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I
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2012 – 2013
Sovereign Wealth Funds: Does the presence of a Sovereign Wealth Fund
alleviate capital flight in times of commodity price volatility?
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de Economische Wetenschappen
Simon Ghiotto
onder leiding van
Prof. Koen Schoors
II
III
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2012 – 2013
Sovereign Wealth Funds: Does the presence of a Sovereign Wealth Fund
alleviate capital flight in times of commodity price volatility?
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de Economische Wetenschappen
Simon Ghiotto
onder leiding van
Prof. Koen Schoors
IV
- PERMISSION Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of gereproduceerd worden, mits bronvermelding. Simon Ghiotto - PERMISSION The undersigned declares that the contents of this master thesis can be consulted and/or reproduced, with respect to citations. Simon Ghiotto
V
Acknowledgements
I
Table of Contents
1. Introduction .................................................................................................................. 1
2. Sovereign Wealth Funds .............................................................................................. 1
2.1 What are Sovereign Wealth Funds? ...................................................................... 1
2.2 Why are Sovereign Wealth Funds established, what is their (intended)
function? ........................................................................................................................... 3
3. Capital Flight...............................................................................................................24
4. Oil Price Volatility ...................................................................................................... 27
5. Methodology ...............................................................................................................28
5.1 Subsample .............................................................................................................28
5.2 Capital Flight ....................................................................................................... 29
5.3 SWF Dummy ....................................................................................................... 29
5.4 Oil price volatility ............................................................................................... 29
5.5 Control Variables ................................................................................................. 30
5.6 Panel Unit Root testing ........................................................................................ 41
6. Empirical Model ......................................................................................................... 43
6.1 The Base Model .................................................................................................... 43
6.2 Models Based on the Maddala-Wu test ............................................................. 44
6.3 Models based on the Phillips-Perron test: contemporaneous .......................... 46
6.4 Models based on the Phillips-Perron test: Lagged and Leading variables ....... 49
7. Estimation Results ...................................................................................................... 54
7.1 The Base Model .................................................................................................... 54
7.2 Models based on the Maddala-Wu unit root test: ADF1 to ADF4 ..................... 55
7.3 Models based on the Phillips-Perron unit root test: PP1 to PP4 ....................... 62
7.4 Models based on the Phillips-Perron test: expanding beyond
contemporaneous values, PP5 to PP9 .......................................................................... 68
8. Discussion ...................................................................................................................76
9. Conclusion ..................................................................................................................78
II
Dutch Synopsis/ Nederlandstalige Samenvatting
In het verloop van deze masterproef zal ik de hypothese onderzoeken dat de
aanwezigheid van een Sovereign Wealth Funds het fenomeen van kapitaal vlucht uit
landen die olie exporteren zou verminderen ten tijde van olie prijs volatiliteit. Een
Sovereign Wealth Fund is een investeringsfonds in handen van de overheid met,
afhankelijk van het type fonds, verschillende functies. In de loop van het laatste
decennium is de belangstelling voor dit soort fondsen enorm gegroeid, zowel door de
enorme groei in aantal als in wereldwijd totaal kapitaal beheert door hen, in 2011 reeds
meer dan 5.000 miljard dollar. Er is echter weinig onderzoek naar de effectiviteit van
deze fondsen voor het thuisland, er wordt vooral gefocust op de landen en bedrijven
waarin deze fondsen investeren. Ik zal dan ook hun effect op kapitaalvlucht, het in het
buitenland bewaren of investeren van binnenlandse inkomsten en fondsen. Specifiek
kijk ik naar een groep olielanden over een periode van 1977 tot 2011 om met behulp van
panel regressies het effect van dit soort fondsen en olie prijs volatiliteit te bepalen. Ik
gebruik ook een groot aantal controle variabelen om de te onderzoeken effecten in
kwestie te isoleren. Ik begin met een eenvoudig basis model, dan vul ik het aan met de
controle variabelen. In een later stadium integreer ik ook waarden van het jaar
voordien en het daaropvolgende jaar, en kijk ik ook naar volatiliteit over een periode
langer dan 1 jaar.
De resultaten zijn niet eenduidig gezien niet enkel de te onderzoeken variabelen
andere tekens hebben in verschillende modellen maar ook controle variabelen zich
ander gedragen dan economische theorie zou voorspellen. Zowel de fondsen als het
effect van olie prijs volatiliteit hebben niet het verwachte effect op kapitaalvlucht, maar
ik haal ook reeds enkele redenen aan waarom de resultaten niet eenduidig zijn.
Ondanks het feit dat verder onderzoek nodig is om de effecten te verduidelijken zijn er
toch interessante resultaten te bespeuren in de berekende regressies.
III
Table of Abbreviations
ADIA Abu Dhabi Investment Corporations
ADF Augmented Dickey Fuller
BoP Balance of Payments
BS Booming Sector
BRIC Brazil, Russia, India and China
CF Capital Flight
CNOOC China National Offshore Oil Corporation
EIA Energy Information Administration
ECA Excess Crude Account
FDI Foreign Direct Investment
GPFG Government Pension Fund Global
GDP Gross Domestic Product
GNI Gross National Income
IFSWF International Forum on Sovereign Wealth Funds
IMF International Monetary Fund
IWG-SWF International Work Group on Sovereign Wealth Funds
LS Lagging Sector
LIA Libyan Investment Authority
M-W Maddala – Wu
NTS Non-Tradable Sector
PP Phillips-Perron
SWF Sovereign Wealth Fund
UNCTAD United Nations Conference on Trade and Development
USD United States Dollar
WTI West Texas Intermediate
IV
Table of Figures, Graphs and Tables
Figure 1: Resource Rents with Grabber friendly institutions p.9
Figure 2: Resource Rents with Producer friendly institutions p.10
Figure 3: Growth Paths p.11
Table 1: Summary presentation of Broad and Hot Money measuring procedure p.26
Table 2: Abbreviations for variables p.40
Table 3: Models ADF1 to ADF4: specification p.46
Table 4: models PP1 to PP4: specification p.48
Table 5: Models PP5 to PP9: specification p.52
Table 6: Base Model and Models ADF1 to ADF4: Results p.59
Table 7: Models PP1 to PP4: Results p.65
Table 8: Models PP5 to PP9: Results p.74
1
1. Introduction
In the process of this thesis I will first examine the current state of Sovereign Wealth
Funds (SWFs) worldwide, such as which countries have them, what is the origin of the
fund, what is their current, or last known, size and other major characteristics of
SWFs. A major part of the current literature focusses on the political economy effects
of SWF investments in a host country or the corporate finance effects on the firm in
which the SWF invests, but the positive effect on the investing country is often merely
true by assumption. Studies regarding the effectiveness of SWFs are limited. This is
why after the descriptive part I will examine whether or not the presence of a SWF
helps against capital flight in the face of volatility in prices of the underlying
commodity, specifically oil. Although I will compile a thorough list of worldwide SWFs
in the first part of the thesis, for my regressions I will have to omit several due to the
origin of SWFs, some are not commodity-based, and lack of data. Since a SWF reduces
uncertainty, I will examine the hypothesis that the presence of a SWF in times of oil
price volatility reduces capital flight. I will also note that I am examining the
effectiveness, not the efficiency. Studying its efficiency would require an extensive
cost-benefit analysis as well as comparison with other ways of dealing with the
problems caused by a large surplus. If found effective, studying the efficiency of SWFs
should be the topic of further study.
2. Sovereign Wealth Funds
2.1 What are Sovereign Wealth Funds?
The use of SWFs in global economics as well as the study of SWFs is a field of
economics which is yet to mature. Although not an entirely new phenomenon, with
the oldest one, the Texas Permanent School Fund, stretching back to 1854 and several
oil and gas based funds having been established in the 1950’, their numbers have
swelled in recent years with 41 out of 72 funds having been formed after 2000. Not only
their numbers but also their assets under management have risen to about 5 trillion
dollars in 2011. As the very term of a Sovereign Wealth Fund is very ambiguous and can
2
cover a wide range of funds, each tailored for the needs of a specific country or region
as well as the fact some of the SWFs I will discuss have been around longer than the
term Sovereign Wealth Fund itself I have chosen to continue with the definition used
by the International Forum for Sovereign Wealth Funds (IFSWF), an organisation
which was established by the International Work Group on Sovereign Wealth Funds
(IWG-SWF). The IWG-SWF was a temporary and now dissolved subsection of the
International Monetary Fund (IMF).
“SWFs are defined as special purpose investment funds or arrangements, owned by the
general government. Created by the general government for macroeconomic purposes,
SWFs hold, manage, or administer assets to achieve financial objectives, and employ a
set of investment strategies which include investing in foreign financial assets. The SWFs
are commonly established out of balance of payments surpluses, official foreign currency
operations, the proceeds of privatizations, fiscal surpluses, and/or receipts resulting from
commodity exports.” (IWG-SWF 2008 p. 27)
There are however many other definitions, which in broad terms overlap, but there are
a few notable differences. Some funds such as the Alberta Heritage Savings Trust Fund
in Canada and the Texas Permanent School Fund in the United States which in my
study and in this definition will be classified as SWFs are not on a national level but on
a regional/state level, and according to some definitions such as the one from
Investopedia, a Forbes Media web site, this would mean these would not qualify as a
Sovereign Wealth Fund. Another important distinction according to many definitions
between SWFs and other government investment vehicles is a foreign outlook on
investments. Foreign investment should be included in the portfolio, and play a major
part in it. A fund such as the Palestine Investment Fund or the Khazanah Nasional
Fund in Malaysia that invests primarily in domestic assets and uses its foreign
investments merely as a tool to diversify or not at all should not be viewed as a SWF
according to this definition since domestic investments negate a lot of the major
reasons for establishing a SWF in the first place. The macroeconomic purposes a SWF
fulfils will be discussed in more detail in paragraph 2.2 (infra, p.3-4). In annex I I have
included a sample of definitions including the previously mentioned Investopedia
3
definition, which gives an idea about the scope of the definitions, but this list is by no
means exhaustive.
I already stated that each SWF is tailored for the need of the investing country, so it
should come as no surprise that the origins of the funds can be myriad. The largest
distinction can be made between commodity and non-commodity funds, respectively
48 and 23 funds in each category. Within commodity funds oil and gas revenues
constitute a vast majority of the funds with 44 out of 48 funds.
As I will research the effect of a SWF on capital flight when faced with oil price
volatility I will focus on the oil and gas based ones.
2.2 Why are Sovereign Wealth Funds established, what is their
(intended) function?
I have already mentioned and will stress several times that SWFs are established by
each country individually which designs the SWF based on its specific needs. Up until
recently there was no inter-governmental organisation that organised regular meetings
of SWF managers and board members. It wasn’t until May 2008, when there were
already 57 SWFs active, that the IWG-SWF was formed, which in turn created the
more permanent organisation the IFSWF in April 2009. The IWG-SWF released its
“Santiago Principles” paper in October 2008 which was the first step towards
standardisation of SWFs, but it is a voluntary charter which has a limited membership
of 24 home countries, most of which are host countries as well. I will use the term
home countries throughout the paper to denote countries that have established a SWF
and host countries to denote countries in which SWFs have invested. The membership
is limited as only 24 home countries participate, out of 51 countries having at least one
SWF. The Generally Accepted Principles and Practices themselves are also only
intended as a framework for the SWFs.
Although it is not unheard of that funds combine functions, or evolve from one
function to another primary function as the global economy changes or the fund
grows, five types of SWFs can be distinguished(IMF 2008).
First we have stabilization funds (IMF 2008), found within the commodity based
SWFs, whose main objective is to insulate the budget and the economy against price
shocks of the underlying commodity. Second are the savings funds (IMF 2008). These
4
are also predominant in the commodity based funds but not limited to them. Their
function is to convert current wealth from often non-renewable assets into a
diversified portfolio which benefits the future generations as well as the current. These
also play a major role in reducing the effects of the Dutch Disease (infra, p.4-7). When
commodity based funds start off with the main purpose of stabilization, their sheer
size can cause an evolution to a savings fund without an explicit intention to do so.
Third we have development funds (IMF 2008) which help fund socio-economic
projects or promote certain targeted sectors. Fourth come the reserve investment
corporations (IMF 2008), who are often still counted as part of the reserve assets but
aim for a higher return on reserves. Fifth at last are the contingent pension reserve
funds (IMF 2008), which provide for contingent unspecified pension liabilities on the
government balance sheet. This is not always a clear cut way of distinguishing SWFs as
for instance the Norwegian SWF, Government Pension Fund Global (GPFG), might
seem like a contingent pension reserve fund, its capital isn’t earmarked and it is
designed so it invests the windfall gains from petroleum exploitation by Norway and
distributes it over current and future generations, thus making it a savings fund. Also
note that there is a difference between pension funds, and sovereign wealth contingent
pension reserve funds. Whereas the former collects its income from the current
working population to invest it and redistribute when said age group retires, a
sovereign wealth contingent pension savings fund gets its inflow of capital from
another source, such as commodity revenues or budget surpluses, but earmarks its
future capital for the specific purpose of paying pension liabilities.
Now that we have an idea about what Sovereign Wealth Funds are, we can look at why
they are founded, what are the problems that they are meant to solve.
2.2.1 Dutch Disease
The Dutch Disease is a term first coined in the magazine The Economist in their 1977
article regarding the decline of the Dutch manufacturing industry1 after the discovery
of natural gas reserves in the North Sea in the 19601.
1 "The Dutch Disease" (November 26, 1977). The Economist, pp. 82-83
5
It is a phenomenon that goes back centuries, as Forsyth and Nicolas have shown in
their 1983 article regarding the decline of Spanish manufacturing after the discovery of
the new world, and the sudden inflow of gold and silver that followed in the sixteenth
century. Forming a sovereign wealth fund is a way to combat this Dutch Disease but
ironically, not only have the Netherlands chosen not to form a SWF but it can be
argued that the Dutch Disease is not Dutch at all, but that the adverse effects on
manufacturing were due to an unsustainable level of social services funded by the gas
revenues (Corden 1984).
The following model is based on Corden (1984), which is to this day still one of the
more influential and often quoted papers on the subject as it provided a
comprehensive overview of several models and ways in which the Dutch Disease
works. I will summarise these ideas, but for further reading on the subject I highly
recommend Corden (1984) as well as the more recent Van der Ploeg (2010).
Although there are many models, many mechanisms through which the Dutch disease
work (most of which are not mutually exclusive, but could actually reinforce each
other) the main principle is that after a discovery of natural resources de-
industrialisation occurs. The core model divides the economy in the Booming Sector
(BS), in which the discovery is made, the Lagging Sector (LS), a grouping of all the
manufacturing sectors that cannot benefit from the discovery, and the Non-Tradable
Sector (NTS). As the BS booms wages and capital rents increase in this sector.
If this increase in income is spent, as is usually at least partly the case, either directly or
through government spending funded through higher tax revenues, this causes the
Spending Effect. The increase of demand will raise the price of Non-Tradable
(produced in NTS) relative to tradable (produced in BS and LS) and cause a real
appreciation of the currency. Resources will be drawn away from BS and LS towards
NTS.
Apart from the spending effect we can also discern the Resource Movement Effect
which entails that the marginal product of labour in BS rises so that demand for labour
in BS rises, drawing wages and labour upward.
So far we can see the direct de-industrialisation resulting from the movement of labour
away from LS towards BS. Additionally there will be a movement of labour out of NTS
6
into BS which will raise demand for non-tradables additional to the excess demand due
to the spending effect, bringing about an appreciation of the currency. This in turn
leads to movement of labour out of LS into NTS, causing indirect de-industrialisation.
As the spending effect and the resource movement effect influence the demand for
NTS in different ways, respectively upward and downwards, the net result on NTS
depends on the size of each effect. We can however conclude that demand for the
Lagging industry, which is usually export oriented manufacturing, will drop. This is in
essence the Dutch Disease. Here we explained the resource movement and spending
effect on the labour market, but on a longer term a similar situation will occur on the
capital market.
We also included 3 sectors in our model, assuming that labour is mobile across sectors,
but it is not a far leap to imagine a situation where mobility across sectors is restricted,
for instance if the Booming Sector, the natural resource extraction sector, uses highly
specialised labour or even has its own workforce from abroad work in the industry.
This causes enclave growth and we will only see the spending effect. The main
instrument of resource allocation will now be the real appreciation of the currency,
here BS employment will be higher, LS employment will be lower and NTS
employment will definitely be lower as well, there is no resource movement effect to
induce upward pressure. Another thing we should mention that although the original
Dutch disease focuses on manufacturing, if agriculture is the main export pre resource
boom, as is the case for many developing countries, the decline of the Lagging Sector
could be a de-agriculturalisation. (Corden 1984)
Now that we have our basic model we can expand by adding complications such as
adding a certain amount of sector specific capital, introducing international capital
and labour mobility, analysing the benefit of the real exchange rate appreciation on
importing sectors, decomposing the lagging sector or introducing measures protecting
the lagging sector during the boom, which in the case of non-renewable resources in
temporary by definition, and many more, but that would lead us to far from our
original topic.
We have also focused on the negative effect from a boom, but through knowledge spill
overs or learning-by-doing effects a temporary sector specific boom could in theory
7
benefit the whole of the economy. Another possibility is that the capital-intensive
manufacturing sector is the booming sector, in which case pro-industrialisation will
occur thanks to the resource movement effect, which in this case will be greater than
the de-industrialisation caused by the spending effect. (Corden and Neary, 1982)
As Van Der Ploeg showed in his 2010 paper: Natural Resources: Curse or Blessing? The
discovery of natural resources need not necessarily lead to de-industrialisation or the
often quoted resource curse, but if a country has good institutions it can benefit greatly
from the discovery, such as Norway and Botswana have clearly illustrated. About forty
per cent of the Gross Domestic Product (GDP) of Botswana stems from diamonds, but
it has beaten the resource curse and discovered a resource blessing. It has one of the
highest public expenditures on education as a fraction of GDP, and its GDP per capita
compared to Nigeria, a country often used as an example of the resource curse, went
from around 65% in the first half of the ‘60 and about equal up until the late ‘70 to
about six fold that of Nigeria today, with heights of almost fourteen fold in the early
’90(Sarraf and Jiwanji 2001 and own calculations based on World Bank Data).
2.2.2 Other explanations for the Resource Curse
Although I have elaborated on the Dutch disease since that is a purely economic
mechanism where the function of a sovereign wealth fund is very clear (supra, p.4-7))
there are several other explanations for the resource curse.
Another such explanation can be found in papers such as Van Wijnenbergen (1984a),
Krugman(1987) or Matsuyama (1992) which state that the resource curse is due to the
fact that endogenous growth stems from human capital spill-over effects such as
learning-by-doing (increased productivity through cumulative production quantities)
and that these spill-over effects are strongest in the manufacturing sector, whereas
they are quite weak in the resource extraction sector. If the company that extracts the
resource is a multinational which employs its own foreign workforce, there hardly is
any spill-over possible. Matsuyama argued that not only manufacturing could be the
declining traded sector where spill-over effects are strong, but for some countries it
could be agriculture.
Other authors argue that it is not the presence of resources, but resource dependence
and volatility of the commodity prices that cause the resource curse (Knack and
8
Keefer, 1995; Mansano and Rigobon, 2001). According to these authors the ’80 debt
crisis was caused by resource rich countries borrowing heavily in the ’70, when
commodity prices were high, thinking that future resource revenues would cover the
debt. When in the ’80 commodity prices fell this triggered the debt crisis. Gylfason et
al. (1999) showed that the real exchange rate volatility caused by the resource price
volatility can be exacerbated by the real exchange rate volatility due to the Dutch
Disease (supra,p.4-7). This real exchange rate volatility can lead to less investment and
less productivity growth (Aghion et al, 2009)
Increased corruption has also been shown to reduce economic growth (Mauro, 1995;
Bardhan, 1997). If a country is resource rich this can not only cause resource induced
rent seeking among politicians (Brunnschweiler, 2008) but it also breaks the link
between taxation and accountability since it provides revenues to pacify dissent, either
through the establishment of a generous welfare state (e.g. Saudi Arabia) or through a
strong police/military/paramilitary presence(Isham et al, 2003).
As we can see a lot of the theories point to what we can call in a broader sense the
institutional quality. Mehlum, Moene and Torvik(2006) show with a quite simple but
very clear model how, depending on intuitional quality, a resource boom can either
benefit or damage the local economy. In their model people with an entrepreneurial
spirit can choose to either start a firm, become a producer, or engage in rent-seeking
activities, become a grabber. Because producing causes positive externalities, the more
producers there are the more the wealth is being created, which is then distributed
among producers and grabbers. Grabbers on the other hand do not produce, but
merely appropriate part of the wealth. This can be for instance through theft,
protection racketeering or government officials asking bribes. As this is not a
productive activity the externalities here are negative, the more grabbers there are the
less can be gained by grabbing. In the absence of ethics people with entrepreneurial
spirit will choose between producing or grabbing based on respective profits. This type
of arbitration will lead to an equilibrium where producers and grabbers earn the same.
