [32] or Bortolotti et al. (2010a,b) [25, 11] find all
that SWFs have a short-term positive e↵ect on target firm performance but more contrasted
long-run e↵ects. In particular, Dewenter et al. (2010) [21] test the impact of SWF investments
on target firms and provide evidence consistent with the tradeo↵ between the monitoring and
lobbying benefits versus tunneling and expropriation costs of SWFs as block holders. Using a
sample of 227 SWF equity purchases and 47 divestments over the period 1996-2008, they find
significantly positive abnormal returns for SWF investments and significantly negative abnormal
returns for divestments. They also find that SWFs are active investors, with slightly more than
half of the target firms experiencing one or more events indicative of SWF monitoring activity
or influence. In the same spirit, Kotter and Lel (2011) [34] analyze whether the investment
strategies and performance of SWFs are friends or foes to target firms. Focusing on 417 SWF
investments from 1980 through February 2009, they conclude that SWFs, when they invest, convey
a positive signal to market participants about the target firm and increased SWF transparency is
enjoyed by both the SWF and existing shareholders. Knill et al (2012) [32] analyze whether SWF
investments stabilize or destabilize trading in both the target firms’ shares and the targets’ home
markets once the SWF deal is announced. Using a sample of 232 SWF investment announcements
and a limited Granger causality analysis, they show that SWF investments Granger-cause the
firm level return/risk relation to deteriorate for some firms, concluding that these investments
are destabilizing. Finally, employing 802 investments by 33 SWFs over the period 1985-2009,
Bortolotti et al. (2010a,b) [10] [11] conclude that target firms experience much larger, significantly
negative abnormal returns over one and two years following the initial SWF investment, while the
purchase announcements yield small but significantly positive stock returns.
Despite the short-term target price response which is generally consistent with other large institutional investors, some of the literature concludes that SWF investment decisions are not only
ruled by profit maximisation but also by considerations other than financial. Karolyi and Liao
(2011) [29] study 4,026 corporate, government and SWF acquisitions over 1998-2008 and conclude
that SWF cross-border acquisitions have di↵erent determinants than corporate cross-border mergers but similar determinants than other government acquisitions. Chhaochharia and Leuven (2009)
3 Unlike Knill et al. (2012b) [33] who estimate the two-tiered model with cross-section data, we estimate this
model with panel data in order to take into account the temporal dimension in the SWF investment decision.
3
[18] analyze how and why SWFs make their investment allocation decisions. Using a large sample
(about 30 000 observations) of equity investments made by four SWFs (Norway’s Government
Pension Fund Global, the National Pension Reserve Fund of Ireland, the Alaska Permanent Fund
and the New Zealand Superannuation Fund) over the period 1998-2007, they find non-financial
motives and more specifically common cultural traits like religion as SWF investment decisions.
Based on a sample of 2662 investments by 29 SWFs over the period 1984-2007, Bernstein et al.
(2013) [7] analyze how the funds vary in their investments styles and performance across di↵erent
geographies and governance structures. They conclude that SWFs are more likely to invest at
home (abroad) when (foreign) equity prices are higher and that the funds where politicians are
involved in the investment policy have a much higher likelihood of investing at home than abroad.
This result suggests that SWF investments are distorted by political or agency considerations. In
the same way, Knill et al. (2012b) [33] suggest that SWFs are more likely to invest in countries
with which they have weaker political relations.
The conclusion according to which SWF investment decisions are dissimilar to those of the
institutions can be explained by to the fact that the SWF is a sovereign-owned institution which
may be managed either by the ministry of finance or by a board composed of government officials. Unlike other funds, the politics or the structure of the fund owned/controlled directly by
the government may influence asset allocation decisions. In terms of social welfare, governments
should have broader goals than wealth maximisation of the firm, like for example the development
of national economy or the maximisation of the employment level. But they also might go away
from their goals through their rent-seeking attitude. As SWFs are state-owned actors, they might
be incited to deviate from the objectives normally associated with private-sector investors and
make investment decisions other than financial. Related to this literature, we try to test whether
SWF investment decisions can be driven by strategic determinants other than financial:
H1 - cross-border SWFs investment decision is driven by target country factors and not only
by financial factors.
