1
Determinants of the Status of an International Financial Centre
Imad Moosa, Larry Li and Riley Jiang
School of Economics, Finance and Marketing
445 Swanston Street
Melbourne 3000, Victoria
Australia
Abstract
To identify the determinants of the status of an international financial centre we consider 24
potential factors and the technique of extreme bounds analysis (EBA). By identifying three
free variables we apply EBA to the remaining 21 variables, running a total of 3990
regressions, 190 regressions for each of the variables of interest. The results of conventional
EBA reveal two robust variables only, and the same finding is obtained by using restricted
EBA. The estimates of the fraction of the cumulative distribution function falling on one side
of zero reveal eight important determinants of the status of an international financial centre,
including the two robust variables identified by using EBA.
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Introduction
An international financial centre is characterised by agglomeration of financial institutions
providing financial services on an international level. According to Zhao et al. (2004), an
international financial centre “refers to a global city that provides a full spectrum of high-end
services, as financial services cannot be independent of other specialized services”. In March
2013, data were released on the ranking of international financial centres as measured by the
global financial centre index (Z/Yen, 2013). This index (GFCI) provides profiles, ratings and
rankings of financial centres from around the world. The index is calculated based on
“external measures” and responses to an online survey completed by international financial
services professionals. Respondents are asked to rate those centres that they are familiar with
and to answer a number of questions pertaining to their perceptions of competitiveness.
The GFCI report was first published by the Z/Yen Group in March 2007 and has
subsequently been updated every six months. According to the latest GFCI report (March
2013), London, New York, Hong Kong and Singapore remain the top four centres—it seems
that London has maintained its number one rank despite the LIBOR scandal. At the bottom,
we find Reykjavik, Budapest and Athens, with Athens being 68 points adrift of Budapest.
Figure 1 displays the top ten (in descending order) and bottom ten (in ascending order)
financial centres measured in terms of the GFCI. A question arises as to what makes London
the “best” and Athens the “worst” international financial centre.
The objective of this study is to identify the factors that determine the status of an
international financial centre as measured by the GFCI. Specifically this is a cross-sectional
study whereby the GFCI is modelled in terms of economic, financial and regulatory factors
(24 of them). In a cross-sectional study like this, we are certain to encounter the problem of
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sensitivity of the estimated coefficients with respect to model specification because no single
theoretical model can be used to identify an explicit set of explanatory variables to be
included in any empirical model. The consequence is that researchers find it tantalising to try
various combinations of the explanatory variables and report the ones they like, typically the
ones that produce “good” results and/or confirm pre-conceived beliefs.
This study is based on the technique of extreme bounds analysis (EBA), as suggested by
Leamer (1983, 1985) and the extensions proposed by Granger and Uhlig (1991) and by SalaI-Martin (1997). By using this technique, we test the robustness of coefficient estimates to
changes in the conditioning set of information (represented by the explanatory variables).
EBA allows us to avoid the problem of selecting the combinations of explanatory variables to
appear in the “optimal model”. Furthermore, the procedure circumvents the problems of data
mining and bias in the reporting of results. We will report the results obtained by using
conventional EBA, restricted EBA and the cumulative distribution function (CDF).
Literature Review
The literature on international financial centres typically deals with issues pertaining to the
factors that can be used to identify a financial centre, why there is spatial agglomeration of
financial activity, and why financial services are spatially concentrated in selected locations.
The basic underlying question is why financial services remain embedded within
international financial centres when technology would seem to facilitate deconcenration and
geographical dispersion (Faulconbridge, 2004). This question falls under the general issue of
enterprise location (where and why firms place specific activities in particular areas), which
is a key area of interest in both international business research (for example, Alcacer and
Chung, 2007; Nachum and Wymbs, 2005; Porter, 2001) and economic geography research
4
(for example, Krugman, 1991; Lorenzen and Mudambi, 2013; Markusen, 1996). Some
scholars express the view that despite rising interest in location, our current understanding of
the geographic aspect of multinational enterprise (MNE) behaviour remains underdeveloped
(McCann, 2011; Ricart et al., 2004).