If in a situation with grabber friendly institutions a resource boom occurs it will raise
the rent-seeking profits for a given number of grabbers. Since entrepreneurs will see
they can earn more by becoming grabbers this is no longer an equilibrium situation.
9
More and more people will make the switch until the new equilibrium is attained, with
lower profits for both grabbers and producers (figure 1).
Figure 1
Resource Rents with Grabber-Friendly Institutions
Figure from Mehlum, Moene and Torvik (2006)
However, if the country either has or creates good institutions, most likely the first
since having resources reduces incentives to improve institutions, the boom can raise
producer profits. This again leads to a disequilibrium situation, but now grabbers will
see they can improve their situation by becoming entrepreneurs. By making this switch
they add to the positive externalities, improving their own wealth and creating
incentives for other grabbers to become entrepreneurs as well, until a new equilibrium
is achieved (figure 2), this time with more producers, less grabbers and higher wealth
for all parties involved.
10
Figure 2
Resource Rents with Producer-Friendly Institutions
Figure from Mehlum, Moene and Torvik (2006)
They summarize their findings with another very simple and clear graph (figure 3,
supra p. 11). They show that although the best case scenario is having a resource rich
country that, thanks to its high institutional quality (B*), will have a high economic
growth. However, if a resource rich country has bad institutions (B) it will have a lower
growth than both the resource poor country with good institutions (A*) and even the
resource poor country with bad institutions (A).
11
Figure 3
Growth paths
Figure from Mehlum, Moene and Torvik (2006)
2.2.3 How can a Sovereign Wealth fund alleviate these problems?
One example of a ‘good’ institution can be a sovereign wealth fund that absorbs at least
part of the revenues from resource wealth. This can be in a number of ways such as
through profits from a state owned enterprise that extracts the resource, selling
exploitation rights to multinationals, specific taxes on extraction and exportation of
the commodity.
First of all the SWF reduces the spending effect caused by the Dutch Disease by
investing the revenues abroad instead of domestically. If all of the revenues would be
put in the fund it would completely eliminate the spending effect, but since usually
only a part of the revenues is invested in the fund a certain amount of spending effect
remains. If the fund is structured like the Norwegian fund, Government Pension Fund
12
– Global, often used as a benchmark for SWFs, only the real interest of the fund can be
used, the capital remains untouched, thus slowing down the inflow significantly.
A fund structured in this way has another benefit, being that it transforms the resource
wealth, which is by its very nature usually a non-renewable stock asset subject to price
volatility, to a continuous and perpetual flow from a diversified financial asset. As long
as the fund is properly managed and only the real interest gains are given back to the
state budget, meaning the capital and a sum equal to inflation remains assets of the
fund, it will keep generating revenue for the country, long after the stock itself is
depleted. Looking back at the classification of SWFs in part 2.2 (supra, p.3-4) it is clear
that these are the Savings Funds previously mentioned.
Some funds such as the National Pension Reserve Fund (Ireland) or Mumtalakat
Holdings (Bahrain) choose to invest domestically, either in part or in some cases such
as the Khazanah Nasional fund (Malaysia) almost completely. In 2012 almost 90% of
the Khazanah Nasional fund was invested in Malaysia itself. This obviously
reintroduces the spending effect, even though the funds are directed to a SWF.
However, these domestic investments might not reduce the spending effect, but they
can be used to diversify the economy away from purely resource extraction and into
related industries, oil states that build refineries, or even completely unrelated fields,
such as when Dubai invested heavily in its tourism sector. If we go back to our three
sector model used in the Dutch Disease example (Booming, Lagging and Non-
Tradable) this means that amount of capital per employee in the Lagging or Non-
Tradable sector rises, and with it their productivity, thereby reducing the resource
movement effect. These funds are the Development Funds mentioned earlier. But not
only the resource movement effect can be tackled using this type of fund, if the sector
in which is invested is one with human capital spill-over effects, the resource curse
according to authors such as Van Wijnenberg (1984a) and Krugman (1987) can also be
mitigated.
Another category was that of the Stabilization Funds. As we saw commodity price
volatility is a significant problem for governments as revenues are variable year after
year, but this can be exacerbated when the country lends money on the financial
market, assuming the debt can be repaid using future commodity revenues (Knack and
13
Keefer, 1995; Mansano and Rigobon, 2001). This could, when triggered by commodity
price fall, cause a debt snowball. Even when commodity prices rise again the
compound interest due to inability to repay debts in the previous period can cripple an
economy. However, stabilization funds even out these price differences and reduce
uncertainty by absorbing revenues in times of high prices and making up the
difference in times of low prices.
The last two types of SWFs, reserve investment funds and contingent pension funds,
aren’t linked with any resource curse problems, but depending on their structure and
investment strategy they can both alleviate or exacerbate the resource curse.
Regardless of which type of Sovereign Wealth Fund is being formed, it is often a way of
putting financial reserves out of arm’s length from the political powers of a country,
without losing control completely. If the current legislators do not expect a change in
government it can have a signalling function, a way to show to the national and
international community that the gains from commodity revenues will be spent wisely,
will not be spent immediately on political prestige projects or will not be extracted
illegally as political rents. Putting a substantial share of revenues in a SWF is a way of
showing good governance, of portraying oneself-justly or unjustly- as responsible
forward thinking leaders. If the current legislators do expect a change in government it
can be a way of denying the competitors/successors these revenues so they cannot
spend or extract them without very explicitly going against this fund. In this way a
sovereign wealth fund can improve the institutional quality. However, if a SWF is not
transparent or designed not as an investment channel but as a way of funnelling
resource revenues into sham corporations institutional quality could even deteriorate.
This issue of transparency is something which has been widely researched and
criticised by authors such as Truman (2007a, 2007b, and 2008) and others. In fact,
when in Truman (2007) A Scoreboard for Sovereign Wealth Funds a number of SWFs
were graded and ranked on issues of Structure, Governance, Behaviour and
transparency & Accountability the Abu Dhabi Investment Authority, which with
estimates ranging from USD(United States Dollars) 650 to 875 billion is by far the
largest (the second largest is Norway with USD 574 billion) and holds between 13 and
17 per cent of all Sovereign Wealth fund capital globally, ranked lowest of all with a
14
score of 0.5 out of a possible 25. Since the average of this scoreboard was 10.75, with a
highest score of 24 by the New Zealand Superannuation Fund it is clear that
transparency is a major issue.
If a SWF truly invests with an entrepreneurial intent, if it truly seeks the highest risk-
adjusted returns, they are not a cause for concern for host countries and should be
treated on an equal basis as mutual funds, private equity funds hedge funds and other
large investors. However, if those funds not only have commercial but also political
interest in mind adverse reactions are possible. Not only when state owned enterprises
such as China’s CNOOC (China National Offshore Oil Corporation) had to withdraw a
takeover bid to acquire the US firm Unocal in 2005 due to the political backlash that
followed its announcement, but also for SWFs. When Singapore’s Temasek acquired a
controlling share in the Thai telecom firm from the then Thai Prime Minister’s family
in January of 2006 it set of a political crisis in Thailand. There were fewer objections
regarding the 2007 investment from Abu Dhabi’s ADIA (Abu Dhabi Investment
Authority) in Citigroup, but that was because it gave assurances no active management
or control would be sought (Aizenman and Glick 2007).
With the rise of SWFs worldwide, both in numbers as in assets under management,
specific regulation and even protectionism regarding SWFs has been a hot issue.
Authors such as Buiter (2007) even argued that SWF should only be allowed to
purchases nonvoting equity shares.
More recently the IWG-SWF published its Santiago Principles in October 2008. It is a
list of 24 generally accepted principles and practices agreed by 24 countries that have
one or several SWFs, but as a voluntary charter it has very limited power, even among
the countries that are members, let alone among the 27 non member states. It does
however produce a benchmark, as well as a blue print for future funds.
15
Name country Year
size in
Billion
USD
Estimation
(e) or
Official
(O)
Data
from
year origin asset allocation (classes)
asset allocation
(geographical)
Revenue Regulation
Fund Algeria 2000 54,8 e 2010 oil
Fundo Soberano de
Angola Angola 2012 5 o 2012 oil
The Future Fund Australia 2006 82,39 o 2012 oil
11,1% Australian equities;
18,1%developed market equities;
5,3% emerging markets equities;
6,8 private equity; 6,6 property;
6,4 infrastructure and
timberland; 19,1 debt securities;
16,3% alternative assets; 10,3%
cash
Western Australian
Future Fund Australia 2012 1,1 o 2012
mining(mainly
Iron ore)
State Oil Fund of the
Republic of
Azerbaijan
Azerbaijan 2000 34,13 o 2013 oil
minimum 85% debt obligations
and money market instruments;
up to 5% equity; up to 5% real
estate; up to 5% gold
Mumtalakat Holdings Bahrain 2006 7,1 o 2012
start-up
capital from
oil, no longer
inflow
cash and bank balances 22%;
non-trading investments 10%;
investment in associates 41%;
investment properties 21%;
other assets 6%
Bahrain and Arabian
Peninsula
16
The Future
Generations Reserve
Fund Bahrain 2006 0,22 e 2012 oil
20% long-term investments;
76% cash deposits; 3 % other
assets
The Pula Fund Botswana 1994 4,6 o 2012 diamond
Sovereign Fund of
Brazil Brazil 2008 11,3 e 2011
non-
commodity
Brazil
Brunei Investment
Agency Brunei 1983 30 e 2009 oil
Alberta Heritage
Savings Trust Fund Canada 1976 15,95 o 2012 oil and gas
Chile Pension Reserve
Fund Chile 2006 5,8 o 2013
government
surplus
46% sovereign and government
related bonds; 17% inflation
indexed sov. Bonds; 20%
corporate bonds; 167% equity
Social and Economic
Stabilization Fund Chile 2007 15 o 2013
initial capital
copper; now
gov. Surplus 15% banks; 85% sovereigns
China Investment
Corporation China 2007 482 o 2011
non-
commodity
31% long term investments; 11%
cash funds and others; 25%
diversified public equities; 21
fixed income securities;
12%absolute return investments
43,8% North America;
29,6% Asia and Pacific;
20,6% Europe; 4,7%
Latin America; 1,3%
Africa
China-Africa
Development Fund China 2007 1 o 2007
non-
commodity
Chinese companies with
activities in Africa
National Social
Security Fund China 2000 139,73 o 2011
non-
commodity
51% fixed income assets; 6%
overseas stock; 16%industrial
investments; 26% domestic
stocks
17
SAFE Investment
Company China 1997 47 o 2011
non-
commodity
8% direct investment abroad;
6% portfolio investment; 18%
other investment; 69% reserve
assets
Fund for Future
Generations
Equatorial
Guinea 2002 0,44 o 2009 oil
Strategic Investment
Fund France 2008 24,54 o 2012
non-
commodity 72,5% stocks focus within France
Sovereign Fund of the
Gabonese Republic Gabon 1998 0,96 e 2012 oil
Ghana Petroleum
Funds Ghana 2011 0,6 o 2012 oil
Hong Kong Monetary
Authority Investment
Portfolio Hong Kong 1993 321 o 2011
non-
commodity
2% cash and money at call; 7%
placement with banks; 89%
financial assets; 2% other assets
Government
Investment Unit Indonesia 2006 0,34 e ?
non-
commodity
14% toll roads; 70% electricity;
9,5% equity; 6% loans
National
Development Fund of
Iran Iran 2011 45 o 2013 oil and gas
Oil Stabilization Fund Iran 2000
replaced by national development fund of
Iran
National Pensions
Reserve Fund Ireland 2001 18,2 o 2012
non-
commodity
57% direct investments in Irish
Banks 17% equity; 4%bonds; 6%
cash; 6% private equity; 3%
property; 2% commodities; 3%
infrastructure; 2% absolute
return funds; 1% equity options
18
Italian Strategic Fund Italy 2011 5,22 o 2012
non-
commodity
Kazakhstan National
Fund Kazakhstan 2000 29 o 2011 oil, gas, metals
formed by merger in
2008 of 2 other Kazakh
SWFs
Revenue Equalization
Reserve Fund Kiribati 1956 0,59 o 2009 phosphate
Korea Investment
Corporation Korea 2005 42,86 o 2011
non-
commodity
49% equities; 43% bonds; 3%
cash; 5% other
45% North America;
24% UK & Europe; 13%
developed Asia; 18%
emerging markets
Kuwait Investment
Authority Kuwait 1953 261 e 2012 oil
Libyan Investment
Authority Libya 2006 53 e 2010 oil
39% cash, deposits and gold;
30% subsidiary companies; 11%
equities; 5%bonds; 14% other
assets
Khazanah Nasional Malaysia 1993 38,77 o 2012
non-
commodity
89,6% Malaysia; 3,7%
Singapore; 2,2% China;
1,4% India; 0,7% Turkey;
2,4% others
National Fund for
Hydrocarbon
Reserves Mauritania 2006 0,3 e
oil and gas
Mauritius Sovereign
Wealth Fund Mauritius 2011 0,5 o 2011
non-
commodity
19
Oil Revenues
Stabilization Fund of
Mexico Mexico 2000 1,4 o 2012 oil
Fiscal Stability Fund Mongolia 2011 not yet operational
mining
New Zealand
Superannuation Fund
New
Zealand 2003 16,37 o 2011
non-
commodity
15% cash; 7 % derivatives; 72%
other financial assets; 5% equity;
1% agriculture
25% New Zealand; 6%
Australia; 11% Asia; 34%
North America; 21%
Europe; 3% Central &
South America; 1%
Africa
Nigerian Sovereign
Investment Authority Nigeria 2012 0,6 o 2012 oil
Government Pension
Fund Norway 1990 574 o 2011 oil 59% equity; 41%fixed income
Oman Investment
Fund Oman 2006
No data nor reliable
estimates found oil
Oman State General
Reserve Fund Oman 1980 8,2 e
Not
found oil
Palestine Investment
Fund Palestine 2003 0,81 o 2011
non-
commodity
100% Palestinian
Regions
Fondo de Ahorro de
Panama Panama 2012 effective from 2014
revenue from
Panama Canal
Papua New Guinea
Sovereign Wealth
Fund
Papua New
Guinea 2011 47 e
effect
ive
2014 oil and gas
Peru Fiscal
Stabilization Fund Peru 1999 7,16 o 2012
non-
commodity
20
Qatar Investment
Authority Qatar 2005 115 e 2012 oil
National Wealth Fund
of the Russian
Federation Russia 2004 87,61 o 2013 oil and gas
Reserve Fund Russia 2004 84,68 o 2013 oil and gas
Russia Direct
Investment Fund Russia 2011 10 o 2011
non-
commodity
Public Investment
Fund Saudi Arabia 1971 15,2 o 2011 oil
Sama Foreign
Holdings Saudi Arabia 1952 517,6 o 2012 oil
calculated by adding investment in foreign reserves and
deposits abroad from central bank balance sheet, data on
SAMA Foreign holding is not publically available
Government of
Singapore Investment
Corporation Pte. Ltd. Singapore 1981 247,5 e 2009
non-
commodity
45% Public Equities; 17% bonds;
10% real estate; 11% private
equity and infrastructure; 6%
other
33% USA; 9% other
Americas; 9% UK; 11%
Eurozone; 6% other
Europe; 12% Japan; 13%
North Asia; 4% Other
Asia; 3% Australasia
Temasek Holdings
(Private) Limited Singapore 1974 158,65 o 2012
non-
commodity
42% Asia excl
Singapore; 30%
Singapore; 24%
Australia& New
Zealand; 11% North
America & Europe; 3%
Other
21
Alabama Trust Fund
The United
States 1985 2,94 o 2012 oil and gas
5%cash; 43% fixed income
securities; 25% equity securities;
19% due from Education trust
and General Fund; 6% land;
Alaska Permanent
Fund Corporation
The United
States 1976 45,46 o 2013 oil
30% stocks; 21% bonds; 12% real
estate; 6% private equity; 6%
absolute return; 4%
infrastructure; 2% cash; 2%
public/private credit; 11% other
New Mexico State
Investment Council
The United
States 1958 14,4 o 2012
non-
commodity
42% US equity; 14% non-us
equity; 21% fixed income; 6%
absolute return; 11% private
equity; 5% real estate; 1% cash
North Dakota Legacy
Fund
The United
States 2011 0,4 o 2012 oil 100% fixed income
Permanent Wyoming
Mineral Trust Fund
The United
States 1974 6 o 2012
mineral
wealth
(mainly oil,
coal and gas)
50% equity; 41% fixed income;
2%wyoming specific investment;
7% cash
Texas Permanent
School Fund
The United
States 1854 29,4 o 2012
land
concession in
1854; oil since
1953
25% domestic equity; 21%
international equity; 17% fixed
income; 10% absolute return; 8%
real estate; 6% private equity;
7% risk parity; 6% real return
Timor-Leste
Petroleum Fund Timor Leste 2005 11,77 o 2012 oil and gas
1% cash; 73% marketable debt
securities; 26% global equity 98% US; 2% other
22
The Heritage and
Stabilization Fund
Trinidad
and Tobago 2000 3,62 o 2011 oil
49% Fixed Income; 28% equity;
23% deposits 17,5% non-US; 82,5% US
Emirates Investment
Authority
United Arab
Emirates 2007 40 e 2008 oil
Abu Dhabi
Investment Authority
United Arab
Emirates -
Abu Dhabi 1976
650-
875 e 2011 oil
(min and max) 48-78% equity;
10-20% bonds; 5-10% credit; 5-
10% alternatives; 6-15% real
estate and infrastructure; 0-10%
cash
(min and max) 35-50%
North America; 20-35%
Europe; 10-20%
Developed Asia; 15-25%
emerging Markets
Abu Dhabi
Investment Council
United Arab
Emirates -
Abu Dhabi 2007 250
oil
International
Petroleum Investment
Company
United Arab
Emirates -
Abu Dhabi 1984 65,4 o 2012 oil
Mubadala
United Arab
Emirates -
Abu Dhabi 2002 50 o 2012 oil over 50 billion
Investment
Corporation of Dubai
United Arab
Emirates -
Dubai 2006 19,6 e
Not
found oil
Ras Al Khaimah
Investment Authority
United Arab
Emirates -
Ras Al
Khaimah 2005 1,2 e
Not
found oil
23
Oil Development
Reserve - Falkland
Islands
United
Kingdom 2012 13,1 o 2013 oil
FEM-
Macroeconomics
Stabilization Fund Venezuela 1998 0,83 o 2009 oil
State capital
Investment
Corporation Vietnam 2005 0,59 o 2012
non-
commodity
24
3. Capital Flight
As stated in my introduction, in this paper I will try and quantify the effect that the
presence of a sovereign wealth fund has on capital flight in times of oil price volatility.
In the first part I have presented a list with all the SWFs worldwide and their main
characteristics, in as far as they were available. The majority of those, accounting for 44
out of 72 funds, are funds from petroleum and natural gas revenues spread over 29
countries. This discrepancy is due to the fact that several countries such as Russia,
Chile or the USA have several funds formed by revenues from oil and gas, based on
geographical location, asset specialisation or risk level of the assets. The United Arab
Emirates are a special situation as most macro-economic data is on a federal level, for
the whole union, but out of 7 oil based sovereign wealth funds only 1 is managed by the
Emirates jointly, 4 are from Abu Dhabi and the others from Dubai and Ras Al
Khaimah.
As well as checking oil exporting countries that have now established a SWF,
comparing capital flight before and after the SWF is formed, I will also include oil
exporting countries without a SWF or where the SWF is not funded from oil and gas
revenues. These countries are Argentina, Brasil, China, Colombia, Angola, India and
Indonesia. China, Brazil and Indonesia have SWFs, but not oil-based. They channel the
oil revenues though state owned enterprises namely CNOOC, PetroBras and
Pertamina. India and Colombia do not have any SWFs and also established state
owned enterprises namely Indian Oil Corporation Limited and EcoPetrol. Argentina
privatised its state oil enterprise in the early ‘90 but last year (2012) decided to
renationalise it and even more recently (mid April 2013) decided to established the
Argentine Hydrocarbon Fund, a 2 billion USD SWF, but details such as further funding
are as of yet unknown. Angola is in a peculiar situation as I intended to include it as
one of the SWF countries but because the SWF was only established in 2012 and my
data only goes as far as 2011 the effect of the SWF cannot be measured.
Although definitions differ, capital flight is the (often large) private outflow of capital.
This can either be from legal or illegal gains, with the economically correct aim of asset
diversification or the somewhat less correct aim of tax evasion or even extraction of
25
political rents, motivated by a general sense of insecurity, lower return on investment
than abroad or a host of other reasons. The bottom line is that capital leaves the
country where it was generated to be held or invested abroad.