We only test here whether country factors are significant for explaining the cross-border SWF
investment decisions. If it is the case, we can conclude these decisions are not based exclusively
on profit-maximizing motives.
Note that the hypothesis H1 does not give information on the way SWFs are going to invest.
Once the decision to invest or not in a particular country has been taken on a macroeconomic
perspective, the amount to invest is decided by SWFs. In line with [33], we consider the complex
decision making process of SWFs by specifying two stages. In the first stage, the SWF chooses
the country in which it will invest. In the second stage, the SWF decides how much it will invest.
Ignoring the two-stage nature of the investment decision assumes that the country factors have
the same impact in both stages. If SWFs are investing for reasons other than financial, we expect
that target countries factors should mostly explain the investment decision and less the amount
to invest:
H2 - Target country factors have not the same impact on the investment decision and the
amount to invest.
The phenomenon of home and familiarity bias in decision making has been largely studied in
the empirical literature on Foreign direct investments (FDIs) or trade (see among others Anderson and Wincoop (2003) [2], Stulz and Williamson (2003) [47] and Kang and Kim (2009) [30]).
All these papers conclude that managers who make the decision regarding FDIs have a strong
preference to invest in countries of social and cultural familiarity. In particular, Coeurdacier,
De Santis and Aviat (2009) [20] show that geographic distance is an important determinant of
cross-border markers and acquisitions, especially among developing countries. Concerning SWFs,
Chhaochharia and Leuven (2009) [18] show that cross-border SWF investment stakes are most
4
importantly explained by geographic distance, ethnicity, language and religion. Related to the
null hypothesis of H1, we test whether the characteristics and attributes (in terms of geographic
distance but also macroeconomic, institutional and cultural characteristics) of the target country
are di↵erent for SWFs country. For that, we take into account the di↵erences in terms of political,
financial and religious risk between SWFs countries and target countries:
H3 - SWFs tend to invest in countries which share the the same macroeconomic, geographical,
institutional and cultural characteristics as their.
If cultural, institutional and macroeconomic di↵erences are associated with more asymmetric
information, we would expect that the more the target country presents similar characteristics to
that of the SWF, the more the latter will tend to invest in this country.
We would like to test if there is an inertia in the SWF investment decision in the sense where
once an investment decision is taken, it is likely that the following years the SWF still invests in
the same country. Considering the dynamics in the panel model allows to test is there is an e↵ect
of inertia in their investment decision-making:
H4 - FDI flows are based mainly on current investment decisions and less a↵ected by the inertia
of past investment decisions.
We expect that once an investment decision is taken, it is likely that the following years the
SWF still invests in the country as learning e↵ect.
3. Data and descriptive analysis
3.1. The SWF sample
We collect the list of SWFs by using di↵erent sources in order to have the most complete list.
We start to build a preliminary sample based on the SWF Institute, the Sovereign Wealth Fund
Center and the International Forum on Sovereign Wealth Funds. We then combine this list with
the names of funds published by JP Morgan (Fernandez and Eschweiler (2008) [24]), Catalano
(2009) [14], Lyons (2007) [37] and the websites of the SWFs (see the Appendix for the complete
list of SWFs and information regarding country of origin, the estimated fund size, the source of
funding and the year in which the fund was established). Sometimes di↵erent names for the same
SWF are found; in this case, we employ the fund website to eliminate duplicates. For selecting
the SWFs, we use the selection criteria given by Miracky and Bortolotti (2009a) [42] described
above and we consider a fund as active if it has made at least one publicly-reported investment
internationally. As we can see in the table in Appendix 1, many funds have been created and
announced on the websites but are not active. Several funds are defined as SWFs even though it
is sometimes ambiguous. It is the case for the Emirates within the UAE that are not organized
in a federal level but are comparable in terms of decision rights to those of a sovereign authority.
