The GFCI ranks cities on the basis of their attractiveness to
financial services professionals. It will be based on what we
describe as a ‘component factor model’. This is a statistical
model built up with different types of data components or
inputs. There will be two specific inputs to the GFCI:
- individual indices measuring the competitive position of
financial centres;
- responses to a regular survey asking finance professionals to
rate centres as locations in which to conduct their
business.
The GFCI is dynamically updated. This process
will permit, for instance, a recently changed index of office
rental costs to adjust the competitiveness of London and the other
financial centres.
The process of creating the GFCI can be outlined diagrammatically
as:
The component factor model would then answer these types of
questions:
"If an investment banker rates Singapore with a certain rating,
then, based on the set of index scores for Singapore, how would
that person rate Paris based on the set of index scores for
Paris?"
or:
If a pension fund manager rates Edinburgh with a certain rating,
then, based on the set of index scores for Edinburgh, how would
that person rate Milan on the set of index scores for
Milan?"
The following features of the GFCI are particularly worth
noting:
- every time an input survey is updated, there will be automatic
adjustments to the competitiveness ratings;
- regular input surveys of people’s impressions will change the
model, thus the ratings;
- there will be several potential inputs to each of the
competitiveness factors;
- sub-indices can be developed by using the sectors represented
by survey respondents;
- the component factor model could be interrogated in a ‘what if’
mode, for instance “how much would London rental costs need to fall
in order to increase London’s ranking against New York?”
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