However, due to the fact that definitions differ and that it is often done covertly
accurate measurements are not available. There are several ways to calculate an
estimate, and after reviewing the literature I have chosen to follow the conclusion of
authors such as Gordon and Levine (1988) and Schneider (2003) to use the broad
measure of Capital Flight (CF), also known as the residual method, as defined by the
World Bank in its World Development Rapport of 1985. Here Capital Flight is
calculated from the Balance of Payment of a country and it is defined as the sum of
Change in Debt, Net Foreign Direct Investment (FDI), Current Account Surplus and
Change in Reserves, always with the sign convention used in Balance of Payments
(BoP) accounts. In Table 1 you can see a summary presentation of the measuring
procedure, taken from Schneider (2003) which shows a stylised version of a BoP and
just a few of the many ways of defining and measuring Capital Flight. Some authors
discussing capital flight for a single country start with this measure and augment it in
several ways, but in the interest of comparability I will continue with the original
version. Possible augmentations are the inclusion of income earned abroad with the
capital that has already fled the country, since choosing not to repatriate the gains can
be seen as a form of capital flight itself or including the capital that fled not only
through private outflows, but also through banks (as was very much the case in the ’82
Mexico Debt Crisis). An alternate measure used by Cuddington (1986) is the Hot
Money Measure also known as the Narrow Measure. This focuses not only on private
outflows, but on illegal and short term private capital outflows. As I said earlier a
generally accepted definition of Capital Flight does not exist, and clearly this
measurement sees capital flight not as the economically sound principle of
geographical diversification to reduce risk, but as a malevolent extraction of capital out
of the domestic economy.
I should note that in certain cases capital flight is not necessarily bad as this reduces
the spending effect I talked about earlier. In fact, when SWFs slow down the inflow of
revenues or invest abroad this can be seen as capital flight as well.
26
Table 1: Summary presentation of Broad and Hot Money measuring procedure
Current Account Surplus A
Net Foreign Direct Investment B
Private short-term Capital
Outflows
C
Portfolio Investments Abroad:
Bonds + Equities
D
Banking System Foreign Assets E
Change in Reserves F
Errors and Omissions G
Change in Debt H
IMF Credit I
Travel(Credit) J
Reinvested FDI Income K
Other Investment Income L
Counterpart Items M
Capital Flight CF
Broad Measure
Erbe and the World Bank:
CF = H+B+A+F
Morgan Stanley Guarantee Trust Company:
CF = H + B + A + F + E
Hot Money Measure
Cuddington:
i) CF = -G-C
ii) CF = -G-C-D
The sign convention used in the Balance of Payments accounts is used here as well, all
the variables in the equations are flow data. (Schneider 2003)
27
It is with this measurement that I encounter my first data collection problems as the
first sovereign wealth fund dates back to 1854 and the first oil based SWFs, which are
the ones that matter for my estimations, were founded in 1952 and 1953. However, pre
1970 only World Bank data for Canada and Australia are available so this limits the
timeframe to 1970-2012. Net FDI flows were even more problematic, being available
only starting in 2005 in the World Bank or IMF databases, but I used data from
UNCTAD (United Nations Conference for Trade and Development) for FDI.
4. Oil Price Volatility
Going back to my research question, whether or not having a SWF reduces capital
flight in times of oil price volatility, I first needed the year when a SWF was formed in a
country, then I needed to measure capital flight for all the countries relevant within
the regression, i.e. those who have a SWF which is funded through oil and natural gas
revenues, and of course a figure for oil price volatility.
Following authors such as Weiner (2009) and the Energy Information Administration
and the common definition of volatility as the standard deviation of the price, I use
data from the United States Energy Information Administration (EIA) on the daily spot
price of West Texas Intermediate (WTI) and Brent oil, two common classifications for
crude oil. I averaged the price per barrel for the two classifications and from this I
calculated the standard deviation of the previous year. In order to check whether
averaging these prices does not delete valuable data, in other words to see if the
standard deviations from WTI, Brent and their average don’t vary I calculated the
differences between WTI and average, Brent and average and WTI and Brent and
found that these values are between -1.05, which was the difference between standard
deviations of WTI and Brent in 1990 and 1.17, which was the difference between the
standard deviations of WTI and the average in 2011. Although these extremes are quite
high, since these are outliers and the average is quite low, about 0.06 I will use the
standard deviation of the average price as my measurement of volatility. I also checked
whether the prices themselves are comparable and found that the difference between
the two on average is minimal. Although in recent years Brent prices have been much
28
higher, reaching differences in spot prices per barrel of almost 30 USD, this has only
been in 2011, prior to 2011 prices were more similar.
However, this data only goes back as far as 1986, severely limiting the size of my
sample and the information therein, thus reducing the accuracy of my regression. In
order to enlarge my sample size I will complement the daily spot price data from the
EIA with monthly average spot price data from the World Bank for the period 1970-
1985. With this I can calculate standard deviation of the monthly price on an annual
basis.
Although using the monthly average instead of the daily spot price evens out the data
and a lower standard deviation can be expected, the data does provide valuable
information and offers a much larger sample size. Using daily data remains preferable,
but when measuring the difference between the annual standard deviation of based on
monthly averages and daily prices the results were quite small, between -0.86 and 0.68
with an average of -0.189. As again we have cases of outliers, deleting the two largest
and smallest values restricts decreases the interval to -0.44 and 0.20. The average
however is quite robust, staying the same up to 3 figures after the decimal point. Using
this I can eliminate the break in the data by adjusting the annual standard deviation
based on monthly averages to be comparable to the standard deviation based on daily
spot prices.
5. Methodology
5.1 Subsample
I already touched upon several data gathering issues in chapters 3 and 4 regarding
capital flight and oil price volatility, but these problems persisted when searching for
control variables. This led me to decide that I would use a subsample in order to
decrease the missing data issue, both in terms of the countries I could discuss as the
timeframe under consideration. My sample of countries was reduced from the original
36 (oil SWF countries + several control oil non-SWF countries) to 22 countries2, and
2 Algeria, Angola, Argentina, Azerbaijan, Brazil, Canada, China, Colombia, Equatorial Guinea, Gabon,
Ghana, India, Indonesia, Iran, Kazakhstan, Mauritania, Mexico, Nigeria, Norway, Papua New Guinea, Russian Federation and Venezuela
29
my timeframe was reduced from 1970-2012 to 1977-2011. Although greatly reduced, this
subsample was still large enough to contain comparative power across countries and
broad enough to provide an evolution over time.
Unless otherwise mentioned, I used World Bank data supplemented with data from
the IMF.
5.2 Capital Flight
Following authors such as Andersen, Johannesen and Lassen(2012) I scaled capital
flight with average GDP over the subsample period. Scaling allows me to compare
countries of different sizes, and using average GDP instead of current GDP has several
advantages. First of all it eliminates measurement errors in GDP, or at least it evens
them out. Secondly, as even within the subsample missing observations remain, using
average GDP allows me to use observations where I can accurately calculate Capital
Flight but where current GDP is missing.
5.3 SWF Dummy
In order to measure the effect of having a SWF I add a dummy variable for each
country that has value 1 if the country had an oil based SWF in that year, and 0 if the
country did not. A subsequent, more in depth research into the relationship of SWFs
and Capital Flight could include instead of a SWF dummy variable a stock variable
related to the size of the SWF, for instance in relation to the size of the GDP or per
capita in order to compare across nations, but that would lead to further missing data
issues and would greatly increase the data gathering difficulties.
I expect the coefficient for SWFs to have a negative sign, as SWFs are a sign of good
governance and sound economic policy, and these should lead to less capital flight.
As there are countries within my sample that have a SWF which is not (directly)
related to its oil revenues, I will also include model where the SWF dummy includes all
SWFs of the countries, not just oil based.
5.4 Oil price volatility
In chapter 4 ( supra, p. 27) I have already introduced the variable for oil price volatility
and how I adjusted the measure to be able to combine different data sources, some
using monthly and some using daily spot prices.
30
I will also include a model to measure the effect of a change in oil price, not just its
volatility, as the correlation between the two is quite high at 0.727.
As it is possible that investors have a longer view than simply the volatility over a one
year period I will later introduce a one year lag ( infra, p. 39) but I will also introduce
volatility over a longer period. I will use a two year period, a three year period and a
five year period, altered in the same way as I did the one year volatility to adjust
standard deviations based on monthly averages to standard deviations based on daily
spot prices. The rationale behind viewing a larger period is that even if the volatility
within one year is volatile, and furthermore even if this is the case year after year, as
long as the country has a steady flow of oil income when averaged over the whole year
the within one year volatility is pretty harmless. However, if volatility persists over a
longer period to the extent that it can no longer be averaged out, this introduces
uncertainty in the system. It is this uncertainty that according to some caused the ’80
debt crisis as mentioned before and is the reason why countries establish stabilization
sovereign wealth funds.
It is important to note that as the focus will be on the volatility on a one year basis,
whenever I mention the oil price volatility without specifying the period I am talking
about the volatility over a one year basis.
5.5 Control Variables
In order to isolate the effect of SWFs and oil price volatility on Capital Flight a set of 14
control variables could be found in the literature measuring capital flight, however due
to missing data issues for the subset of countries relevant in the regression it is not
possible to include all 14 controls. After reducing the sample to the subsample the data
was much more complete but there were still some issues left. There are also several
highly related control variables from different studies, as I will include several models
to see which one has the most explanatory power. I will for instance try to control for
economic development, but in order to do this previous literature used current GDP,
current GDP per capita, GDP growth and GDP per capita growth, which can all be seen
as proxies for the same underlying idea, economic development.
31
5.5.1 Economic development
In most if not all of the studies the authors included a control for economic
development. Which one they used however differed. I will include four of these
namely GDP in current USD, GDP per capita in current USD, GDP growth in per cent
and GDP per capita growth in per cent. GDP and GDP per capita represent the state of
the economy, whereas the growth figures indicate an outlook. Higher developed
countries tend to have a higher GDP per capita, but growth figures are often noticeably
lower compared to middle income countries such as the BRIC (Brazil, Russia, India,
and China) countries, who are all included in the subsample. As both a higher state of
economic development and a higher growth rate are positive signs for the economy, I
expect all four of the variables to have negative signs as they will lower capital flight.
However, it will be interesting to see which one is most significant. We will later see
that GDP and GDP per capita are non-stationary, requiring differencing in order to be
included in the regression. When I talk about the difference I mean the absolute
difference, whereas growth is the relative growth compared to the previous year.
5.5.2 Institutional Quality and Political Participation
I will use figures from the Polity IV Project by Marshall and Jaggers(2011). This dataset
includes large amounts of data and a great deal of variables, but I will only use the
Polity2 index, the scores on Democracy and the scores on Autocracy, ranging from 1977
until 2011. The last two are additive eleven point scales ranging from 0 to 10 that
include measures of competitiveness and openness of executive recruitment,
constraints on the chief executive and competitiveness of political participation. The
higher the Democracy score, the more democratic a country is, the higher the
Autocracy score, the more autocratic it is. Polity2 is an index made by the authors that
combines the previous two. Although it originally ranges from -10 to +10, with a higher
number signifying better polity, I recalibrated it to range from 0 to 10 in order to
facilitate comparison.
Coefficients are expected to be negative for autocracy and positive for democracy and
polity2, but it is also possible that the presence of an authoritarian regime hinders the
possibility of capital flight, even if the residents would like to invest or store their
32
wealth elsewhere, whereas in a more democratic regime capital might flee more freely
if an external shock occurs, thus reversing the signs of the coefficients.
5.5.3 Financial openness
In order to measure the financial openness of a country I will use an index of de jure
capital account openness as calculate by Chin and Ito (2008), but using their own
updated version with data until 2011. The index includes measurements based on
several restrictions on external accounts namely the presence of multiple exchange
rates, restrictions on capital account or current account transactions and the
requirement on the surrender of export proceeds. As the index itself is relative to the
scores of other countries and as a whole has a mean of zero by construction,
restructuring within a subsample limited in countries and timeframe to improve
comparability risks changing the data itself.
Again we might have a dual relation, where more financial openness could lead to less
capital flight as residents know that they can safely store their capital domestically
because in the case of a negative domestic shock or interesting foreign investment
opportunities they can easily invest internationally, or increased openness could lead
to more capital flight as residents want to diversify investments internationally,
knowing that if they need it they can easily repatriate the funds.
5.5.4 Deposit interest rate and interest rate spread
Another factor which is can be controlled for regarding capital flight is domestic
interest rate. If for any reason, be it the specific current economic situation of a
country, be it government imposed interest rates, the deposit interest rates are low,
one could expect that the incentive to invest elsewhere increases. I therefore expect the
sign of the coefficient to be negative.
Closely linked to the domestic deposit interest rate, is the domestic interest rate
spread. This is the difference between the deposit interest rates and the interest rates
one has to pay when applying for a loan. A smaller spread points to close competition
in the banking sector as profit margins decrease. It is for instance possible that the
deposit interest rate is quite high but that this simply reflects high inflation numbers,
and that the lending rate is not that much higher. In that case investing domestically
33
remains attractive as long as inflation is not all too rampant and is predictable. This is
why I would expect the sign of the interest rate spread to be positive, a higher spread
leads to higher capital flight.
5.5.5 Economic integration in the global market
We will measure the economic integration of a country using the proxy exports as
percentage of GDP. This shows how open the country is, but also how dependent on
foreign trade. As with financial openness also economic openness could lead both
ways. On the one hand a more open economy often performs better and is more
resistant to country specific effects, which should lead to less capital flight, but on the
other hand an open economy increases to possibilities, the number of methods
available, to flee with capital and eases repatriation of funds. A more integrated
economy will also have a higher rate of international diversification which leads to
higher capital flight, but again this could also mean that other countries will use the
country for international diversification, evening out this effect. As a whole I do not
know which effect will be largest and do not know the sign of the coefficient.
5.5.6 External Debt
External Debt plays a major role in capital flight, as the change in external debt stock is
even a part of the formula to calculate capital flight. At least using the formula
suggested by the World Bank as I did, others such as the ‘Hot Money Measure’ do not
include external debt. However, merely measuring the external debt stock gives an
eschewed picture as this is biased towards smaller countries. Smaller countries will
have less external debt than larger countries with comparable economic situations.
This is why a common control variable is the external debt, scaled to the country’s
current GNI. As higher external debt to GNI ratios are worrisome I expect this variable
to be positively correlated to Capital Flight, i.e. have a positive sign.
However, since we are using external debt and not total debt, one could also assert
that it is not the ratio of external debt to GNI, which is the total economic activity by a
country’s nationals, but the ratio of external debt to exports, being the external
economic activity. The total debt of Japan of over 200% of GDP could hardly be
compared to the total debt of the USA, just over 100% of GDP, because most of the
34
debt of Japan is held domestically, with about half owed to the central bank of Japan
and a major part in the hands of the public, but also domestically, whereas the major
creditors to the USA are foreign.
This is why I expect the external debt to exports ratio will be both stronger (a higher
absolute value) and more significant. The sign however should still be positive.
However, it is possible that since external debt stock is a part of the formula that we
risk introducing almost perfect multicollinearity, especially in the case where we scale
external debt to current GNI, since I also scale capital flight to GDP, albeit the average
of current GDP over the whole period, which is closely linked to GNI. When we check
the correlogram table (Annex III) we find a value of -0.295, so there is no risk of
multicollinearity.
5.5.7 Foreign Direct Investment
Another part of the formula for capital flight which we will examine more closely is the
Foreign Direct Investments, net inflows. This could be seen as another measure for
integration in the global market, but more importantly it reflects if the international
community views the country as an interesting business opportunity. Again the capital
flight formula uses FDI as a stock variable, an absolute number, but in order to be able
to compare between countries of different sizes I used FDI scaled to current GDP. As
FDI is an integral part of the formula the risk seen with external debt is repeated, so we
could again avertedly introduce multicollinearity in the regression. However, at a
correlation of 0.14 this does not seem an issue.
As a positive sign for net FDI means more money is being invested in the country by
foreign investors than is being invested abroad by its citizens, this reduces capital
flight and I expect the sign to be negative.
5.5.8 Total Reserves as percentage of External Debt
Although reserves are again related to the formula, the formula uses change in reserves
whereas as a control variable I will use the stock variable, total reserves. As measuring
the reserves by itself would lack comparative power, I will scale it using external debt.
This to measure to what extent a country is able to fulfil its obligations to its foreign
creditors using its own reserves. A country with a low reserve to external debt ratio will
35
be hard pressed to pay back loans when the creditors refuse to roll-over loans when a
negative shock to the system occurs. This lowers confidence in the country and that is
why we can expect the variable to have a clear, significant and negative correlation to
capital flight.
5.5.9 Inflation
Another control variable that I could not omit is inflation. As this is a sign of the
economic stability of a country, this could greatly influence the capital flight
experienced in a country. I used inflation based on the GDP deflator, supplemented
with data based on consumer prices where the former was missing.
Another option is instead of simply including the inflation rate, a high inflation
dummy, where the cut off point for ‘High Inflation’ is 40%. As inflation could wage
rampantly in some countries, with the highest inflation measured in the subsample an
astonishing 5399% on annual basis in Angola in 1996, this could lead to distorted
results as the economic impact of inflation of 5399% is not 54 times as hard as the
economic impact of inflation at 100%.
As high inflation introduces economic uncertainty I expect the signs to be positive.
5.5.10 Oil Rents and Oil Price
Not only the international oil price volatility or the international oil price, but also the
country’s dependency on oil could lead to changes in capital flight. A country such as
Norway with an enormous oil based SWF fund but which also has a developed
industrial and post-industrial economy, highly skilled workers and other assets, will
suffer less from problems on the oil market than Middle Eastern countries that highly
depend on oil. This is why I will include oil rents as a percentage of GDP in the
regression and I expect the sign to be positive. The more the economy is dependent on
oil the more likely investors will invest elsewhere. I expect however to see the most
telling results later on when I introduce oil rents as percentage of GDP among my
interaction effects. As it is quite possible, and this was the result of studies such as
Ndikumana and Boyce (2012), I will also control for the oil price, which I also expect to
have a positive sign.
36
5.5.11 Conflict
Another clear influence on capital flight is conflict in the country, or even the outbreak
of war. Since this can be expected to damage infrastructure, reducing future
production capacities, hinder international trade and in some cases even induce
international economic sanctions I have no doubt that conflict will have an impact on
capital flight. However, not all conflicts are equal and that is why I will use data from
the Armed Conflict and Intervention Dataset found on the Integrated Network for
Societal Conflict Research. This data allows me to differentiate among the type of the
conflict and the intensity. Although further differentiation is possible I will compare
international conflicts, such as international violence and interstate wars, with civil
conflict, these are civil wars but also violent protests and demonstrations that end in
fatalities. Both of these types of conflicts are measured by intensity on ten point scales.
Since it is possible that the type of the conflict does not matter, I will also include a
total conflict variable, being the sum of international and civil conflict, which I
rescaled from a twenty point to a ten point scale to ease comparison.
Another possibility is that the intensity does not matter, since we can only measure
this ex post and it is the ex-ante expectations or fears that induce capital flight. A
minor conflict that could have led to a major war might have been halted thanks to
international diplomacy, but the capital could already have fled with the first signs of
conflict. This is why I will also include a conflict dummy. If there was any conflict in
the country in a certain year, be it international or civil, this dummy has a value of 1,
otherwise 0.
I expect all signs to be positive, but the level of significance could differ between
international conflict, civil unrest, the total conflict or the conflict dummy.
5.5.12 Domestic credit as a percentage of GDP
In the same way as we can see the FDI inflows as a show of confidence from the
international community in the country, we can also take the credit granted
domestically as a show of confidence that the domestic financial sector has in its own
country. This is scaled to current GDP in order to facilitate comparison among
countries of different sizes. I expect the sign to be negative as a country which has no
37
confidence in itself will experience both capital flight, people want to invest abroad,
and a lower level of domestic credit since the capital has already left the country.
5.5.13 Interaction Effects
Aside from the control variables I will also include some interaction effects to measure
the effect of combinations of variables. I have high hopes for this category of variables
as SWFs are created for a reason, and it is precisely the surrounding circumstances that
will determine the effectiveness of a SWF. First of all I will measure the interaction
effect of SWFs and oil price volatility, meaning that this variable will be equal to the oil
price volatility if and when the country had an oil-based SWF in that year, and zero in
any other situation. I will also measure the effect of combining SWFs, oil price
volatility and oil rents. This adds to the previous interaction effect in that it includes a
measure of dependency from hydrocarbon revenues.
Another interaction which should be interesting to measure is that of SWFs and
institutional quality. As I stated earlier a SWF can be seen as a sign, either justly or
unjustly, of good economic governance. But as SWFs are designed by each country as it
sees fit, a country with bad institutions might design an ineffective SWF, or even a bad
SWF. Although this should be examined and discussed on a case by case basis, a
possible example could be the SWF of Nigeria, a country included in my subsample.