These funds are the Abu Dhabi Investment Authority, The Investment Corporation of Dubai,
Mubadala Development Company, the International Petroleum Investment Corporation (IPIC),
Ras Al Khaimah Investment Authority, Dubai Holding4 and Dubai World. This search yields a
sample of 92 funds but only 31 of these funds from 15 countries5 are retained for the analysis as
they are considered as active.6 . Our sample consists of investments by a total of 31 SWFs from
4 Dubai Holding is a holding company that belongs to the Government of Dubai; Sheikh Mohammed bin Rashid
Al Maktoum as the Ruler of Dubai holds the majority of the company.
5 As our analysis focuses on the amounts, we retain only the transactions for which the the deal value is available
6 Our search criteria allows to consider several SWFs which are not retained by Miracky and Bortolotti (2009a)
[42] but which respect nevertheless the five criteria described above for defining a SWF and have been active
internationally. Examples of these funds are International Petroleum Investment Co., Dubai World and Kingdom
Holding Company that have invested a lot abroad.
5
around the world (from 15 countries).
3.2. Investment data
We construct our sample of SWF investments in listed firms by using two di↵erent sources;
First, we conducted a search in four financial databases (SDC Platinum, Zephyr, Capital IQ and
Thomson Reuters Eikon) of all known SWFs and their subsidiaries in order to identify transactions
involving SWFs. Second, we use the online database Factiva to complete the missing acquisitions.
We extract investment data for both the SWFs and their subsidiaries defined as entities in which
the fund holds at least a 50% ownership stake (Bernstein et al. (2013) [7]).7 We collect a number
of data items, including information about the targeted firms (name, country), information about
the SWFs (name, subsidiary, country), the date of the transaction, the pre- and post-acquisition
share of the SWF in the targeted firm and the value of the deal.
Table 1 presents summary statistics - overall and by year - on the number and average value
of cross-border SWF deals. The combined sample of both sources from 2000 to 2013 allows to
capture 609 cross-border acquisitions with an average value of USD 585,7 million (USD 278,406
million in cumulative value) by 15 countries (see the Appendix for the detail of these funds).
Table 1: Annual Distribution of SWF Foreign Investments
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Total
Number of
foreign investments
17
4
8
13
13
42
87
118
36
34
60
41
94
42
609
Proportion
(in %)
2,8
0,7
1,3
2,1
2,1
6,9
14,3
19,4
5,9
5,6
9,9
6,7
15,4
6,9
100
Average value of
foreign investments (USD million)
215,6
2315,1
112,3
208,7
392,9
279,2
240,0
366,9
1635,0
629,8
415,1
688,7
346,1
354,2
585,7
Proportion
(in %)
2,6
28,2
1,3
2,5
4,7
3,4
2,9
4,4
19,9
7,6
5,1
8,4
4,2
4,3
100
As described in Table 1, the number of SWF cross-border investments grew dramatically from
2005 to 2007 with fast-growing influxes of revenue combined with the search for better returns and
reached a peak in 2007, with 118 investments representing about 19% of the total of the foreign
transactions over the period 2000-2013.8 During the crisis, many funds shifted their investment
strategies, retreating from foreign markets and increasing domestic investments. The number
of foreign investments begun to drop sharply in 2008 even if the volume of investment activity
remained substantial (with 19,9 % of the total value of all transactions in 2008). Since 2010, SWFs
continued to intervene actively abroad both in number and in amounts, with 15% of the total of
the foreign transactions for only year of 2012.
