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What makes an International Financial Centre (IFC) Competitive? An empirical study of the determinants responsible for the competitiveness of an IFC

Naeem , Mian Muhammad Farooq (2023) What makes an International Financial Centre (IFC) Competitive? An empirical study of the determinants responsible for the competitiveness of an IFC. Doctoral thesis, Durham University.



International Financial Centres (IFCs) serve as focal points for implementing international agreements and other transactions between financial sectors located around the world. The competitiveness of an IFC depends on its function to provide easy access to the capital, stability in financial markets and a dynamic business eco-system. The purpose of conducting this study is to identify the most relevant determinants that significantly affect the Global Financial Centres Index (GFCI) ranking of the countries across the world. First published in 2007, the GFCI is considered as the primary source for ranking IFCs globally. GFCI is an index which ranks financial centres based on over 61,499 assessments of financial centres across the world provided by 10,252 respondents to an online questionnaire of GFCI (GFCI33, 2023). The collected date represents 153 key indices provided by sources including the World Bank, the Organisation of Economic Cooperation and Development, and the Economist Intelligence Unit. It utilises qualitative (online questionnaires) and quantitative (economic indices) dataset to publish reports biannually.
Through this paper, an attempt has been made to conduct an empirical study of the determinants responsible for the competitiveness of an IFC based on GFCI ranking. To facilitate this study, extensive data has been collected for 196 IFCs (unique financial jurisdictions) along with 238 key factors (determinants) over a period of fourteen years (2007 till 2020). In addition to revisiting some of the existing empirical studies on this subject, this dissertation attempts to further build on the existing empirical research and analyses the impact of unique key factors on the GFCI ranking through the application of a panel regression. From extensive set of variables, the study adopts 17 most relevant determinants (summarised below) by using a Decision Tree approach.
The variable of Business Regulations is constructed by using the Ease of doing business index source from the World Bank (GFCI 33). The variable of corporate taxes is constructed by the sum of tax bases and tax rates dataset source from KPMG (GFCI 33). Indexed sourced from Transparency International is used to construct the variable of Corruption Perception Index (GFCI 33). The variable of Credit Market Regulations is constructed by international consortium group by measuring the deposit based financing source from World Bank (GFCI 33). Government size, Property Rights and the Legal System, Reliable Money, Freedom to Trade Internationally Regulation, and Gender equality in legal rights are five broad categories used to construct Economic Freedom Overall Index Variable source from Fraser Institute (GFCI 33). The study adopts the variable of freedom of trade which is sourced from WTO constructed upon non-tariff barrier in exports and imports of a country (GFCI 33). The variable of Global Competitiveness Index is constructed by the macroeconomic and the micro/business aspects of competitiveness into a single index (GFCI 33). The data on volume of high tech exports is modelled and calculated as a function of foreign demand and of price competitiveness in order to construct variable of High Tech Exports source. The variable of inflation is constructed by using Consumer Price Index (CPI). The variable of Internet uses as a percentage of population is derived by dividing the number of Internet users by total population and multiplying by 100. The variable of Labour Market Regulations is constructed through using of the Rigidity of Employment Index. The variable of Legal System Property Rights is constructed by encompassing index of Legal verification and guarantee systems, fair legal rules, and formal compensation mechanism. The variable of quality of roads is constructed through collecting data on the transportation infrastructure and financial spending by using (IRI) International Roughness Index (GFCI 33). Spending, revenue, and employment are all ways to construct the variable of size of a government. An aggregate of money growth (money supply growth minus real GDP growth), standard deviation of inflation (GDP deflator), CPI inflation in most recent year, and freedom to hold foreign currency in bank accounts are used to construct the variable of the sound money index. The index is measured on a scale of 0 (worst) to 10 (best). The variable of percentage of Urban Population is constructed by Individuals living in urban areas as a percentage of total population. A long and solid life, being educated and have a respectable way of life are the three indicators to construct the variable of HDI.
The results of the Panel regression show that all the variables positively impact the GFCI ranking except business regulations, labour market regulation, legal system property rights and HDI.
This dissertation also establishes to arrange the IFCs in groups (Clusters) based on similar shared characteristics. This has been possible by adopting criteria of developing a centroid for each cluster against each determinant for a number of observations (Years). As a result, each cluster includes all those countries that are experiencing similar characteristics throughout the range of observations (years). By introducing the Elbow method of clustering, the study has identified five optimal groups (clusters). In order to deal with complexities of missing values in the dataset and arranging the IFCs in these five optimal groups based upon a centroid (mean) value, this study has undergone an appropriate clustering methodology using the Majorisation-Minimisation Algorithm named as K-POD means clustering. It is evident that each centroid is seen as representing the average observation within a cluster across all the variables in the analysis. All the observations in a cluster ranging between maxima and minima centrifuge around centroid value. The distances between cluster centroids show how far apart the centroids of the clusters in the final partition are from one another.
The study suggests that by minimising the hurdles created by business regulation laws, labour market regulation procedures and legalised process of property rights, the GFCI ranking will improve for the countries. It will help to pave the path of financial stability and creation of wealth. Similarly, by providing better health and education facilities, the Human development Index will help positively to improve the GFCI ranking of countries.

Item Type:Thesis (Doctoral)
Award:Doctor of Business Administration
Faculty and Department:Faculty of Business
Thesis Date:2023
Copyright:Copyright of this thesis is held by the author
Deposited On:15 Aug 2023 15:25

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