This established the Excess Crude Account (ECA), an oil based SWF, in 2004, but in
2011 this was replaced by the Nigerian Sovereign Investment Authority, which manages
three separate funds. The reasoning was that the ECA was a SWF formed by the
previous administration with no legal backing, and that the country would benefit
from the new arrangement. This could indicate that the previous SWF, the ECA, was
not a tool for economic development but for extraction of the oil rents by those in
power. Including institutional quality together with the presence of a SWF will control
for these types of situations. I will use not only the index for institutional quality, but
an interaction effect between SWFs and a measure for democracy and an interaction
effect between SWFs and a measure for autocracy. (cfr. 5.5.2) I will also include the
interaction effect of having a SWF in times of oil price volatility, but include the index
measure for institutional quality as well.
38
Another series of interaction effect that I will include are that of SWFs in times of
conflict. As often commodity based SWFs such as the ones in the subsample have an
economically stabilising role, and conflict tends to destabilise the economy, this effect
should not be omitted. In essence, it tells us if the SWF mitigates or worsens the effects
of conflict when we compare it with the coefficient of the variable without the SWF
interaction. As with institutional quality I have several interactions to test, namely
with civil conflict, total conflict and a war dummy (cfr 5.5.11). I had intended to use the
fourth war variable, international conflict, as well, but it seems that within the
subsample there was only one occurrence out of 770 observations where a country that
had a SWF was in a state of international conflict, which was Russia in 2008. This
conflict was known as the Russia-Georgia Five-Day or South Ossetia war. The fact that
all other observations of this series are equal to 0 make it impossible to use it in the
regression, it would result in a near singular matrix. Another SWF that gained
notoriety in recent times of conflict is the Libyan Investment Authority (LIA).
Although Libya is not within the sample due to data gathering issues, prior to the
Libyan Civil war it had a SWF that, although very opaque, had assets rumoured to be
around 60-70 billion dollars. If we add to that foreign investments of the Central Bank
and other Government investment authorities this figure reaches over 150 billion USD.
As the conflict grew and civil unrest turned to civil war, the LIA deposited large sums
abroad which were then frozen by the financial authorities of the United States (circa
32 billion dollars) and the EU (circa 45 billion Euros, 58 billion USD). Many other
assets were frozen or even nationalised due to their link with the Gaddafi regime. But
in recent months these assets have been released to the new regime. Granted, this is a
painstaking process and the LIA has to challenge some claims in court, but the SWF
kept hands out of the Gaddafi regime and these funds are now being used to rebuild
post-war Libya. Whether or not the LIA will take up its role as a SWF again is not
certain. The reason that international conflicts occur so seldom together with having a
SWF could be myriad. One could argue that a SWF stabilises a country and integrates
the country in the global economy, to the point that the economic costs of waging a
war are too high, or one could argue that a country only creates a SWF to focus on
economic development once it has reached a certain level of international diplomatic
39
stability. Examining the correlation or even causation could provide interesting results
but that is not what this research question is about, and the subsample I gathered is
not suited for this subject as it was compiled with another goal in mind.
5.5.14 Lags and Leads
So far I have introduced variables and in a regression these will show how much of the
capital flight of the current year they explain. However, in the field of economics it
would be wrong to view each year on itself. This is why I introduce lags of one year for
each variable. This will allow us to examine how investors react to figures from the
previous year. As figures from the current year are seldom unavailable investors have
to base themselves on expectations, be it from econometric forecasts or intuition, for
the current year and on figures from the previous year. To further introduce
expectations into the model I will not only use lagged values but also leading values,
values from the year following the year in which capital flight was measured. Although
these do not show the ex-ante expectations but the ex-post results, they are not perfect
proxies. Under rational expectations there should not be a systemic deviation between
ex-post results and ex-ante expectations authors such as Kamin and Rachlinski(1995)
and others within fields such as psychology, behavioural economics and behavioural
finance time and time again showed that human behaviour is not rational. However
since finding reliable data on expectations for every single variable, for every single
year and every single country, would be very hard I shall assume the ex-post results to
be equal to ex-ante expectations. I should however state that although I use leading
values to introduce expectations for the independent variables as relevant variables in
the model, the use of the word expectations states a causal relationship from the
leading value to capital flight. The other way around is also possible, that it is capital
flight in one year that influences the supposed independent variable in the following
year, or that there is a variable that I did not integrate in the model that influences
both, but in different periods. To facilitate specifications and illustrations of the
models I used abbreviations, in table 2 you can see a full explanatory list. To indicate
interaction effects with oil price volatilities over periods over than 1 year, the suffix _Y
is added where Y is the number of years. Interact1_2 for instance is the interaction
40
between SWFs and oil price volatility over 2 years. An additional “D” before the
variable abbreviation indicates that it has been differenced.
Table 2: abbreviations for variables
Cap flight ratio average GDP
cap_flight_rat
Constant
c
Total scaled conflict actot
Autocracy autoc
Civil conflict civtot
Democracy democ
Deposit interest rate dep_int
Domestic credit % GDP dom_cred_rat
Exports % GDP export_rat
External debt % GNI extdebt_gni
External debt %export extdebt_ratex
Fdi net in % GDP fdi_in_rat
Gdp gdp
Gdp per cap gdp_cap
Gdp per cap growth gdp_cap_growth
Gdp growth gdp_growth
Inflation inflation
Inflation Dummy inflation_dummy
Interest rate spread int_rate_spread
SWF*Oil price volatility
interact1
SWF*Oil price volatility* oil rents% GDP interact2
SWF*polity2
interact3
SWF_dummy*democracy
interact4
SWF_dummy*autocracy
interact5
SWF_dummy*polity2*oil_p_vol interact6
SWF_dummy*actotal
interact7
SWF_dummy*inttotal
interact8
SWF_dummy*civtotal
interact9
SWF_dummy*war_dummy interact10
International conflict
inttot
Financial openness kaopen
Oil price volatility over 1 year period oil_p_vol
Oil price volatility over 2 year period oil_p_vol_2y
Oil price volatility over 3 year period oil_p_vol_3y
Oil price volatility over 5 year period oil_p_vol_5y
Oil rents % GDP oil_rents_rat
Polity2 polity2
Reserves to external debt ratio reserves_rat_exdebt
Swf dummy swf_dummy
Wardummy wardummy
Oil price oil_p
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5.6 Panel Unit Root testing
In order to test if a unit root is present in the variables I first use two different Fisher
type tests on both the key variables, Capital Flight scaled to average GDP, the SWF
dummy , the oil price volatility and the oil price itself, and the different control
variables. First I used the panel version of the Augmented Dickey Fuller (ADF) unit
root test designed by Maddala and Wu (1999) (M-W). As a second test I use the
Phillips-Perron (PP) test (1988). Both of these allow for an unbalanced panel, which
would be a necessary condition as there are is missing data in all the variables. Not
only missing observations in some cases, but several countries have whole gaps in
some variables and the former communist states in the subsample only existed since
the early 90’. Although the PP test is also a Fisher type test which tries to overcome the
Dickey-Fuller test’s weakness, namely that the process generating data for the variable
in question might have a higher order of autocorrelation that is allowed for in the test,
thus making y(t-1) endogenous and the results of the DF test void, it differs from the
ADF test. The latter introduces lags of d(Yt) to overcome this issue, whereas the PP
test makes a non-parametric adjustment to the t-test statistic. In the ADF test I used
the Schwarz criteria to select the appropriate lag length, but when using the non-
parametric adjustment this is not necessary and we circumvent this problem.
However, both these tests share the assumption of independence between individual
series which is quite strong and not likely to be true for cross-country data. Some
economic variables such as the GDP growth are likely to be linked, as we can see in
today’s worldwide recession. However, this example is only a probable relation, the
worldwide oil price and oil price volatility are not just dependent but even identical
across the countries by definition. Unfortunately the econometrics programme used in
the regressions does not allow second generation panel unit root tests to be performed.
The first generation unit root tests mostly agreed with each other, out of 37 variables
tested 9 were found by both tests to have a unit root in levels when including a trend
and an intercept, whereas there were 3 cases where only the Maddala-Wu test
identified a unit root, and another 3 where only the Phillips-Perron test identified a
unit root, each time on a 10% significance level when including a trend and an
intercept in levels. In one case I could not preform the test, the interaction effect of a
42
SWF and international conflict. As I mentioned before (supra, p48) there was not
enough variability in the data to use it in regressions, so it should come as no surprise
that there is not enough variability to perform a unit root test, neither Maddala-Wu
nor Phillips-Perron. None of the variables in none of the tests had a unit root in second
or first differences. Mostly this was true on a lower than a 1% probability.
The variables identified by both tests as having a unit root are: external debt as
percentage of export, current GDP, GDP/cap, oil price, reserves as percentage of
external debt, the SWF dummy, the interaction effect between SWFs and institutional
quality, the interaction effect between SWFs and autocracy and the interaction effect
between SWFs and the war dummy. Those where only the Maddala-Wu test identified
a unit root were domestic credit as a percentage of GDP (p-value of PP test was 0.0314),
the interaction effect between SWFs and active conflicts (p-value of PP test was
0.0587) and the interaction effect between civil conflict (p-value of PP test was 0.0756).
Those where only the PP test identified a unit root where the institutional quality
variables, namely democracy (p-value of M-W test was 0.0497), autocracy (p-value of
M-W test was 0) and polity2 (p-value of ADF test was 0).
In order to eliminate non-stationary from the regression I will implement the variables
identified to have a unit root in first differences. As the Maddala-Wu test and the
Phillips-Perron test do not always agree I will include some models based on the
Maddala-Wu test, but when I go further in depth I will follow the results of the PP test.
Not only does it have the previously mentioned advantage of making the non-
parametric adjustment, thus eliminating the risk of choosing the level of serial
correlation, but since the sample is quite large, 770 observations in total, we can view
this as a large sample. Even when observations are dropped during the regressions for
a variety of reasons, the remainder still number several hundreds. This is a condition
for the PP test to be valid as it is based on asymptotic theory. The SWF dummy was
also positively identified by both tests as having a unit root in levels, but differencing
this would negate the point of introducing a dummy variable so I will keep the SWF
dummy in levels.
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6. Empirical Model
6.1 The Base Model
The base model for my regressions is quite simple, I check to what extent the presence
of a SWF, volatility of the oil price in the previous year and its interaction effect can
explain capital flight, scaled to average GDP over the period 1977 to 2011. The latter,
capital flight as scaled to average GDP, is the dependent variable for every model. This
base model does not seem to contain much valuable data as its R², the variance of the
dependent variable explained with the independent variables, is only 2.18%. The
adjusted R², as well a measure for explained variance but with a penalty for the number
of variables used, is even slightly negative. A further review of these and all other
regression results will be in chapter 7 (infra, p. 54-75).
In this model and throughout the regressions I will be using fixed cross-section effects
as this improves comparability between countries. Using fixed cross-sectional effects
allows for time-invariant country specific variables to enter the model, in essence, it
creates a dummy variable for each country which is constant over the whole sample
period. I already scaled the data, either with average GDP in the case of capital flight or
with current GDP in the case of many of the control variables, but certain exogenous
country specific effects could remain. One could easily think that cultural or
geographical factors directly affect capital flight, not just indirectly through the control
variables used. A small country such as Equatorial Guinea, Azerbaijan or Papua New
Guinea might offer less option for domestic risk diversification than large countries
such as China, Russia and Brazil, even when having scaled the variables. Or cultural
factors could play a role, a very closed and introvert culture might have international
trade, but at the end of the day still prefer to invest domestically. Introducing fixed
cross-sectional effects removes or at least reduces all these factors from the regression
and allows us to examine the exogenous variables over the whole group.
As I have many control variables there is the need for many different models. I will
only suggest a few of the options, but since some of the variables are proxies of the
same underlying idea, such as GDP and GDP/cap measuring the state of the economy,
GDP growth and GDP/cap growth measuring economic growth or inflation and a high
44
inflation dummy, many are possible. There are also some variables that cannot be in
the same regression by design as the linear relationship between the two causes perfect
multi collinearity, such as polity2 with democracy and autocracy, and total active
conflict with civil conflict and international conflict.
In total I will present to different extents 14 models including the base model, but with
26 exogenous variables, not counting possible interaction effects, different tests for
stationarity, lags and leads this is only the tip of the iceberg of possibilities.
Cap_Flight_Rat = C(1)*Swf_Dummy + C(2)*Oil_P_Vol + C(3)*Interact1 + C(4)
6.2 Models Based on the Maddala-Wu test
First I will use the Maddala-Wu test to determine stationarity, and create models
where I differenced the variables the M-W test positively identified as having a unit
root. These are the models ADF1 through ADF4, the specifications of which can be
found in table 3. In ADF1 I include a quite full range of exogenous variables and
interaction effects, totalling at 30. For this model I chose to split up the composite
variables for institutional quality, polity2, into its separate elements, autocracy and
democracy, and for conflict, total active conflict, into civil conflict and international
conflict. This also means that I only included the interaction effects which used these
variables, and not those using polity2 and total active conflict. As the majority of the
coefficients of the variables in this model do not appear to be different from zero at the
10% significance level I start refining the model. I do this by continuously eliminating
the variable with the highest P- value. Having the highest p-value means that there is
the least possibility that the coefficient is different from zero. The exception to my
procedure is when a variable is part of an interaction effect, if this is the case I allow
the variable to remain in the model as long as the interaction effect is not the variable
with the highest p-value. Once the interaction effect is removed from the model the
variable can be deleted. I repeat these steps until I am left with a model that exists only
out of variables that are at least significant at the 10% or not significant but are part of
an interaction effect which is significant. The only coefficient that remains in the
regression regardless of its significance is the constant. This is because it is introduced
as part of the fixed cross-sectional effects used in the regression which in our case
requires a constant.
45
Following the steps I just described results in another model which, although having a
slightly lower R² and adjusted R², uses much less variables to come to its conclusion.
This model, consisting of 9 variables, namely civil conflict, democracy, exports as a
percentage of GDP, the inflation dummy, financial openness, oil rents as a percentage
of GDP, the difference in reserves as a percentage of external debt, the SWF dummy
and the difference in oil price. No interaction effects remained significant. I called this
model ADF2 and will further review in the next section. Surprising however is that
variables that appeared significant, some even highly significant above the 1%
confidence bound, are no longer found in the refined model. On the other hand,
variables that at first did not seem significant, not even within the 10% confidence
bound, are now highly significant.
In the first model, ADF1, I started by using the separate variables for institutional
quality and conflict, but another possibility is using their composite variables, polity2
and total active conflict. Aside from adding these and removing democracy, autocracy,
civil conflict and international conflict I must also adjust the interaction effects,
including those and only those that are calculated using variables used within the
regression. Formulating this regression provides me with the model ADF3, which
although smaller than ADF1 is still quite extensive numbering 28 variables and
interaction effects.
Again we see that although some variables are highly significant the vast majority,
about three quarters of the variables, are not significant at the 10% confidence level. I
therefore repeat the procedure outlined earlier to refine the model and end up with
ADF4. Again we see that some at first significant variables have disappeared whereas
other variables have strongly gained in significance. The end result looks a lot like the
model ADF2, not surprising considering the similar starting position and equal refining
procedure. The only difference is that total active conflict and polity2 have taken the
place of civil conflict and democracy.
46
Table 3: Models ADF1 to ADF4: specification
ADF1 Cap_Flight_Rat = C(1)*Autoc + C(2)*Civtot + C(3)*Ddom_Cred_Rat + C(4)*Democ +
C(5)*Dep_Int + C(6)*Dextdebt_Ratex + C(7)*Dgdp + C(8)*Dgdp_Cap +
C(9)*Dinteract10 + C(10)*Dinteract5 + C(11)*Dinteract9 + C(12)*Doil_P +
C(13)*Dreserves_Rat_Exdebt + C(14)*Export_Rat + C(15)*Extdebt_Ratgni +
C(16)*Fdi_In_Rat + C(17)*Gdp_Cap_Growth + C(18)*Gdp_Growth + C(19)*Inflation +
C(20)*Inflation_Dummy + C(21)*Int_Rate_Spread + C(22)*Interact1 + C(23)*Interact2 +
C(24)*Interact4 + C(25)*Kaopen + C(26)*Oil_P_Vol + C(27)*Oil_Rents_Rat +
C(28)*Swf_Dummy + C(29)*Wardummy + C(30) + C(31)*Inttot
ADF2 Cap_Flight_Rat = C(1)*Civtot + C(2)*Democ + C(3)*Doil_P + C(4)*Dreserves_Rat_Exdebt +
C(5)*Export_Rat + C(6)*Inflation_Dummy + C(7)*Kaopen + C(8)*Oil_Rents_Rat +
C(9)*Swf_Dummy + C(10)
ADF3 Cap_Flight_Rat = C(1) + C(2)*Actotal + C(3)*Ddom_Cred_Rat + C(4)*Dep_Int +
C(5)*Dextdebt_Ratex + C(6)*Dgdp + C(7)*Dgdp_Cap + C(8)*Dinteract10 +
C(9)*Dinteract3 + C(10)*Dinteract7 + C(11)*Doil_P + C(12)*Dreserves_Rat_Exdebt +
C(13)*Export_Rat + C(14)*Extdebt_Ratgni + C(15)*Fdi_In_Rat +
C(16)*Gdp_Cap_Growth + C(17)*Gdp_Growth + C(18)*Inflation +
C(19)*Inflation_Dummy + C(20)*Int_Rate_Spread + C(21)*Interact2 + C(22)*Interact1 +
C(23)*Interact6 + C(24)*Kaopen + C(25)*Oil_P_Vol + C(26)*Oil_Rents_Rat +
C(27)*Polity2 + C(28)*Swf_Dummy + C(29)*Wardummy
ADF4 Cap_Flight_Rat = C(1) + C(2)*Actotal + C(3)*Doil_P + C(4)*Dreserves_Rat_Exdebt +
C(5)*Export_Rat + C(6)*Inflation_Dummy + C(7)*Kaopen + C(8)*Oil_Rents_Rat +
C(9)*Swf_Dummy + C(10)*Polity2
6.3 Models based on the Phillips-Perron test: contemporaneous
However, these previous 4 models were all based on the same test for stationarity, the
Maddala-Wu test, whereas I explained in section 5.6 why this test could be wrong and
why I prefer to use the Phillips-Perron test. Although broadly similar, there were a few
notable differences in the results for the unit root tests, namely three cases where only
the Maddala-Wu test identified a unit root, and three cases where only the Phillips-
Perron test identified a unit root. In the following models I accept the Phillips-Perron
test as producing the correct results. The specifications for these next four models can
be found in table 4.
47
Firstly I calculate the model PP1, which is equal to ADF1 in the sense that it does not
contain the composite variables for institutional quality and conflict, but rather their
separate elements. Again only the corresponding interaction effects are included.
However, it differs from ADF1 in the variables that were identified as non-stationary
and as a results were differenced to de-trend them. Unlike ADF1 domestic credit as a
percentage of GDP and the interaction effect between having a SWF and the presence
and scale of civil conflict were not differenced, whereas the institutional quality
measurements, being democracy and autocracy, were in fact differenced. Just like its
Maddala-Wu counterpart the PP1 model is again a quite extensive model with many
variables of which only a few are significantly different from zero.
This is why we repeat the steps outlined earlier to refine the model and try and find
which variables truly matter. As there are only some differences but not many, only 4
out of 30 variables differ, it should not be surprising that for our PP2 model we arrive
at a similar result to ADF2. However, aside from the similarities in the forms of exports
as percentage of GDP, oil rents as percentage of GDP, the difference in the ratio of
reserves to external debt, the presence of a SWF and the difference in oil price, there
are also notable differences. First of all no institutional or conflict variables have
remained in the regression, and neither has the measure for financial openness, which
is also linked to institutional quality. On the macro economical side GDP per capita
growth has entered the regression, whereas the inflation dummy is now replaced by
the measure for inflation.
Just as PP1 was the Phillips-Perron test equivalent of the ADF1 regression, so is PP3
equivalent to ADF3, being the regression that starts will most of the variables, but with
the composite variables for institutional quality and conflict. Again the difference lies
in the way that some variables have been de-trended, in this case the composite
measure for institutional quality, polity2, whereas others have not been de-trended,
being domestic credit as a percentage of GDP and the interaction effect between the
presence of a SWF and the scale of total conflict.