Table 2 presents the distribution of investments in value (panel A) and amounts (panel B)
done by the 15 acquirer countries and shows that Singapore, with its two SWFs and GIC, made
more cross-border investments than any other country (265 foreign deals which represents 43,5%
of all SWF investments by number but only 8 % by value) followed by SWFs from the United
Arab Emirates (22% of deals, 13% of value)9 , Qatar (14,3% of deals, 8% of value) and China (7%
of deals, 17% of value). We can observe that funds of Kuwait made few investments compared to
the others (2,3% of deals) but with very large amounts (18,6% of all investments by value.)10
Finally, Table 3 outlines the geographical distribution by number and by amount of SWF
countries in target firm regions. The clear trend revealed by this table is SWF’s preference to invest
7 Newswires cited above report information regarding the name of the fund, the name of the subsidiary, the name
of the target firm and the size of the stake.
8 In 2007, SWFs emerged as major players on the world financial stage, mostly when they pumped $60bn into
Western banks during the financial meltdown.
9 The Abu Dhabi Investment Authority (ADIA) is considered as the second bigger world fund.
10 This is also the case for funds from South Korea which invested three times abroad but these deals represent
15% of the total value.
6
in the developed countries of North America (18% of total deals, 20% in value) and West Europe
(27% of total deals, 16% in value), particularly in the English common law countries of Canada,
the United States and Great Britain. This is clearly the case for SWFs from the United Arab
Emirates, Qatar, China and in a lesser extent Singapore that have invested both in number and in
value in both regions over this period. The other target regions are Far East (15% of total deals,
6% in value) and Indian Subcontinent (14% of total deals, 10% in value) The fact that the majority
of SWF investments are targeted towards developed countries with safe institutions, high revenues
and financial regulation reveal that macroeconomic factors matter in their investment decision.
The second less clear trend is the tendency of SWFs to invest in their own geographical region.
More precisely, SWFs from Middle East and South Asia also have a preference to invest in their
own geographical region even if they seem to have a strategy of geographical diversification. Note
that geographical diversification of SWF cross-border investments is sometimes really di↵erent in
number and in amounts, which suggests that the SWF decision to invest in a particular country
and the decision about the amount to be invest in this country are not based on the same criteria.
A revealing example is the only stake done by the fund of Qatar in Central and South America
but for an impressive amount of USD 2,716 million11 .
Table 2: Geographic Distribution of SWF Foreign Investments - Acquirer country
Australia
Bahrain
China
France
Kazakhstan
Kuwait
Libya
Malaysia
New Zealand
Oman
Qatar
Saudi Arabia
Singapore
South Korea
UAE
Total
11 Qatar
Number of
foreign investments
4
1
43
2
2
14
7
25
3
16
87
4
265
3
133
609
Proportion
(in %)
0,7
0,2
7,1
0,3
0,3
2,3
1,1
4,1
0,5
2,6
14,3
0,7
43,5
0,5
21,8
100
Average value of
foreign investments (USD million)
119,4
46,0
802,8
83,5
149,5
881,4
150,6
204,3
61,5
119,7
386,2
94,0
378,9
715,5
644,6
8200,3
Proportion
(in %)
2,5
0,9
16,9
1,76
0,71
18,6
3,1
4,3
1,3
2,5
8,1
1,9
8,0
15,1
13,6
100
Holding invested USD2,716 million in Banco Santander Brazil, which represents 5% of stakes
7
8
SWF countries
Australia