Many scholars believe that the economic role of space has become increasingly insignificant,
in the sense that location of the enterprise does not matter in the age of electronic
communications and electronic money (for example, Castellas, 1989; O’Brien, 1992;
Cairncross, 1997; Ohame, 1990, 1995a, 1995b; Kobrin, 1997). However, there are those who
believe the opposite—that spatial proximity is still critical because not all types of
information can be transmitted over distance with constant costs (for example, Berry et al.,
1997, Sassen, 1995; Short and Kim, 1999). According to Cantwell (2009), the revival of
interest in the locational concentration or dispersion of activity can be attributed in large part
to the paradox between the apparent death of distance, as pointed out by Cairncross (1997),
and the renewed significance of local clusters that are poles of attraction to innovation and
entrepreneurship, as stressed by Breschi and Malerba (2001). Cantwell attributes the
resurgence of interest in issues of the location of international business to the pioneering
work of Dunning (1998).
Goerzen et al. (2013) combine the concept of location derived by economic geographers with
theories of the multinational enterprise (MNE) and the liability of foreignness developed by
international business scholars to examine the factors that propel MNEs towards, or away
from, “global cities”. They argue that three distinctive characteristics of global cities (global
interconnectedness, cosmopolitanism, and abundance of advanced producer services) help
MNEs overcome the costs of doing business abroad. Based on a multilevel multinomial
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model, their analysis of a large sample of MNE location decisions suggests not only that
MNEs have a strong propensity to locate within global cities, but also that these choices are
associated with a nuanced interplay of firm (and subsidiary-level) factors, including
investment motives, proprietary capabilities and business strategy.
The development of international financial centres offers a good example to illustrate the
ongoing significance of enterprise location. A characteristic of the literature on international
financial centres is that there is no consensus on a single definition of an international
financial centre, or even just a financial centre. A financial centre is defined by Reed (1998)
as a “place where providers of and customers for financial services meet to transact
business”. According to Lai (2006) a financial centre is a “conglomeration of financial and
service enterprises and corporate headquarters, particularly foreign ones”. Jao (1997) defines
an international financial centre as a “place in which there is a high concentration of banks
and other financial institutions, and in which a comprehensive set of financial markets are
allowed to exist and develop, so that financial activities and transactions can be effectuated
more efficiently than at any other locality”. Fakitesi (2009) lists some of the characteristics of
an international financial centre as follows: (i) it is conducive to the conduct of international
financial business profitably, easily and efficiently; (ii) there is abundance of skilled
management and intellectual talent covering business, finance and interdependent services;
(iii) it offers deep liquid and sophisticated capital markets and world competitive tax and
regulatory regimes with foreign investment and offshore business flow; (iv) it can add
significant value to financial services through a workforce that can respond promptly and in
an innovative manner; (v) it offers high quality telecommunications and IT capacity as well
as well educated, multilingual workforce; (vi) all facets of financial services can be located
efficiently; and (vii) it provides a convivial and alluring environment for business. Park
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(2011) distinguishes between an international financial centre and a domestic one on the
following grounds: (i) international centres deal in various major currencies of the world, not
just the currency of the country where a centre is located; (ii) most of the financial
transactions conducted in foreign currencies in international centres are generally free of
taxes and exchange controls; and (iii) international financial centres provide various financial
services to both resident and non-resident clients.
Several studies have been conducted on what makes a particular city an international
financial centre. By using London as an example, Thrift (1994) concludes that international
financial centres have a particular set of locational determinants, arguing that local
characteristics and localised information jointly define the advantages of a given location as a
financial centre. Wojcik (2009) concludes on the basis of previous research that “an
important part of information used in financial markets is not easily transferable across space,
resulting in the significance of local financial relations and spatial concentration of financial
firms”. Martin (1999) asserts that the friction of information flows across physical distance
affects the location of financial activities, as information collection and verification are
particularly crucial for financial business. Leyshon (1995, 1997) emphasises the politicaleconomic approach to the formation of geographies of money and finance, arguing that a
wide range of social factors might contribute to the survival and success of international
financial centres in particular places. Zhao et al. (2004) attempt to explain why foreign
financial services are spatially concentrated in a particular city to form an international
financial centre. By examining various forces behind the formation of a financial centre, they
argue that “information problems have created the necessity of the geographic agglomeration
of financial activities in the source of information even in the era when financial markets
have worked through sophisticated telecommunication networks”.