As before, this leads to an unnecessarily large model with only a few variables with
coefficients significantly different from zero which needs to be refined. Refining this
leads us to model PP4, which bears similarities both to ADF4, with which it shares the
48
type of variables, and with PP2, with which it shares the test for stationarity. We can
see that exports as percentage of GDP and the difference in oil price remain
throughout the three models, but it shares its macro-economic variables, GDP per
capita growth and inflation, with PP3 but its institutional variables, capital openness
and polity2, with ADF4. However, since polity2 was found to be non-stationary
according to the Phillips-Perron test this time I am using the difference in institutional
quality, the once differenced version of polity2. But it also introduces new variables
which were not present in previous refined models, being the difference in external
debt as percentage of export, the interest rate spread and the war dummy. I should
note that although I followed the steps explained earlier, I was not strict about the 10%
significance levels for this model as some variables, namely the difference in external
debt as percentage of exports, the interest rate spread, the difference in institutional
quality and the war dummy do not seem different from zero at the 10% significance
level, but eliminating them from the model greatly reduced R², the percentage of
variance explained in the model. This is why I allowed them to remain.
Table 4: Models PP1 to PP4: specification
PP1 Cap_Flight_Rat = C(1)*Civtot + C(2)*Dautoc + C(3)*Ddemoc + C(4)*Dextdebt_Ratex + C(5)*Dep_Int
+ C(6)*Dgdp + C(7)*Dgdp_Cap + C(8)*Dinteract10 + C(9)*Dinteract5 + C(10)*Doil_P +
C(11)*Dom_Cred_Rat + C(12)*Dreserves_Rat_Exdebt + C(13)*Export_Rat +
C(14)*Extdebt_Ratgni + C(15)*Fdi_In_Rat + C(16)*Gdp_Cap_Growth + C(17)*Gdp_Growth +
C(18)*Inflation + C(19)*Inflation_Dummy + C(20)*Interact1 + C(21)*Int_Rate_Spread +
C(22)*Interact2 + C(23)*Interact4 + C(24)*Interact9 + C(25)*Inttot + C(26)*Kaopen +
C(27)*Oil_P_Vol + C(28)*Oil_Rents_Rat + C(29)*Swf_Dummy + C(30)*Wardummy + µ
PP2 Cap_Flight_Rat = C(1)*Doil_P + C(2)*Dreserves_Rat_Exdebt + C(3)*Export_Rat +
C(4)*Gdp_Cap_Growth + C(5)*Inflation + C(6)*Oil_Rents_Rat + C(7)*Swf_Dummy + µ
PP3 Cap_Flight_Rat = C(1)*Actotal + C(2)*Dep_Int + C(3)*Dextdebt_Ratex + C(4)*Dgdp_Cap +
C(5)*Dgdp + C(6)*Dinteract10 + C(7)*Dinteract3 + C(8)*Doil_P + C(9)*Dom_Cred_Rat +
C(10)*Dpolity2 + C(11)*Dreserves_Rat_Exdebt + C(12)*Export_Rat + C(13)*Extdebt_Ratgni +
C(14)*Fdi_In_Rat + C(15)*Gdp_Cap_Growth + C(16)*Gdp_Growth + C(17)*Inflation +
C(18)*Inflation_Dummy + C(19)*Int_Rate_Spread + C(20)*Interact1 + C(21)*Interact2 +
C(22)*Interact6 + C(23)*Interact7 + C(24)*Kaopen + C(25)*Oil_P_Vol + C(26)*Oil_Rents_Rat
+ C(27)*Swf_Dummy + C(28)*Wardummy + µ
PP4 Cap_Flight_Rat = C(1)*Doil_P + C(2)*Export_Rat + C(3)*Gdp_Cap_Growth + C(4)*Inflation +
C(5)*Kaopen + C(6)*Swf_Dummy + C(8)*Dextdebt_Ratex + C(9)*Int_Rate_Spread +
C(10)*Dpolity2 + C(11)*Wardummy +µ
49
6.4 Models based on the Phillips-Perron test: Lagged and Leading
variables
All the previous models looked at the contemporaneous impact of variables on capital
flight, but as I explained earlier in section 5.5.13 (supra, p. 37-38) this seems a very
limited point of view in the field of economics. Shocks to the system can persist for
several years and people can anticipate shocks and adjust their behaviour accordingly.
It is even possible that their anticipation of the shock is exactly what causes the shock
to happen. The field of economics is rife with these kinds of self-fulfilling prophecies.
To name one that is present in the model, inflation persists when the public believes
inflation to happen. Employees will raise their wage demands in order to protect their
purchasing power anticipating the higher prices, cutting the profit margin of
companies. These will in response try to pass of these higher costs to the consumer.
On a micro or even a meso scale these actions make sense, but when aggregated over
the whole economy the increased purchasing power through wage increases will be
eroded by the inflation that follows. The fear and expectation of inflation has triggered
inflation. Conflict is another variable that could easily have a not only
contemporaneous effect but also lagged and leading effects. When conflict is brewing,
be it international or civil, investors might be inclined to funnel capital out of the
country in anticipation of the conflict. It could also be that the destruction through the
conflict reduces interesting investment opportunities, forcing capital abroad, or in fact
creates the need for reconstruction and offers highly interesting domestic investment
opportunities. This is why the next model PP5 will incorporate lagged and leading
values. As a starting point I will use PP3 since that is the PP model which has the
highest adjusted R² so far. I will use a one year lag and one year lead as this will already
result in a very large model, including the constant it will total 85 variables. Although
the sample size available is quite extensive, 22 countries over a 35 year period,
including further lags and leads would be over specifying the model, something which
is already a risk with just a one year lag and lead. This model and the next three can be
found in table 5.
50
As before I refine the model to see which variables were actually relevant for the
regression in questions to end up at model PP6. Although this is a refined model due
to the many starting variables, even after refining it is quite extensive counting 28
variables, interaction effects, lagged and leading variables. The specification for this
model and the complete list of variables can be found in table 5. To highlight a few
variables, it is notable that this is the first refined model that still contains interaction
effects, namely the interactions between SWFs and oil price volatility, SWFs together
with oil price volatility and oil rents as percentage of GDP, and the presence of and
SWF in times of conflict, be it civil or international. It also seems that several lagged
values for the previous year are significant, some of which were never
contemporaneously relevant in refined models namely the difference in GDP per
capita, the deposit interest rate and external debt as percentage of Gross National
Income(GNI). Export as a percentage of GDP seems to have both a contemporaneous
impact as a lagged impact, as did reserves as a percentage of interest rates and the
presence of a SWF. Interest rate spread also seems to have a lagged impact, but no
longer a contemporaneous one, as it did when it appeared in model PP4. After refining
the model also leading values, meaning the variables for which the value in the
following year remained in the model. Firstly there were the interaction effects
regarding conflict and the presence of a SWF. On the one hand the interaction
between the presence of a SWF and total conflict on a zero to ten scale remained, but
also the once differenced interaction between the presence of a SWF and the war
dummy stayed in the model. Also the difference in oil price, which in every previous
model had a significant contemporaneous impact, seems to have lost it
contemporaneous effect but had gained a leading effect. It seems that not the rise but
the expectation of a rise has an impact. Inflation and oil rents are two variables that
not only have a contemporaneous impact, but also their leading coefficient remained
in the model. This was also true for the interaction effect between sovereign wealth
funds and scaled conflicted I discussed earlier.
When I introduced SWFs I emphasised that each country designs its own fund,
although there are several best practices to model, and that is it possible that the SWF
is not constructed with the best intentions. This is why I will again refine the model
51
PP5, being the model with the composite variables for institutions and conflict, but
now I will limit the sample to democracies. I define democracies in the same way as
Andersen, Johannesen and Lassen (2012) by demanding that the democracy value from
the PolityIV dataset as calculated by Marshall and Jaggers (2011) should be at least five.
In this way I demand a certain institutional quality, which should improve the design
of the SWF. By calculating PP5 for this subsample and then refining as I did all
previous times I calculated model PP7. Again there are quite a few variables so I will
not mention them all, the specification can be found in table 5. Notable however is
that no contemporaneous conflict variables or their interaction effects remain. We also
see some newcomers in the contemporaneous variables, being FDI inflows as
percentage to current GDP and the difference in GDP per capita. It is also the first
refined PP model that started with Polity2 where it is no longer in the refined
regression. Most of the lagged and leading variables which were present in PP6, the
refined model for all countries, are no longer present in PP7, the refined model for
democracies. Exceptions are the lagged difference in GDP per capita and the lagged
value of external debt as a percentage of GNI. There are also newcomers in the leading
and lagged values. Here percentual GDP growth enters a refined model for the first
time, albeit the lagged version. The only conflict variable which stays in the model is
the lagged value for total conflict scaled from 0 to ten. It is also the first time that the
interaction effect between SWFs and oil price volatility, an element in the base model,
is able to remain the model, but as with GDP growth it is also the lagged version. The
only leading value in the model is the percentual GDP growth per capita. It is
interesting to note that for the lagged and contemporaneous effect it was the
difference in GDP per capita, being the absolute growth in GDP per capita, but that for
the leading value the percentual change matters.
Although the brunt of the regressions and the focus of my research question was on a
one year volatility, in section 5.4 (supra, p.30) I already introduced volatility over
longer period, being two, three and five years. This why I will repeat model PP3, the
model with composite variables for institutional quality and conflict, supplemented
with the lagged and leading variables that seemed relevant in model 6 to create model
PP8. However this time I will use instead of only the one year volatility, volatilities over
52
all four timeframes. As there were several interaction effects that contained oil price
volatility over a one year period I will recreate these interaction effects with the two
year, three year and five year volatility. I will also introduce lagged and leading effects
for the newly introduced variables and their respective interaction effects. As this
creates another very extensive model I will not include it in here but refer to annex III
(attachment).
In order to limit the size of the model somewhat I shall refine it as I did with the
previous models to calculate the model PP9. However, even though the model had less
starting variables than PP5, the refined version is still quite large with 40 variables,
including interaction effects, lagged and leading values. It seems that including
volatilities over a longer period greatly enhanced the model as many contemporaneous
interaction effects, together with lagged and leading variables and interaction effects,
stayed in the model. As it is again too large to include here I shall include it in annex
III.
Table 5: Models PP5 to PP9: specification
PP5 Cap_Flight_Rat = C(1)*Actotal + C(2)*Dep_Int + C(3)*Dextdebt_Ratex + C(4)*Dgdp_Cap +
C(5)*Dgdp + C(6)*Dinteract10 + C(7)*Dinteract3 + C(8)*Doil_P + C(9)*Dom_Cred_Rat
+ C(10)*Dpolity2 + C(11)*Dreserves_Rat_Exdebt + C(12)*Export_Rat +
C(13)*Extdebt_Ratgni + C(14)*Fdi_In_Rat + C(15)*Gdp_Cap_Growth +
C(16)*Gdp_Growth + C(17)*Inflation + C(18)*Inflation_Dummy +
C(19)*Int_Rate_Spread + C(20)*Interact1 + C(21)*Interact2 + C(22)*Interact6 +
C(23)*Interact7 + C(24)*Kaopen + C(25)*Oil_P_Vol + C(26)*Oil_Rents_Rat +
C(27)*Swf_Dummy + C(28)*Wardummy + C(30)*Actotal(-1) + C(31)*Dep_Int(-1) +
C(32)*Dextdebt_Ratex(-1) + C(33)*Dgdp_Cap(-1) + C(34)*Dgdp(-1) +
C(35)*Dinteract10(-1) + C(36)*Dinteract3(-1) + C(37)*Doil_P(-1) +
C(38)*Dom_Cred_Rat(-1) + C(39)*Dpolity2(-1) + C(40)*Dreserves_Rat_Exdebt(-1) +
C(41)*Export_Rat(-1) + C(42)*Extdebt_Ratgni(-1) + C(43)*Fdi_In_Rat(-1) +
C(44)*Gdp_Cap_Growth(-1) + C(45)*Gdp_Growth(-1) + C(46)*Inflation(-1) +
C(47)*Inflation_Dummy(-1) + C(48)*Int_Rate_Spread(-1) + C(49)*Interact1(-1) +
C(50)*Interact2(-1) + C(51)*Interact6(-1) + C(52)*Interact7(-1) + C(53)*Kaopen(-1) +
C(54)*Oil_P_Vol(-1) + C(55)*Oil_Rents_Rat(-1) + C(56)*Swf_Dummy(-1) +
C(57)*Wardummy(-1) + C(58)*Actotal(1) + C(59)*Dep_Int(1) +
C(60)*Dextdebt_Ratex(1) + C(61)*Dgdp_Cap(1) + C(62)*Dgdp(1) + C(63)*Dinteract10(1)
53
+ C(64)*Dinteract3(1) + C(65)*Doil_P(1) + C(66)*Dom_Cred_Rat(1) + C(67)*Dpolity2(1)
+ C(68)*Dreserves_Rat_Exdebt(1) + C(69)*Export_Rat(1) + C(70)*Extdebt_Ratgni(1) +
C(71)*Fdi_In_Rat(1) + C(72)*Gdp_Cap_Growth(1) + C(73)*Gdp_Growth(1) +
C(74)*Inflation(1) + C(75)*Inflation_Dummy(1) + C(76)*Int_Rate_Spread(1) +
C(77)*Interact1(1) + C(78)*Interact2(1) + C(79)*Interact6(1) + C(80)*Interact7(1) +
C(81)*Kaopen(1) + C(82)*Oil_P_Vol(1) + C(83)*Oil_Rents_Rat(1) +
C(84)*Swf_Dummy(1) + C(85)*Wardummy(1) +µ
PP6 Cap_Flight_Rat = C(1)*Actotal + C(2)*Dgdp_Cap + C(3)*Dom_Cred_Rat + C(4)*Dpolity2 +
C(5)*Dreserves_Rat_Exdebt + C(6)*Export_Rat + C(7)*Extdebt_Ratgni + C(8)*Inflation
+ C(9)*Interact1 + C(10)*Interact2 + C(11)*Interact7 + C(12)*Kaopen + C(13)*Oil_P_Vol
+ C(14)*Oil_Rents_Rat + C(15)*Swf_Dummy + C(16)*Wardummy + C(18)*Dep_Int(-1) +
C(19)*Dgdp_Cap(-1) + C(20)*Dreserves_Rat_Exdebt(-1) + C(21)*Export_Rat(-1) +
C(22)*Extdebt_Ratgni(-1) + C(23)*Int_Rate_Spread(-1) + C(24)*Swf_Dummy(-1) +
C(25)*Dinteract10(1) + C(26)*Doil_P(1) + C(27)*Inflation(1) + C(28)*Interact7(1) +
C(29)*Oil_Rents_Rat(1) +µ
PP7 Cap_Flight_Rat = C(1)*Dgdp_Cap + C(2)*Export_Rat + C(3)*Extdebt_Ratgni + C(4)*Fdi_In_Rat
+ C(5)*Kaopen + C(6)*Oil_P_Vol + C(7)*Oil_Rents_Rat + C(8)*Swf_Dummy +
C(10)*Actotal(-1) + C(11)*Dgdp_Cap(-1) + C(12)*Extdebt_Ratgni(-1) +
C(13)*Gdp_Growth(-1) + C(14)*Interact1(-1) + C(15)*Gdp_Cap_Growth(1) +µ
PP8 Cap_Flight_Rat = C(1)*Actotal + C(2)*Dep_Int + C(3)*Dextdebt_Ratex + C(4)*Dgdp +
C(5)*Dgdp_Cap + C(6)*Dinteract10 + C(7)*Dinteract3 + C(8)*Doil_P +
C(9)*Dom_Cred_Rat + C(10)*Dpolity2 + C(11)*Dreserves_Rat_Exdebt +
C(12)*Export_Rat + C(13)*Extdebt_Ratgni + C(14)*Fdi_In_Rat +
C(15)*Gdp_Cap_Growth + C(16)*Gdp_Growth + C(17)*Inflation +
C(18)*Inflation_Dummy + C(19)*Int_Rate_Spread + C(20)*Interact1 +
C(21)*Interact1_2(-1) + C(22)*Interact1_2 + C(23)*Interact1_2(1) + C(24)*Interact1_3(-1)
+ C(25)*Interact1_3 + C(26)*Interact1_3(1) + C(27)*Interact1_5(-1) + C(28)*Interact1_5 +
C(29)*Interact1_5(1) + C(30)*Interact2 + C(31)*Interact2_2(-1) + C(32)*Interact2_2 +
C(33)*Interact2_2(1) + C(34)*Interact2_3(-1) + C(35)*Interact2_3 + C(36)*Interact2_3(1)
+ C(37)*Interact2_5(-1) + C(38)*Interact2_5 + C(39)*Interact2_5(1) + C(40)*Interact6 +
C(41)*Interact6_2(-1) + C(42)*Interact6_2 + C(43)*Interact6_2(1) + C(44)*Interact6_3(-
1) + C(45)*Interact6_3 + C(46)*Interact6_3(1) + C(47)*Interact6_5(-1) +
C(48)*Interact6_5 + C(49)*Interact6_5(1) + C(50)*Interact7 + C(51)*Kaopen +
C(52)*Oil_P_Vol + C(53)*Oil_P_Vol_2y(-1) + C(54)*Oil_P_Vol_2y +
C(55)*Oil_P_Vol_2y(1) + C(56)*Oil_P_Vol_3y(-1) + C(57)*Oil_P_Vol_3y +
C(58)*Oil_P_Vol_3y(1) + C(59)*Oil_P_Vol_5y(-1) + C(60)*Oil_P_Vol_5y +
C(61)*Oil_P_Vol_5y(1) + C(62)*Oil_Rents_Rat + C(63) + C(64)*Dep_Int(-1) +
54
C(65)*Dgdp_Cap(-1) + C(66)*Dreserves_Rat_Exdebt(-1) + C(67)*Dinteract10(1) +
C(68)*Doil_P(1) + C(69)*Inflation(1) + C(70)*Interact7(1) + C(71)*Oil_Rents_Rat(1) +
C(72)*Export_Rat(-1) + C(73)*Extdebt_Ratgni(-1) + C(74)*Int_Rate_Spread(-1) +
C(75)*Swf_Dummy(-1) + µ
PP9 Cap_Flight_Rat = C(1)*Actotal + C(2)*Dgdp_Cap + C(3)*Dom_Cred_Rat + C(4)*Dpolity2 +
C(5)*Dreserves_Rat_Exdebt + C(6)*Export_Rat + C(7)*Extdebt_Ratgni +
C(8)*Gdp_Cap_Growth + C(9)*Inflation + C(10)*Int_Rate_Spread + C(11)*Interact1_2(-
1) + C(12)*Interact1_2(1) + C(13)*Interact1_5(1) + C(14)*Interact2 + C(15)*Interact2_2(-1)
+ C(16)*Interact2_3 + C(17)*Interact2_5(-1) + C(18)*Interact6_2 + C(19)*Interact6_3(-1)
+ C(20)*Interact6_3 + C(21)*Interact6_3(1) + C(22)*Interact6_5(1) + C(23)*Interact7 +
C(24)*Kaopen + C(25)*Oil_P_Vol + C(26)*Oil_P_Vol_3y(-1) + C(27)*Oil_P_Vol_3y +
C(28)*Oil_P_Vol_5y + C(29)*Oil_Rents_Rat + C(30) + C(31)*Dgdp_Cap(-1) +
C(32)*Export_Rat(-1) + C(33)*Extdebt_Ratgni(-1) + C(34)*Int_Rate_Spread(-1) +
C(35)*Dinteract10(1) + C(36)*Inflation(1) + C(37)*Interact7(1) + C(38)*Oil_Rents_Rat(1)
+ C(39)*Wardummy + C(40)*Swf_Dummy + C(41)*Oil_P_Vol_2y +µ
7. Estimation Results
7.1 The Base Model
The first model I will discuss will be the base model. This is very limited in terms of the
variables that I included and I therefore it should come as no surprise that the
explanative power is quite low as I already mentioned at the start of section 6. The
advantage from including limited variables is that this model included most
observations, 614 out of 770, and the only model that could include all the countries
over the whole period. Other models suffered from missing observation issues and
therefore the sample was automatically limited in the other cases.
As we can see neither SWFs nor oil price volatility seems significantly different from
zero, nor does their interaction effect. We can however see that where the prediction
for the sign of oil price volatility was correct, the coefficient for a SWF is positive and
quite large at 0.0856, meaning the presence of a capital flight actually increases capital
flight. As the coefficient is not significantly different from zero I will not investigate
this further. Also the interaction effect is positive, but this as I said this is not
significant at the 10% level and it is also quite small. The base model is one of only
55
three models, together with PP3 and PP7, where the constant is significantly different
from zero, here measuring in at 0.0418. The full regression results can be found in table
6 (infra, p. 60-61) at the end of section 7.2.
7.2 Models based on the Maddala-Wu unit root test: ADF1 to ADF4
When calculating the first model, ADF1, that includes control variables we can
immediately see that the R² and adjusted R² values do not just rise, they shoot up.