Bahrain
China
France
Kazakhstan
Kuwait
Libya
Malaysia
New Zealand
Oman
Qatar
Saudi Arabia
Singapore
South Korea
UAE
Total
SWF countries
Australia
Bahrain
China
France
Kazakhstan
Kuwait
Libya
Malaysia
New Zealand
Oman
Qatar
Saudi Arabia
Singapore
South Korea
UAE
Total
0
0
157
0
0
0
45
0
0
0
0
8
898
0
2
1109
Africa
0
0
8
0
0
0
1
0
0
0
0
1
3
0
4
17
Africa
Carribean
West indies
0
0
850
0
0
0
0
0
0
0
0
0
0
0
0
850
Carribean
West indies
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
Central and South
America
0
0
200
0
0
0
0
0
0
0
2719
0
202
0
188
3309
Central & South
America
0
0
1
0
0
0
0
0
0
0
1
0
5
0
4
11
Central
Asia
0
0
946
0
0
0
300
0
0
0
0
0
207
0
0
1453
Central
Asia
0
0
7
0
0
0
1
0
0
0
0
0
2
0
0
10
Target firm regions
Central
East
Far
Europe
Europe
East
0
0
0
0
0
0
0
0
631
0
0
0
0
0
0
0
0
327
0
0
0
0
0
71
0
0
2
0
129
0
0
0
78
0
0
0
43
0
297
0
0
0
181
0
318
224
129
1724
Target firm regions
Central
East
Far
Europe
Europe
East
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
3
0
0
0
0
0
5
0
0
1
0
1
0
0
0
1
0
0
0
1
0
73
0
0
0
2
0
5
3
1
90
Indian
Subcontinent
0
0
0
0
0
1800
0
86
0
62
200
0
113
0
560
2821
Indian
Subcontinent
0
0
0
0
0
2
0
7
0
7
4
0
53
0
10
83
0
0
0
166
59
0
183
0
79
123
15
0
0
93
717
Middle
East
0
0
0
1
2
0
4
0
1
2
1
0
0
10
21
Middle
East
North
America
0
0
1332
84
0
1290
320
0
91
0
280
200
495
716
871
5678
North
America
0
0
12
2
0
5
1
0
2
0
7
1
49
3
29
111
North
Europe
0
0
0
0
0
0
0
0
0
0
44
0
180
0
1225
1540
North
Europe
0
0
0
0
0
0
0
0
0
0
1
0
2
0
5
8
Table 3: Geographical Distribution of SWF Foreign Investments - Number and Average Value of foreign Investments
Oceanic
Bassin
0
0
292
0
0
0
0
0
0
2
0
0
794
0
567
1657
South East
Asia
0
0
502
0
0
0
0
380
0
43
1195
0
152
0
513
2785
South East
Asia
0
0
2
0
0
0
0
9
0
2
2
0
28
0
16
59
West
Europe
119
46
1028
0
133
596
97
0
0
297
368
154
821
0
798
4457
West
Europe
4
1
6
0
1
2
4
0
0
4
69
1
28
0
42
162
119
46
5939
84
299
4072
762
720
93
612
5006
376
4203
716
5315
28362
Total
4
1
43
2
2
14
7
25
3
16
87
4
265
3
133
609
Total
Panel A: Number of Investments
Oceanic
Bassin
0
0
4
0
0
0
0
0
0
1
0
0
21
0
6
32
4. Methodology: The two-Tiered Dynamic panel model
The two-tiered panel dynamic tobit model o↵ers many advantageous to better evaluate the
determinants of the Sovereign Wealth Funds’ (SWFs) investments. First, the ”two-tiered” dimension allows to take into account the two-step decision usually performed by a SWF. In a first
instance the choice consists to make or not the investment in a pre-determined countries, relying
on a specific set of determinants. It is clearly a binary decision: either yit > 0 either yit = 0. This
is only in a second instance, once the green light for the investment has been given, that the SWF
decides about the amount to be invest in the specific country. It is obvious that the determinants
of this second decision are potentially di↵erent from the one determining the first decision and
that the second decision is conditional to the first one. Such a feature was initially raised by Cragg
(1971) who proposed a two-tiered model to allow the parameters which characterize the decision
regarding y > 0 versus y = 0 to be distinct from the parameters with determine the decision
regarding how much y is given that y > 012 .
Second, the SWF decision to invest in a particular country is also persistent over time. It
means that if a first investment has been made in year t, intimacy links are created and it is
likely that the SWFs will invest again in the future. Therefore, dynamic should be included via
an autoregressive term in the first but also the second decision.