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Kerr (1965), Park and Essayyad (1989) and Porteous (1999) suggest that several measures
can be used to identify specific cities that function as financial centres. These measures
include employment in the financial sector relative to total employment, assets of financial
institutions, the proportion of cheques cashed, the turnover value of stock exchange, the
volume of communications (particularly express mail and telecommunications), and the
presence of foreign banks and head offices of large multinational non-financial corporations.
Kayral et al. (2012) use a logistic regression and the GFIC (as the dependent variable) to find
positive relations with the efficiency and strength of the legal rights, the variability of the
labour force participation rate, and the underlying centres being in the top 20% group.
Porteous (1995) has proposed a theoretical framework to find out why financial activities
tend to agglomerate in one particular location rather than another. His framework emphasises
the key role of information flow (with respect to information accessibility and reliability) in
influencing the location of financial activities. He focuses on two information concepts for
their effect on the development of a financial centre: information hinterland and information
asymmetry. The information hinterland is defined as the region for which a particular core
city, acting as that regional centre, provides the best access point for the profitable
exploitation of valuable information flows. The effect of information asymmetry is to push
financial firms closer to an information source in order to find and interpret non-standardised
information that a financial firm can use to make profit.
Some studies deal with the current and expected (or hoped for) status of particular financial
centres. Sarigul (2012) uses SWOT analysis to show that Istanbul has significant strengths
to become an international financial centre. In particular he stresses that Istanbul is “well with
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its geographical position, young and increasingly educated labour force, no numerical
shortage in the labour market, high quality and skilled labour force in the field of banking, two
modern airports that are well connected to the financial business centres and other major global
cities”. Roberts (2008) attributes the status of London to the developments in Britain in the
scope of world economics, explaining how London appeared as the first financial centre and
how it kept its place by adapting to new economic conditions. For example, it was at one time
thought that the launch of the euro and the location of the European Central Bank in
Frankfurt could form a threat to the status of London. This, however, has not happened
(Faulconbridge, 2004). Schenk (2002) attributes the status of Hong Kong as an international
financial centre to the transparency and effectiveness of financial services. Seade (2009)
examines East Asian financial centres and concludes that Hong Kong, Tokyo, Shanghai and
Singapore are about to catch up with their most powerful competitors, London and New
York. Giap (2009) studies the progress of Singapore and appraises its rise in Asia. According
to Shirai (2009) and Kawai (2009), although Japan is one of the most economically powerful
countries in the world, Tokyo remains behind New York and London because of “its
unsatisfactory trading volume”. Mingqi (2009) and Bhattacharya (2010) attribute the status of
Shanghai to economic reform in China.
The literature, therefore, does not provide a limited set of the factors that determine the status
of an international financial centre. A large number of potential explanatory variables must be
considered in the absence of a theoretical model and the availability of a diverse set of
hypotheses emphasising different factors. This is why a straightforward cross-sectional
analysis is likely to produce subjective results that exhibit confirmation and publication bias.
This is also why the methodology described in the following section is chosen to conduct the
analysis.
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Methodology
Cross-sectional studies are typically based on a cross-sectional regression of the form:
n
y   0    i xi  
(1)
i 1
where the dependent variable y is explained in terms of n explanatory variables, x i ’s. These
studies invariably report a sample of regressions encompassing various combinations of the
explanatory variables. The reported regressions are chosen for convenience because they
vindicate the researcher’s pre-conceived notions. This problem arises because the theory is
not adequately explicit about what variables should appear in the “true” model. For example,
the following situation is often encountered: x1 may be significant when the regression
includes x 2 and x3 , but not when x 4 is included. So, which combination of all available x i ’s
do we choose?
Extreme bounds analysis can be used to find out if there is robustness in the determinants of
the dependent variable. Hussain and Brookins (2001) argue that the usual practice of
reporting a preferred model with its diagnostic tests need not be sufficient to convey the
degree of reliability of the determinants (explanatory variables). By calculating upper and
lower bounds for the parameter of interest from all possible combinations of potential
explanatory variables, it is possible to assess and report the sensitivity of the estimated
coefficients to specification changes. The relation between the dependent variable and a
given explanatory variable is considered to be robust if the estimated coefficient on that
variable remains statistically significant without a change of sign when the set of explanatory
variables are changed.