From +2.2% and -1.8% respectively here we see 60.20% and 50.69%. The downside of
extending the model with other variables is that the size of the sample is severely
limited, with only 14 cross-sections over 30 periods. The number of observations
included drops to just over a third of previously used. Although 30 variables are
included in the model, only 6 of them are significantly different from 0. As discussing
the signs and expected signs of all the variables would be superfluous as we are not
even at the 10% confidence bound certain that these are in fact the correct signs, I will
limit myself here and in the following models to those that are significant unless
otherwise relevant. The full list of coefficients for this model and the other ADF
models can be found in table 6 (infra, p. 60-61) at the end of this section.
At the one per cent confidence bound only two variables are significant, being exports
as percentage of GDP and inflation. Although inflation has the positive sign we
expected, I was not sure which sign exports would be. Here scaled exports has a
positive sign, which is consistent for every model included, and a value of 0.0036. This
value roughly comes back in every model before we introduce lagged and leading
values, after that there is some variation in the value. Inflation has a smaller coefficient
of 0.0009, but also this is quite robust for the different models.
When looking at the variables significant at the five per cent level we see the difference
in external debt as a percentage of exports, the country’s financial openness and the
difference in oil price. The differences in oil price and external debt as percentage of
exports have the predicted sign, but for financial openness we did not know what to
expect. Now we see that this seems to have a positive impact on capital flight, which is
consistent over the different models but with varying values for the coefficients. Of
these three, the financial openness has a strong impact in this model but the difference
56
in oil price much less, and the external debt as percentage of exports even less than
that.
In the broadest confidence bound of ten per cent we find the deposit interest rate and
the presence of a SWF. Both have positive signs, both contrary to what I expected, but
where the presence of a SWF has a large impact compared to other coefficients, the
deposit interest rate is only very slightly positive. Albeit both insignificantly different
from zero, it is strange that both autocracy and democracy, which are different end of
the same spectrum, have a negative coefficient. To fast forward, this is also true for the
PP1 model, which uses the differences in democracy and autocracy. Although
democracy has a stronger negative coefficient we could take this as a sign that
moderate versions of government increase capital flight whereas extremes reduce it.
This could be another interesting follow up research question but a possible
explanation could be that in a strong autocracy there is little or no freedom of capital
movement, thus reducing capital flight, and a very democratic country has not only
freedom of movement of capital but also a high enough institutional quality to create
confidence, but a country that introduces freedom of capital movement before earning
the confidence can avertedly increase capital flight.
When we look at the refined version of this last model, the ADF2 model, we see that it
not only contains fewer variables but that the omission of the variables allowed for a
greater number of observations, which almost doubled. Also the cross-sections and
number of periods included, although less spectacularly. When we look at the variance
explained by the model the R² is significantly lower at 51,64%, almost 9 percentage
points lower, but the adjusted R² only dropped by just over 3 percentage points.
Although I only use the 10% confidence bound to refine the model, 5 of the 9
remaining variables are significant at a 1% level and the other 4 are on the 5% level. No
variables on the 10% level remain. In the 1% level we have firstly democracy, with again
a negative sign albeit slightly lower in absolute value. Secondly we see exports as a
percentage of GDP which has a similar value to before. Reserves as percentage of
external debt has a highly significant but very small positive sign, contrary to what I
expected. This value is quite robust for the refined models, but in the unrefined model
we find a much smaller but not significant value. The SWF dummy is smaller than in
57
the unrefined model, but is still positive. As this goes against the hypotheses of my
research question I will discuss this further in section 8 (infra, p.75). The difference in
oil price is even more significant than before, but its value is halved to 0.0020. This
value is to some extent robust for all refined models before introducing lags and leads,
whereas the value from ADF1, 0.0039, is quite robust across the non-refined models. At
the five per cent confidence level we find civil conflict. Where international conflict
did not make it to the refined model, civil conflict did but has a negative value. This
means that civil conflict decreases capital flight, which seems counter intuitive but in
section 6.4 I suggested that the need for reconstruction could create very interesting
domestic investment opportunities. The next valuable significant at 5% is the high
inflation dummy with a quite strong positive sign, as expected. In the unrefined model
it was inflation itself that had a positive and significant sign, whereas the inflation
dummy was negative and not significant. The value of the coefficient for the high
inflation dummy is also much higher than that of the inflation variable in ADF1. The
last significant variable is financial openness, with again a positive sign but just over
half the size of previously. As with the difference in oil price, this value is quite robust
over the different refined models, whereas its value in ADF1 was quite robust over the
unrefined models.
The next model is ADF3, the unrefined model using composite variables for
institutional quality and conflict. If we compare this to the previous unrefined model
we see the same number of observations but a very slight fall of explained variance,
both in R² and adjusted R². Exactly the same variables are significant as in ADF1 with
even roughly the same values. Also most of the non-significant variables are roughly
equal, the exceptions being the constant, the interaction effect between SWFs and oil
price volatility and the interaction effect between SWFs, oil price volatility and oil
rents as percentage of GDP. The constant went from negative -0.01 to positive 0.03, but
the negative effect of both interaction effects is much stronger. These are still quite
small numbers, but relative to previous model a notable difference.
As before this model is too extensive with too many variables, some of which highly
unlikely to be significant, so the refined model ADF4 emerges. This model is very
similar to ADF3, which should come as no surprise given their similar starting
58
variables. The R², adjusted R² and number of observations included are almost exactly
the same. The only difference is that democracy is no longer present in the model, but
polity2 is, and civil conflict has been replaced by total conflict. Striking is that the
coefficients of these variables have almost the same value as the ones they are each
replacing.
59
Table 6: Base Model and Models ADF1 to ADF4: Results
Abbreviation Base_Model ADF1 ADF2 ADF3 ADF4
Cap Flight Ratio Av Gdp Cap_Flight_Rat x x x x x
Constant C 0,0418 * (0,0777)
-0,0110 (0,0664)
-0,170 (0,0192)
0,0345 (0,0462)
-0,0057 (0,0209)
Total Conflict Actot
-0,0075 (0,0056)
-0,0067 ** (0,0030)
Autocracy Autoc
-0,0019 (0,0068)
Civil Conflict Civtot
-0,0088 (0,0057)
-0,0066 ** (0,0031)
Democracy Democ
-0,0087 (0,0065)
-0,0068 *** (0,0022)
Deposit Interest Rate Dep_Int
5,77E-05 * (3,26E-05)
5,97E-05 * (3,27E-05)
Domestic Credit % Gdp Dom_Cred_Rat
-0,0024 (0,0017)
-0,0024 (0,0017)
Exports % Gdp Export_Rat
0,0036*** (0,0010)
0,0034 *** (0,0006)
0,0037 *** (0,0010)
0,0035*** (0,00058)
External Debt % Gni Extdebt_Gni
9,85E-05 (0,0003)
0,0001 (0,0003)
External Debt %Export Extdebt_Ratex
0,0004 ** (0,0002)
0,0004 * (0,0002)
Fdi Net In % Gdp Fdi_In_Rat
-0,0012 (0,0030)
-0,0004 ( 0,0029)
Gdp Gdp
-9,98E-14 (1,81E-13)
-1,47 E-13 (1,80 E-13)
Gdp Per Cap Gdp_Cap
7,95E-06 (1,63E-05)
7,99 E-06 (1,63 E-05)
Gdp Per Cap Growth Gdp_Cap_Growth
0,0022 (0,0013)
0,0024 (0,0015)
Gdp Growth Gdp_Growth
0,0035 (0,0039)
0,004 (0,0039)
60
Inflation Inflation
0,0009 *** (0,0002)
0,0009 *** (0,0002)
Inflation Dummy Inflation_Dummy
-0,0239 (0,0274)
0,0296 ** (0,0146)
-0,0227 (0,0275)
0,0302** (0,0146)
Interest Rate Spread Int_Rate_Spread
-0,0012 (0,0009)
-0,0012 (0,0009)
Swfxoil Price Volatility Interact1 0,0015
(0,0068) -0,0008 (0,0042)
-0,0082 (0,0133)
Swfxoil Price Volatilityxoil Rents%Gdp Interact2
6,46E-05 (0,0001)
0,0002 (0,0002)
Swf*Polity2 Interact3
-0,0046 (0,0065)
Swf_Dummy*Democ Interact4
-0,0040 (0,0075)
Swf_Dummy*Autoc Interact5
-0,0160 (0,0118)
Swf_Dummy*Polity2*Oil_P_Vol Interact6
0,0002 (0,0013)
Swf_Dummy*Actotal Interact7
0,0092 (0,0243)
Swf_Dummy*Civtotal Interact9
-0,0105 (0,0260)
Swf_Dummy*War_Dummy Interact10
0,0245 (0,1061)
-0,0555 (0,0850)
International Conflict Inttot
-0,2070 (0,1572)
Financial Openness Kaopen
0,0247 ** (0,0098)
0,0132 ** (0,0061)
0,0266 *** (0,0097)
0,0133** (0,0060)
Oil Price Volatility Oil_P_Vol 0,0008
(0,0042) -0,0024 (0,0022)
-0,0021 (0,0022)
Oil Rents % Gdp Oil_Rents_Rat
-0,0008 (0,0006)
0,0011 ** (0,0004)
-0,0009 (0,0006)
0,0010 ** (0,0004)
Polity2 Polity2
-0,0062 (0,0044)
-0,0072 *** (0,0023)
Reserves Reserves_Rat_Exdebt
8,66E-05 0,0003 *** 7,26E-05E-05 0,0004 ***
61
(0,0002) (6,87E-05) (0,0002) (0,0004)
Swf Dummy Swf_Dummy 0,0856
(0,0068) 0,0729 * (0,0423)
0,0516 *** (0,0154)
0,0680 ** (0,0281)
0,0550 *** (0,0154)
Wardummy Wardummy
0,0195 (0,0293)
0,0218 (0,0294)
Oil Price Oil_P
0,0039 ** (0,0016)
0,0020 *** (0,0006)
0,0037 ** (0,0016)
0,0020 *** (0,0006)
R²
2,18% 60,20% 51,64% 59,36% 51,72%
adjusted R²
-1,80% 50,69% 48,32% 50,20% 48,40%
total panel (unbalanced) observations
614 224 406 224 406
Cross-sections included
22 14 18 14 18
periods included
35 30 31 30 31
cross section effects fixed
cross section effects fixed
cross section effects fixed
cross section effects fixed
cross section effects fixed
cross section effects fixed
Standard error between parentheses: * = 10% significance, ** = 5% significance, *** = 1% significance
Figures in bold indicate that the variable was differenced before including it in the model
62
7.3 Models based on the Phillips-Perron unit root test: PP1 to PP4
These first four models were based on the Maddala-Wu unit root test for stationarity,
but now we pass to the models based on the Phillips-Perron test that use only
contemporaneous variables.
First there is the PP1 model, which is the equivalent of the ADF1 model but with slight
differences in terms of which variables are differenced. Both the R² and adjusted R² are
slightly lower than those of ADF1 but as a whole the model is quite similar. The
variables that were significant in ADF1 are again significant and within the same
confidence level, even with similar values. The only exceptions are external debt as
percentage of exports that goes from being significant on the 5% level to the 10% level
and the interaction effect between the presence of a SWF and civil conflict which gains
significance, albeit only on a 10% level, and changes heavily. The interaction effect also
goes from quite negative to strongly positive.
Next we have the refined version of the previous model, PP2. Although its R² is much
lower, just over 11 percentage points, its adjusted R² is only 4.5 percentage points lower.
There are however many more observations included in the model due to fewer
included variables. As PP1 was quite similar to ADF1 we could reasonably expect a
similar refined model as well, but this is not true. In section 6.3 (supra, p.46-48) I
already stated which variables are part of the model and you could already see that
there were notable differences there. When we look at the significance and values of
the coefficient of the included variables we can again see differences. Similarities can
be found in the exports to GDP ratio, which has the same level of significance and
almost the same value, as is the case for oil rents as percentage of GDP and the
difference in reserves as percentage of external debt. However, here the similarities
stop and differences start. The constant in this model is still negative, but went from
non-significant to significant on a 1% level. You already knew that GDP per capita
growth was significant since it was a part of this model, but it seems that it is slightly
positive, contrary as to what one would expect, and this on a 5% confidence level.
Inflation took the place of the inflation dummy, and is just like in the PP1 model
significant on the 1% level. However, the value of the coefficient has greatly decreased.
Unlike the ADF2 and PP1 model which both had, albeit sometimes different,
63
significant conflict and institutional variables, this model has none of those. There are
also other variables that were significant in the P1 model, domestic deposit interest
rate and differences in the ratio of external debt to exports, which lost their
significance.
Another quite general model is the Phillips-Perron test equivalent to ADF3, PP3. As
opposed to PP1 composite variables were used here. As with P2 I will make a dual
comparison, both with ADF3 and PP1. As far as similarities go in terms of significant
variables we see quite a few. Firstly its explanative power, both in terms of R² and
adjusted R², is almost equal to ADF3 and PP1. It has the highest adjusted R² of the
three, but only by 0.25 percentage points. The deposit interest rate, the export to GDP
ratio, the inflation, the financial openness and the difference in oil price all have the
same level of significance and almost the same value as in ADF3 and PP1. The SWF
dummy is similar in terms of sign and significance, but going from almost 0.07 to
barely 0.05 is quite a drop in effect. The differences include a significant negative
constant but more notably, a significant interaction effect between the presence of a
sovereign wealth fund and the scale measure for total conflict which is significant on a
5% level and quite strongly positive. The variable total conflict itself is negative but
much smaller and not significant.
The refined version of the model just discussed is PP4, and it is the first that actually
performs better than its unrefined version with a lower R² but with a slightly higher
adjusted R² than PP4. This could be due to a better specification of variables, but more
likely it is because as I said in section 6.3 (supra, p.46-48) that I did not follow the steps
for refining as strictly as I could. The difference in external debt as percentage of
export, the interest rate spread, the difference in institutional quality and the war
dummy are not significant at a 10% level and are not part of an interaction effect that is
significant, so I should have dropped it, but this greatly reduces explanatory power of
the regression. In the case of the difference in external debt dropping it means a
reduction of adjusted R² as large as 52.75 percentage points, it actually becomes
negative after dropping a variable that is supposed to be not significant. If we examine
the variables that are significant on the 1% level we first see the constant which has a
highly negative value, slightly larger in absolute value than in PP3. The export to GDP
64
ratio is still positive but also slightly larger than in all of the previous models. Inflation
and financial openness, which were also highly significant in model PP3, both have a
smaller value than previously, although the drop is more noticeable regarding the
financial openness. Compared to PP2 GDP per capita growth gained more significance
but was even slightly more positive than previously, contrary to expectations. This is
another model where the oil price volatility does not feature anymore, but where the
difference in oil price is significant and positive.
65
Table 7: Models PP1 to PP4: Results
Abbreviation PP1 PP2 PP3 PP4
Cap Flight Ratio Av Gdp Cap_Flight_Rat x x x x
Constant C -0,06687 (0,0471)
-0,0636 *** (0,0152)
-0,0783 * (0,0454)
-0,0845 *** (0,0252)
Total Conflict Actot
-0,0066 (0,0055)
Autocracy Autoc -0,0043 (0,0164)
Civil Conflict Civtot -0,0066 (0,0056)
Democracy Democ -0,0036 (0,0135)
Deposit Interest Rate Dep_Int 5,45E-05 * (3,23 E-05)
5,70E-05 * (3,18E-05)
Domestic Credit % Gdp Dom_Cred_Rat 0,0001
(0,0009)
0,0003 (0,0008)
Exports % Gdp Export_Rat 0,0036 *** (0,0010)
0,0035 *** (0,0005)
0,0037 *** (0,0010)
0,0043 *** (0,0007)
External Debt % Gni Extdebt_Gni 0,0004
(0,0003)
0,0004 (0,0003)
External Debt %Export Extdebt_Ratex 0,0003 * (0,0002)
0,0003 (0,0002)
0,0002 (0,0002)
Fdi Net In % Gdp Fdi_In_Rat -0,0004 (0,0027)
-0,0002 (0,0027)
Gdp Gdp -2,57E-13 (1,85E-13)
-269E-13 (1,76 E-13)
Gdp Per Cap Gdp_Cap 1,64E-05
(1,62E-05)
1,83 E-05 (1,61 E-05)
Gdp Per Cap Growth Gdp_Cap_Growth 0,0022
(0,0015) 0,0018 ** (0,0008)
0,0021 (0,0015)
0,0026 ** (0,0012)
Gdp Growth Gdp_Growth 0,0023
(0,0038)
0,0022 (0,0038)
66
Inflation Inflation 0,0008*** (0,0002)
2,86E-05 *** (1,11E-05)
0,0008 *** (0,0002)
0,0006 *** (0,0001)
Inflation Dummy Inflation_Dummy -0,0197 (0,0272)
-0,0207 (0,0271)
Interest Rate Spread Int_Rate_Spread -0,0009 (0,0009)
-0,0008 (0,0009)
-0,0006 (0,0007)
Swfxoil Price Volatility Interact1 -0,0034 (0,0045)
-0,0079 (0,0129)
Swfxoil Price Volatilityxoil Rents%Gdp Interact2 0,0001
(0,0001)
0,0002 (0,0002)
Swf*Polity2 Interact3
-0,0066 (0,0062)
Swf_Dummy*Democ Interact4 -0,0053 (0,0073)
Swf_Dummy*Autoc Interact5 -0,0164 (0,0118)
Swf_Dummy*Polity2*Oil_P_Vol Interact6
0,0003 (0,0012)
Swf_Dummy*Actotal Interact7
0,0335 ** (0,0158)
Swf_Dummy*Civtotal Interact9 0,0320 * (0,0188)
Swf_Dummy*War_Dummy Interact10 -0,0904 (0,0631)
-0,0681 (0,0495)
International Conflict Inttot 0,0258
(0,1604)
Financial Openness Kaopen 0,0281 *** (0,0094)
0,0285 *** (0,0091)
0,0234 *** (0,0077)
Oil Price Volatility Oil_P_Vol -0,002452 (0,0022)
-0,0022 (0,0021)
Oil Rents % Gdp Oil_Rents_Rat -0,0010
(0,0006) 0,0010 *** (0,0004)
-0,0010 (0,0022)
Polity2 Polity2
7,58E-05 (0,0080)
0,0022 (0,0075)
67
Reserves Reserves_Rat_Exdebt 8,94E-05 (0,0002)
0,0004 *** (6,34 E-05)
7,86 E-05 (0,0001)
Swf Dummy Swf_Dummy 0,0697 * (0,0391)
0,0400 *** (0,0133)
0,0507 * (0,0284)
Wardummy Wardummy 0,0046
(0,0289)
0,0031 (0,0286)
-0,0159 (0,0148)
Oil Price Oil_P 0,0040 ** (0,0016)
0,0010 ** (0,0004)
0,0037 ** (0,0016)
0,0019 ** (0,0008)
R²
59,86% 48,83% 59,62% 55,13%
adjusted R²
50,37% 45,88% 50,62% 50,76%
total panel (unbalanced) observations
226 457 226 283
Cross-sections included
14 18 14 16
periods included
30 34 30 30
cross section effects fixed
cross section effects fixed
cross section effects fixed
cross section effects fixed
cross section effects fixed
Standard error between parentheses: * = 10% significance, ** = 5% significance, *** = 1% significance
Figures in bold indicate that the variable was differenced before including it in the model
68
7.4 Models based on the Phillips-Perron test: expanding beyond
contemporaneous values, PP5 to PP9
All previous models only included contemporaneous variables, in section 5.5.14 (infra,
p.39) however I explained why this could be a severe limitation. This is why the next
models will include a one year lagged and leading term. As before a full review would
be very extensive considering the 85 explanatory variables including the constant,
interaction effects, lagged and leading values. It would also not be very useful as only 11
of these variables are significant on a 10% level or better. The full results can be found
in annex IV. Within the contemporary variables only the difference of GDP per capita
is significant on a 1% level and with a sign that goes against expectations but with a
value of 0.00006 its effect is very limited. On a 5% level we find the difference in
reserves to external debt ratio, external debt as percentage of GNI, inflation and oil
rents in percentage of GDP. These are all positive, whereas of these we only expected
inflation and oil rents as ratio to GDP to be positive. Exports to GDP ratio is significant
on a 10% level, with a positive value along the same lines as the previous models.
Among the lagged values we can see three significant variables, all on a 5% level. These
are the deposit interest rate of the previous year, the external debt to GNI ratio of the
previous year and the interest rate spread of the previous year. Of these only the
external debt ratio to GNI was also contemporaneously significant, but where it went
against expectations contemporaneously its lagged value changed the sign and has
become negative, following expectations. The lagged values of deposit interest rate and
interest rate spread, which are linked to each other by definition, both go against
expectations being respectively positive and negative. Only two leading values seem
significant, the differenced interaction effect between the war dummy and SWF
dummy and the ratio of oil rents to GDP. The difference in interaction effect is
strongly positive with one of the largest values seen in any model so far, but the oil
rents to GDP is negative. Just like the case of current versus lagged value of external
debt to GNI here the leading value changed sign and now follows expectations. If we
look at the descriptive statistics of the model we see a very high R² of 84.13% but a
large difference with its adjusted R² of 67.75%, which is still the best result we had so
far. This large difference suggests severe over fitting of the model, which is also shown
69
by the significantly higher Schwarz information criterion compared to previous
models.
