Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
Determinants of the Status of an International Financial
Centre
Imad Moosa, Larry Li and Riley Jiang
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.
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 crosssectional 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 sensitivity of the estimated coefficients with respect to
____________
School of Economics, Finance and Marketing, 445 Swanston Street, Melbourne 3000, Victoria,
Australia. Email: imad.moosa@rmit.edu.au
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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 Sala-I-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 (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).
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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 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 (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‖.
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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
political-economic 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‖.
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 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
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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.
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.
EBA is based on a linear regression of the form
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
n
m
i 1
i 1
y     i X i Q    i Zi 
(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-IMartin (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.
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
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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).
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 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.
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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 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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Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
Table 1: Estimates of  min and  max (All Regressions)
 min
Variable
Energy usage
0.01
High-tech exports
2.10
Inflation
5.52
Internet usage
-0.27
Market capitalisation
0.87
Real interest rate
2.32
Urban population
2.14
Corporate tax rate
5.42
Corruption perceptions index
30.91
Global competitiveness index
291.12
Global credit rating
7.03
Occupancy cost
1.17
Quality of ground transport
network
32.81
Size of government
32.69
Legal system and property rights 10.09
Sound money
29.67
Freedom to trade internationally
25.22
Regulation
Credit market regulation
Labour market regulation
Business regulation
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
Significant
 (%)
0.5
24.7
0.5
19.5
56.8
0
0
92.1
7.4
100
18.9
100
-2.89
-0.23
-3.74
-0.58
-0.92
4.2
6.8
17.4
0
0
-4.17
-3.36
-1.37
-2.99
27.9
85.8
9.5
43.7
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
Table 2: Estimates of  min and  max (Regressions with the highest 60% R 2 )
 min
Variable
Energy usage
High-tech exports
0.008
2.105
t
0.841
2.790
Inflation
Internet usage
Market capitalisation
Real interest rate
Urban population
Corporate tax rate
5.516
-0.270
0.834
2.319
2.136
5.416
0.670
-0.175
2.725
1.236
1.802
3.697
Corruption perceptions index
Global competitiveness index
Global credit rating
Occupancy cost
30.905
291.122
7.032
1.157
1.780
5.976
2.868
4.380
Quality of ground transport network
Size of government
32.813
32.687
1.617
3.000
Legal system and property rights
10.089
0.329
Sound money
29.673
1.528
Freedom to trade internationally
25.216
1.528
Regulation
9.551
0.285
Credit market regulation
2.298
0.086
Labour market regulation
59.369
3.669
Business regulation
3.057
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
Significant
 (%)
0.9
41.2
-1.978
-4.343
0.593
-1.601
-1.051
1.615
0.9
32.5
49.1
0
0
90.4
-3.139
2.092
-0.434
2.235
12.3
100
22.8
100
-2.885
-0.227
7.0
11.4
-3.735
28.9
-0.577
0
-0.892
0
-4.173
37.7
-3.363
78.1
-1.368
15.8
-2.985
49.1
Proceedings of Global Business Research Conference
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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
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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
Proceedings of Global Business Research Conference
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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
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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
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
and database, and
World Bank estimates
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
Fraser Institute
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
total investment, and top marginal tax rate.
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, reliability of police, and business
costs of crime.
Fraser Institute
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
Fraser Institute
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
regulations, mandated cost of worker dismissal,
and conscription.
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