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EBA is based on a linear regression of the form
n
m
i 1
i 1
y      i X i Q    i Z i 
(2)
where X i is an explanatory variable that is always included in the regression because its
importance has been established by previous studies (and because it makes sense theoretically
or even intuitively), Q is the variable whose robustness is under consideration, and Z i is a
potentially important variable. The X i ’s are called “free variables”, whereas Q is called the
“variable of interest”. According to Sala-I-Martin (1997), the free variables “need to be
‘good’ a priori”, in the sense that “they have to be widely used in the literature” and that they
are “somewhat ‘robust’ in the sense that they systematically seem to matter in all regressions
in the previous literature”. Otherwise, the choice of the free variables may be justified on the
basis of theoretical and intuitive considerations.
The procedure involves varying the set of Z variables included in the regression to find the
widest range of coefficients on the variable of interest,  , that standard tests of significance
do not reject. If the extreme values remain significant and of the same sign, then one can infer
that the result (and hence, the variable of interest) is “robust”. Otherwise, the variable is
“fragile”. In other words, for a variable of interest to be robust,  min and  max must be
significant and of the same sign. This is equivalent to reporting  j  2 and  j  2 ,
where  is the standard deviation of  for j  1,  m , where m is the number of estimated
regressions for each variable of interest. In this case if the minimum value of  j  2 is
negative and the maximum value of  j  2 is positive, the variable of interest is not robust.
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A typical finding of studies using these criteria to determine whether a variable is robust or
fragile is that very few (or no) variables are robust (for example, Levine and Renelt, 1992).
While the results can be interpreted to imply that the variable under consideration are not
robust, Sala-I-Martin suggests that the test is too strong for any variable to pass. He argues
that “if the distribution of  has some positive and some negative support, then one is bound
to find one regression for which the estimated coefficient changes signs if enough regressions
are run”. It is for this reason that a number of attempts have been made to refine the
robustness criteria in order to reduce the probability of obtaining “unreasonable” extreme
bounds. As a result, a “reasonable” EBA test has been developed to estimate the extreme
bounds on the coefficient of interest by eliminating models with poor goodness of fit as
measured by R 2 . Granger and Uhling (1990) proposed this refinement of EBA by imposing a
condition on the level of goodness of fit, such that all models with low R 2 are irrelevant for
the calculation of extreme bounds. This criterion is represented by
2
2
R2  [(1   ) Rmax
 Rmin
]
(3)
where 0    1 . This modification results in the so-called “restricted extreme bounds
analysis”.
Sala-I-Martin (1997) suggests further refinement by departing from the zero-one labelling as
robust and fragile. Instead he assigns a level of confidence to each of the variables by looking
at the entire distribution of  . Specifically this procedure is based on the fraction of the
density function lying on each side of zero, CDF(0). If 95% of the density function lies to the
right of zero, the variable is considered robust. The cumulative distribution function is
calculated from the weighted average of the point estimates of  where the weights are the
integrated likelihoods (for details, see Sala-I-Martin, 1997).
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Data and Empirical Results
The data used in this study, covering 53 international financial centres, were obtained from a
variety of independent and reputable institutions including the World Bank, the World
Economic Forum, and the Fraser Institute. While the GFCI is reported for more than 70
financial centres, only 53 are used because data on (at least some) of the explanatory
variables are not available for other centres. A total of 24 potential explanatory variables are
used—the variables, their definitions and sources can be found in the appendix.
The implementation of the EBA requires each regression to contain one or more free
variables, X, that are always included in the model, the variable of interest, Q and the Z
variables. The capital access index, human development index and economic freedom index
are used as free variables because they summarise (in one number each) multiple dimensions
of the financial, economic and social environment. The constituent components of these
variables include factors that are intuitively accepted as being important for the ability of a
city to attract international financial firms, including the macroeconomic environment,
institutional environment, equity market development, bond market development, access to
knowledge, standard of living, business freedom, trade freedom, financial freedom, freedom
from corruption, and others (see appendix for a complete listing). In addition to the free
variable and the variable of interest, two Z variables from a remaining list of 21 are included
in each regression. This exercise involves running a total of 3990 regressions, 190 regressions
for each of the variables of interest with two Z variables and three X variables.