The two-tiered panel dynamic tobit model can be expressed as follows:
⇤
yit
= xit + yit
1
+ ci + ✏it ,
(1)
⇤
yit = max(yit
, 0),
(2)
where yit is the natural log of the investment amount in a given country and year and xit a
vector of explanatory variables. The component ci is an unobserved individual specific random
disturbance which is constant over time, and uit is an idiosyncratic error which varies across time
and individuals. The errors are assume to have the following autoregressive structure:
✏it = dit + vit ,
uit = ⇣vit
1
(3)
+ uit ,
(4)
where uit is a gaussian i.i.d. process. The two-tiered model provides in a first step with an estimation of the parameters ( 1 , 1 , c1 ) as well as the probability P rob(yt⇤ ) to realize the investment.
It also delivers in a second step an estimates of the parameters ( 2 , 2 , c2 ) conditional to the previous step estimation. All other parameters are common to both steps. Chang (1991) proposed to
estimate this two-tiered dynamic panel Tobit model using the Geweke, Hajivassiliou and Keane
(1996) (GHK) simulator. Still Xun and Lubrano (2015) show that the estimators are depending
on the initial values used to initiate the estimation algorithm. They thus propose to follow the
treatment of initial values proposed by Wooldbridge (2005). For our two-tiered model, it results
in including two di↵erent s when censored or not.
⇤
yit
= xit + yit
1 1 1I(yit 1
> 0) + yit
1 1 2I(yit 1
= 0) + ci + ✏it ,
(5)
where I(.) is a function which takes a 1 if the condition is verified otherwise a 0.
5. Empirical part
5.1. Description of the macroeconomic variables
We estimate the models using a set of 13 explanatory variables which give information about
both the funds’ countries and the targeted countries : i) geographic proximity; i) economic situation; ii) level of development; iii) level of political risk; iv) cultural features. The financial aspect
12 Note
that the Tobit model is a special case of the Cragg’s two-tiered model.
9
is taken into account by considering the MSCI World index and the OECB Volatility index (VIX).
We also consider 5 SWF level characteristics as control variables, taking into account their size,
their level of transparency and their governance.
⇤
n the econometric model described in Model (1), the dependent variable yit
represents the
number of foreign purchases done by SWFs in the target countries from 1989 to 2010 on a yearly
basis. 73 target countries are considered. The explanatory variables that belong to the vector of
exogenous variables x are supposed to describe the economic and institutional factors of the target
countries: GDP, Government Stability, Exchange Rate Stability, Crude Oil Price, Governance and
Democracy. The financial aspect is also taken into account as determinant of the SWFs foreign
investments by considering the MSCI World index, an index of volatility (V IX) and a step dummy
variable, which takes a 1 (resp. a 0) after (resp. before) the crisis, representing the crisis financial
period. In order to avoid endogeneity issues, we used the lagged variables (except the dummy
variable) in the model. Table ?? reports the source and the definition of each variable employed
in our study. The correlation between these variables is low, stressing that the information cannot
be condensed in a subset of variables.13
5.2. Results
BERTRAND ET CHRISTELLE
6. Conclusion
13 For
sake of space we do not report the correlation, but it is available upon request from the authors.