If we refine this model to PP6, which can be found in table 8, we immediately see an
improvement in the descriptive statistics of the model as although the R² drops to
75.38%, which is a bad sign, the adjusted R² rises to 69.36% and the Schwarz
information criterion drops, which are both good signs. We can say with a degree of
certainty that the previous model was over specified. The refined model however still
uses 28 explanatory variables, including contemporaneous, lagged, leading and
interaction effects. Contemporaneously we see that this is the first refined model
where the difference in oil price is no longer featured, and although the oil price
volatility is still not relevant, its interaction effect with the presence of a SWF is
significant on a 5% level and has a quite strong negative value as stated in my
hypotheses. If we include the oil rents ratio to GDP in the interaction it becomes
positive on a 5% level, but with a much lower absolute value than before. Of the 28
variables included 6 are contemporaneously relevant on a 1% level. Exports to GDP
ratio is one, which is still positive and with a higher value than previous models, as is
external debt to GNI, with a positive value very similar to in PP5. Inflation also has a
similar value as in previous models as does financial openness. The interaction effect
between SWFs and total conflict is again positive and significant but doubles in value
compared to the previous model where it was significant, PP3. Oil rents to GDP ratio is
the last variable contemporaneously significant on a 1% level and is positive, following
expectations. On a 5% level we see the negative constant, the already mentioned
interaction effects as well as the difference in reserves as percentage of external debt
and the difference of the composite variable for institutional quality. These are both
positive, both going against expectations, although polity2 has a much larger value. On
the broadest 10% level we only see one contemporaneous variable namely domestic
credit as percentage of GDP. This is negative as we expected. Looking at the lagged
values four are significant on the 1% level. Three of these were significant
contemporaneously as well namely the difference in reserves to external debt ratio, the
export to GDP ratio and the external debt to GNI. Where the lagged difference in
reserves to external debt ratio has exactly the same value as the contemporaneous
70
variable the two others not just change signs, but reach a negative value almost the
same as the positive current variable, in absolute terms. The last lagged value on the
1% level is the deposit interest rate. On a 5% level we find the difference in GDP per
capita which is negative as expected, but with a very small value. The interest rate
spread is also significant and negative on the 5% level as it was in PP5, but with a much
smaller value. The last significant lagged variable is the SWF dummy with a quite
strong positive value, again on the 5% level. On the leading values only one was
significant on a 5% level, being the interaction effect between a SWF and total conflict,
which was also significant contemporaneously. However, this is another case where
the variable changes sign when accounting for time effects and follows expectation as
it is negative. It seems that during conflict a SWF worsens capital flight, but in the run
up to conflict is reduces it. The other four leading variables are all significant on a 1%
level. These are the difference of the interaction effect between the SWF and the war
dummy, the difference in oil price, inflation and oil rents as percentage of GDP. The
latter three were present contemporaneously as well, but with an almost equal but
opposite value for the coefficient. Interestingly enough, the leading value for the
difference in interaction effect between SWF and war dummy which we would expect
to be similar to the leading interaction effect between SWF and scaled conflict not only
has an opposite sign but the former also has a very strong positive value.
Another model I designed in section 6.4 (supra, p.51) is PP7, also found in table 8
where I started with PP5, but before refining I limited the sample to only include
democracies instead of all the possible observations. The explanatory power of this
model is much lower than before as both the R² and adjusted R² fall by about ten
percentage points. When we first look at the contemporaneous variables we see that 6
of the 8 variables were also present in PP6, the two exceptions being the ratio of FDI
inflows to GDP and the difference in GDP per capita. For the former it is the first time
that it appears in a refined model and with its positive value significant at the 5% level
it goes against expectations. The latter has a highly (1%) significant positive coefficient,
but with a very small value. Although the next 6 variables were also present in PP6 and
had the same sign, each and every one of them saw significant changes in value.
External debt to GNI, oil rents to GDP and exports to GDP ratios are all significant on
71
the 1% level as they were in PP6, but whereas the coefficient for exports to GDP ratio
was halved, the other two doubled in value. The financial openness also kept its sign
but it lost significance going from the 1% to the 10% level and more than halved in
value. The remaining two variables, oil price volatility and the SWF dummy, are not
significant on a 10% level but are the elements of a lagged interaction effect. This
lagged effect has a positive value, significant on a 5% level, which goes against
expectations. Another lagged effect is scaled total conflict with a quite strong negative
value, significant on a 10% level. On a 1% level we see the difference in GDP per capita
with a very small and similar value to contemporaneous, but with a reversed sign. We
also see external debt to GNI ratio which has almost the same value as
contemporaneously, with the same sign. Lastly there is GDP growth with another
negative value which although still not large is much larger relative to the lagged effect
of the difference in GDP per capita. There is only one leading effect in this regression,
and that is GDP per capita growth with a quite strong positive value significant on a 1%
level, contrary to expectations.
Another expansion of the model could be to include volatility over a longer period as I
did in model PP8. As including these, together with the adapted versions of the
respective interaction effects and the lagged and leading values of all these, to PP3
supplemented with the significant lags and leads from PP4 creates another very big
model with 74 variables not including the constant, I will restrict myself to reviewing
only the significant variables. If we look at the descriptive statistics of the model we see
a high R² and adjusted R² but with a big difference between the two and neither can
rival PP5. Its adjusted R² is also lower than PP6. If we look at the contemporaneous
variables on the 1% level we see 9 variables. The only negative coefficient is the
domestic credit ratio, which follows expectations. Inflation, the ratio of oil rents to
GDP, the difference in reserves to external debt ratio, the export ratio, the external
debt to GNI ratio, the GDP per capita growth, and the interaction effect between a
SWF and scaled conflict are all positive, but only the first two follow expectations, the
others go against them. On a 5% level we see the difference in GDP per capita having a
small but positive value, again going against expectations, and the interaction effect
between the presence of a SWF, oil rents to GDP ratio and volatility on a 5 year period
72
which has a negative value. On the 10% level we find only 1 contemporaneous variable,
being the oil price volatility over a five year period which has a quite strong positive
value. Of the four significant lagged values three reach the 1% level namely the
difference in reserves to external debt, the export ratio to GDP and the external debt to
GNI ratio. The first has a very similar value to its contemporaneous version, but the
latter two both have a similar absolute value but with a reversed, now negative, sign.
The last lagged significant value is in the 1% level and is the interaction effect between
the presence of a SWF, oil rents to GDP and the oil price volatility over a two year
period. It has a strong negative impact on capital flight, almost triple that of the
contemporaneous interaction effect between SWF, oil rents to GDP and the volatility
over the five year period. Of the four leading significant variables three achieve the 1%
level being inflation with a weak negative value, against expectations, the interaction
effect between SWFs and the war dummy with a very strong negative value and the
difference in interaction effect between the SWF dummy and the war dummy, with the
strongest significant effect seen so far, which is positive. Again we see these two related
interaction effects to have different signs. The last leading value is significant at the
5% level and is oil rents to GDP ratio with a moderate negative value, almost the
opposite of its contemporaneous counterpart.
At first glance it should appear that the one year oil price volatility does not matter as
much because nor it nor any of its interaction effects appears to be significant, but
since I already stated that over specification of model PP8 was not just possible but in
fact very probable it is possible that this causes significant variables to appear
insignificant. This is why the next model, also the last model I will review, is the
refined version of PP8. If we look at the descriptive statistics it is clear that this is the
best model constructed so far because even though it includes many variables, 41
including the constant, interactions, lags and leads, it has a quite low Schwarz criterion
which indicates that there is not much risk of over-specification. It also has the highest
adjusted R² value by far, although it R² is lower than that of PP5. Since the adjusted R²
contains a penalty for over-specification this is to be relied on over the standard R². Of
the variables included in the model there are 7 which are not significant, among which
the constant, but the remaining 6 are included because they are part of significant
73
interaction effects. The contemporaneous variables significant at the 1% level are
external debt to GNI ratio, inflation, the interaction effect between SWFs, oil rents to
GDP and oil price volatility over a three year period, the interaction effect between
SWFs, institutional quality and oil price volatility, oil price volatility over a five year
period, oil rents to GDP ratio, export to GDP ratio, GDP per capita growth, the
interaction effect between SWFs and scaled total conflict and financial openness. Of
these 10 the first 7 follow expectations, but for the other three expectations were not
clear. On a 5% level we see 6 variables namely the domestic credit ratio, the interaction
effect between SWFs and oil price volatility over a one year period, the difference in
GDP per capita, the difference in reserves to external debt ratio, the interaction effect
between SWFs, institutional quality and oil price volatility over a two year period and
the oil price volatility over a three year period. Of these only the first two follow
expectations whereas the difference in GDP per capita, the difference in reserves to
external debt ratio and the oil price volatility clearly go against expectations. However
only the latter has a sizeable effect, the other two are quite small. There is only one
contemporaneous variable on the 10% level which is the interest rate spread, having a
positive value as was expected. Of the 9 lagged variables most can be found in the 1%
confidence level. The difference in GDP per capita is the only one which is significant
on the 10% level, with again almost the opposite value of its contemporaneous
counterpart. On the 5% level we see the oil price volatility over a three year period,
which has a smaller but still negative value compared to the contemporaneous
variable, again going against expectations. On the 1% level we find the interaction
effect between SWFs, oil price volatility over a two year period and oil rents to GDP
ratio, the interaction effect between SWFs, institutional quality and oil price volatility
over a three year period, the interest rate spread, the interaction effect between SWFs
and oil price volatility over a two year period, the interaction effect between SWFs, oil
rents to GDP and oil price volatility over a five year period, exports to GDP ratio and
external debt to GNI ratio. Of these the first three follow expectations. Of the leading
effects only two out of eight can be found in the 5% level, the remaining six are
significant on the 1% level. These two are the interaction effect between the presence
of a SWF and oil price volatility over a two year period and inflation, both of which go
74
against expectations. Those significant on a 1% level are the interaction effect between
SWFs and oil price volatility over a five year period, the interaction effect between
SWFs, institutional quality and oil price volatility over a three year period, the
interaction effect of SWFs and scaled total conflict, the interaction effect of SWFs,
institutional quality and oil price volatility over a five year period, the differenced
interaction effect between SWFs and the war dummy and lastly oil rents to GDP ratio.
Only the first three follow expectations. Striking is the difference between the
interaction effects of SWFs, institutional quality and oil price volatility over a three
year period, and the same interaction effect with oil price volatility over a five year
period. Whereas the first reduces capital flight, the second increases this by almost the
same amount. This reversal between the same variables over a different timed effect
has already been mentioned several times, but when viewing the whole model this
appears a total of seven times. Aside from the just mentioned interaction effect also
the difference of GDP per capita, exports to GDP ratio, external debt to GNI ratio,
inflation, interest rate spread, the interaction effect between SWFs and scaled conflict
and oil rents show this reversal. A possible cause for this could be the tendency to
adapt one’s behaviour, but over compensate to be sure.
Table 8: Models PP5 to PP9: Results
Abbreviations PP6 PP7
cap flight ratio average gdp cap_flight_rat x x
constant c 0,0554
(0,0416) -0,0654 **
(0,0253)
total conflict actot -0,0092 **
(0,0045)
autocracy autoc
civil conflict civtot
democracy democ
deposit interest rate dep_int
domestic credit % gdp dom_cred_rat -0,0012 * (0,0007)
exports % gdp export_rat 0,0067 ***
(0,0011) 0,0030 ***
(0,0009)
external debt % gni extdebt_gni 0,0014 ***
(0,0004) 0,0030 ***
(0,0006)
external debt %export extdebt_ratex
fdi net in % gdp fdi_in_rat 0,0047 **
(0,0018)
gdp gdp
75
gdp per cap gdp_cap 6,08E-05 ***
(1,24E-05)
gdp per cap growth gdp_cap_growth
gdp growth gdp_growth
inflation inflation 0,0005 ***
(0,0002)
Inflation Dummy inflation_dummy
interest rate spread int_rate_spread
SWFxOil price volatility interact1 -0,0188 **
(0,0073)
SWFxOil price volatilityxoil rents%gdp interact2
0,0004 ** (0,0002)
swf*polity2 interact3
swf_dummy*democ interact4
swf_dummy*autoc interact5
swf_dummy*polity2*oil_p_vol interact6
swf_dummy*actotal interact7 0,0708 ***
(0,0212)
swf_dummy*inttotal interact8
swf_dummy*civtotal interact9
swf_dummy*war_dummy interact10
international conflict inttot
financial openness kaopen 0,0205 ***
(0,0078) 0,0097 * (0,0057)
oil price volatility oil_p_vol -0,0059 (0,0039)
-0,0002 (0,0012)
oil rents % gdp oil_rents_rat 0,0048 ***
(0,0011) 0,0070 ***
(0,0012)
polity2 polity2 0,0136 ** (0,0065)
reserves reserves_rat_exdebt 0,0002 ** (9,15E-05)
swf dummy swf_dummy 0,0122
(0,0371) 0,0284
(0,0217)
wardummy wardummy 0,0055
(0,0231)
oil price oil_p
cross sectional effects fixed
cross sectional effects fixed
R²
75,38% 64,88%
adjusted R²
69,36% 59,19%
total panel (unbalanced) observations
210 166
Cross-sections included
14 10
periods included
28 32
76
8. Discussion
Although the previously suggested models seem to have quite a lot of explanative
power regarding capital flight, we have seen some surprising results. Not only in terms
of control variables that went against expectations but also regarding the elements that
were the subject of this study. Of the control variables we saw more that often than not
economic development, be it total or per capita, be it difference in absolute value or
growth in relative terms, actually increases capital flight. That a higher state of the
economy, total GDP and GDP results in more disposable income and thus more capital
available to be invested abroad relative to average GDP over the period is not
unthinkable, but since due to non-stationarity I had to de-trend the state variables
these also became a measure of growth, albeit in absolute terms, and should have
improved confidence in the economy, thus reducing capital flight. It is possible that
due to the limited timeframe the public did not alter its confidence level in the
economy. Even when lagged and leading effects were included it was still only one year
back and one year forward. Larger lag lengths could provide different results.
When we look at the explanatory variables that we were examining the results for the
presence of a SWF are quite disappointing. In our hypotheses I stated that since a SWF
reduces uncertainty the presence should help reduce capital flight. I specified in times
in oil price volatility but expected a general reduction. However, the reverse is true, the
contemporaneous variable for SWFs was consistently positive and quite strong at that.
Only the leading value in PP5 was negative, but not-significant. When we look at
interaction effects results with a variety of control variables and with oil price volatility
results are mixed. However, this negative effect is due to the sheer size and activity of
the SWF. By reducing the inflow of in the case of this regression oil money it
contributes to capital flight, and by investing abroad instead of domestically this is
another increase in capital flight. Since some of the SWFs in the regression are very
large, such as the Norwegian GPFG of more than 500 billion USD, this could have such
an impact on capital flight that there is a positive effect among the general public but
that it is overshadowed by the SWF itself. I already suggested a follow up study that
77
uses the scaled size of the SWF instead of a dummy, but if one knows the size or at
least the assets invested abroad one could take the effect of a SWF out of capital flight
and investigate how the private sector reacts.
A strange result could be seen in the interaction effects of SWFs and conflict, namely
SWFs and scaled conflict on one hand, and SWFs and a war dummy on the other hand.
Although the former was differenced to eliminate non-stationarity in the later models
we often see that in the same period (both current, both lagged of both leading) the
two have different signs. Since the war dummy is based on the scaled total conflict
variable I had expected the two to be similar, but with varying levels of significance
and strengths of effects.
Another surprising result was that oil price volatility, at least that over a one year
period, was never significant and only once negative. The volatility over a five year
period was significant twice, in PP8 and PP9, and was positive both times, but in PP9
we could also see a significant negative effect from the volatility over three years, from
the current and lagged value. If we look at the interaction effects with oil price
volatility there is some good news, as these are less clearly positive but more show
mixed results. Depending on what is interacted and whether it was lagged, current or
leading the sign and significance altered.
78
9. Conclusion
In the course of this thesis I started with a brief overview of Sovereign Wealth Funds,
their history and their function. I also included the current state of the global SWF
funds as there are no databases available online which are as complete and as recent.
This was a clear illustration of the importance that SWFs have come to play in the
global economy in the last decade or so. Despite this proliferation the brunt of the
research was on the effect in host countries, whereas the effectiveness of SWFs for the
home country was true by assumption.
Next came a review of capital flight, principally how it could be calculated as there is
no consensus within economic literature, and my measure of oil price volatility.
Although economic theory would expect a strong negative effect of a SWF on capital
flight it seems that in several models capital flight actually increases, and that the oil
price volatility is not as significant as first hypothesised. The difference in oil price and
interaction effects regarding SWFs seem to be more important, as are many of the
control variables.
The somewhat disappointing result however is not a full criticism towards SWFs as
these have many other functions as well, which were outside the scope of this thesis
and were not researched. The point remains that clearly the effectiveness of SWFs
should not be taken for granted but that one surprising result does not negate other
benefits. Further research in this field is needed in order to ascertain clear results, both
in the forms of case studies and large scale econometric research.
79
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Table of Attachments
Annex I List of SWF Definitions
Annex II Correlogram
Annex III Models PP5, PP8 and PP9: results
83
Annex I List of SWF Definitions
1. OECD: November 2007 in international investment of sovereign wealth funds:
“Government-owned investment vehicles that are funded by foreign exchange
assets.”
2. Investopedia—Internet site for Forbes Media: December 2007
“Pools of money derived from a country’s reserves, which are set aside for
investment purposes to benefit the country’s economy and citizens. The funding
for SWFs comes from central bank reserves that accumulate as a result of
budget and trade surpluses, and even from revenue generated from the exports
of natural resources.”
3. Edwin M. Truman—before the U.S. House Committee on Banking, Housing, and
Urban Affairs, November 2007
“Separate pools of international assets owned and managed by governments to
achieve a variety of economic and financial objectives. They sometimes hold
domestic assets as well.”
4. Deutsche Bank, September 2007
“Sovereign wealth funds—or state investment funds—are financial vehicles
owned by states which hold, manage, or administer public funds and invest
them in a wider range of assets of various kinds. Their funds are mainly derived
from excess liquidity in the public sector stemming from government fiscal
surpluses or from official reserves at central banks.”
5. U.S. Treasury, June 2007
“There is no single universally accepted definition of an SWF. [In this paper,]
the term “SWF” means a government investment vehicle which is funded by
foreign exchange assets, and which manages those assets separately from the
official reserves of the monetary authorities.”
6. BPM6: March 2011 draft following world-wide consultation
“Some governments create special-purpose government funds, usually called
sovereign wealth funds, to hold assets of the economy for long-term objectives.
The funds to be invested commonly arise from commodity sales, the proceeds of
84
privatizations, and/or the accumulation of foreign financial assets by the
authorities.”
7. McKinsey Global Institute, October 2007
“Sovereign wealth funds are usually funded by the nation’s central bank reserves
and have the objective of maximizing financial returns within certain risk
boundaries.” McKinsey contrast these funds with government holding
corporations such as Temasek (Singapore) and Khazanah (Malaysia).
8. Morgan Stanley, October 2007
“An SWF needs to have five ingredients: sovereign; high foreign currency
exposure; no explicit liabilities; high-risk tolerance; and long-term investment
horizon.”
9. Aizenman (2009)
“SWFs: savings funds controlled by sovereign governments that hold and
manage foreign assets”
10. Park (2008)
“SWFs are state-owned institutions that use publicly owned foreign exchange to
pursue active profit-maximizing investments rather than passive liquidity
management. In other words, in contrast to central banks, which manage
foreign exchange assets largely to protect the country from sudden shortages of
international liquidity, SWFs use foreign exchange assets to maximize risk-
adjusted returns.”
11. Kern (Deutsche Bank) 2008
“SWFs are government-owned investment funds which are commonly funded
by the transfer of foreign exchange assets, and which are set up to serve the
objectives of a stabilisation fund, a savings fund for future generations, a reserve
investment corporation, a development fund, or a contingent pension reserve
fund by investing the funds on a longterm basis, often overseas. In doing so,
SWFs fulfil functions complementary to other state-operated entities, such as
central banks, development banks and pension funds, and to other state-owned
assets, like state-owned enterprises, and other public entities.”
85
12. Jost(2009)
SWFs are state-owned special investment funds that invest in foreign currencies
and are separately managed from foreign exchange reserves of the central bank.