Table 1 reports the estimated maximum and minimum values of the coefficients on the
variables of interest. Only two variables turn out to be robust: global competitiveness index
and occupancy costs. The coefficients on these two variables are significantly positive. While
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a positive coefficient on the global competitiveness index sounds intuitive, a positive
coefficient on occupancy costs may sound strange, because it means that financial institutions
prefer to operate in centres where the costs of running offices is high. However, there is
nothing strange about this result. Occupancy costs are high in highly-ranked international
financial centres because of the high demand for office space. What is more important about
these results, as far as this study is concerned, is that a large number of variables that should
be robust, at least intuitively, turn out to be fragile. These include the corporate tax rate,
global credit rating, size of government and legal system and property right. The size of
government may provide some indication of the tendency to introduce restrictive regulation.
The reason why only two variables turn out to be robust may be that the extreme bounds test
is too difficult to pass as argued by Sala-I-Martin (1997). To consider this possibility, we use
restricted EBA as suggested by Granger and Uhling (1990). For this purpose we consider the
regressions with the highest 80% and 60% R 2 . Table 2 reports the results of restricted EBA
with the highest 60% R 2 —these results show no change, as the only two robust variables are
still global competitiveness and occupancy costs. The results obtained from regressions with
the highest 80% R 2 are qualitatively similar.
The other alternative to conventional EBA is to use the cumulative distribution function
approach suggested by Sala-I-Martin. The results are reported in Table 3 and Figure 2. We
can see now that eight explanatory variables have a CDF(0) of more than 0.95, which means
that these variables are important for determining the status of an international financial
centre as measured by the GFCI. These variables are high-tech exports, internet usage,
corporate tax rate, global competitiveness, global credit rating, occupancy cost, size of
government, and the legal system and property rights. These results are more reasonable and
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realistic than those obtained from either conventional or restricted extreme bounds analysis.
A policy implication of these results is that if a country aims to establish a highly ranked
international financial centre, it must pay particular attention to these eight factors as well as
the three free variables—that is, capital access, human development and economic freedom.
Conclusion
Countries in all parts of the world have been striving to establish one of their cities as a
highly-ranked international financial centre that attracts financial institutions from various
parts of the world. To adopt policies that are conducive to the achievement of these objectives
the first step is to identify the factors that determine the status of an international financial
centre. To that end we considered 24 possible determinants while trying to avoid the problem
arising from subjective model selection and data mining by using extreme bounds analysis.
By identifying three free variables that have been established as important determinants, the
results of EBA revealed that only two of the 21 variables of interest turned out to be
important: global competitiveness and occupancy cost. The results did not change by using
restricted EBA where inference is based on the regressions with the highest 60% R 2 .
However, by estimating the fraction of cumulative distribution function falling on one side of
zero, we identified eight robust variables that are important determinants of the status of
international financial centres.
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Appendix: Variables, Definitions and Sources
Variable
Description
Source
Capital access index
An index that is assessed across seven components of
each country’s economic, financial and social
infrastructures. These components are macroeconomic
environment, institutional environment, financial and
banking institutions, equity market development, bond
market development, alternative sources of capital and
international funding.
Milken Institute
Human development index
A composite index measuring average achievement in
three basic dimensions of human development: a long
and healthy life, access to knowledge and a decent
standard of living.
United Nations
Development
Programme
Economic Freedom index
An index that is built upon analysis of ten components
of economic freedom, some of which are themselves
composites of additional quantifiable measures. The ten
component scores are equally weighted and averaged to
get an overall economic freedom score for each
country. The components are business freedom, trade
freedom, fiscal freedom, government size, monetary
freedom, investment freedom, financial freedom,
property rights, freedom from corruption and labour
freedom
The Heritage
Foundation and Wall
Street Journal
Energy usage
The use of primary energy before transformation to
other end-use fuels, which is equal to indigenous
production plus imports and stock changes minus
exports and fuels supplied to ships and aircraft engaged
in international transport. It is measured as kg of oil
equivalent per capita.
International Energy
Agency
High-tech exports
High-technology exports are products with high R&D
intensity, such as in aerospace, computers,
pharmaceuticals, scientific instruments and electrical
machinery. It is measured as a percentage of
manufactured exports.
United Nations,
Comrade database
Inflation
Percentage change in the consumer price index
International Monetary
Fund (International
Financial Statistics)
Internet usage
The number of Internet users per 100 people. Internet
users are people with access to the worldwide network.
International
Telecommunication
Union, World
Telecommunication/IC
T Development Report
and database, and
World Bank estimates
16
Market capitalisation
Market capitalization (also known as market value) is
the share price times the number of shares outstanding.