10
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13
Appendix ?. List of SWFs and characteristics
14
15
Victorian Funds
ment Corporation
Australian Future Fund
BAHRAIN MUMTALAKAT
HOLDING COMPANY BSC
Brunei investment authority
China Investment Corporation
China SAFE Investment Ltd
National
Fund
CHINA-AFRICA
DEVELOPMENT
FUND
CO.,
LTD
France Strategic investment
fund
Samruk Kazyna
Wealth Fund
Kuwait Investment Authority
Libyan Investment Authority
KHAZANAH
BHD
Australia
Australia
Bahrain
Brunei
China
China
China
China
France
Kazakhstan
Kuwait
libya
Malaysia
National
Security
STATE
GENERAL
SERVE FUND
Oman Investment Fund
Qatar Investment Authority
Kingdom Holding Co
Government of Singapore Investment Corporation
Temasek
KOREA
INVESTMENT
CORPORATION
DUBAI HOLDING LLC
Dubai World
Abu Dhabi Mubadala Development Company
Abu
Dhabi
Petroleum
Company
Abu Dhabi Investment Authority
RAS
AL-KHAIMAH
INVESTMENT AUTHORITY
INVESTMENT CORPORATION OF DUBAI, THE
Abu
Dhabi
Council
Oman
Oman
Qatar
Saudi Arabia
Singapore
Singapore
South Korea
UAE
UAE
UAE
UAE
UAE
UAE
UAE
UAE
Investment
International
Investment
RE-
NEW ZEALAND SUPERANNUATION FUND, THE
New Zealand
NASIONAL
Social
Queensland Investment Corporation
Australia
Manage-
Fund name
Country
subnational affiliation
90
70
1.2
773
68.4
60.9
NA
NA
72
177
320
19.6
170
6
13
28.98
40.5
66
548
77.5
25.5
5
201.6
567.9
652.7
40
10.5
95
46.6
70.6
Assets Under Management
2007
2006
2005
1976
1984
2002
2004
2004
2005
1974
1981
1996
2005
2006
1980
2001
1993
2006
1953
2008
2008
2007
2000
1997
2007
1983
2006
2006
1994
1992
Founding date
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Non-commodity
Non-commodity
Non-commodity
Oil and gas
Oil and gas
Oil and gas
Oil and gas
Non-commodity
Oil and gas
Oil and gas
Non-commodity
Non-commodity
Non-commodity
Non-commodity
Non-commodity
Non-commodity
Oil and gas
Non-commodity
Non-commodity
Unknown
Fiscal
Source of the funds
Sav-
Reserve investment
Reserve investment
Reserve investment
Saving Reserve investment
Reserve investment
Reserve investment
Reserve investment
Unknown
Reserve investment
Saving Reserve investment
Saving Reserve investment
Reserve investment
Saving Reserve investment
Reserve investment
Stabilisation
Reserve investment
Pension reserve
Saving
Saving
Stabilisation
ing
Stabilisation Saving Pension reserve
Pension reserve
Reserve investment
Reserve investment
Reserve investment
Reserve investment
Saving
Saving Reserve investment
Saving
Unknown
Unknown
Policy purpose
Yes
Yes
Yes
Yes
Unknown
Yes
Yes
Unknown
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Unknown
Reliance on external management
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
No
No
Unknown
No
No
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Unknown
No
No
Yes
Presence of politicians on the board
Appendix ?. Description of the variables
16
17
VALUE DEALS
MAJORITY DEALS
VALUE MAJORITY DEALS
DIST GEO
GDPGD
INFLATIOND
REERD
Average value of deals from
country j to country i
Number of deals from country j to country i (majority
stakes only)
Average value of deals from
country j to country i (majority stakes only)
Geographic proximity
GDP Growth Differences
Inflation Rate Difference
Real
Effective
Rate Differences
LANGUAGEDUMMY
EDUCATIOND
HEALTHD
Language Dummy
Mean Years of Schooling of
Adults Differences
Life Expectancy
Differences
Birth
RELIGIONDUMMY
Religion Dummy
at
CHINNITOD
Index
KAOPEN Index Differences
PolityIV Democracy
Differences
POLITYD
DEALS
Number of deals from country j to country i
Exchange
Code
Variable
Difference in the Life Expectancy at Birth between acquirer and target country. The measure of the Life
Expectancy at Birth is based on the number of years a newborn infant could expect to live if prevailing
patterns of age-specific mortality rates at the time of birth stay the same throughout the infants life.
Difference in the Mean Years of Schooling of Adults between acquirer and target country. The measure
of the Mean Years of Schooling of Adults is based on the average number of years of education received
by people ages 25 and older, converted from education attainment levels using official durations of each
level.
Dummy variable which is equal to 1 if country i and j has the same official and/or most widely spoken
language and 0 otherwise.
Dummy variable which is equal to 1 if country i and j has the same predominant religion and 0 otherwise.
Difference in the Normalized KAOPEN index taken between acquirer and target country. Initially introduced by Chinn and Ito (2006), this index measures a country’s degree of capital account openness. As
the index is not available for 2013, the values for this year has been estimated by the authors (linear
interpolation). The higher the index is, the more the country is financially opened.