They have no or only limited liabilities and therefore differ from sovereign
pension funds. SWFs undertake long-term investments in search of commercial
returns but they are not operating state owned companies
13. GAO (2008)
“Sovereign wealth fund[…] are government-chartered or government-sponsored
investment vehicles[…] invest some or all of their funds in assets other than
sovereign debt outside the country that established them[…] are funded through
government transfers arising primarily from sovereign budget surpluses, trade
surpluses, central bank currency reserves, or revenues from the commodity
wealth of a country; and […] are not actively functioning as a pension fund.”
14. Beck and Fiora (2008)
“Although there exists no commonly accepted definition of SWFs, three
elements can be identified that are common to such funds: First, SWFs are
state-owned. Second, SWFs have no or only very limited explicit liabilities and,
third, SWFs are managed separately from official foreign exchange reserves.2 In
addition, most SWFs share certain characteristics that originate in the specific
nature of SWFs.”
15. Gieve (2008)
“There is no off the shelf definition of an SWF. What I have in mind is a
government investment vehicle that manages foreign assets with a higher risk
tolerance and higher expected returns than for central bank foreign currency
reserves.”
16. Balding (2008)
“A sovereign wealth fund is a pool of capital controlled by a government or
government related entity that invests in assets seeking returns above the risk
free rate of return.”
17. Fotak and Megginson (2008)
“Sovereign wealth funds (are) a pool of domestic and international assets owned
86
and managed by government to achieve a variety of economic and financial
objectives, including the accumulation and management of reserve assets, the
stabilization of macroeconomic effects and the transfer of wealth across
generations.”
18. Blundell-Wignall, Hu, and Yermo (2008)
“Sovereign Wealth Funds are pools of assets owned and managed directly or
indirectly by governments to achieve national objectives.”
19. McKinsey Global Institute (2007)
“Sovereign wealth funds…have diversified portfolios that range across equity,
fixed income, real estate, bank deposits, and alternative investments, such as
hedge funds and private equity.”
87
Annex II Correlogram
DEP_INT DOM_CRED_
RAT EXPORT_RAT EXTDEBT_
RATEX EXTDEBT_RA
TGNI FDI_IN_RAT GDP GDP_CAP GDP_CAP_G
ROWTH GDP_GROWTH INFLATIO
N DEP_INT 1.000000 0.063073 -0.030401 0.185970 0.223962 -0.283233 -0.141975 -0.128183 -0.011552 -0.306450 -0.009896
DOM_CRED_RAT 0.063073 1.000000 -0.412021 0.139074 -0.223867 -0.112812 0.506427 0.300182 -0.010396 -0.048352 -0.117690
EXPORT_RAT -0.030401 -0.412021 1.000000 -0.494635 0.297873 0.129252 -0.344617 -0.177389 0.181001 0.198326 0.184413 EXTDEBT_RAT
EX 0.185970 0.139074 -0.494635 1.000000 0.520072 0.099787 -0.070721 -0.166758 -0.197054 -0.308137 0.031109 EXTDEBT_RAT
GNI 0.223962 -0.223867 0.297873 0.520072 1.000000 0.174843 -0.333846 -0.393263 -0.155654 -0.256107 0.262957
FDI_IN_RAT -0.283233 -0.112812 0.129252 0.099787 0.174843 1.000000 0.026123 -0.006969 -0.049242 0.247014 0.107674
GDP -0.141975 0.506427 -0.344617 -0.070721 -0.333846 0.026123 1.000000 0.662347 0.050703 0.211691 -0.053484
GDP_CAP -0.128183 0.300182 -0.177389 -0.166758 -0.393263 -0.006969 0.662347 1.000000 -0.008045 0.191219 -0.083325 GDP_CAP_GR
OWTH -0.011552 -0.010396 0.181001 -0.197054 -0.155654 -0.049242 0.050703 -0.008045 1.000000 0.391795 0.048351
GDP_GROWTH -0.306450 -0.048352 0.198326 -0.308137 -0.256107 0.247014 0.211691 0.191219 0.391795 1.000000 0.014071
INFLATION -0.009896 -0.117690 0.184413 0.031109 0.262957 0.107674 -0.053484 -0.083325 0.048351 0.014071 1.000000 INT_RATE_SPR
EAD -0.107050 -0.101328 0.097352 0.020414 0.045899 0.241724 0.167823 -0.022016 -0.026161 0.076589 0.242682
INTERACT1 -0.130278 0.018399 0.092324 -0.408057 -0.342457 -0.029218 0.246948 0.412536 0.114867 0.203767 -0.036318
INTERACT2 -0.111880 -0.047513 0.194040 -0.380917 -0.292853 -0.017964 0.032930 0.245944 0.223615 0.215328 -0.029080
KAOPEN 0.090775 0.330671 -0.095181 0.059411 0.037014 0.067103 0.142677 0.085557 0.016254 -0.007547 -0.091464
OIL_P -0.223141 0.205266 0.092218 -0.543584 -0.525945 -0.186932 0.484459 0.524799 0.239756 0.432753 -0.056687
OIL_P_VOL -0.122272 0.165787 0.083309 -0.389271 -0.360637 -0.036065 0.339311 0.373484 0.178569 0.264660 -0.041233 OIL_RENTS_RA
T -0.008286 -0.411571 0.662483 -0.268296 0.395777 0.035336 -0.312367 -0.156035 0.150811 0.045790 0.137908 RESERVES_RA
T_EXDEBT -0.073088 -0.041781 0.047235 -0.294984 -0.274952 -0.058456 2.71E-05 0.058498 0.018952 0.115719 -0.024943
RESID 0.106388 0.023898 -0.010002 0.100680 0.086537 -0.013937 -0.003663 -0.060476 -0.025634 -0.008969 0.128177
VAR01 -0.293854 0.149575 0.182916 -0.356151 -0.295716 0.367430 0.445716 0.441263 0.269192 0.575557 -0.028516
88
INT_RATE_S
PREAD INTERACT1 INTERACT2 KAOPEN OIL_P OIL_P_VOL OIL_RENTS_R
AT RESERVES_RAT_EXDEBT RESID VAR01 DEP_INT -0.107050 -0.130278 -0.111880 0.090775 -0.223141 -0.122272 -0.008286 -0.073088 0.106388 -0.293854
DOM_CRED_RAT -0.101328 0.018399 -0.047513 0.330671 0.205266 0.165787 -0.411571 -0.041781 0.023898 0.149575
EXPORT_RAT 0.097352 0.092324 0.194040 -0.095181 0.092218 0.083309 0.662483 0.047235 -0.010002 0.182916 EXTDEBT_RA
TEX 0.020414 -0.408057 -0.380917 0.059411 -0.543584 -0.389271 -0.268296 -0.294984 0.100680 -0.356151 EXTDEBT_RA
TGNI 0.045899 -0.342457 -0.292853 0.037014 -0.525945 -0.360637 0.395777 -0.274952 0.086537 -0.295716
FDI_IN_RAT 0.241724 -0.029218 -0.017964 0.067103 -0.186932 -0.036065 0.035336 -0.058456 -0.013937 0.367430
GDP 0.167823 0.246948 0.032930 0.142677 0.484459 0.339311 -0.312367 2.71E-05 -0.003663 0.445716
GDP_CAP -0.022016 0.412536 0.245944 0.085557 0.524799 0.373484 -0.156035 0.058498 -0.060476 0.441263 GDP_CAP_GR
OWTH -0.026161 0.114867 0.223615 0.016254 0.239756 0.178569 0.150811 0.018952 -0.025634 0.269192 GDP_GROWT
H 0.076589 0.203767 0.215328 -0.007547 0.432753 0.264660 0.045790 0.115719 -0.008969 0.575557
INFLATION 0.242682 -0.036318 -0.029080 -0.091464 -0.056687 -0.041233 0.137908 -0.024943 0.128177 -0.028516 INT_RATE_SP
READ 1.000000 -0.111109 -0.083649 -0.225026 -0.058578 -0.039570 0.110641 -0.057833 0.044964 0.110389
INTERACT1 -0.111109 1.000000 0.862333 -0.003377 0.555033 0.693475 0.074149 0.411820 -0.006555 0.418925
INTERACT2 -0.083649 0.862333 1.000000 -0.073680 0.475546 0.598807 0.180391 0.307128 0.064796 0.352025
KAOPEN -0.225026 -0.003377 -0.073680 1.000000 -0.057968 -0.029262 -0.333354 -0.179517 -0.070232 0.061081
OIL_P -0.058578 0.555033 0.475546 -0.057968 1.000000 0.732198 0.026406 0.321841 0.019117 0.685021
OIL_P_VOL -0.039570 0.693475 0.598807 -0.029262 0.732198 1.000000 0.036359 0.300540 0.043388 0.525224 OIL_RENTS_R
AT 0.110641 0.074149 0.180391 -0.333354 0.026406 0.036359 1.000000 0.005183 0.041494 -0.007123 RESERVES_RAT_EXDEBT -0.057833 0.411820 0.307128 -0.179517 0.321841 0.300540 0.005183 1.000000 -0.030442 0.238075
RESID 0.044964 -0.006555 0.064796 -0.070232 0.019117 0.043388 0.041494 -0.030442 1.000000 -0.046115
VAR01 0.110389 0.418925 0.352025 0.061081 0.685021 0.525224 -0.007123 0.238075 -0.046115 1.000000
89
Annex III: Models PP5, PP8 and PP9: results
PP5
Variable Coefficient Std. Error
ACTOTAL -0.008400 0.015847
DEP_INT 3.00E-05 4.80E-05
DEXTDEBT _RATEX 0.000181 0.000279
DGDP_CAP 5.99E-05 1.97E-05
DGDP -1.41E-13 2.24E-13
DINTERACT10 0.106089 0.185558
DINTERACT3 0.000461 0.015220
DOIL_P -0.003191 0.002777
DOM_CRED_RAT -0.002812 0.002804
DPOLITY2 0.011091 0.008621
DRESERVES_RAT _EXDEBT 0.000538 0.000257
EXPORT_RAT 0.004297 0.002392
EXTDEBT_RATGNI 0.002306 0.000881
FDI_IN_RAT -0.004894 0.004157
GDP_CAP_GROWTH 0.000996 0.001781
GDP_GROWTH 0.006812 0.007402
INFLATION 0.000815 0.000410
INFLATION_DUMMY -0.042517 0.032717
INT_RATE_SPREAD 0.002795 0.002045
INTERACT1 -0.071133 0.049784
INTERACT2 0.000800 0.000853
INTERACT6 0.003837 0.004670
INTERACT7 0.044153 0.050987
KAOPEN 0.020164 0.016737
OIL_P_VOL -0.005529 0.012482
OIL_RENTS_RAT 0.006184 0.002783
SWF_DUMMY 0.101317 0.155034
WARDUMMY -0.029827 0.043870
C 0.004156 0.089267
ACTOTAL(-1) 0.008860 0.011246
DEP_INT(-1) 0.000138 5.72E-05
DEXTDEBT_RATEX(-1) 0.000225 0.000206
DGDP_CAP(-1) -1.91E-05 1.66E-05
DGDP(-1) 6.54E-14 2.34E-13
DINTERACT10(-1) 0.061337 0.105490
DINTERACT3(-1) -0.004295 0.007128
DOIL_P(-1) 0.000489 0.002619
DOM_CRED_RAT(-1) 0.002135 0.001872
DPOLITY2(-1) -0.003873 0.009044
DRESERVES_RAT_EXDEBT(-1) 0.000276 0.000341
EXPORT_RAT(-1) -0.002788 0.002051
EXTDEBT_RATGNI(-1) -0.001678 0.000766
FDI_IN_RAT(-1) 0.003091 0.005386
GDP_CAP_GROWTH(-1) 0.002631 0.001609
GDP_GROWTH(-1) 0.000190 0.006982
INFLATION(-1) -0.000108 0.000289
INFLATION_DUMMY(-1) 0.035776 0.031452
INT_RATE_SPREAD(-1) -0.002355 0.001165
INTERACT1(-1) 0.003990 0.041900
INTERACT2(-1) -0.000322 0.000672
90
INTERACT6(-1) -0.003549 0.004008
INTERACT7(-1) 0.047290 0.057162
KAOPEN(-1) 0.011685 0.014400
OIL_P_VOL(-1) -0.009107 0.007938
OIL_RENTS_RAT(-1) -0.002188 0.002007
SWF_DUMMY(-1) 0.166030 0.133649
WARDUMMY(-1) 0.002886 0.041081
ACTOTAL(1) -0.007619 0.013260
DEP_INT(1) -3.89E-05 4.42E-05
DEXTDEBT_RATEX(1) 0.000178 0.000337
DGDP_CAP(1) -5.82E-06 2.28E-05
DGDP(1) -9.70E-14 2.41E-13
DINTERACT10(1) 0.222238 0.105182
DINTERACT3(1) 0.007489 0.018274
DOIL_P(1) 0.002360 0.002932
DOM_CRED_RAT(1) -0.000876 0.002276
DPOLITY2(1) -0.002699 0.011027
DRESERVES_RAT_EXDEBT(1) -0.000129 0.000515
EXPORT_RAT(1) 0.000319 0.002226
EXTDEBT_RATGNI(1) -0.000385 0.000722
FDI_IN_RAT(1) -0.003052 0.004898
GDP_CAP_GROWTH(1) 0.001385 0.001790
GDP_GROWTH(1) 0.008366 0.006884
INFLATION(1) -0.000626 0.000581
INFLATION_DUMMY(1) 0.013519 0.037419
INT_RATE_SPREAD(1) -0.001949 0.002102
INTERACT1(1) -0.002932 0.023122
INTERACT2(1) 0.000116 0.000403
INTERACT6(1) 0.000937 0.002038
INTERACT7(1) -0.058380 0.031610
KAOPEN(1) -0.006722 0.013171
OIL_P_VOL(1) 0.002264 0.003502
OIL_RENTS_RAT(1) -0.004125 0.002396
SWF_DUMMY(1) -0.050029 0.142465
WARDUMMY(1) 0.043685 0.039892
Fixed cross-sectional effects
R² 84,13%
Adjusted R² 67,75%
Number of observations 192
Number of cross-sections 14
Number of periods 28
91
PP8
Variable Coefficient Std. Error
ACTOTAL -0.000669 0.003083
DEP_INT 8.76E-06 3.66E-05
DEXTDEBT_RATEX 0.000233 0.000226
DGDP -3.02E-14 1.80E-13
DGDP_CAP 3.95E-05 1.70E-05
DINTERACT10 -0.060174 0.134179
DINTERACT3 0.014429 0.016008
DOIL_P 0.000716 0.001787
DOM_CRED_RAT -0.001640 0.000591
DPOLITY2 0.006020 0.007322
DRESERVES_RAT_EXDEBT 0.000498 0.000139
EXPORT_RAT 0.005450 0.001427
EXTDEBT_RATGNI 0.001673 0.000556
FDI_IN_RAT 0.004134 0.003364
GDP_CAP_GROWTH 0.004763 0.001322
GDP_GROWTH -0.001687 0.005909
INFLATION 0.000709 0.000197
INFLATION_DUMMY -0.023112 0.022721
INT_RATE_SPREAD 0.000981 0.000854
INTERACT1 -0.054026 0.208893
INTERACT1_2(-1) 0.658407 1.077974
INTERACT1_2 -0.389797 1.317695
INTERACT1_2(1) -0.112569 0.595153
INTERACT1_3(-1) -0.408112 1.007688
INTERACT1_3 -0.034838 1.294218
INTERACT1_3(1) 0.382711 1.063220
INTERACT1_5(-1) -0.114024 0.211965
INTERACT1_5 0.280777 0.291259
INTERACT1_5(1) -0.268397 0.538062
INTERACT2 -0.000981 0.005269
INTERACT2_2(-1) -0.019683 0.011493
INTERACT2_2 -0.000664 0.014122
INTERACT2_2(1) 0.000800 0.005710
INTERACT2_3(-1) 0.010155 0.011019
INTERACT2_3 0.011815 0.015990
INTERACT2_3(1) -0.002830 0.008323
INTERACT2_5(-1) 0.007428 0.005310
INTERACT2_5 -0.007919 0.003296
INTERACT2_5(1) 0.000882 0.006263
INTERACT6 -0.001330 0.020469
INTERACT6_2(-1) 0.013532 0.108913
INTERACT6_2 0.102034 0.124852
INTERACT6_2(1) 0.045355 0.060888
INTERACT6_3(-1) -0.030461 0.104542
INTERACT6_3 -0.068976 0.126596
INTERACT6_3(1) -0.096729 0.107505
INTERACT6_5(-1) 0.008995 0.020186
INTERACT6_5 -0.000419 0.029859
INTERACT6_5(1) 0.049622 0.057038
INTERACT7 0.230189 0.058032
KAOPEN 0.005167 0.005705
OIL_P_VOL 0.010682 0.008375
OIL_P_VOL_2Y(-1) 0.018230 0.022874
OIL_P_VOL_2Y 0.031103 0.028853
OIL_P_VOL_2Y(1) 0.010180 0.014123
92
OIL_P_VOL_3Y(-1) -0.022094 0.022129
OIL_P_VOL_3Y -0.042989 0.034566
OIL_P_VOL_3Y(1) -0.030734 0.027507
OIL_P_VOL_5Y(-1) -0.007284 0.006915
OIL_P_VOL_5Y 0.020143 0.011093
OIL_P_VOL_5Y(1) 0.018252 0.020639
OIL_RENTS_RAT 0.003496 0.001248
C 0.047240 0.046902
DEP_INT(-1) 3.47E-05 5.15E-05
DGDP_CAP(-1) -1.49E-05 1.16E-05
DRESERVES_RAT_EXDEBT(-1) 0.000379 0.000120
DINTERACT10(1) 0.620706 0.190335
DOIL_P(1) -0.001113 0.002194
INFLATION(1) -0.000889 0.000195
INTERACT7(1) -0.139288 0.050199
OIL_RENTS_RAT(1) -0.002864 0.001354
EXPORT_RAT(-1) -0.005223 0.001444
EXTDEBT_RATGNI(-1) -0.001487 0.000554
INT_RATE_SPREAD(-1) -0.000519 0.000518
SWF_DUMMY(-1) 0.173735 0.298825
Fixed Cross sectional Effects
R² 78,50%
adjusted R² 66,26%
total panel (unbalanced) observations 205
Cross-sections included 14
periods included 28
PP9
Variable Coefficient Std. Error
ACTOTAL -0.006795 0.004277
DGDP_CAP 2.59E-05 1.22E-05
DOM_CRED_RAT -0.001558 0.000693
DPOLITY2 0.007248 0.006443
DRESERVES_RAT_EXDEBT 0.000202 9.68E-05
EXPORT_RAT 0.006301 0.001100
EXTDEBT_RATGNI 0.001783 0.000398
GDP_CAP_GROWTH 0.003967 0.001180
INFLATION 0.000472 0.000170
INT_RATE_SPREAD 0.001977 0.001091
INTERACT1_2(-1) 0.100511 0.025453
INTERACT1_2(1) 0.054680 0.027424
INTERACT1_5(1) -0.071827 0.025543
INTERACT2 -0.000905 0.000427
INTERACT2_2(-1) -0.003083 0.000693
INTERACT2_3 0.001269 0.000406
INTERACT2_5(-1) 0.002180 0.000502
INTERACT6_2 0.013931 0.005712
INTERACT6_3(-1) -0.008821 0.002634
INTERACT6_3 -0.013907 0.005092
INTERACT6_3(1) -0.016019 0.004781
INTERACT6_5(1) 0.017785 0.004859
INTERACT7 0.097111 0.023549
93
KAOPEN 0.034349 0.007864
OIL_P_VOL 0.005203 0.003198
OIL_P_VOL_3Y(-1) -0.009486 0.004468
OIL_P_VOL_3Y -0.014895 0.007389
OIL_P_VOL_5Y 0.016103 0.004807
OIL_RENTS_RAT 0.004061 0.000941
C 0.041923 0.043652
DGDP_CAP(-1) -1.79E-05 9.59E-06
EXPORT_RAT(-1) -0.004318 0.001077
EXTDEBT_RATGNI(-1) -0.001597 0.000418
INT_RATE_SPREAD(-1) -0.001665 0.000497
DINTERACT10(1) 0.212260 0.071095
INFLATION(1) -0.000496 0.000237
INTERACT7(1) -0.071634 0.021291
OIL_RENTS_RAT(1) -0.005352 0.001207
WARDUMMY -0.014728 0.023343
SWF_DUMMY 0.041549 0.048634
OIL_P_VOL_2Y 0.003069 0.007275
Fixed Cross sectional Effects
R² 80,16%
adjusted R² 73,41%
total panel (unbalanced) observations 206
Cross-sections included 14
periods included 28