Listed domestic companies are the domestically
incorporated companies listed on the country’s stock
exchanges at the end of the year. Listed companies do
not include investment companies, mutual funds or
other collective investment vehicles. It is measured as a
percentage of GDP.
Standard & Poor’s,
Global Stock Markets
Fact book and
supplementary S&P
data.
Real interest rate
The lending interest rate adjusted for inflation as
measured by the GDP deflator.
International Monetary
Fund, (International
Financial Statistics)
Urban population
Urban population refers to people living in urban areas
as defined by national statistical offices. It is calculated
using World Bank population estimates and urban ratios
from the United Nations World Urbanization Prospects.
It is measured as a percentage of total population.
United Nations, World
Urbanization Prospects
Corporate tax rate
The tax imposed on corporate earnings.
KPMG
Corruption perceptions index
The Transparency International’s corruptions
perceptions index is an aggregate indicator that ranks
countries in terms of the degree to which corruption is
perceived to exist among public officials and
politicians. It is a composite index drawing on
corruption-related data by a variety of independent and
reputable institutions.
Transparency
International
Global competitiveness
index
A comprehensive indicator that measures the
microeconomic and macroeconomic foundations of
national competitiveness.
World Economic Forum
Global credit rating
Rating of sovereign borrowers as determined by rating
agencies.
Fitch, Moody's and S&P
Occupancy cost
A measure of local office costs which comprises all
occupancy costs. It is measured on an annual basis in
U.S. dollar per square foot.
C.B. Richard Ellis
Quality of ground transport
network
An index that measures the degree to which national
ground transport network (buses, trains, trucks, taxis,
etc.) offer efficient transportation within a nation.
World Economic Forum
Size of government
An aggregate rating of government consumption to total
consumption, transfers and subsidies to GDP,
government enterprises and investment to total
investment, and top marginal tax rate.
Fraser Institute
Legal system and property
rights
An aggregate rating of judicial independence, impartial
courts, protection of property rights, military
interference in the rule of law and politics, integrity of
the legal system, legal enforcement of contracts,
regulatory restrictions on the sale of real property,
Fraser Institute
17
reliability of police, and business costs of crime.
Sound money
An aggregate rating of money growth (money supply
growth minus real GDP growth), standard deviation of
inflation (GDP deflator), inflation in most recent year
(CPI), and freedom to own foreign currency bank
accounts. It is a measure of the healthiness of a nation’s
“money”.
Fraser Institute
Freedom to trade
internationally
An aggregate rating of tariffs (revenue from trade taxes
as a percentage of trade sector, mean tariff rate,
standard deviation of tariff rates), regulatory trade
barriers (non-tariff trade barriers, compliance costs of
importing and exporting), black-market exchange rates
(percentage spread between the official and the
parallel/black market exchange rate), and controls of the
movement of capital and people (foreign ownership,
investment restrictions, capital controls, freedom of
foreigners to visit).
Fraser Institute
Regulation
An aggregate rating of credit market regulations
(ownership of banks, private sector credit, interest rate
controls/negative real interest rates), labour market
regulations (hiring regulations and minimum wage,
hiring and firing regulations, centralised collective
bargaining, hours regulations, mandated cost of worker
dismissal, conscription), and business regulations
(administrative requirements, bureaucracy costs,
starting a business, extra payments/bribes/favouritism,
licensing restrictions, cost of tax compliance).
Fraser Institute
Credit market regulation
An aggregate rating of ownership of banks (percentage
of bank deposits held in privately owned banks), private
sector credit (the extent to which government
borrowing crowds out private borrowing), and interest
rate controls/negative real interest rates (a measure of
credit-market controls and regulations).
Fraser Institute
Labour market regulation
An aggregate rating of hiring regulations and minimum
wage, hiring and firing regulations, centralised
collective bargaining, hours regulations, mandated cost
of worker dismissal, and conscription.
Fraser Institute
Business regulation
An aggregate rating of administrative requirements,
bureaucracy costs, starting a business, extra
payments/bribes/favouritism, licensing restrictions, and
cost of tax compliance.