Polity score of the Polity IV Project that captures the level of authority of a regime, ranging from -10
(hereditary monarchy) to 10 (consolidated democracy),taken in difference between countries j and i.
Annual Consumer Price Index Based Real Effective Exchange Rates considering 41 trading partners from
2000 to 2013 taken in difference between countries j and i.
Difference in the Inflation Rate measured by the Consumer Price Index from 2000 to 2013 between
acquirer and target country.
Difference in the Average Annual Real Growth Rate of the Gross Domestic Product from 2000 to 2013
between acquirer and target country.
Geographic Distance in kilometers between the capital of countries i and j. We obtained latitude and
longitudes of capital cities of each country and apply the formula : 6378* arcos [sin(lat Acquirer) * sin(lat
Target) + cos (lat Acquirer) * cos (lat Target) * cos (lon Target lon Acquirer)], where lat and lon are
latitudes and longitudes. (Following the methodology of Knill and al., 2012)
Average value of the majority control acquisitions in which the target is from country i and the acquirer
is a Sovereign Wealth Fund from country j (where i = j)
Number of majority control acquisitions (total share hold by the SWF after the transaction > 10%) in
which the target is from country i and the acquirer is a Sovereign Wealth Fund from country j (where i
= j)
Average value of the deals in which the target is from country i and the acquirer is a Sovereign Wealth
Fund from country j (where i = j)
Number of deals in which the target is from country i and the acquirer is a Sovereign Wealth Fund from
country j (where i = j)
Definition
Human development reports
Human development reports
CIA World Factbook
CIA World Factbook
Center for Systemic Peace
Bruegel
World Bank Development Indicators
World Bank Development Indicators
Maps of World
Source
18
TRANSPARENCY
Linaburg-Maduell
parency Index
on
Reliance on External Managers
Politicians
EXTERNAL MANAGERS
POLITICIANS
LARGE
Size of the Fund Dummy
Presence of
the Board
COMMODITY
Origin of the Funds Dummy
Trans-
MSCI Return
MSCI Return
CBOE Market Volatility Index
VIX
CORRUPTIOND
Differ-
Corruption
ences
Index
GOVSTABD
Government Stability Index
Differences
RELIGIOUSTENSIOND
Religious
Tensions
Differences
Index
Code
Variable
Dummy variable the is equal to 1 if at least one of the funds of a country relies on external managers,
and 0 otherwise
Dummy variable that is equal to 1 if there is at least one politician on the board of one of the SWFs of
a country, and 0 otherwise.
Latest value of the Linaburg-Maduell Transparency Index, that assesses SWFs transparency, aggregated
at a country level.
Dummy variable that is equal to 1 if the assets under management of a SWF are superior to USD 100
billion, and 0 otherwise.
Dummy variable that is equal to 1 if the SWF’s funds come from commodity revenues (oil, gas, minerals)
and 0 otherwise.
A dtailler BERTRAND
Average annual value of the CBOE Market Volatility index. This index is a measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices.
Difference in ICRG Corruption index between acquirer and target country. The ICRG corruption index
assess the corruption within the political system. The higher the index is, the lower is the corruption.
Difference in the ICRG Government Stability index between acquirer and target country. The ICRG
government stability index assesses both the ability of a country to carry out its declared program, and
its ability to stay in office. The higher the index is, the lower is the risk.
Difference in the ICRG Religious Tensions index between acquirer and target country. The ICRG religious
tensions index assess the risk that has the governance of a country to be disturbed by a religious group.
The risk involved here range from inexperienced people imposing inappropriate policies through civil
dissent to civil war.
Definition
SWF Institute
SWFs’ websites
SWF Institute
SWFs’ websites
SWF Institute
SWFs’ websites
SWF Institute
SWFs’ websites
SWF Institute
SWFs’ websites
Datastream
Eikon
ICRG
ICRG
ICRG
Source