Fraser Institute
18
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Table 1: Estimates of  min and  max (All Regressions)
Variable
 min
Energy usage
High-tech exports
Inflation
Internet usage
Market capitalisation
Real interest rate
Urban population
Corporate tax rate
Corruption perceptions index
Global competitiveness index
Global credit rating
Occupancy cost
Quality of ground transport network
Size of government
Legal system and property rights
Sound money
Freedom to trade internationally
Regulation
Credit market regulation
Labour market regulation
Business regulation
0.01
2.10
5.52
-0.27
0.87
2.32
2.14
5.42
30.91
291.12
7.03
1.17
32.81
32.69
10.09
29.67
25.22
9.55
2.30
59.37
3.06
t
0.84
2.79
0.67
-0.18
2.73
1.24
1.80
3.70
1.78
5.98
2.87
4.38
1.62
3.00
0.33
1.53
1.53
0.28
0.09
3.67
0.11
 max
-0.02
0.54
-17.17
-5.25
0.16
-3.59
-1.13
2.47
-39.59
83.72
-0.86
0.81
-49.69
-2.61
-71.43
-13.35
-23.98
-212.30
-74.86
-17.65
-78.13
t
-2.22
0.71
-1.98
-4.34
0.59
-1.60
-1.05
1.62
-3.14
2.09
-0.43
2.24
-2.89
-0.23
-3.74
-0.58
-0.92
-4.17
-3.36
-1.37
-2.99
Significant
 (%)
0.5
24.7
0.5
19.5
56.8
0
0
92.1
7.4
100
18.9
100
4.2
6.8
17.4
0
0
27.9
85.8
9.5
43.7
24
Table 2: Estimates of  min and  max (Regressions with the highest 60% R 2 )
Variable
Energy usage
High-tech exports
Inflation
Internet usage
Market capitalisation
Real interest rate
Urban population
Corporate tax rate
Corruption perceptions index
Global competitiveness index
Global credit rating
Occupancy cost
Quality of ground transport network
Size of government
Legal system and property rights
Sound money
Freedom to trade internationally
Regulation
Credit market regulation
Labour market regulation
Business regulation
 min
0.008
2.105
5.516
-0.270
0.834
2.319
2.136
5.416
30.905
291.122
7.032
1.157
32.813
32.687
10.089
29.673
25.216
9.551
2.298
59.369
3.057
t
0.841
2.790
0.670
-0.175
2.725
1.236
1.802
3.697
1.780
5.976
2.868
4.380
1.617
3.000
0.329
1.528
1.528
0.285
0.086
3.669
0.108
 max
-0.016
0.544
-17.170
-5.245
0.162
-3.593
-1.131
2.466
-39.590
83.724
-0.855
0.812
-49.687
-2.611
-71.432
-13.351
-21.036
-212.30
-74.857
-17.649
-77.410
t
-2.223
0.708
-1.978
-4.343
0.593
-1.601
-1.051
1.615
-3.139
2.092
-0.434
2.235
-2.885
-0.227
-3.735
-0.577
-0.892
-4.173
-3.363
-1.368
-2.985
Significant
 (%)
0.9
41.2
0.9
32.5
49.1
0
0
90.4
12.3
100
22.8
100
7.0
11.4
28.9
0
0
37.7
78.1
15.8
49.1
25
Table 3: Estimated CDF(0)
Variable
Energy usage
High-tech exports
Inflation
Internet usage
Market capitalisation
Real interest rate
Urban population
Corporate tax rate
Corruption perceptions index
Global competitiveness index
Global credit rating
Occupancy cost
Quality of ground transport network
Size of government
Legal system and property rights
Sound money
Freedom to trade internationally
CDF(0)
0.647
1.000
0.932
0.995
1.000
0.711
0.937
1.000
0.590
1.000
0.958
1.000
0.853
0.963
0.968
0.890
0.526
26
Figure 1: Top Ten and Bottom Ten Financial Centres Measured by the GFCI
Top Ten
820
800
780
760
740
720
700
680
660
640
Bottom Ten
700
600
500
400
300
200
100
0
27
Figure 2: Estimated CDF(0)
Occupancy cost
Global competitiveness index
Corporate tax rate
Market capitalisation
High-tech exports
Internet usage
Legal system and property rights
Size of government
Global credit rating
Urban population
Inflation
Sound money
Quality of ground transport network
Real interest rate
Energy usage
Corruption perceptions index
Freedom to trade internationally
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00